# Konabayev — B2B Marketing, Browser Extensions, Apify Actors — Full Content Index > B2B marketing knowledge base and tool portfolio by Tugelbay Konabayev. 200 published articles across English, Russian, and Kazakh on B2B growth, SEO, AI tools, CRM, and marketing automation; 7 free browser extensions; 23 active Apify scrapers and data tools; source-referenced B2B datasets and authored models. This is a scoped index of published source-record excerpts and article pointers, not full article bodies or fresh primary-source verification. Each dataset includes at most its first 10 records or first 10 explicitly hero-eligible records in file order. Repeated source claims across datasets are not independent observations. Article update dates are editorial dates. Record audit/review dates may describe an editorial check; retain the original source period and each qualification. Source assertions and methodology wording are attributed excerpts, not this site’s independent validation or causal findings. Titles and descriptions are article metadata, not proof of current vendor terms, measured SEO/GEO/AIO uplift or guaranteed citation. Author: Tugelbay Konabayev (Almaty, Kazakhstan) — Senior Digital Marketing Manager and independent B2B marketing consultant with 10+ years of experience. ## Professional Profile - Canonical profile: https://konabayev.com/author/tugelbay-konabayev/ - Background and credentials: https://konabayev.com/about/ - B2B and AI marketing consulting in Kazakhstan: https://konabayev.com/consulting/ - Russian local service page for Almaty and Kazakhstan: https://konabayev.com/ru/marketing-consultant-almaty/ - Current role: Senior Digital Marketing Manager at Promise Group Kazakhstan (Promise Group). - Location: Almaty, Kazakhstan. Service area: Kazakhstan and Central Asia, with remote international delivery. - Core expertise: B2B marketing strategy, performance marketing, lead generation, marketing analytics, CRM automation, and AI implementation for marketing. ## Sponsorship Rates & Editorial Requirements - Canonical sponsorship page: https://konabayev.com/advertise/ - Structured rates and requirements: https://konabayev.com/advertise.json - Rates updated: 2026-10-19. Currency: USD. Starting rates, subject to editorial approval and a written quote. - Sponsored mention: from $200 for 12 months. - Sponsored mention: from $400 for 24 months. - Supplied sponsored article: from $300 for 12 months. - Supplied sponsored article: from $600 for 24 months. - Custom content: from $600 for 12 months. - Payment: Full payment upfront via PayPal Business after editorial approval and before work or publication. An invoice is available. - Paid links use rel="sponsored" and visible disclosure. Permanent placements and paid dofollow links are not offered. A brand mention without a link does not receive an automatic discount. - Renewal: Separately agreed at the then-current rate; no automatic renewal. Existing written agreements retain their individually agreed rates and terms. - Placement term: Starts on publication. After expiry, the commercial placement may be removed unless renewed; useful editorial content may remain with its disclosure. - Cancellation: Before work starts, cancellation receives a full refund. After work starts, completed work is charged as agreed in the written quote and the remainder refunded. - Early removal: If the publisher ends a compliant placement early, an agreed replacement or prorated refund for the unused placement term is offered. The quote separates editorial work from placement for this calculation. - Included: One commercial destination per placement. No paid ranking position, positive verdict, traffic or SEO result is guaranteed. - Custom content: A 24-month term for custom-written content requires a separate quote. - Accepted topics: B2B SaaS; Marketing; SEO; AI tools and workflows. - Excluded topics: Casino and gambling; CBD and drugs; Adult content; Payday loans and speculative finance; Fraudulent or misleading products; Unrelated backlink-only submissions. - Brief: Advertiser and agency if applicable; Product website and exact destination URL; Format and placement term; Topic or existing article URL; Budget in USD; Deadline; Draft or evidence if available. Send to info@konabayev.com or use the form on the canonical page. - Sending a proposal does not reserve a placement. Payment does not buy a ranking position, positive verdict, guaranteed traffic or SEO results. ## Datasets: Sources and Authored Models Article update dates describe editorial revisions, not fresh measurement. Preserve each claim’s source, audit date and qualification when using it. ### ABM Statistics 2026: ROI, Pipeline and Buying Groups URL: https://konabayev.com/blog/abm-statistics-2026/ Full article summary (may cover material outside this subset): Source-backed 2026 ABM statistics on buying groups, pipeline conversion, ROI, AI adoption, advertising and measurement. Updated: 2026-10-04 Selected records: first 10 of 77 published records in file order. Full published dataset: https://konabayev.com/data/abm-statistics-2026.jsonl. The selected records do not cover every topic in the full article summary. - [abm-statistics-2026-001] Recorded assertion from demandbase.com: "Demandbase says its 2026 ABM benchmark analyzes 1,452 Demandbase tenants.". Record category: benchmark. Source-record methodology (retained wording): "Demandbase Labs 2026 ABM benchmark article based on 1,452 Demandbase tenants, 429,634 ad campaigns, 38 million marketing activities and 9.7 million sales interactions. Verified May 10, 2026.". Qualification: First-party Demandbase tenant benchmark. Source: https://www.demandbase.com/resources/labs/state-of-abm-2026-benchmark-report/. Record audit/review date: 2026-05-10. - [abm-statistics-2026-002] Recorded assertion from demandbase.com: "Demandbase says the same benchmark analyzes 429,634 ad campaigns.". Record category: benchmark. Source-record methodology (retained wording): "Demandbase Labs 2026 ABM benchmark article based on 1,452 Demandbase tenants, 429,634 ad campaigns, 38 million marketing activities and 9.7 million sales interactions. Verified May 10, 2026.". Qualification: First-party Demandbase platform benchmark. Source: https://www.demandbase.com/resources/labs/state-of-abm-2026-benchmark-report/. Record audit/review date: 2026-05-10. - [abm-statistics-2026-003] Recorded assertion from demandbase.com: "Demandbase says the same benchmark analyzes 38 million marketing activities.". Record category: benchmark. Source-record methodology (retained wording): "Demandbase Labs 2026 ABM benchmark article based on 1,452 Demandbase tenants, 429,634 ad campaigns, 38 million marketing activities and 9.7 million sales interactions. Verified May 10, 2026.". Qualification: First-party Demandbase platform benchmark. Source: https://www.demandbase.com/resources/labs/state-of-abm-2026-benchmark-report/. Record audit/review date: 2026-05-10. - [abm-statistics-2026-004] Recorded assertion from demandbase.com: "Demandbase says the same benchmark analyzes 9.7 million sales interactions.". Record category: benchmark. Source-record methodology (retained wording): "Demandbase Labs 2026 ABM benchmark article based on 1,452 Demandbase tenants, 429,634 ad campaigns, 38 million marketing activities and 9.7 million sales interactions. Verified May 10, 2026.". Qualification: First-party Demandbase platform benchmark. Source: https://www.demandbase.com/resources/labs/state-of-abm-2026-benchmark-report/. Record audit/review date: 2026-05-10. - [abm-statistics-2026-005] Recorded assertion from demandbase.com: "Demandbase reports that organizations aligned around buying groups achieve up to 2-3x higher win rates than teams centered on individual leads.". Record category: benchmark. Source-record methodology (retained wording): "Demandbase Labs 2026 ABM benchmark article based on 1,452 Demandbase tenants, 429,634 ad campaigns, 38 million marketing activities and 9.7 million sales interactions. Verified May 10, 2026.". Qualification: First-party Demandbase benchmark. Source: https://www.demandbase.com/resources/labs/state-of-abm-2026-benchmark-report/. Record audit/review date: 2026-05-10. - [abm-statistics-2026-006] Recorded assertion from demandbase.com: "Demandbase describes a typical buying group as including 13-17 stakeholders.". Record category: benchmark. Source-record methodology (retained wording): "Demandbase Labs 2026 ABM benchmark article based on 1,452 Demandbase tenants, 429,634 ad campaigns, 38 million marketing activities and 9.7 million sales interactions. Verified May 10, 2026.". Qualification: First-party Demandbase benchmark. Source: https://www.demandbase.com/resources/labs/state-of-abm-2026-benchmark-report/. Record audit/review date: 2026-05-10. - [abm-statistics-2026-007] Recorded assertion from demandbase.com: "Demandbase reports that win rates peak when teams focus on 2-3 buying groups per product.". Record category: benchmark. Source-record methodology (retained wording): "Demandbase Labs 2026 ABM benchmark article based on 1,452 Demandbase tenants, 429,634 ad campaigns, 38 million marketing activities and 9.7 million sales interactions. Verified May 10, 2026.". Qualification: First-party Demandbase benchmark. Source: https://www.demandbase.com/resources/labs/state-of-abm-2026-benchmark-report/. Record audit/review date: 2026-05-10. - [abm-statistics-2026-008] Recorded assertion from demandbase.com: "Demandbase reports that buying groups receiving 180-190 touches reach a 94% conversion rate.". Record category: conversion. Source-record methodology (retained wording): "Demandbase Labs 2026 ABM benchmark article based on 1,452 Demandbase tenants, 429,634 ad campaigns, 38 million marketing activities and 9.7 million sales interactions. Verified May 10, 2026.". Qualification: First-party Demandbase benchmark. Source: https://www.demandbase.com/resources/labs/state-of-abm-2026-benchmark-report/. Record audit/review date: 2026-05-10. - [abm-statistics-2026-009] Recorded assertion from demandbase.com: "Demandbase reports that organizations connecting CRM, MAP and predictive models achieve 22%+ MQA-to-pipeline conversion.". Record category: conversion. Source-record methodology (retained wording): "Demandbase Labs 2026 ABM benchmark article based on 1,452 Demandbase tenants, 429,634 ad campaigns, 38 million marketing activities and 9.7 million sales interactions. Verified May 10, 2026.". Qualification: First-party Demandbase benchmark. Source: https://www.demandbase.com/resources/labs/state-of-abm-2026-benchmark-report/. Record audit/review date: 2026-05-10. - [abm-statistics-2026-010] Recorded assertion from demandbase.com: "Demandbase reports a 14% MQA-to-pipeline conversion baseline for teams with limited integration.". Record category: conversion. Source-record methodology (retained wording): "Demandbase Labs 2026 ABM benchmark article based on 1,452 Demandbase tenants, 429,634 ad campaigns, 38 million marketing activities and 9.7 million sales interactions. Verified May 10, 2026.". Qualification: First-party Demandbase benchmark. Source: https://www.demandbase.com/resources/labs/state-of-abm-2026-benchmark-report/. Record audit/review date: 2026-05-10. ### AI Agents for Marketing Statistics 2026: Deployment and Barriers URL: https://konabayev.com/blog/ai-agents-for-marketing-statistics-2026/ Full article summary (may cover material outside this subset): Eleven source-bounded AI-agent figures from the 2025 Martech for 2026 report, covering deployment, autonomy, integrations and adoption barriers. Updated: 2026-07-14 Selected records: first 10 of 11 published records in file order. Full published dataset: https://konabayev.com/data/ai-agents-for-marketing-statistics-2026.jsonl. The selected records do not cover every topic in the full article summary. - [ai-agents-for-marketing-statistics-2026-001; scope=martech_supporting_missing_n] Recorded assertion from martechday.com: "The Martech for 2026 report says 90.3% of its tech-forward participants used AI agents somewhere in the martech stack.". Recorded figure: 90.3%. Period: 2025 survey published December 2, 2025. Population: tech-forward marketing and martech participants; respondent count not disclosed in the accessible report. Evidence type: Chiefmartec and MartechTribe AI and Data in Marketing survey. Record category: ai_agents. Source-record methodology (retained wording): "The report asks participants about AI agents, integrations, data sources, autonomy, and barriers. Several questions allow multiple selections.". Qualification: Supporting evidence only. N is not disclosed and the authors say participants skew more tech-savvy than average. Percentages must not be presented as market-representative or hero claims. Source: https://content.martechday.com/martech-for-2026.pdf. Record audit/review date: 2026-07-14. - [ai-agents-for-marketing-statistics-2026-002; scope=martech_supporting_missing_n] Recorded assertion from martechday.com: "The Martech for 2026 report says 23.3% of participants had AI agents in full production.". Recorded figure: 23.3%. Period: 2025 survey published December 2, 2025. Population: tech-forward marketing and martech participants; respondent count not disclosed in the accessible report. Evidence type: Chiefmartec and MartechTribe AI and Data in Marketing survey. Record category: ai_deployment. Source-record methodology (retained wording): "The report asks participants about AI agents, integrations, data sources, autonomy, and barriers. Several questions allow multiple selections.". Qualification: Supporting evidence only. N is not disclosed and the authors say participants skew more tech-savvy than average. Percentages must not be presented as market-representative or hero claims. Do not infer that every other respondent was in one mutually exclusive status without the original response table. Source: https://content.martechday.com/martech-for-2026.pdf. Record audit/review date: 2026-07-14. - [ai-agents-for-marketing-statistics-2026-003; scope=martech_supporting_missing_n] Recorded assertion from martechday.com: "The Martech for 2026 report says 80.6% used AI agents in assistant-only mode.". Recorded figure: 80.6%. Period: 2025 survey published December 2, 2025. Population: tech-forward marketing and martech participants; respondent count not disclosed in the accessible report. Evidence type: Chiefmartec and MartechTribe AI and Data in Marketing survey. Record category: ai_autonomy. Source-record methodology (retained wording): "The report asks participants about AI agents, integrations, data sources, autonomy, and barriers. Several questions allow multiple selections.". Qualification: Supporting evidence only. N is not disclosed and the authors say participants skew more tech-savvy than average. Percentages must not be presented as market-representative or hero claims. Source: https://content.martechday.com/martech-for-2026.pdf. Record audit/review date: 2026-07-14. - [ai-agents-for-marketing-statistics-2026-004; scope=martech_supporting_missing_n] Recorded assertion from martechday.com: "The Martech for 2026 report says 85.4% used AI to enhance existing functionality.". Recorded figure: 85.4% enhanced existing functionality. Period: 2025 survey published December 2, 2025. Population: tech-forward marketing and martech participants; respondent count not disclosed in the accessible report. Evidence type: Chiefmartec and MartechTribe AI and Data in Marketing survey. Record category: ai_stack_effect. Source-record methodology (retained wording): "The report asks participants about AI agents, integrations, data sources, autonomy, and barriers. Several questions allow multiple selections.". Qualification: Supporting evidence only. N is not disclosed and the authors say participants skew more tech-savvy than average. Percentages must not be presented as market-representative or hero claims. Source: https://content.martechday.com/martech-for-2026.pdf. Record audit/review date: 2026-07-14. - [ai-agents-for-marketing-statistics-2026-005; scope=martech_supporting_missing_n] Recorded assertion from martechday.com: "The Martech for 2026 report says 42.7% added new functionality while 30.1% replaced existing functionality with AI.". Recorded figure: 42.7% new; 30.1% replaced. Period: 2025 survey published December 2, 2025. Population: tech-forward marketing and martech participants; respondent count not disclosed in the accessible report. Evidence type: Chiefmartec and MartechTribe AI and Data in Marketing survey. Record category: ai_stack_effect. Source-record methodology (retained wording): "The report asks participants about AI agents, integrations, data sources, autonomy, and barriers. Several questions allow multiple selections.". Qualification: Supporting evidence only. N is not disclosed and the authors say participants skew more tech-savvy than average. Percentages must not be presented as market-representative or hero claims. The question allowed multiple selections, so the values are not shares of one exclusive whole. Source: https://content.martechday.com/martech-for-2026.pdf. Record audit/review date: 2026-07-14. - [ai-agents-for-marketing-statistics-2026-006; scope=martech_supporting_missing_n] Recorded assertion from martechday.com: "The Martech for 2026 report lists custom integrations at 56.3%, prebuilt integrations at 47.6%, and iPaaS at 40.8%.". Recorded figure: 56.3% custom; 47.6% prebuilt; 40.8% iPaaS. Period: 2025 survey published December 2, 2025. Population: tech-forward marketing and martech participants; respondent count not disclosed in the accessible report. Evidence type: Chiefmartec and MartechTribe AI and Data in Marketing survey. Record category: integration_method. Source-record methodology (retained wording): "The report asks participants about AI agents, integrations, data sources, autonomy, and barriers. Several questions allow multiple selections.". Qualification: Supporting evidence only. N is not disclosed and the authors say participants skew more tech-savvy than average. Percentages must not be presented as market-representative or hero claims. The question allowed multiple selections. Source: https://content.martechday.com/martech-for-2026.pdf. Record audit/review date: 2026-07-14. - [ai-agents-for-marketing-statistics-2026-007; scope=martech_supporting_missing_n] Recorded assertion from martechday.com: "The Martech for 2026 report says 37.9% connected cloud warehouse or lakehouse data directly to AI agents.". Recorded figure: 37.9%. Period: 2025 survey published December 2, 2025. Population: tech-forward marketing and martech participants; respondent count not disclosed in the accessible report. Evidence type: Chiefmartec and MartechTribe AI and Data in Marketing survey. Record category: data_integration. Source-record methodology (retained wording): "The report asks participants about AI agents, integrations, data sources, autonomy, and barriers. Several questions allow multiple selections.". Qualification: Supporting evidence only. N is not disclosed and the authors say participants skew more tech-savvy than average. Percentages must not be presented as market-representative or hero claims. Source: https://content.martechday.com/martech-for-2026.pdf. Record audit/review date: 2026-07-14. - [ai-agents-for-marketing-statistics-2026-008; scope=martech_supporting_missing_n] Recorded assertion from martechday.com: "Poor data quality was the most-selected AI and data challenge in the Martech for 2026 report, at 56.3%.". Recorded figure: 56.3%. Period: 2025 survey published December 2, 2025. Population: tech-forward marketing and martech participants; respondent count not disclosed in the accessible report. Evidence type: Chiefmartec and MartechTribe AI and Data in Marketing survey. Record category: barrier. Source-record methodology (retained wording): "The report asks participants about AI agents, integrations, data sources, autonomy, and barriers. Several questions allow multiple selections.". Qualification: Supporting evidence only. N is not disclosed and the authors say participants skew more tech-savvy than average. Percentages must not be presented as market-representative or hero claims. The question allowed multiple selections. Source: https://content.martechday.com/martech-for-2026.pdf. Record audit/review date: 2026-07-14. - [ai-agents-for-marketing-statistics-2026-009; scope=martech_supporting_missing_n] Recorded assertion from martechday.com: "Organizational and process readiness was selected by 52.4% in the Martech for 2026 report.". Recorded figure: 52.4%. Period: 2025 survey published December 2, 2025. Population: tech-forward marketing and martech participants; respondent count not disclosed in the accessible report. Evidence type: Chiefmartec and MartechTribe AI and Data in Marketing survey. Record category: barrier. Source-record methodology (retained wording): "The report asks participants about AI agents, integrations, data sources, autonomy, and barriers. Several questions allow multiple selections.". Qualification: Supporting evidence only. N is not disclosed and the authors say participants skew more tech-savvy than average. Percentages must not be presented as market-representative or hero claims. The question allowed multiple selections. Source: https://content.martechday.com/martech-for-2026.pdf. Record audit/review date: 2026-07-14. - [ai-agents-for-marketing-statistics-2026-010; scope=martech_supporting_missing_n] Recorded assertion from martechday.com: "Integration friction was selected by 50.5% in the Martech for 2026 report.". Recorded figure: 50.5%. Period: 2025 survey published December 2, 2025. Population: tech-forward marketing and martech participants; respondent count not disclosed in the accessible report. Evidence type: Chiefmartec and MartechTribe AI and Data in Marketing survey. Record category: barrier. Source-record methodology (retained wording): "The report asks participants about AI agents, integrations, data sources, autonomy, and barriers. Several questions allow multiple selections.". Qualification: Supporting evidence only. N is not disclosed and the authors say participants skew more tech-savvy than average. Percentages must not be presented as market-representative or hero claims. The question allowed multiple selections. Source: https://content.martechday.com/martech-for-2026.pdf. Record audit/review date: 2026-07-14. ### AI Code Assistant Statistics 2026: Copilot, Cursor and Claude Code URL: https://konabayev.com/blog/ai-code-assistant-statistics-2026/ Full article summary (may cover material outside this subset): Source-backed 2026 AI code assistant statistics on developer adoption, Copilot, Cursor, Claude Code, agents, productivity, trust and risk. Updated: 2026-10-04 Selected records: first 10 of 98 published records in file order. Full published dataset: https://konabayev.com/data/ai-code-assistant-statistics-2026.jsonl. The selected records do not cover every topic in the full article summary. - [ai-code-assistant-statistics-2026-001] Recorded assertion from survey.stackoverflow.co: "Stack Overflow says 84% of 2025 Developer Survey respondents use or plan to use AI tools in their development process, up from 76% in 2024.". Record category: benchmark. Source-record methodology (retained wording): "Stack Overflow 2025 Developer Survey AI section. The AI tools question had 33,662 responses from all respondents and 26,004 responses from professional developers. Firecrawl snapshot saved on May 11, 2026.". Qualification: Survey response base changes by question. Use the response count or subgroup when making detailed comparisons. Source: https://survey.stackoverflow.co/2025/ai. Record audit/review date: 2026-05-10. - [ai-code-assistant-statistics-2026-002] Recorded assertion from survey.stackoverflow.co: "Stack Overflow says 47.1% of all respondents use AI tools daily in their development process.". Record category: benchmark. Source-record methodology (retained wording): "Stack Overflow 2025 Developer Survey AI section. The AI tools question had 33,662 responses from all respondents and 26,004 responses from professional developers. Firecrawl snapshot saved on May 11, 2026.". Qualification: Survey response base changes by question. Use the response count or subgroup when making detailed comparisons. Source: https://survey.stackoverflow.co/2025/ai. Record audit/review date: 2026-05-10. - [ai-code-assistant-statistics-2026-003] Recorded assertion from survey.stackoverflow.co: "Stack Overflow says 17.7% of all respondents use AI tools weekly in their development process.". Record category: benchmark. Source-record methodology (retained wording): "Stack Overflow 2025 Developer Survey AI section. The AI tools question had 33,662 responses from all respondents and 26,004 responses from professional developers. Firecrawl snapshot saved on May 11, 2026.". Qualification: Survey response base changes by question. Use the response count or subgroup when making detailed comparisons. Source: https://survey.stackoverflow.co/2025/ai. Record audit/review date: 2026-05-10. - [ai-code-assistant-statistics-2026-004] Recorded assertion from survey.stackoverflow.co: "Stack Overflow says 13.7% of all respondents use AI tools monthly or infrequently in their development process.". Record category: benchmark. Source-record methodology (retained wording): "Stack Overflow 2025 Developer Survey AI section. The AI tools question had 33,662 responses from all respondents and 26,004 responses from professional developers. Firecrawl snapshot saved on May 11, 2026.". Qualification: Survey response base changes by question. Use the response count or subgroup when making detailed comparisons. Source: https://survey.stackoverflow.co/2025/ai. Record audit/review date: 2026-05-10. - [ai-code-assistant-statistics-2026-005] Recorded assertion from survey.stackoverflow.co: "Stack Overflow says 16.2% of all respondents do not plan to use AI tools in their development process.". Record category: benchmark. Source-record methodology (retained wording): "Stack Overflow 2025 Developer Survey AI section. The AI tools question had 33,662 responses from all respondents and 26,004 responses from professional developers. Firecrawl snapshot saved on May 11, 2026.". Qualification: Survey response base changes by question. Use the response count or subgroup when making detailed comparisons. Source: https://survey.stackoverflow.co/2025/ai. Record audit/review date: 2026-05-10. - [ai-code-assistant-statistics-2026-006] Recorded assertion from survey.stackoverflow.co: "Stack Overflow says 50.6% of professional developers use AI tools daily in their development process.". Record category: benchmark. Source-record methodology (retained wording): "Stack Overflow 2025 Developer Survey AI section. The AI tools question had 33,662 responses from all respondents and 26,004 responses from professional developers. Firecrawl snapshot saved on May 11, 2026.". Qualification: Survey response base changes by question. Use the response count or subgroup when making detailed comparisons. Source: https://survey.stackoverflow.co/2025/ai. Record audit/review date: 2026-05-10. - [ai-code-assistant-statistics-2026-007] Recorded assertion from survey.stackoverflow.co: "Stack Overflow says 17.4% of professional developers use AI tools weekly in their development process.". Record category: benchmark. Source-record methodology (retained wording): "Stack Overflow 2025 Developer Survey AI section. The AI tools question had 33,662 responses from all respondents and 26,004 responses from professional developers. Firecrawl snapshot saved on May 11, 2026.". Qualification: Survey response base changes by question. Use the response count or subgroup when making detailed comparisons. Source: https://survey.stackoverflow.co/2025/ai. Record audit/review date: 2026-05-10. - [ai-code-assistant-statistics-2026-008] Recorded assertion from survey.stackoverflow.co: "Stack Overflow says 12.8% of professional developers use AI tools monthly or infrequently.". Record category: benchmark. Source-record methodology (retained wording): "Stack Overflow 2025 Developer Survey AI section. The AI tools question had 33,662 responses from all respondents and 26,004 responses from professional developers. Firecrawl snapshot saved on May 11, 2026.". Qualification: Survey response base changes by question. Use the response count or subgroup when making detailed comparisons. Source: https://survey.stackoverflow.co/2025/ai. Record audit/review date: 2026-05-10. - [ai-code-assistant-statistics-2026-009] Recorded assertion from survey.stackoverflow.co: "Stack Overflow says 14.7% of professional developers do not plan to use AI tools in their development process.". Record category: benchmark. Source-record methodology (retained wording): "Stack Overflow 2025 Developer Survey AI section. The AI tools question had 33,662 responses from all respondents and 26,004 responses from professional developers. Firecrawl snapshot saved on May 11, 2026.". Qualification: Survey response base changes by question. Use the response count or subgroup when making detailed comparisons. Source: https://survey.stackoverflow.co/2025/ai. Record audit/review date: 2026-05-10. - [ai-code-assistant-statistics-2026-010] Recorded assertion from survey.stackoverflow.co: "Stack Overflow says 55.5% of early-career developers use AI tools daily.". Record category: benchmark. Source-record methodology (retained wording): "Stack Overflow 2025 Developer Survey AI section. The AI tools question had 33,662 responses from all respondents and 26,004 responses from professional developers. Firecrawl snapshot saved on May 11, 2026.". Qualification: Survey response base changes by question. Use the response count or subgroup when making detailed comparisons. Source: https://survey.stackoverflow.co/2025/ai. Record audit/review date: 2026-05-10. ### AI Referral Traffic Benchmarks 2026: ChatGPT, Claude, Gemini URL: https://konabayev.com/blog/ai-search-referral-traffic-benchmarks-2026/ Full article summary (may cover material outside this subset): Dated AI referral observations from June 2026, a small owned-site example, and Seer Q3 2025 CTR comparisons, with measurement limits and data. Updated: 2026-10-04 Selected records: first 10 of 23 published records in file order. Full published dataset: https://konabayev.com/data/ai-search-referral-traffic-benchmarks-2026.jsonl. The selected records do not cover every topic in the full article summary. - Recorded assertion from https://konabayev.com/blog/ai-search-referral-traffic-benchmarks-2026/: "28-day pageviews". Recorded value: 655. Unit: pageviews. Period: Snapshot generated 2026-06-08. Source-record methodology: not recorded. Qualification: Historical owned analytics snapshot generated June 8, 2026; preserve the source-table unit; visits are not interchangeable with pageviews or visitors. Source: https://konabayev.com/blog/ai-search-referral-traffic-benchmarks-2026/. Record audit/review date: not recorded. - Recorded assertion from https://konabayev.com/blog/ai-search-referral-traffic-benchmarks-2026/: "28-day visitors". Recorded value: 506. Unit: visitors. Period: Snapshot generated 2026-06-08. Source-record methodology: not recorded. Qualification: Historical owned analytics snapshot generated June 8, 2026; preserve the source-table unit; visits are not interchangeable with pageviews or visitors. Source: https://konabayev.com/blog/ai-search-referral-traffic-benchmarks-2026/. Record audit/review date: not recorded. - Recorded assertion from https://konabayev.com/blog/ai-search-referral-traffic-benchmarks-2026/: "7-day pageviews". Recorded value: 185. Unit: pageviews. Period: Snapshot generated 2026-06-08. Source-record methodology: not recorded. Qualification: Historical owned analytics snapshot generated June 8, 2026; preserve the source-table unit; visits are not interchangeable with pageviews or visitors. Source: https://konabayev.com/blog/ai-search-referral-traffic-benchmarks-2026/. Record audit/review date: not recorded. - Recorded assertion from https://konabayev.com/blog/ai-search-referral-traffic-benchmarks-2026/: "7-day visitors". Recorded value: 130. Unit: visitors. Period: Snapshot generated 2026-06-08. Source-record methodology: not recorded. Qualification: Historical owned analytics snapshot generated June 8, 2026; preserve the source-table unit; visits are not interchangeable with pageviews or visitors. Source: https://konabayev.com/blog/ai-search-referral-traffic-benchmarks-2026/. Record audit/review date: not recorded. - Recorded assertion from https://konabayev.com/blog/ai-search-referral-traffic-benchmarks-2026/: "ChatGPT visible referral visits". Recorded value: 18. Unit: visits. Period: 7-day window in snapshot generated 2026-06-08. Source-record methodology: not recorded. Qualification: chatgpt.com in top referring-domain table; preserve the source-table unit; visits are not interchangeable with pageviews or visitors. Source: https://konabayev.com/blog/ai-search-referral-traffic-benchmarks-2026/. Record audit/review date: not recorded. - Recorded assertion from https://konabayev.com/blog/ai-search-referral-traffic-benchmarks-2026/: "Claude visible referral visits". Recorded value: 8. Unit: visits. Period: 7-day window in snapshot generated 2026-06-08. Source-record methodology: not recorded. Qualification: claude.ai in top referring-domain table; preserve the source-table unit; visits are not interchangeable with pageviews or visitors. Source: https://konabayev.com/blog/ai-search-referral-traffic-benchmarks-2026/. Record audit/review date: not recorded. - Recorded assertion from https://konabayev.com/blog/ai-search-referral-traffic-benchmarks-2026/: "Gemini visible referral visits". Recorded value: 2. Unit: visits. Period: 7-day window in snapshot generated 2026-06-08. Source-record methodology: not recorded. Qualification: gemini.google.com in top referring-domain table; preserve the source-table unit; visits are not interchangeable with pageviews or visitors. Source: https://konabayev.com/blog/ai-search-referral-traffic-benchmarks-2026/. Record audit/review date: not recorded. - Recorded assertion from https://konabayev.com/blog/ai-search-referral-traffic-benchmarks-2026/: "Measured AI visible referral visits". Recorded value: 28. Unit: visits. Period: 7-day window in snapshot generated 2026-06-08. Source-record methodology: not recorded. Qualification: ChatGPT plus Claude plus Gemini; preserve the source-table unit; visits are not interchangeable with pageviews or visitors. The measured subset is ChatGPT, Claude and Gemini visible domains; it does not represent all AI referrals. Source: https://konabayev.com/blog/ai-search-referral-traffic-benchmarks-2026/. Record audit/review date: not recorded. - Recorded assertion from https://konabayev.com/blog/ai-search-referral-traffic-benchmarks-2026/: "ChatGPT share of measured AI referrals". Recorded value: 64.3. Unit: percent. Period: 7-day window in snapshot generated 2026-06-08. Source-record methodology: not recorded. Qualification: 18 divided by 28; preserve the source-table unit; visits are not interchangeable with pageviews or visitors. The measured subset is ChatGPT, Claude and Gemini visible domains; it does not represent all AI referrals. Source: https://konabayev.com/blog/ai-search-referral-traffic-benchmarks-2026/. Record audit/review date: not recorded. - Recorded assertion from https://konabayev.com/blog/ai-search-referral-traffic-benchmarks-2026/: "Claude share of measured AI referrals". Recorded value: 28.6. Unit: percent. Period: 7-day window in snapshot generated 2026-06-08. Source-record methodology: not recorded. Qualification: 8 divided by 28; preserve the source-table unit; visits are not interchangeable with pageviews or visitors. The measured subset is ChatGPT, Claude and Gemini visible domains; it does not represent all AI referrals. Source: https://konabayev.com/blog/ai-search-referral-traffic-benchmarks-2026/. Record audit/review date: not recorded. ### AI Search Statistics 2026: Adoption, Usage & Click Data URL: https://konabayev.com/blog/ai-search-statistics-2026/ Full article summary (may cover material outside this subset): Primary-source AI search statistics for 2026 covering ChatGPT adoption, Google AI Overview usage, clicks, citations, query patterns and traffic effects. Updated: 2026-10-04 Selected records: first 10 of 39 published records in file order. Full published dataset: https://konabayev.com/data/ai-search-statistics-2026.jsonl. The selected records do not cover every topic in the full article summary. - [ai-search-statistics-2026-001] Recorded assertion from abc.xyz: "Google said AI Overviews had more than 1.5 billion users per month in April 2025.". Record category: adoption. Source-record methodology (retained wording): "Alphabet management disclosure on the April 24, 2025 Q1 earnings call.". Qualification: Company-reported product usage; Alphabet did not publish an independently audited denominator or raw user table. Source: https://abc.xyz/investor/events/event-details/2025/2025-Q1-Earnings-Call/. Record audit/review date: 2026-07-14. - [ai-search-statistics-2026-002] Recorded assertion from blog.google: "Google said AI Overviews increased Google usage by more than 10% for the query types that showed AI Overviews in the U.S. and India.". Record category: adoption. Source-record methodology (retained wording): "Google Search product disclosure published May 20, 2025 by the VP, Head of Search.". Qualification: Official product claim without public raw data; usage-lift evidence is limited to query types that showed AI Overviews in the U.S. and India. Source: https://blog.google/products-and-platforms/products/search/google-search-ai-mode-update/. Record audit/review date: 2026-07-14. - [ai-search-statistics-2026-003] Recorded assertion from blog.google: "Google said more than 1.5 billion people used Google Lens each month to search what they saw.". Record category: visual_search. Source-record methodology (retained wording): "Google Search product disclosure published May 20, 2025 by the VP, Head of Search.". Qualification: Official product claim without public raw data; usage-lift evidence is limited to query types that showed AI Overviews in the U.S. and India. Source: https://blog.google/products-and-platforms/products/search/google-search-ai-mode-update/. Record audit/review date: 2026-07-14. - [ai-search-statistics-2026-004] Recorded assertion from blog.google: "Google said Deep Search in AI Mode could issue hundreds of searches before producing a cited report.". Record category: product. Source-record methodology (retained wording): "Google Search product disclosure published May 20, 2025 by the VP, Head of Search.". Qualification: Official product claim without public raw data; usage-lift evidence is limited to query types that showed AI Overviews in the U.S. and India. Source: https://blog.google/products-and-platforms/products/search/google-search-ai-mode-update/. Record audit/review date: 2026-07-14. - [ai-search-statistics-2026-005] Recorded assertion from abc.xyz: "Alphabet said the Gemini App had more than 750 million monthly active users in February 2026.". Record category: adoption. Source-record methodology (retained wording): "Alphabet management disclosure on the February 4, 2026 Q4 earnings call.". Qualification: Company-reported metrics; Gemini App usage is broader than search, and Search revenue growth cannot be attributed to AI features alone. Source: https://abc.xyz/investor/events/event-details/2026/2025-Q4-Earnings-Call-2026-Dr_C033hS6/default.aspx. Record audit/review date: 2026-07-14. - [ai-search-statistics-2026-006] Recorded assertion from abc.xyz: "Alphabet said it shipped more than 250 product launches across AI Mode and AI Overviews in Q4 2025.". Record category: product. Source-record methodology (retained wording): "Alphabet management disclosure on the February 4, 2026 Q4 earnings call.". Qualification: Company-reported metrics; Gemini App usage is broader than search, and Search revenue growth cannot be attributed to AI features alone. Source: https://abc.xyz/investor/events/event-details/2026/2025-Q4-Earnings-Call-2026-Dr_C033hS6/default.aspx. Record audit/review date: 2026-07-14. - [ai-search-statistics-2026-007] Recorded assertion from abc.xyz: "Alphabet reported that Search revenue grew 17% year over year in Q4 2025.". Record category: monetization. Source-record methodology (retained wording): "Alphabet management disclosure on the February 4, 2026 Q4 earnings call.". Qualification: Company-reported metrics; Gemini App usage is broader than search, and Search revenue growth cannot be attributed to AI features alone. Source: https://abc.xyz/investor/events/event-details/2026/2025-Q4-Earnings-Call-2026-Dr_C033hS6/default.aspx. Record audit/review date: 2026-07-14. - [ai-search-statistics-2026-008] Recorded assertion from openai.com: "OpenAI reported more than 700 million weekly active ChatGPT users in its September 2025 consumer-use study.". Record category: adoption. Source-record methodology (retained wording): "OpenAI and NBER working-paper analysis of 1.5 million consumer-plan conversations using privacy-preserving automated classification.". Qualification: Consumer ChatGPT usage is not the same as ChatGPT Search query volume; researchers did not manually read messages. Source: https://openai.com/index/how-people-are-using-chatgpt/. Record audit/review date: 2026-07-14. - [ai-search-statistics-2026-009] Recorded assertion from openai.com: "OpenAI and its research collaborators analyzed 1.5 million consumer-plan ChatGPT conversations.". Record category: research_scope. Source-record methodology (retained wording): "OpenAI and NBER working-paper analysis of 1.5 million consumer-plan conversations using privacy-preserving automated classification.". Qualification: Consumer ChatGPT usage is not the same as ChatGPT Search query volume; researchers did not manually read messages. Source: https://openai.com/index/how-people-are-using-chatgpt/. Record audit/review date: 2026-07-14. - [ai-search-statistics-2026-010] Recorded assertion from openai.com: "Three-quarters of consumer ChatGPT conversations focused on practical guidance, seeking information, or writing.". Record category: usage. Source-record methodology (retained wording): "OpenAI and NBER working-paper analysis of 1.5 million consumer-plan conversations using privacy-preserving automated classification.". Qualification: Consumer ChatGPT usage is not the same as ChatGPT Search query volume; researchers did not manually read messages. Source: https://openai.com/index/how-people-are-using-chatgpt/. Record audit/review date: 2026-07-14. ### B2B Buyer Journey Statistics 2026: 30 Data Points URL: https://konabayev.com/blog/b2b-buyer-journey-statistics/ Full article summary (may cover material outside this subset): 30 source-backed B2B buyer journey statistics covering cycle length, Day One shortlists, seller contact, buying groups, AI and observed touchpoints. Updated: 2026-07-11 Selected records: first 10 of 30 published records in file order. Full published dataset: https://konabayev.com/data/b2b-buyer-journey-statistics.jsonl. The selected records do not cover every topic in the full article summary. - [BUYER-001] Recorded assertion from 6sense.com: "6sense collected nearly 4,000 responses from B2B buyers in its main 2025 study.". Recorded value: nearly 4,000. Period: 2025 study with 2023-2024 comparisons where stated. Population: nearly 4,000 B2B buyers in the main study plus a 766-response companion survey. Denominator: main-study or 766-response companion-survey base as identified by the claim; exact question base can vary. Source-record methodology: not recorded. Qualification: The main findings are buyer self-reports, not observed clickstream or CRM events. Bases vary between the main study and the 766-response companion survey. Source: https://6sense.com/science-of-b2b/buyer-experience-report-2025/. Record audit/review date: 2026-07-11. - [BUYER-002] Recorded assertion from 6sense.com: "A companion survey on AI and economic conditions contributed another 766 responses.". Recorded value: 766. Period: 2025 study with 2023-2024 comparisons where stated. Population: nearly 4,000 B2B buyers in the main study plus a 766-response companion survey. Denominator: main-study or 766-response companion-survey base as identified by the claim; exact question base can vary. Source-record methodology: not recorded. Qualification: The main findings are buyer self-reports, not observed clickstream or CRM events. Bases vary between the main study and the 766-response companion survey. Source: https://6sense.com/science-of-b2b/buyer-experience-report-2025/. Record audit/review date: 2026-07-11. - [BUYER-003] Recorded assertion from 6sense.com: "Average B2B buying-cycle length was 10.1 months in 2025, down from 11.3 months in 2024.". Recorded value: 10.1 months. Period: 2025 study with 2023-2024 comparisons where stated. Population: nearly 4,000 B2B buyers in the main study plus a 766-response companion survey. Denominator: main-study or 766-response companion-survey base as identified by the claim; exact question base can vary. Source-record methodology: not recorded. Qualification: The main findings are buyer self-reports, not observed clickstream or CRM events. Bases vary between the main study and the 766-response companion survey. Source: https://6sense.com/science-of-b2b/buyer-experience-report-2025/. Record audit/review date: 2026-07-11. - [BUYER-004] Recorded assertion from 6sense.com: "The point of first seller contact moved to 61% through the journey from 69% in 2024.". Recorded value: 61%. Period: 2025 study with 2023-2024 comparisons where stated. Population: nearly 4,000 B2B buyers in the main study plus a 766-response companion survey. Denominator: main-study or 766-response companion-survey base as identified by the claim; exact question base can vary. Source-record methodology: not recorded. Qualification: The main findings are buyer self-reports, not observed clickstream or CRM events. Bases vary between the main study and the 766-response companion survey. Source: https://6sense.com/science-of-b2b/buyer-experience-report-2025/. Record audit/review date: 2026-07-11. - [BUYER-005] Recorded assertion from 6sense.com: "The point-of-first-contact change meant buyers contacted sellers roughly six to seven weeks earlier.". Recorded value: 6-7 weeks. Period: 2025 study with 2023-2024 comparisons where stated. Population: nearly 4,000 B2B buyers in the main study plus a 766-response companion survey. Denominator: main-study or 766-response companion-survey base as identified by the claim; exact question base can vary. Source-record methodology: not recorded. Qualification: The main findings are buyer self-reports, not observed clickstream or CRM events. Bases vary between the main study and the 766-response companion survey. Source: https://6sense.com/science-of-b2b/buyer-experience-report-2025/. Record audit/review date: 2026-07-11. - [BUYER-006] Recorded assertion from 6sense.com: "The winning vendor was already on the Day One shortlist in 95% of purchases.". Recorded value: 95%. Period: 2025 study with 2023-2024 comparisons where stated. Population: nearly 4,000 B2B buyers in the main study plus a 766-response companion survey. Denominator: main-study or 766-response companion-survey base as identified by the claim; exact question base can vary. Source-record methodology: not recorded. Qualification: The main findings are buyer self-reports, not observed clickstream or CRM events. Bases vary between the main study and the 766-response companion survey. Source: https://6sense.com/science-of-b2b/buyer-experience-report-2025/. Record audit/review date: 2026-07-11. - [BUYER-007] Recorded assertion from 6sense.com: "The favorite at the end of the Selection Phase became the final winner 77% of the time, down from 83% in prior years.". Recorded value: 77%. Period: 2025 study with 2023-2024 comparisons where stated. Population: nearly 4,000 B2B buyers in the main study plus a 766-response companion survey. Denominator: main-study or 766-response companion-survey base as identified by the claim; exact question base can vary. Source-record methodology: not recorded. Qualification: The main findings are buyer self-reports, not observed clickstream or CRM events. Bases vary between the main study and the 766-response companion survey. Source: https://6sense.com/science-of-b2b/buyer-experience-report-2025/. Record audit/review date: 2026-07-11. - [BUYER-008] Recorded assertion from 6sense.com: "Buyers initiated 79% of seller engagements in 2025, compared with 82% in prior years.". Recorded value: 79%. Period: 2025 study with 2023-2024 comparisons where stated. Population: nearly 4,000 B2B buyers in the main study plus a 766-response companion survey. Denominator: main-study or 766-response companion-survey base as identified by the claim; exact question base can vary. Source-record methodology: not recorded. Qualification: The main findings are buyer self-reports, not observed clickstream or CRM events. Bases vary between the main study and the 766-response companion survey. Source: https://6sense.com/science-of-b2b/buyer-experience-report-2025/. Record audit/review date: 2026-07-11. - [BUYER-009] Recorded assertion from 6sense.com: "Buyers evaluated an average of 5.1 vendors, up from 4.5.". Recorded value: 5.1 vendors. Period: 2025 study with 2023-2024 comparisons where stated. Population: nearly 4,000 B2B buyers in the main study plus a 766-response companion survey. Denominator: main-study or 766-response companion-survey base as identified by the claim; exact question base can vary. Source-record methodology: not recorded. Qualification: The main findings are buyer self-reports, not observed clickstream or CRM events. Bases vary between the main study and the 766-response companion survey. Source: https://6sense.com/science-of-b2b/buyer-experience-report-2025/. Record audit/review date: 2026-07-11. - [BUYER-010] Recorded assertion from 6sense.com: "Buyers had prior experience with an average of 3.8 of the vendors they evaluated.". Recorded value: 3.8 vendors. Period: 2025 study with 2023-2024 comparisons where stated. Population: nearly 4,000 B2B buyers in the main study plus a 766-response companion survey. Denominator: main-study or 766-response companion-survey base as identified by the claim; exact question base can vary. Source-record methodology: not recorded. Qualification: The main findings are buyer self-reports, not observed clickstream or CRM events. Bases vary between the main study and the 766-response companion survey. Source: https://6sense.com/science-of-b2b/buyer-experience-report-2025/. Record audit/review date: 2026-07-11. ### B2B Cold Calling Statistics 2026: Success, Connect & Meeting Rates URL: https://konabayev.com/blog/b2b-cold-calling-statistics-2026/ Full article summary (may cover material outside this subset): B2B cold calling statistics for 2026: success, connect and meeting rates from Cognism, Belkins, Nooks, HubSpot and Orum datasets. Updated: 2026-10-04 Selected records: first 10 of 109 published records in file order. Full published dataset: https://konabayev.com/data/b2b-cold-calling-statistics-2026.jsonl. The selected records do not cover every topic in the full article summary. - [b2b-cold-calling-statistics-2026-001] Recorded assertion from cognism.com: "Cognism titles its 2026 cold calling report as an analysis of over 200K calls.". Record category: benchmark. Source-record methodology (retained wording): "Cognism State of Cold Calling in 2026 report, based on analysis of 2025 cold calling data and WHAM cold-call performance data. Scraped with Firecrawl on May 10, 2026.". Qualification: Cognism is a sales intelligence vendor. Treat Cognism team performance figures as first-party vendor benchmarks, not neutral market averages. Source: https://www.cognism.com/reports/cold-calling-report-2026. Record audit/review date: 2026-05-10. - [b2b-cold-calling-statistics-2026-002] Recorded assertion from cognism.com: "Cognism says its 2026 report analyzes 2025 cold calling data to inform how teams should approach cold calling in 2026.". Record category: benchmark. Source-record methodology (retained wording): "Cognism State of Cold Calling in 2026 report, based on analysis of 2025 cold calling data and WHAM cold-call performance data. Scraped with Firecrawl on May 10, 2026.". Qualification: Cognism is a sales intelligence vendor. Treat Cognism team performance figures as first-party vendor benchmarks, not neutral market averages. Source: https://www.cognism.com/reports/cold-calling-report-2026. Record audit/review date: 2026-05-10. - [b2b-cold-calling-statistics-2026-003] Recorded assertion from cognism.com: "Cognism reports that the industry average cold calling success rate climbed to 2.7%, up from 2.3% the previous year.". Record category: benchmark. Source-record methodology (retained wording): "Cognism State of Cold Calling in 2026 report, based on analysis of 2025 cold calling data and WHAM cold-call performance data. Scraped with Firecrawl on May 10, 2026.". Qualification: Cognism is a sales intelligence vendor. Treat Cognism team performance figures as first-party vendor benchmarks, not neutral market averages. Source: https://www.cognism.com/reports/cold-calling-report-2026. Record audit/review date: 2026-05-10. - [b2b-cold-calling-statistics-2026-004] Recorded assertion from cognism.com: "Cognism reports that its sales team entered 2026 with an 11.3% cold call success rate.". Record category: benchmark. Source-record methodology (retained wording): "Cognism State of Cold Calling in 2026 report, based on analysis of 2025 cold calling data and WHAM cold-call performance data. Scraped with Firecrawl on May 10, 2026.". Qualification: Cognism is a sales intelligence vendor. Treat Cognism team performance figures as first-party vendor benchmarks, not neutral market averages. Source: https://www.cognism.com/reports/cold-calling-report-2026. Record audit/review date: 2026-05-10. - [b2b-cold-calling-statistics-2026-005] Recorded assertion from cognism.com: "Cognism reports that its cold call success rate increased from 6.7% in the previous report to 11.3% in the 2026 report.". Record category: benchmark. Source-record methodology (retained wording): "Cognism State of Cold Calling in 2026 report, based on analysis of 2025 cold calling data and WHAM cold-call performance data. Scraped with Firecrawl on May 10, 2026.". Qualification: Cognism is a sales intelligence vendor. Treat Cognism team performance figures as first-party vendor benchmarks, not neutral market averages. Source: https://www.cognism.com/reports/cold-calling-report-2026. Record audit/review date: 2026-05-10. - [b2b-cold-calling-statistics-2026-006] Recorded assertion from cognism.com: "Cognism reports that 1.55 calls are now needed on average to reach a prospect.". Record category: benchmark. Source-record methodology (retained wording): "Cognism State of Cold Calling in 2026 report, based on analysis of 2025 cold calling data and WHAM cold-call performance data. Scraped with Firecrawl on May 10, 2026.". Qualification: Cognism is a sales intelligence vendor. Treat Cognism team performance figures as first-party vendor benchmarks, not neutral market averages. Source: https://www.cognism.com/reports/cold-calling-report-2026. Record audit/review date: 2026-05-10. - [b2b-cold-calling-statistics-2026-007] Recorded assertion from cognism.com: "Cognism reports that its 2025 data required an average of 2.9 call attempts to get through.". Record category: benchmark. Source-record methodology (retained wording): "Cognism State of Cold Calling in 2026 report, based on analysis of 2025 cold calling data and WHAM cold-call performance data. Scraped with Firecrawl on May 10, 2026.". Qualification: Cognism is a sales intelligence vendor. Treat Cognism team performance figures as first-party vendor benchmarks, not neutral market averages. Source: https://www.cognism.com/reports/cold-calling-report-2026. Record audit/review date: 2026-05-10. - [b2b-cold-calling-statistics-2026-008] Recorded assertion from cognism.com: "Cognism reports that Thursday remained the standout day to get through to prospects, while Tuesday overtook Wednesday as the second-best day.". Record category: benchmark. Source-record methodology (retained wording): "Cognism State of Cold Calling in 2026 report, based on analysis of 2025 cold calling data and WHAM cold-call performance data. Scraped with Firecrawl on May 10, 2026.". Qualification: Cognism is a sales intelligence vendor. Treat Cognism team performance figures as first-party vendor benchmarks, not neutral market averages. Source: https://www.cognism.com/reports/cold-calling-report-2026. Record audit/review date: 2026-05-10. - [b2b-cold-calling-statistics-2026-009] Recorded assertion from cognism.com: "Cognism reports that Friday still trails behind the rest of the week for cold calling performance.". Record category: benchmark. Source-record methodology (retained wording): "Cognism State of Cold Calling in 2026 report, based on analysis of 2025 cold calling data and WHAM cold-call performance data. Scraped with Firecrawl on May 10, 2026.". Qualification: Cognism is a sales intelligence vendor. Treat Cognism team performance figures as first-party vendor benchmarks, not neutral market averages. Source: https://www.cognism.com/reports/cold-calling-report-2026. Record audit/review date: 2026-05-10. - [b2b-cold-calling-statistics-2026-010] Recorded assertion from cognism.com: "Cognism reports that the most effective cold calling window is between 10am and 11am.". Record category: benchmark. Source-record methodology (retained wording): "Cognism State of Cold Calling in 2026 report, based on analysis of 2025 cold calling data and WHAM cold-call performance data. Scraped with Firecrawl on May 10, 2026.". Qualification: Cognism is a sales intelligence vendor. Treat Cognism team performance figures as first-party vendor benchmarks, not neutral market averages. Source: https://www.cognism.com/reports/cold-calling-report-2026. Record audit/review date: 2026-05-10. ### B2B Lead Generation Statistics 2026: Conversion and CAC URL: https://konabayev.com/blog/b2b-lead-generation-statistics-2026/ Full article summary (may cover material outside this subset): Eighteen audited B2B lead-generation benchmarks covering website conversion, SaaS acquisition cost, email engagement and marketing-to-sales handoff. Updated: 2026-07-14 Selected records: first 10 of 18 published records in file order. Full published dataset: https://konabayev.com/data/b2b-lead-generation-statistics-2026.jsonl. The selected records do not cover every topic in the full article summary. - [b2b-lead-generation-statistics-2026-001] Recorded assertion from firstpagesage.com: "First Page Sage reports a 1.1% average website conversion rate for B2B SaaS in its 2026 industry table.". Recorded figure: 1.1%. Period: client data gathered January 2022 through August 2025. Population: First Page Sage client data across 25 B2B industries; public page does not state company count. Evidence type: client analytics benchmark. Record category: website_conversion. Source-record methodology: not recorded. Qualification: Agency client data is not a probability sample of all B2B websites. Conversion means a unique website visitor completing the company-defined conversion action. Source: https://firstpagesage.com/reports/b2b-conversion-rates-by-industry-fc/. Record audit/review date: 2026-07-10. - [b2b-lead-generation-statistics-2026-002] Recorded assertion from firstpagesage.com: "First Page Sage reports a 1.9% average website conversion rate for financial services.". Recorded figure: 1.9%. Period: client data gathered January 2022 through August 2025. Population: First Page Sage client data across 25 B2B industries; public page does not state company count. Evidence type: client analytics benchmark. Record category: website_conversion. Source-record methodology: not recorded. Qualification: Agency client data is not a probability sample of all B2B websites. Conversion means a unique website visitor completing the company-defined conversion action. Source: https://firstpagesage.com/reports/b2b-conversion-rates-by-industry-fc/. Record audit/review date: 2026-07-10. - [b2b-lead-generation-statistics-2026-003] Recorded assertion from firstpagesage.com: "First Page Sage reports a 2.2% average website conversion rate for manufacturing.". Recorded figure: 2.2%. Period: client data gathered January 2022 through August 2025. Population: First Page Sage client data across 25 B2B industries; public page does not state company count. Evidence type: client analytics benchmark. Record category: website_conversion. Source-record methodology: not recorded. Qualification: Agency client data is not a probability sample of all B2B websites. Conversion means a unique website visitor completing the company-defined conversion action. Source: https://firstpagesage.com/reports/b2b-conversion-rates-by-industry-fc/. Record audit/review date: 2026-07-10. - [b2b-lead-generation-statistics-2026-004] Recorded assertion from firstpagesage.com: "First Page Sage reports a 1.5% average website conversion rate for IT and managed services.". Recorded figure: 1.5%. Period: client data gathered January 2022 through August 2025. Population: First Page Sage client data across 25 B2B industries; public page does not state company count. Evidence type: client analytics benchmark. Record category: website_conversion. Source-record methodology: not recorded. Qualification: Agency client data is not a probability sample of all B2B websites. Conversion means a unique website visitor completing the company-defined conversion action. Source: https://firstpagesage.com/reports/b2b-conversion-rates-by-industry-fc/. Record audit/review date: 2026-07-10. - [b2b-lead-generation-statistics-2026-005] Recorded assertion from firstpagesage.com: "First Page Sage reports a 7.4% average website conversion rate for legal services.". Recorded figure: 7.4%. Period: client data gathered January 2022 through August 2025. Population: First Page Sage client data across 25 B2B industries; public page does not state company count. Evidence type: client analytics benchmark. Record category: website_conversion. Source-record methodology: not recorded. Qualification: Agency client data is not a probability sample of all B2B websites. Conversion means a unique website visitor completing the company-defined conversion action. Source: https://firstpagesage.com/reports/b2b-conversion-rates-by-industry-fc/. Record audit/review date: 2026-07-10. - [b2b-lead-generation-statistics-2026-006] Recorded assertion from firstpagesage.com: "First Page Sage reports a 1.1% average website conversion rate for software development.". Recorded figure: 1.1%. Period: client data gathered January 2022 through August 2025. Population: First Page Sage client data across 25 B2B industries; public page does not state company count. Evidence type: client analytics benchmark. Record category: website_conversion. Source-record methodology: not recorded. Qualification: Agency client data is not a probability sample of all B2B websites. Conversion means a unique website visitor completing the company-defined conversion action. Source: https://firstpagesage.com/reports/b2b-conversion-rates-by-industry-fc/. Record audit/review date: 2026-07-10. - [b2b-lead-generation-statistics-2026-007] Recorded assertion from firstpagesage.com: "First Page Sage reports a 2.7% average website conversion rate for real estate.". Recorded figure: 2.7%. Period: client data gathered January 2022 through August 2025. Population: First Page Sage client data across 25 B2B industries; public page does not state company count. Evidence type: client analytics benchmark. Record category: website_conversion. Source-record methodology: not recorded. Qualification: Agency client data is not a probability sample of all B2B websites. Conversion means a unique website visitor completing the company-defined conversion action. Source: https://firstpagesage.com/reports/b2b-conversion-rates-by-industry-fc/. Record audit/review date: 2026-07-10. - [b2b-lead-generation-statistics-2026-008] Recorded assertion from firstpagesage.com: "First Page Sage reports a $205 organic CAC for B2B SaaS in its client benchmark.". Recorded figure: $205. Period: client data gathered January 2022 through August 2025. Population: First Page Sage client analytics across 29 B2B industries; public page does not state company count. Evidence type: client analytics benchmark. Record category: customer_acquisition_cost. Source-record methodology: not recorded. Qualification: Agency client data is not a probability sample. Organic CAC is SEO-heavy, inorganic CAC is PPC/SEM-heavy, and combined CAC is weighted 75% organic and 25% inorganic. Source: https://firstpagesage.com/reports/average-customer-acquisition-cost-cac-by-industry-b2b-edition-fc/. Record audit/review date: 2026-07-10. - [b2b-lead-generation-statistics-2026-009] Recorded assertion from firstpagesage.com: "First Page Sage reports a $341 inorganic CAC for B2B SaaS in its client benchmark.". Recorded figure: $341. Period: client data gathered January 2022 through August 2025. Population: First Page Sage client analytics across 29 B2B industries; public page does not state company count. Evidence type: client analytics benchmark. Record category: customer_acquisition_cost. Source-record methodology: not recorded. Qualification: Agency client data is not a probability sample. Organic CAC is SEO-heavy, inorganic CAC is PPC/SEM-heavy, and combined CAC is weighted 75% organic and 25% inorganic. Source: https://firstpagesage.com/reports/average-customer-acquisition-cost-cac-by-industry-b2b-edition-fc/. Record audit/review date: 2026-07-10. - [b2b-lead-generation-statistics-2026-010] Recorded assertion from firstpagesage.com: "First Page Sage reports a $239 combined average CAC for B2B SaaS, using its stated 75% organic and 25% inorganic weighting.". Recorded figure: $239. Period: client data gathered January 2022 through August 2025. Population: First Page Sage client analytics across 29 B2B industries; public page does not state company count. Evidence type: client analytics benchmark. Record category: customer_acquisition_cost. Source-record methodology: not recorded. Qualification: Agency client data is not a probability sample. Organic CAC is SEO-heavy, inorganic CAC is PPC/SEM-heavy, and combined CAC is weighted 75% organic and 25% inorganic. Source: https://firstpagesage.com/reports/average-customer-acquisition-cost-cac-by-industry-b2b-edition-fc/. Record audit/review date: 2026-07-10. ### Latest B2B Marketing Benchmarks 2026: CAC, ROI, Conversion Stats URL: https://konabayev.com/blog/b2b-marketing-benchmarks-2026/ Full article summary (may cover material outside this subset): B2B conversion, CAC and ROI benchmarks with a September 2026 Benchmarkit update, source-level caveats, and downloadable comparison tables. Updated: 2026-10-04 Selected records: first 10 of 50 published records in file order. Full published dataset: https://konabayev.com/data/b2b-marketing-benchmarks-2026.jsonl. The selected records do not cover every topic in the full article summary. - [B2B-001] Recorded assertion from firstpagesage.com: "First Page Sage reports a 1.1% average website conversion rate for B2B SaaS in its 2026 industry table.". Recorded figure: 1.1%. Period: client data gathered January 2022 through August 2025. Population: First Page Sage client data across 25 B2B industries; public page does not state company count. Evidence type: client analytics benchmark. Record category: website_conversion. Source-record methodology: not recorded. Qualification: Agency client data is not a probability sample of all B2B websites. Conversion means a unique website visitor completing the company-defined conversion action. Source: https://firstpagesage.com/reports/b2b-conversion-rates-by-industry-fc/. Record audit/review date: 2026-07-10. - [B2B-002] Recorded assertion from firstpagesage.com: "First Page Sage reports a 1.9% average website conversion rate for financial services.". Recorded figure: 1.9%. Period: client data gathered January 2022 through August 2025. Population: First Page Sage client data across 25 B2B industries; public page does not state company count. Evidence type: client analytics benchmark. Record category: website_conversion. Source-record methodology: not recorded. Qualification: Agency client data is not a probability sample of all B2B websites. Conversion means a unique website visitor completing the company-defined conversion action. Source: https://firstpagesage.com/reports/b2b-conversion-rates-by-industry-fc/. Record audit/review date: 2026-07-10. - [B2B-003] Recorded assertion from firstpagesage.com: "First Page Sage reports a 2.2% average website conversion rate for manufacturing.". Recorded figure: 2.2%. Period: client data gathered January 2022 through August 2025. Population: First Page Sage client data across 25 B2B industries; public page does not state company count. Evidence type: client analytics benchmark. Record category: website_conversion. Source-record methodology: not recorded. Qualification: Agency client data is not a probability sample of all B2B websites. Conversion means a unique website visitor completing the company-defined conversion action. Source: https://firstpagesage.com/reports/b2b-conversion-rates-by-industry-fc/. Record audit/review date: 2026-07-10. - [B2B-004] Recorded assertion from firstpagesage.com: "First Page Sage reports a 1.5% average website conversion rate for IT and managed services.". Recorded figure: 1.5%. Period: client data gathered January 2022 through August 2025. Population: First Page Sage client data across 25 B2B industries; public page does not state company count. Evidence type: client analytics benchmark. Record category: website_conversion. Source-record methodology: not recorded. Qualification: Agency client data is not a probability sample of all B2B websites. Conversion means a unique website visitor completing the company-defined conversion action. Source: https://firstpagesage.com/reports/b2b-conversion-rates-by-industry-fc/. Record audit/review date: 2026-07-10. - [B2B-005] Recorded assertion from firstpagesage.com: "First Page Sage reports a 7.4% average website conversion rate for legal services.". Recorded figure: 7.4%. Period: client data gathered January 2022 through August 2025. Population: First Page Sage client data across 25 B2B industries; public page does not state company count. Evidence type: client analytics benchmark. Record category: website_conversion. Source-record methodology: not recorded. Qualification: Agency client data is not a probability sample of all B2B websites. Conversion means a unique website visitor completing the company-defined conversion action. Source: https://firstpagesage.com/reports/b2b-conversion-rates-by-industry-fc/. Record audit/review date: 2026-07-10. - [B2B-006] Recorded assertion from firstpagesage.com: "First Page Sage reports a 1.1% average website conversion rate for software development.". Recorded figure: 1.1%. Period: client data gathered January 2022 through August 2025. Population: First Page Sage client data across 25 B2B industries; public page does not state company count. Evidence type: client analytics benchmark. Record category: website_conversion. Source-record methodology: not recorded. Qualification: Agency client data is not a probability sample of all B2B websites. Conversion means a unique website visitor completing the company-defined conversion action. Source: https://firstpagesage.com/reports/b2b-conversion-rates-by-industry-fc/. Record audit/review date: 2026-07-10. - [B2B-007] Recorded assertion from firstpagesage.com: "First Page Sage reports a 2.7% average website conversion rate for real estate.". Recorded figure: 2.7%. Period: client data gathered January 2022 through August 2025. Population: First Page Sage client data across 25 B2B industries; public page does not state company count. Evidence type: client analytics benchmark. Record category: website_conversion. Source-record methodology: not recorded. Qualification: Agency client data is not a probability sample of all B2B websites. Conversion means a unique website visitor completing the company-defined conversion action. Source: https://firstpagesage.com/reports/b2b-conversion-rates-by-industry-fc/. Record audit/review date: 2026-07-10. - [B2B-008] Recorded assertion from firstpagesage.com: "First Page Sage reports a $205 organic CAC for B2B SaaS in its client benchmark.". Recorded figure: $205. Period: client data gathered January 2022 through August 2025. Population: First Page Sage client analytics across 29 B2B industries; public page does not state company count. Evidence type: client analytics benchmark. Record category: customer_acquisition_cost. Source-record methodology: not recorded. Qualification: Agency client data is not a probability sample. Organic CAC is SEO-heavy, inorganic CAC is PPC/SEM-heavy, and combined CAC is weighted 75% organic and 25% inorganic. Source: https://firstpagesage.com/reports/average-customer-acquisition-cost-cac-by-industry-b2b-edition-fc/. Record audit/review date: 2026-07-10. - [B2B-009] Recorded assertion from firstpagesage.com: "First Page Sage reports a $341 inorganic CAC for B2B SaaS in its client benchmark.". Recorded figure: $341. Period: client data gathered January 2022 through August 2025. Population: First Page Sage client analytics across 29 B2B industries; public page does not state company count. Evidence type: client analytics benchmark. Record category: customer_acquisition_cost. Source-record methodology: not recorded. Qualification: Agency client data is not a probability sample. Organic CAC is SEO-heavy, inorganic CAC is PPC/SEM-heavy, and combined CAC is weighted 75% organic and 25% inorganic. Source: https://firstpagesage.com/reports/average-customer-acquisition-cost-cac-by-industry-b2b-edition-fc/. Record audit/review date: 2026-07-10. - [B2B-010] Recorded assertion from firstpagesage.com: "First Page Sage reports a $239 combined average CAC for B2B SaaS, using its stated 75% organic and 25% inorganic weighting.". Recorded figure: $239. Period: client data gathered January 2022 through August 2025. Population: First Page Sage client analytics across 29 B2B industries; public page does not state company count. Evidence type: client analytics benchmark. Record category: customer_acquisition_cost. Source-record methodology: not recorded. Qualification: Agency client data is not a probability sample. Organic CAC is SEO-heavy, inorganic CAC is PPC/SEM-heavy, and combined CAC is weighted 75% organic and 25% inorganic. Source: https://firstpagesage.com/reports/average-customer-acquisition-cost-cac-by-industry-b2b-edition-fc/. Record audit/review date: 2026-07-10. ### B2B Marketing Budget Benchmarks 2026: Spend Ranges and Allocation URL: https://konabayev.com/blog/b2b-marketing-budget-benchmarks-2026/ Full article summary (may cover material outside this subset): B2B budget planning with retained 2024–2025 spend ranges, scoped Dreamdata customer observations, EUR costs, ROAS subgroups and an exclusive cash-budget model. Updated: 2026-10-04 Selected records: first 10 of 40 published records in file order. Full published dataset: https://konabayev.com/data/b2b-marketing-budget-benchmarks-2026.jsonl. The selected records do not cover every topic in the full article summary. - [b2b-marketing-budget-benchmarks-2026-001] Recorded assertion from directiveconsulting.com: "Retained secondary summary: Most B2B companies invest 7.7%-8% of revenue in marketing". Record category: revenue. Source-record methodology (retained wording): "Forrester, 2024; Gartner, 2025; BDC; various benchmarks". Qualification: Retained Directive secondary synthesis of differently defined underlying studies, including 2024–2025 research; not a newly measured 2026 B2B average or independently revalidated primary research. Allocation categories are not a reconciled additive budget. Preserve source labels and stated revenue/ARR units without borrowing thresholds from another row. Editorial qualification October 4, 2026; original audit dates remain historical. Source: https://directiveconsulting.com/blog/blog-b2b-marketing-budget/. Record audit/review date: 2026-05-10. - [b2b-marketing-budget-benchmarks-2026-002] Recorded assertion from directiveconsulting.com: "Retained secondary summary: Digital channels now absorb 61.1% of total marketing spend". Record category: channel. Source-record methodology (retained wording): "Forrester, 2024; Gartner, 2025; BDC; various benchmarks". Qualification: Retained Directive secondary synthesis of differently defined underlying studies, including 2024–2025 research; not a newly measured 2026 B2B average or independently revalidated primary research. Allocation categories are not a reconciled additive budget. Preserve source labels and stated revenue/ARR units without borrowing thresholds from another row. Editorial qualification October 4, 2026; original audit dates remain historical. Source: https://directiveconsulting.com/blog/blog-b2b-marketing-budget/. Record audit/review date: 2026-05-10. - [b2b-marketing-budget-benchmarks-2026-003] Recorded assertion from directiveconsulting.com: "Retained secondary summary: Paid media takes roughly 30.6% of the average marketing budget". Record category: benchmark. Source-record methodology (retained wording): "Forrester, 2024; Gartner, 2025; BDC; various benchmarks". Qualification: Retained Directive secondary synthesis of differently defined underlying studies, including 2024–2025 research; not a newly measured 2026 B2B average or independently revalidated primary research. Allocation categories are not a reconciled additive budget. Preserve source labels and stated revenue/ARR units without borrowing thresholds from another row. Editorial qualification October 4, 2026; original audit dates remain historical. Source: https://directiveconsulting.com/blog/blog-b2b-marketing-budget/. Record audit/review date: 2026-05-10. - [b2b-marketing-budget-benchmarks-2026-004] Recorded assertion from directiveconsulting.com: "Retained secondary summary: B2B SaaS marketing budgets can run 15%-25%+ of ARR in early growth stages". Record category: revenue. Source-record methodology (retained wording): "Forrester, 2024; Gartner, 2025; BDC; various benchmarks". Qualification: Retained Directive secondary synthesis of differently defined underlying studies, including 2024–2025 research; not a newly measured 2026 B2B average or independently revalidated primary research. Allocation categories are not a reconciled additive budget. Preserve source labels and stated revenue/ARR units without borrowing thresholds from another row. Editorial qualification October 4, 2026; original audit dates remain historical. Source: https://directiveconsulting.com/blog/blog-b2b-marketing-budget/. Record audit/review date: 2026-05-10. - [b2b-marketing-budget-benchmarks-2026-005] Recorded assertion from directiveconsulting.com: "Retained secondary summary: 75% of CMOs report being asked to do more with less". Record category: benchmark. Source-record methodology (retained wording): "Forrester, 2024; Gartner, 2025; BDC; various benchmarks". Qualification: Retained Directive secondary synthesis of differently defined underlying studies, including 2024–2025 research; not a newly measured 2026 B2B average or independently revalidated primary research. Allocation categories are not a reconciled additive budget. Preserve source labels and stated revenue/ARR units without borrowing thresholds from another row. Editorial qualification October 4, 2026; original audit dates remain historical. Source: https://directiveconsulting.com/blog/blog-b2b-marketing-budget/. Record audit/review date: 2026-05-10. - [b2b-marketing-budget-benchmarks-2026-006] Recorded assertion from directiveconsulting.com: "Retained secondary summary: Agency and outsourced services receive 22%-25% of total marketing budgets". Record category: benchmark. Source-record methodology (retained wording): "Forrester, 2024; Gartner, 2025; BDC; various benchmarks". Qualification: Retained Directive secondary synthesis of differently defined underlying studies, including 2024–2025 research; not a newly measured 2026 B2B average or independently revalidated primary research. Allocation categories are not a reconciled additive budget. Preserve source labels and stated revenue/ARR units without borrowing thresholds from another row. Editorial qualification October 4, 2026; original audit dates remain historical. Source: https://directiveconsulting.com/blog/blog-b2b-marketing-budget/. Record audit/review date: 2026-05-10. - [b2b-marketing-budget-benchmarks-2026-007] Recorded assertion from directiveconsulting.com: "Retained secondary summary: In-house labor accounts for roughly 25%-27% of total marketing budgets". Record category: benchmark. Source-record methodology (retained wording): "Forrester, 2024; Gartner, 2025; BDC; various benchmarks". Qualification: Retained Directive secondary synthesis of differently defined underlying studies, including 2024–2025 research; not a newly measured 2026 B2B average or independently revalidated primary research. Allocation categories are not a reconciled additive budget. Preserve source labels and stated revenue/ARR units without borrowing thresholds from another row. Editorial qualification October 4, 2026; original audit dates remain historical. Source: https://directiveconsulting.com/blog/blog-b2b-marketing-budget/. Record audit/review date: 2026-05-10. - [b2b-marketing-budget-benchmarks-2026-008] Recorded assertion from directiveconsulting.com: "Retained secondary summary: Smaller B2B firms often operate with marketing budgets of 2%-5% of revenue". Record category: revenue. Source-record methodology (retained wording): "Forrester, 2024; Gartner, 2025; BDC; various benchmarks". Qualification: Retained Directive secondary synthesis of differently defined underlying studies, including 2024–2025 research; not a newly measured 2026 B2B average or independently revalidated primary research. Allocation categories are not a reconciled additive budget. Preserve source labels and stated revenue/ARR units without borrowing thresholds from another row. Editorial qualification October 4, 2026; original audit dates remain historical. Source: https://directiveconsulting.com/blog/blog-b2b-marketing-budget/. Record audit/review date: 2026-05-10. - [b2b-marketing-budget-benchmarks-2026-009] Recorded assertion from directiveconsulting.com: "Retained secondary summary: High-growth B2B companies frequently invest 10%-20%+ of revenue during expansion phases". Record category: revenue. Source-record methodology (retained wording): "Forrester, 2024; Gartner, 2025; BDC; various benchmarks". Qualification: Retained Directive secondary synthesis of differently defined underlying studies, including 2024–2025 research; not a newly measured 2026 B2B average or independently revalidated primary research. Allocation categories are not a reconciled additive budget. Preserve source labels and stated revenue/ARR units without borrowing thresholds from another row. Editorial qualification October 4, 2026; original audit dates remain historical. Source: https://directiveconsulting.com/blog/blog-b2b-marketing-budget/. Record audit/review date: 2026-05-10. - [b2b-marketing-budget-benchmarks-2026-010] Recorded assertion from directiveconsulting.com: "Retained secondary summary: Content and SEO typically receive 10%-15% of the total marketing budget". Record category: benchmark. Source-record methodology (retained wording): "Forrester, 2024; Gartner, 2025; BDC; various benchmarks". Qualification: Retained Directive secondary synthesis of differently defined underlying studies, including 2024–2025 research; not a newly measured 2026 B2B average or independently revalidated primary research. Allocation categories are not a reconciled additive budget. Preserve source labels and stated revenue/ARR units without borrowing thresholds from another row. Editorial qualification October 4, 2026; original audit dates remain historical. Source: https://directiveconsulting.com/blog/blog-b2b-marketing-budget/. Record audit/review date: 2026-05-10. ### B2B Marketing Statistics 2026: LinkedIn, AI, and Buyers URL: https://konabayev.com/blog/b2b-marketing-statistics-2026/ Full article summary (may cover material outside this subset): Source-backed B2B marketing statistics for 2026 on LinkedIn creator content, employee reach, AI-assisted buying, trust, and influencer budgets. Updated: 2026-08-04 Selected records: first 10 of 18 published records in file order. Full published dataset: https://konabayev.com/data/b2b-marketing-statistics-2026.jsonl. The selected records do not cover every topic in the full article summary. - Recorded assertion from 6 B2B Marketing Insights for 2026: Why Creators Are Up Next in B2B: "LinkedIn reports that the combined networks of employees are about 12 times larger than a company's own following.". Evidence type: publisher observation. Source-record methodology: not recorded. Qualification: Combined network size can overlap and does not equal organic reach, engagement, or revenue. Source: https://www.linkedin.com/business/marketing/blog/trends-tips/b2b-marketing-insights-creators-thought-leadership. Record audit/review date: 2026-08-04. - Recorded assertion from 6 B2B Marketing Insights for 2026: Why Creators Are Up Next in B2B: "LinkedIn describes its community as more than 1 billion professionals worldwide.". Evidence type: platform figure. Source-record methodology: not recorded. Qualification: This is a LinkedIn-published community figure, not an independent audience audit. Source: https://www.linkedin.com/business/marketing/blog/trends-tips/b2b-marketing-insights-creators-thought-leadership. Record audit/review date: 2026-08-04. - Recorded assertion from B2B Creators: The Future of Thought Leadership on LinkedIn: "LinkedIn reports that 82% of buyers say B2B creator content influences them.". Evidence type: publisher buyer statistic. Source-record methodology: not recorded. Qualification: Influence is broader than purchase causation; the captured page does not expose the full sample. Source: https://www.linkedin.com/business/marketing/blog/linkedin-ads/b2b-creator-thought-leadership. Record audit/review date: 2026-08-04. - Recorded assertion from B2B Creators: The Future of Thought Leadership on LinkedIn: "LinkedIn reports that nearly 80% of buyers engage with B2B creator content monthly.". Evidence type: publisher buyer statistic. Source-record methodology: not recorded. Qualification: Engagement is not defined as a pipeline or revenue event on the captured page. Source: https://www.linkedin.com/business/marketing/blog/linkedin-ads/b2b-creator-thought-leadership. Record audit/review date: 2026-08-04. - Recorded assertion from B2B Creators: The Future of Thought Leadership on LinkedIn: "LinkedIn reports that 59% of buyers discover new brands through creator content.". Evidence type: publisher buyer statistic. Source-record methodology: not recorded. Qualification: This is an awareness-stage observation, not a conversion rate. Source: https://www.linkedin.com/business/marketing/blog/linkedin-ads/b2b-creator-thought-leadership. Record audit/review date: 2026-08-04. - Recorded assertion from B2B Creators: The Future of Thought Leadership on LinkedIn: "LinkedIn reports that 67% of buyers say creator content helps them assess potential solutions.". Evidence type: publisher buyer statistic. Source-record methodology: not recorded. Qualification: Assessment influence should not be rewritten as purchase causation. Source: https://www.linkedin.com/business/marketing/blog/linkedin-ads/b2b-creator-thought-leadership. Record audit/review date: 2026-08-04. - Recorded assertion from B2B Creators: The Future of Thought Leadership on LinkedIn: "LinkedIn reports that 47% of buyers visited a vendor website after engaging with creator content.". Evidence type: publisher buyer statistic. Source-record methodology: not recorded. Qualification: A website visit is not a qualified lead or sale. Source: https://www.linkedin.com/business/marketing/blog/linkedin-ads/b2b-creator-thought-leadership. Record audit/review date: 2026-08-04. - Recorded assertion from B2B Creators: The Future of Thought Leadership on LinkedIn: "LinkedIn reports that 38% of buyers engaged with a sales team after creator content.". Evidence type: publisher buyer statistic. Source-record methodology: not recorded. Qualification: The statistic does not establish creator content as the sole cause of sales engagement. Source: https://www.linkedin.com/business/marketing/blog/linkedin-ads/b2b-creator-thought-leadership. Record audit/review date: 2026-08-04. - Recorded assertion from B2B Creators: The Future of Thought Leadership on LinkedIn: "LinkedIn reports that short-form video uploads on the platform grew 45% year over year.". Evidence type: platform activity statistic. Source-record methodology: not recorded. Qualification: Upload growth measures supply, not buyer response or revenue. Source: https://www.linkedin.com/business/marketing/blog/linkedin-ads/b2b-creator-thought-leadership. Record audit/review date: 2026-08-04. - Recorded assertion from B2B Creators: The Future of Thought Leadership on LinkedIn: "LinkedIn reports that campaigns using Thought Leader Ads had 252% higher click-through rates.". Evidence type: relative product benchmark. Source-record methodology: not recorded. Qualification: No absolute CTR baseline or full sample is supplied in the captured page. Source: https://www.linkedin.com/business/marketing/blog/linkedin-ads/b2b-creator-thought-leadership. Record audit/review date: 2026-08-04. ### B2B SaaS Conversion Rates: Recorded Ranges and Cohort Definitions URL: https://konabayev.com/blog/b2b-saas-conversion-benchmarks-2026/ Full article summary (may cover material outside this subset): B2B SaaS funnel conversion definitions, recorded benchmark ranges, unresolved MQL-to-SQL discrepancies, and a practical cohort measurement worksheet. Updated: 2026-10-04 Selected records: first 10 of 40 published records in file order. Full published dataset: https://konabayev.com/data/b2b-saas-conversion-benchmarks-2026.jsonl. The selected records do not cover every topic in the full article summary. - [b2b-saas-conversion-benchmarks-2026-001] Recorded assertion from saashero.net: "Average B2B SaaS visitor-to-lead conversion rates sit at 1.5-2.5%". Record category: conversion. Source-record methodology (retained wording): "No reproducible sample, weights, denominator or observation window is available in the retained record. Original publisher-methodology labels are preserved in the historical brief.". Qualification: Secondary aggregator assertion recorded May 10, 2026; not a verified market benchmark. Sample size, denominator, event definition and observation window are unconfirmed. Editorial caveat reviewed October 2, 2026 without reacquiring the source. Repeated statements are not independent observations. Retained aggregator wording may be a fragment. Do not infer a missing cohort, tier, metric or denominator, or use it as a standalone benchmark. Source: https://www.saashero.net/content/2026-b2b-saas-conversion-benchmarks/. Record audit/review date: 2026-10-02. - [b2b-saas-conversion-benchmarks-2026-002] Recorded assertion from saashero.net: "top 10% reach 8-15%". Record category: benchmark. Source-record methodology (retained wording): "No reproducible sample, weights, denominator or observation window is available in the retained record. Original publisher-methodology labels are preserved in the historical brief.". Qualification: Secondary aggregator assertion recorded May 10, 2026; not a verified market benchmark. Sample size, denominator, event definition and observation window are unconfirmed. Editorial caveat reviewed October 2, 2026 without reacquiring the source. Repeated statements are not independent observations. Retained aggregator wording may be a fragment. Do not infer a missing cohort, tier, metric or denominator, or use it as a standalone benchmark. Source: https://www.saashero.net/content/2026-b2b-saas-conversion-benchmarks/. Record audit/review date: 2026-10-02. - [b2b-saas-conversion-benchmarks-2026-003] Recorded assertion from saashero.net: "MQL-to-SQL rates average 32-40% for SaaS". Record category: benchmark. Source-record methodology (retained wording): "No reproducible sample, weights, denominator or observation window is available in the retained record. Original publisher-methodology labels are preserved in the historical brief.". Qualification: Secondary aggregator assertion recorded May 10, 2026; not a verified market benchmark. Sample size, denominator, event definition and observation window are unconfirmed. Editorial caveat reviewed October 2, 2026 without reacquiring the source. Repeated statements are not independent observations. Unresolved MQL-to-SQL cohort labels across 32-40%, 39-40%, 40%, 10-30%, 13%, <20% and <10% records; not comparable and no weighted average or diagnostic threshold can be derived. Retained aggregator wording may be a fragment. Do not infer a missing cohort, tier, metric or denominator, or use it as a standalone benchmark. Source: https://www.saashero.net/content/2026-b2b-saas-conversion-benchmarks/. Record audit/review date: 2026-10-02. - [b2b-saas-conversion-benchmarks-2026-004] Recorded assertion from saashero.net: "SQL-to-close conversions average 20-25%". Record category: conversion. Source-record methodology (retained wording): "No reproducible sample, weights, denominator or observation window is available in the retained record. Original publisher-methodology labels are preserved in the historical brief.". Qualification: Secondary aggregator assertion recorded May 10, 2026; not a verified market benchmark. Sample size, denominator, event definition and observation window are unconfirmed. Editorial caveat reviewed October 2, 2026 without reacquiring the source. Repeated statements are not independent observations. Retained aggregator wording may be a fragment. Do not infer a missing cohort, tier, metric or denominator, or use it as a standalone benchmark. Source: https://www.saashero.net/content/2026-b2b-saas-conversion-benchmarks/. Record audit/review date: 2026-10-02. - [b2b-saas-conversion-benchmarks-2026-005] Recorded assertion from saashero.net: "top performers exceed 30%". Record category: benchmark. Source-record methodology (retained wording): "No reproducible sample, weights, denominator or observation window is available in the retained record. Original publisher-methodology labels are preserved in the historical brief.". Qualification: Secondary aggregator assertion recorded May 10, 2026; not a verified market benchmark. Sample size, denominator, event definition and observation window are unconfirmed. Editorial caveat reviewed October 2, 2026 without reacquiring the source. Repeated statements are not independent observations. Retained aggregator wording may be a fragment. Do not infer a missing cohort, tier, metric or denominator, or use it as a standalone benchmark. Source: https://www.saashero.net/content/2026-b2b-saas-conversion-benchmarks/. Record audit/review date: 2026-10-02. - [b2b-saas-conversion-benchmarks-2026-006] Recorded assertion from saashero.net: "HR Tech at 3-6% top-of-funnel". Record category: benchmark. Source-record methodology (retained wording): "No reproducible sample, weights, denominator or observation window is available in the retained record. Original publisher-methodology labels are preserved in the historical brief.". Qualification: Secondary aggregator assertion recorded May 10, 2026; not a verified market benchmark. Sample size, denominator, event definition and observation window are unconfirmed. Editorial caveat reviewed October 2, 2026 without reacquiring the source. Repeated statements are not independent observations. Retained aggregator wording may be a fragment. Do not infer a missing cohort, tier, metric or denominator, or use it as a standalone benchmark. Source: https://www.saashero.net/content/2026-b2b-saas-conversion-benchmarks/. Record audit/review date: 2026-10-02. - [b2b-saas-conversion-benchmarks-2026-007] Recorded assertion from saashero.net: "Cybersecurity at 1-2%". Record category: benchmark. Source-record methodology (retained wording): "No reproducible sample, weights, denominator or observation window is available in the retained record. Original publisher-methodology labels are preserved in the historical brief.". Qualification: Secondary aggregator assertion recorded May 10, 2026; not a verified market benchmark. Sample size, denominator, event definition and observation window are unconfirmed. Editorial caveat reviewed October 2, 2026 without reacquiring the source. Repeated statements are not independent observations. Retained aggregator wording may be a fragment. Do not infer a missing cohort, tier, metric or denominator, or use it as a standalone benchmark. Source: https://www.saashero.net/content/2026-b2b-saas-conversion-benchmarks/. Record audit/review date: 2026-10-02. - [b2b-saas-conversion-benchmarks-2026-008] Recorded assertion from saashero.net: "Visitor-to-Lead: 1.5-2.5%". Record category: benchmark. Source-record methodology (retained wording): "No reproducible sample, weights, denominator or observation window is available in the retained record. Original publisher-methodology labels are preserved in the historical brief.". Qualification: Secondary aggregator assertion recorded May 10, 2026; not a verified market benchmark. Sample size, denominator, event definition and observation window are unconfirmed. Editorial caveat reviewed October 2, 2026 without reacquiring the source. Repeated statements are not independent observations. Retained aggregator wording may be a fragment. Do not infer a missing cohort, tier, metric or denominator, or use it as a standalone benchmark. Source: https://www.saashero.net/content/2026-b2b-saas-conversion-benchmarks/. Record audit/review date: 2026-10-02. - [b2b-saas-conversion-benchmarks-2026-009] Recorded assertion from saashero.net: "Visitor-to-Lead: 8-15%". Record category: benchmark. Source-record methodology (retained wording): "No reproducible sample, weights, denominator or observation window is available in the retained record. Original publisher-methodology labels are preserved in the historical brief.". Qualification: Secondary aggregator assertion recorded May 10, 2026; not a verified market benchmark. Sample size, denominator, event definition and observation window are unconfirmed. Editorial caveat reviewed October 2, 2026 without reacquiring the source. Repeated statements are not independent observations. Retained aggregator wording may be a fragment. Do not infer a missing cohort, tier, metric or denominator, or use it as a standalone benchmark. Source: https://www.saashero.net/content/2026-b2b-saas-conversion-benchmarks/. Record audit/review date: 2026-10-02. - [b2b-saas-conversion-benchmarks-2026-010] Recorded assertion from saashero.net: "Visitor-to-Lead: <0.7%". Record category: benchmark. Source-record methodology (retained wording): "No reproducible sample, weights, denominator or observation window is available in the retained record. Original publisher-methodology labels are preserved in the historical brief.". Qualification: Secondary aggregator assertion recorded May 10, 2026; not a verified market benchmark. Sample size, denominator, event definition and observation window are unconfirmed. Editorial caveat reviewed October 2, 2026 without reacquiring the source. Repeated statements are not independent observations. Retained aggregator wording may be a fragment. Do not infer a missing cohort, tier, metric or denominator, or use it as a standalone benchmark. Source: https://www.saashero.net/content/2026-b2b-saas-conversion-benchmarks/. Record audit/review date: 2026-10-02. ### B2B Sales Benchmarks 2026: Win Rates, Quota & Cycles URL: https://konabayev.com/blog/b2b-sales-benchmarks/ Full article summary (may cover material outside this subset): 50 source-backed B2B sales benchmarks for 2026 covering win rates, quota attainment, sales cycles, seller time, AI, pipeline, stakeholders and expansion. Updated: 2026-10-04 Selected records: first 10 of 50 published records in file order. Full published dataset: https://konabayev.com/data/b2b-sales-benchmarks.jsonl. The selected records do not cover every topic in the full article summary. - [SALES-001] Recorded assertion from salesforce.com: "Salesforce respondents spent 40% of an average workweek selling.". Recorded figure: 40%. Period: survey fielded August-September 2025; report published as the 2026 edition. Population: 4,050 sales professionals across sales leadership, quota-carrying, partner, SDR/BDR, and sales-operations roles. Evidence type: anonymous third-party-panel survey. Record category: seller_time. Source-record methodology: not recorded. Qualification: These are respondent-reported practices and perceptions, not audited CRM outcomes. Bases vary by question, especially for agent, usage-pricing, and partner-selling subsets. Source: https://www.salesforce.com/en-us/wp-content/uploads/sites/4/documents/reports/sales/salesforce-state-of-sales-report-2026.pdf. Record audit/review date: 2026-07-11. - [SALES-002] Recorded assertion from salesforce.com: "Meeting with customers accounted for 13% of respondents' average workweek.". Recorded figure: 13%. Period: survey fielded August-September 2025; report published as the 2026 edition. Population: 4,050 sales professionals across sales leadership, quota-carrying, partner, SDR/BDR, and sales-operations roles. Evidence type: anonymous third-party-panel survey. Record category: seller_time. Source-record methodology: not recorded. Qualification: These are respondent-reported practices and perceptions, not audited CRM outcomes. Bases vary by question, especially for agent, usage-pricing, and partner-selling subsets. Source: https://www.salesforce.com/en-us/wp-content/uploads/sites/4/documents/reports/sales/salesforce-state-of-sales-report-2026.pdf. Record audit/review date: 2026-07-11. - [SALES-003] Recorded assertion from salesforce.com: "Prospecting accounted for 17% of respondents' average workweek.". Recorded figure: 17%. Period: survey fielded August-September 2025; report published as the 2026 edition. Population: 4,050 sales professionals across sales leadership, quota-carrying, partner, SDR/BDR, and sales-operations roles. Evidence type: anonymous third-party-panel survey. Record category: seller_time. Source-record methodology: not recorded. Qualification: These are respondent-reported practices and perceptions, not audited CRM outcomes. Bases vary by question, especially for agent, usage-pricing, and partner-selling subsets. Source: https://www.salesforce.com/en-us/wp-content/uploads/sites/4/documents/reports/sales/salesforce-state-of-sales-report-2026.pdf. Record audit/review date: 2026-07-11. - [SALES-004] Recorded assertion from salesforce.com: "Sixty-nine percent said measurable ROI was more important to customers than a year earlier.". Recorded figure: 69%. Period: survey fielded August-September 2025; report published as the 2026 edition. Population: 4,050 sales professionals across sales leadership, quota-carrying, partner, SDR/BDR, and sales-operations roles. Evidence type: anonymous third-party-panel survey. Record category: buyer_expectations. Source-record methodology: not recorded. Qualification: These are respondent-reported practices and perceptions, not audited CRM outcomes. Bases vary by question, especially for agent, usage-pricing, and partner-selling subsets. Source: https://www.salesforce.com/en-us/wp-content/uploads/sites/4/documents/reports/sales/salesforce-state-of-sales-report-2026.pdf. Record audit/review date: 2026-07-11. - [SALES-005] Recorded assertion from salesforce.com: "Sixty-nine percent said personalization was more important to customers than a year earlier.". Recorded figure: 69%. Period: survey fielded August-September 2025; report published as the 2026 edition. Population: 4,050 sales professionals across sales leadership, quota-carrying, partner, SDR/BDR, and sales-operations roles. Evidence type: anonymous third-party-panel survey. Record category: buyer_expectations. Source-record methodology: not recorded. Qualification: These are respondent-reported practices and perceptions, not audited CRM outcomes. Bases vary by question, especially for agent, usage-pricing, and partner-selling subsets. Source: https://www.salesforce.com/en-us/wp-content/uploads/sites/4/documents/reports/sales/salesforce-state-of-sales-report-2026.pdf. Record audit/review date: 2026-07-11. - [SALES-006] Recorded assertion from salesforce.com: "Sixty-seven percent said customers required extensive education before buying.". Recorded figure: 67%. Period: survey fielded August-September 2025; report published as the 2026 edition. Population: 4,050 sales professionals across sales leadership, quota-carrying, partner, SDR/BDR, and sales-operations roles. Evidence type: anonymous third-party-panel survey. Record category: buyer_expectations. Source-record methodology: not recorded. Qualification: These are respondent-reported practices and perceptions, not audited CRM outcomes. Bases vary by question, especially for agent, usage-pricing, and partner-selling subsets. Source: https://www.salesforce.com/en-us/wp-content/uploads/sites/4/documents/reports/sales/salesforce-state-of-sales-report-2026.pdf. Record audit/review date: 2026-07-11. - [SALES-007] Recorded assertion from salesforce.com: "Fifty-seven percent said customers took longer to decide than they used to.". Recorded figure: 57%. Period: survey fielded August-September 2025; report published as the 2026 edition. Population: 4,050 sales professionals across sales leadership, quota-carrying, partner, SDR/BDR, and sales-operations roles. Evidence type: anonymous third-party-panel survey. Record category: sales_cycle. Source-record methodology: not recorded. Qualification: These are respondent-reported practices and perceptions, not audited CRM outcomes. Bases vary by question, especially for agent, usage-pricing, and partner-selling subsets. Source: https://www.salesforce.com/en-us/wp-content/uploads/sites/4/documents/reports/sales/salesforce-state-of-sales-report-2026.pdf. Record audit/review date: 2026-07-11. - [SALES-008] Recorded assertion from salesforce.com: "Fifty-four percent of sales teams said they already used AI agents.". Recorded figure: 54%. Period: survey fielded August-September 2025; report published as the 2026 edition. Population: 4,050 sales professionals across sales leadership, quota-carrying, partner, SDR/BDR, and sales-operations roles. Evidence type: anonymous third-party-panel survey. Record category: ai_adoption. Source-record methodology: not recorded. Qualification: These are respondent-reported practices and perceptions, not audited CRM outcomes. Bases vary by question, especially for agent, usage-pricing, and partner-selling subsets. Source: https://www.salesforce.com/en-us/wp-content/uploads/sites/4/documents/reports/sales/salesforce-state-of-sales-report-2026.pdf. Record audit/review date: 2026-07-11. - [SALES-009] Recorded assertion from salesforce.com: "Another 34% expected to use AI agents within two years.". Recorded figure: 34%. Period: survey fielded August-September 2025; report published as the 2026 edition. Population: 4,050 sales professionals across sales leadership, quota-carrying, partner, SDR/BDR, and sales-operations roles. Evidence type: anonymous third-party-panel survey. Record category: ai_adoption. Source-record methodology: not recorded. Qualification: These are respondent-reported practices and perceptions, not audited CRM outcomes. Bases vary by question, especially for agent, usage-pricing, and partner-selling subsets. Source: https://www.salesforce.com/en-us/wp-content/uploads/sites/4/documents/reports/sales/salesforce-state-of-sales-report-2026.pdf. Record audit/review date: 2026-07-11. - [SALES-010] Recorded assertion from salesforce.com: "Thirty-four percent of sales teams with agents used them for prospecting.". Recorded figure: 34%. Period: survey fielded August-September 2025; report published as the 2026 edition. Population: 4,050 sales professionals across sales leadership, quota-carrying, partner, SDR/BDR, and sales-operations roles. Evidence type: anonymous third-party-panel survey. Record category: prospecting. Source-record methodology: not recorded. Qualification: These are respondent-reported practices and perceptions, not audited CRM outcomes. Bases vary by question, especially for agent, usage-pricing, and partner-selling subsets. Source: https://www.salesforce.com/en-us/wp-content/uploads/sites/4/documents/reports/sales/salesforce-state-of-sales-report-2026.pdf. Record audit/review date: 2026-07-11. ### Customer Acquisition Cost Benchmarks 2026 by Industry and Channel URL: https://konabayev.com/blog/cac-benchmarks-2026/ Full article summary (may cover material outside this subset): Source-backed 2026 CAC benchmarks by channel, industry, SaaS segment, payback period, and LTV:CAC planning threshold. Updated: 2026-10-04 Selected records: first 10 of 31 published records in file order. Full published dataset: https://konabayev.com/data/cac-benchmarks-2026.jsonl. The selected records do not cover every topic in the full article summary. - [cac-benchmarks-2026-001] Recorded assertion from firstpagesage.com: "It is generally healthy to shoot for 3:1 meaning you would pay $1 to make $3 in revenue.". Record category: revenue. Source-record methodology (retained wording): "This report utilizes a dataset drawn from our agency's clients between 2022 and 2024, representing ~120 firms. Our dataset contains a large and diverse range of B2B companies and is statistically significant for those businesses.". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://firstpagesage.com/marketing/cac-by-channel-fc/. Record audit/review date: 2026-05-10. - [cac-benchmarks-2026-002] Recorded assertion from firstpagesage.com: "Our dataset's average LTV for B2B companies is $32,414.". Record category: revenue. Source-record methodology (retained wording): "This report utilizes a dataset drawn from our agency's clients between 2022 and 2024, representing ~120 firms. Our dataset contains a large and diverse range of B2B companies and is statistically significant for those businesses.". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://firstpagesage.com/marketing/cac-by-channel-fc/. Record audit/review date: 2026-05-10. - [cac-benchmarks-2026-003] Recorded assertion from firstpagesage.com: "Our dataset's average LTV for B2C companies is $10,089.". Record category: revenue. Source-record methodology (retained wording): "This report utilizes a dataset drawn from our agency's clients between 2022 and 2024, representing ~120 firms. Our dataset contains a large and diverse range of B2B companies and is statistically significant for those businesses.". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://firstpagesage.com/marketing/cac-by-channel-fc/. Record audit/review date: 2026-05-10. - [cac-benchmarks-2026-004] Recorded assertion from userpilot.com: "CAC in B2B SaaS can range anywhere from $300 to $5,000+". Record category: cac. Source-record methodology (retained wording): "Benchmarkit's 2025 data; FinancialModelslab's analysis; First Page Sage's analysis; First Page Sage's CAC analysis of 22 SaaS industries; HubSpot; First Page Sage's LTV-to-CAC analysis of 10 SaaS industries; Userpilot's 2025 benchmark report; Rocketbots case study; Braze's 2024 Cross-Channel Guide; Cleeng case study; HubSpot; Slack case study; Shopify's 2026 research; Lillian Pierson, P.E. (Data-Mania); Gartner". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://userpilot.com/blog/average-customer-acquisition-cost/. Record audit/review date: 2026-05-10. - [cac-benchmarks-2026-005] Recorded assertion from userpilot.com: "median of $2 to acquire $1 of new customer ARR". Record category: revenue. Source-record methodology (retained wording): "Benchmarkit's 2025 data; FinancialModelslab's analysis; First Page Sage's analysis; First Page Sage's CAC analysis of 22 SaaS industries; HubSpot; First Page Sage's LTV-to-CAC analysis of 10 SaaS industries; Userpilot's 2025 benchmark report; Rocketbots case study; Braze's 2024 Cross-Channel Guide; Cleeng case study; HubSpot; Slack case study; Shopify's 2026 research; Lillian Pierson, P.E. (Data-Mania); Gartner". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://userpilot.com/blog/average-customer-acquisition-cost/. Record audit/review date: 2026-05-10. - [cac-benchmarks-2026-006] Recorded assertion from userpilot.com: "fully loaded CAC of $1,800, an ARPU of $150, and an 80% gross margin, it takes around 15 months to recover the acquisition cost". Record category: revenue. Source-record methodology (retained wording): "Benchmarkit's 2025 data; FinancialModelslab's analysis; First Page Sage's analysis; First Page Sage's CAC analysis of 22 SaaS industries; HubSpot; First Page Sage's LTV-to-CAC analysis of 10 SaaS industries; Userpilot's 2025 benchmark report; Rocketbots case study; Braze's 2024 Cross-Channel Guide; Cleeng case study; HubSpot; Slack case study; Shopify's 2026 research; Lillian Pierson, P.E. (Data-Mania); Gartner". Qualification: This is a worked payback calculation under stated inputs, not an observed industry benchmark. Source: https://userpilot.com/blog/average-customer-acquisition-cost/. Record audit/review date: 2026-05-10. - [cac-benchmarks-2026-007] Recorded assertion from userpilot.com: "$10,000 on sales and marketing in Q1 and acquires 100 new customers during the same period. On applying the customer acquisition cost formula, CAC = $10,000 ÷ 100 = $100". Record category: cac. Source-record methodology (retained wording): "Benchmarkit's 2025 data; FinancialModelslab's analysis; First Page Sage's analysis; First Page Sage's CAC analysis of 22 SaaS industries; HubSpot; First Page Sage's LTV-to-CAC analysis of 10 SaaS industries; Userpilot's 2025 benchmark report; Rocketbots case study; Braze's 2024 Cross-Channel Guide; Cleeng case study; HubSpot; Slack case study; Shopify's 2026 research; Lillian Pierson, P.E. (Data-Mania); Gartner". Qualification: Division operator restored for display; this is an illustrative calculation, not an observed industry benchmark. Source: https://userpilot.com/blog/average-customer-acquisition-cost/. Record audit/review date: 2026-05-10. - [cac-benchmarks-2026-008] Recorded assertion from userpilot.com: "referral-driven acquisition... generates 5%-20% of total customer acquisition". Record category: cac. Source-record methodology (retained wording): "Benchmarkit's 2025 data; FinancialModelslab's analysis; First Page Sage's analysis; First Page Sage's CAC analysis of 22 SaaS industries; HubSpot; First Page Sage's LTV-to-CAC analysis of 10 SaaS industries; Userpilot's 2025 benchmark report; Rocketbots case study; Braze's 2024 Cross-Channel Guide; Cleeng case study; HubSpot; Slack case study; Shopify's 2026 research; Lillian Pierson, P.E. (Data-Mania); Gartner". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://userpilot.com/blog/average-customer-acquisition-cost/. Record audit/review date: 2026-05-10. - [cac-benchmarks-2026-009] Recorded assertion from userpilot.com: "Consumer-focused SaaS companies have CACs under $300". Record category: cac. Source-record methodology (retained wording): "Benchmarkit's 2025 data; FinancialModelslab's analysis; First Page Sage's analysis; First Page Sage's CAC analysis of 22 SaaS industries; HubSpot; First Page Sage's LTV-to-CAC analysis of 10 SaaS industries; Userpilot's 2025 benchmark report; Rocketbots case study; Braze's 2024 Cross-Channel Guide; Cleeng case study; HubSpot; Slack case study; Shopify's 2026 research; Lillian Pierson, P.E. (Data-Mania); Gartner". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://userpilot.com/blog/average-customer-acquisition-cost/. Record audit/review date: 2026-05-10. - [cac-benchmarks-2026-010] Recorded assertion from userpilot.com: "ecommerce at $64 and retail at $76 recording the lowest acquisition costs". Record category: cac. Source-record methodology (retained wording): "Benchmarkit's 2025 data; FinancialModelslab's analysis; First Page Sage's analysis; First Page Sage's CAC analysis of 22 SaaS industries; HubSpot; First Page Sage's LTV-to-CAC analysis of 10 SaaS industries; Userpilot's 2025 benchmark report; Rocketbots case study; Braze's 2024 Cross-Channel Guide; Cleeng case study; HubSpot; Slack case study; Shopify's 2026 research; Lillian Pierson, P.E. (Data-Mania); Gartner". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://userpilot.com/blog/average-customer-acquisition-cost/. Record audit/review date: 2026-05-10. ### Chrome Extension Statistics 2026: Store Size, Adoption and Updates URL: https://konabayev.com/blog/chrome-extension-statistics-2026/ Full article summary (may cover material outside this subset): Audited Chrome Web Store statistics for 2026: active listings, user distribution, categories, ratings, reviews and update cadence. Updated: 2026-08-04 Selected records: first 10 of 19 published records in file order. Full published dataset: https://konabayev.com/data/chrome-extension-statistics-2026.jsonl. The selected records do not cover every topic in the full article summary. - [chrome-extension-statistics-2026-001] Recorded assertion from exstats.com: "Exstats reported 178,299 active Chrome Web Store extensions in March 2026.". Record category: benchmark. Source-record methodology (retained wording): "Daily parsing of public extension-store data.". Qualification: Exstats active-listing count can differ from other crawlers because crawl scope and definitions vary. Source: https://exstats.com/blog/state-of-browser-extensions-q1-2026. Record audit/review date: 2026-08-04. - [chrome-extension-statistics-2026-002] Recorded assertion from exstats.com: "Exstats reported 83,465 Firefox Add-ons and 28,741 Microsoft Edge Add-ons in March 2026.". Record category: benchmark. Source-record methodology (retained wording): "Daily parsing of public store data across Chrome, Firefox and Edge.". Qualification: Store definitions and public availability differ by ecosystem. Source: https://exstats.com/blog/state-of-browser-extensions-q1-2026. Record audit/review date: 2026-08-04. - [chrome-extension-statistics-2026-003] Recorded assertion from exstats.com: "Exstats reported Firefox extension listings grew 74.16% year over year from March 2025 to March 2026, compared with 22.70% for Chrome and 21.55% for Edge.". Record category: benchmark. Source-record methodology (retained wording): "Year-over-year listing comparison in the Exstats Q1 2026 dataset.". Qualification: Growth in listings is not the same as growth in paid demand. Source: https://exstats.com/blog/state-of-browser-extensions-q1-2026. Record audit/review date: 2026-08-04. - [chrome-extension-statistics-2026-004] Recorded assertion from exstats.com: "Exstats reported Chrome added 38,015 new extensions and removed 7,890 in Q1 2026, for net growth of 30,125 listings.". Record category: benchmark. Source-record methodology (retained wording): "Q1 2026 gross new, deleted and net new listing counts.". Qualification: Listing growth can include low-adoption products. Source: https://exstats.com/blog/state-of-browser-extensions-q1-2026. Record audit/review date: 2026-08-04. - [chrome-extension-statistics-2026-005] Recorded assertion from exstats.com: "Exstats reported Chrome had a net loss of 27,249 listings in Q4 2025 before rebounding in Q1 2026.". Record category: benchmark. Source-record methodology (retained wording): "Quarterly net-new comparison between Q4 2025 and Q1 2026.". Qualification: Deletion waves may reflect policy or compliance resets, not demand collapse. Source: https://exstats.com/blog/state-of-browser-extensions-q1-2026. Record audit/review date: 2026-08-04. - [chrome-extension-statistics-2026-006] Recorded assertion from exstats.com: "Exstats reported Chrome removed 47,288 extensions in October 2025.". Record category: benchmark. Source-record methodology (retained wording): "Monthly listing and deletion table from September 2025 through March 2026.". Qualification: The source connects the deletion wave to platform cleanup context but does not prove one cause for every deletion. Source: https://exstats.com/blog/state-of-browser-extensions-q1-2026. Record audit/review date: 2026-08-04. - [chrome-extension-statistics-2026-007] Recorded assertion from exstats.com: "Exstats reported Chrome's extension total fell from 175,423 in September 2025 to 134,888 in October 2025, then recovered to 178,299 by March 2026.". Record category: benchmark. Source-record methodology (retained wording): "Monthly extension listing totals from Exstats.". Qualification: Store total is a supply metric, not a monetization metric. Source: https://exstats.com/blog/state-of-browser-extensions-q1-2026. Record audit/review date: 2026-08-04. - [chrome-extension-statistics-2026-008] Recorded assertion from exstats.com: "Exstats reported the median Chrome extension had 18 users in Q1 2026.". Record category: benchmark. Source-record methodology (retained wording): "Public store usersCount distribution.". Qualification: Chrome usersCount is an adoption metric, not revenue or active usage. Source: https://exstats.com/blog/state-of-browser-extensions-q1-2026. Record audit/review date: 2026-08-04. - [chrome-extension-statistics-2026-009] Recorded assertion from exstats.com: "Exstats reported the median Edge add-on had 62 active installs and the median Firefox add-on had one average daily user.". Record category: benchmark. Source-record methodology (retained wording): "Closest public adoption metric for each extension store.". Qualification: Chrome, Edge and Firefox adoption metrics are not directly equivalent. Source: https://exstats.com/blog/state-of-browser-extensions-q1-2026. Record audit/review date: 2026-08-04. - [chrome-extension-statistics-2026-010] Recorded assertion from exstats.com: "Exstats reported 70.4% of Chrome extensions had 100 users or fewer in Q1 2026.". Record category: benchmark. Source-record methodology (retained wording): "Adoption bucket distribution from public store data.". Qualification: A low-user listing can still be a private, enterprise or niche product. Source: https://exstats.com/blog/state-of-browser-extensions-q1-2026. Record audit/review date: 2026-08-04. ### Cold Email Statistics 2026: Reply and Follow-Up Benchmarks URL: https://konabayev.com/blog/cold-email-statistics-2026/ Full article summary (may cover material outside this subset): Twenty-three audited cold-email benchmarks from Sales.co and Instantly covering reply rates, positive replies, sequence position, copy and deliverability. Updated: 2026-08-20 Selected records: first 10 of 23 published records in file order. Full published dataset: https://konabayev.com/data/cold-email-statistics-2026.jsonl. The selected records do not cover every topic in the full article summary. - [cold-email-statistics-2026-001] Recorded assertion from instantly.ai: "Instantly reports a 3.43% overall cold-email reply rate across its 2026 benchmark dataset.". Record category: reply_rate. Source-record methodology (retained wording): "Aggregated, anonymized send, reply, sequence and timing events across billions of interactions and thousands of active workspaces.". Qualification: Overall reply rate includes replies to follow-ups and is defined as all replies divided by total emails sent. Source: https://instantly.ai/cold-email-benchmark-report-2026. Record audit/review date: 2026-08-20. - [cold-email-statistics-2026-002] Recorded assertion from instantly.ai: "Instantly's top 25% of cold-email campaigns achieved reply rates of at least 5.5% in its 2026 benchmark.". Record category: reply_rate. Source-record methodology (retained wording): "Aggregated and anonymized platform benchmark by performance tier.". Qualification: The public report does not expose a workspace-level sample count for this tier. Source: https://instantly.ai/cold-email-benchmark-report-2026. Record audit/review date: 2026-08-20. - [cold-email-statistics-2026-003] Recorded assertion from instantly.ai: "Instantly's top 10% of cold-email campaigns achieved reply rates of at least 10.7% in its 2026 benchmark.". Record category: reply_rate. Source-record methodology (retained wording): "Aggregated and anonymized platform benchmark by performance tier.". Qualification: High-performing platform users are not representative of every outbound team. Source: https://instantly.ai/cold-email-benchmark-report-2026. Record audit/review date: 2026-08-20. - [cold-email-statistics-2026-004] Recorded assertion from instantly.ai: "Instantly reports that the first email in a sequence generated 58% of replies and follow-ups generated the remaining 42%.". Record category: sequence. Source-record methodology (retained wording): "Sequence-position analysis across active campaigns in the 2026 platform benchmark.". Qualification: Reply share is not the same as an incremental causal lift from each follow-up. Source: https://instantly.ai/cold-email-benchmark-report-2026. Record audit/review date: 2026-08-20. - [cold-email-statistics-2026-005] Recorded assertion from instantly.ai: "Instantly reports that elite cold-email campaigns averaged fewer than 80 words in their first-touch emails.". Record category: message_design. Source-record methodology (retained wording): "Content-pattern analysis for Instantly's elite-performing campaign tier.". Qualification: The report does not isolate email length as the sole cause of performance. Source: https://instantly.ai/cold-email-benchmark-report-2026. Record audit/review date: 2026-08-20. - [cold-email-statistics-2026-006] Recorded assertion from instantly.ai: "Instantly identifies four to seven touchpoints as the sequence-length range in which its benchmark saw the best balance before diminishing returns.". Record category: sequence. Source-record methodology (retained wording): "Sequence-position analysis across active campaigns.". Qualification: The recommendation is vendor guidance derived from its customer base, not a universal optimum. Source: https://instantly.ai/cold-email-benchmark-report-2026. Record audit/review date: 2026-08-20. - [cold-email-statistics-2026-007] Recorded assertion from instantly.ai: "Instantly reports that teams with stable domain health and consistent sending saw 15% to 20% more replies in its dataset.". Record category: deliverability. Source-record methodology (retained wording): "Platform comparison of domain-health and sending-consistency patterns.". Qualification: The public report does not expose the full model or controls, so treat the result as observational. Source: https://instantly.ai/cold-email-benchmark-report-2026. Record audit/review date: 2026-08-20. - [cold-email-statistics-2026-008] Recorded assertion from instantly.ai: "Instantly recommends keeping cold-email bounce rates below 2% in its 2026 benchmark guidance.". Record category: deliverability. Source-record methodology (retained wording): "Operational guidance accompanying the platform's 2026 telemetry report.". Qualification: This is a vendor-recommended ceiling, not a measured distribution percentile. Source: https://instantly.ai/cold-email-benchmark-report-2026. Record audit/review date: 2026-08-20. - [cold-email-statistics-2026-009] Recorded assertion from sales.co: "Sales.co reports a 2.09% reply rate in a funnel covering 1,369,823 contacts emailed and 28,632 replies.". Record category: reply_rate. Source-record methodology (retained wording): "Observed cold-email campaign data; the published funnel reports contacts emailed, any replies and positive replies.". Qualification: This funnel denominator differs from Instantly's emails-sent denominator, so the rates must not be averaged. Source: https://sales.co/research/cold-email-statistics. Record audit/review date: 2026-08-20. - [cold-email-statistics-2026-010] Recorded assertion from sales.co: "Sales.co reports 8,737 positive replies from 1,369,823 contacts emailed, an interested-reply rate of about 0.64%.". Record category: positive_reply. Source-record methodology (retained wording): "Positive replies were classified from observed campaign responses using six sentiment categories.". Qualification: A positive reply is not necessarily a booked meeting, qualified opportunity or sale. Editorial clarification October 3, 2026: the report-level 61,770 classified replies and the contact funnel's 28,632 replies are not established as the same cohort. Do not apply the 14.1% classified-reply share to that funnel or compare it directly with 8,737 / 1,369,823 (about 0.64%) as if the denominators matched. No new source acquisition. Source: https://sales.co/research/cold-email-statistics. Record audit/review date: 2026-08-20. ### Content Marketing Statistics 2026: ROI, Channel Mix and Pipeline URL: https://konabayev.com/blog/content-marketing-statistics-2026/ Full article summary (may cover material outside this subset): Source-backed 2026 content marketing statistics for ROI, channel mix, blog and SEO performance, B2B effectiveness, AI usage, and pipeline impact. Updated: 2026-10-04 Selected records: first 10 of 40 published records in file order. Full published dataset: https://konabayev.com/data/content-marketing-statistics-2026.jsonl. The selected records do not cover every topic in the full article summary. - [content-marketing-statistics-2026-001] Recorded assertion from hubspot.com: "After just one year, HubSpot customers acquire 129% more leads, close 36% more deals, and see a 37% improvement in ticket closure rates.". Record category: benchmark. Source-record methodology (retained wording): "HubSpot State of Marketing Report; Statista; FirstPageSage; HubSpot Web Strategy & Analytics Report; Deloitte; StatCounter; DataReportal; eMarketer; The Leap; Wyzowl; Orbit Media; CNBC; X Data; Snapchat; The HubSpot Customer Platform; PewResearch; Sprout Social; Pinterest; INFLOW Network; Plainly; Wistia; Social Media Examiner; MailButler; Litmus; Constant Contact; Exploding Topics; Simple Texting; WebFX; SimilarWeb; Wordstream; Ranktracker; Prezentor; Zippia; Content Marketing Institute; Spotify; Basis Technologies; HubSpot Blog Marketing Trends Report; HubSpot Research; HubSpot State of Sales Report; HubSpot". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://www.hubspot.com/marketing-statistics. Record audit/review date: 2026-05-10. - [content-marketing-statistics-2026-002] Recorded assertion from hubspot.com: "Conversion Rate Optimization is the second-most-used optimization technique among marketers (50%).". Record category: conversion. Source-record methodology (retained wording): "HubSpot State of Marketing Report; Statista; FirstPageSage; HubSpot Web Strategy & Analytics Report; Deloitte; StatCounter; DataReportal; eMarketer; The Leap; Wyzowl; Orbit Media; CNBC; X Data; Snapchat; The HubSpot Customer Platform; PewResearch; Sprout Social; Pinterest; INFLOW Network; Plainly; Wistia; Social Media Examiner; MailButler; Litmus; Constant Contact; Exploding Topics; Simple Texting; WebFX; SimilarWeb; Wordstream; Ranktracker; Prezentor; Zippia; Content Marketing Institute; Spotify; Basis Technologies; HubSpot Blog Marketing Trends Report; HubSpot Research; HubSpot State of Sales Report; HubSpot". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://www.hubspot.com/marketing-statistics. Record audit/review date: 2026-05-10. - [content-marketing-statistics-2026-003] Recorded assertion from hubspot.com: "Lead-to-customer conversion is the second most important KPI for marketers across businesses of all sizes.". Record category: conversion. Source-record methodology (retained wording): "HubSpot State of Marketing Report; Statista; FirstPageSage; HubSpot Web Strategy & Analytics Report; Deloitte; StatCounter; DataReportal; eMarketer; The Leap; Wyzowl; Orbit Media; CNBC; X Data; Snapchat; The HubSpot Customer Platform; PewResearch; Sprout Social; Pinterest; INFLOW Network; Plainly; Wistia; Social Media Examiner; MailButler; Litmus; Constant Contact; Exploding Topics; Simple Texting; WebFX; SimilarWeb; Wordstream; Ranktracker; Prezentor; Zippia; Content Marketing Institute; Spotify; Basis Technologies; HubSpot Blog Marketing Trends Report; HubSpot Research; HubSpot State of Sales Report; HubSpot". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://www.hubspot.com/marketing-statistics. Record audit/review date: 2026-05-10. - [content-marketing-statistics-2026-004] Recorded assertion from hubspot.com: "Nearly 56% of marketers say it's much easier to improve conversion rates now than it was ten years ago.". Record category: conversion. Source-record methodology (retained wording): "HubSpot State of Marketing Report; Statista; FirstPageSage; HubSpot Web Strategy & Analytics Report; Deloitte; StatCounter; DataReportal; eMarketer; The Leap; Wyzowl; Orbit Media; CNBC; X Data; Snapchat; The HubSpot Customer Platform; PewResearch; Sprout Social; Pinterest; INFLOW Network; Plainly; Wistia; Social Media Examiner; MailButler; Litmus; Constant Contact; Exploding Topics; Simple Texting; WebFX; SimilarWeb; Wordstream; Ranktracker; Prezentor; Zippia; Content Marketing Institute; Spotify; Basis Technologies; HubSpot Blog Marketing Trends Report; HubSpot Research; HubSpot State of Sales Report; HubSpot". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://www.hubspot.com/marketing-statistics. Record audit/review date: 2026-05-10. - [content-marketing-statistics-2026-005] Recorded assertion from hubspot.com: "In 2024, the top marketing channels driving ROI for B2B brands were their (1) website, blog, and SEO efforts, (2) paid social media content, and (3) social media shopping tools.". Record category: channel. Source-record methodology (retained wording): "HubSpot State of Marketing Report; Statista; FirstPageSage; HubSpot Web Strategy & Analytics Report; Deloitte; StatCounter; DataReportal; eMarketer; The Leap; Wyzowl; Orbit Media; CNBC; X Data; Snapchat; The HubSpot Customer Platform; PewResearch; Sprout Social; Pinterest; INFLOW Network; Plainly; Wistia; Social Media Examiner; MailButler; Litmus; Constant Contact; Exploding Topics; Simple Texting; WebFX; SimilarWeb; Wordstream; Ranktracker; Prezentor; Zippia; Content Marketing Institute; Spotify; Basis Technologies; HubSpot Blog Marketing Trends Report; HubSpot Research; HubSpot State of Sales Report; HubSpot". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://www.hubspot.com/marketing-statistics. Record audit/review date: 2026-05-10. - [content-marketing-statistics-2026-006] Recorded assertion from hubspot.com: "For B2C brands, the channels with the best ROI were (1) email marketing, (2) paid social media content, and (3) content marketing.". Record category: channel. Source-record methodology (retained wording): "HubSpot State of Marketing Report; Statista; FirstPageSage; HubSpot Web Strategy & Analytics Report; Deloitte; StatCounter; DataReportal; eMarketer; The Leap; Wyzowl; Orbit Media; CNBC; X Data; Snapchat; The HubSpot Customer Platform; PewResearch; Sprout Social; Pinterest; INFLOW Network; Plainly; Wistia; Social Media Examiner; MailButler; Litmus; Constant Contact; Exploding Topics; Simple Texting; WebFX; SimilarWeb; Wordstream; Ranktracker; Prezentor; Zippia; Content Marketing Institute; Spotify; Basis Technologies; HubSpot Blog Marketing Trends Report; HubSpot Research; HubSpot State of Sales Report; HubSpot". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://www.hubspot.com/marketing-statistics. Record audit/review date: 2026-05-10. - [content-marketing-statistics-2026-007] Recorded assertion from hubspot.com: "The average conversion rate across all e-commerce sites is under 2%.". Record category: conversion. Source-record methodology (retained wording): "HubSpot State of Marketing Report; Statista; FirstPageSage; HubSpot Web Strategy & Analytics Report; Deloitte; StatCounter; DataReportal; eMarketer; The Leap; Wyzowl; Orbit Media; CNBC; X Data; Snapchat; The HubSpot Customer Platform; PewResearch; Sprout Social; Pinterest; INFLOW Network; Plainly; Wistia; Social Media Examiner; MailButler; Litmus; Constant Contact; Exploding Topics; Simple Texting; WebFX; SimilarWeb; Wordstream; Ranktracker; Prezentor; Zippia; Content Marketing Institute; Spotify; Basis Technologies; HubSpot Blog Marketing Trends Report; HubSpot Research; HubSpot State of Sales Report; HubSpot". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://www.hubspot.com/marketing-statistics. Record audit/review date: 2026-05-10. - [content-marketing-statistics-2026-008] Recorded assertion from hubspot.com: "Conversion rates are highest for skincare online shopping at 2.7% and lowest for luxury apparel at just 0.4%.". Record category: conversion. Source-record methodology (retained wording): "HubSpot State of Marketing Report; Statista; FirstPageSage; HubSpot Web Strategy & Analytics Report; Deloitte; StatCounter; DataReportal; eMarketer; The Leap; Wyzowl; Orbit Media; CNBC; X Data; Snapchat; The HubSpot Customer Platform; PewResearch; Sprout Social; Pinterest; INFLOW Network; Plainly; Wistia; Social Media Examiner; MailButler; Litmus; Constant Contact; Exploding Topics; Simple Texting; WebFX; SimilarWeb; Wordstream; Ranktracker; Prezentor; Zippia; Content Marketing Institute; Spotify; Basis Technologies; HubSpot Blog Marketing Trends Report; HubSpot Research; HubSpot State of Sales Report; HubSpot". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://www.hubspot.com/marketing-statistics. Record audit/review date: 2026-05-10. - [content-marketing-statistics-2026-009] Recorded assertion from hubspot.com: "Email marketing is one of the most effective channels for driving conversions, with a 2.8% conversion rate for B2C brands and a 2.4% conversion rate for B2Bs.". Record category: conversion. Source-record methodology (retained wording): "HubSpot State of Marketing Report; Statista; FirstPageSage; HubSpot Web Strategy & Analytics Report; Deloitte; StatCounter; DataReportal; eMarketer; The Leap; Wyzowl; Orbit Media; CNBC; X Data; Snapchat; The HubSpot Customer Platform; PewResearch; Sprout Social; Pinterest; INFLOW Network; Plainly; Wistia; Social Media Examiner; MailButler; Litmus; Constant Contact; Exploding Topics; Simple Texting; WebFX; SimilarWeb; Wordstream; Ranktracker; Prezentor; Zippia; Content Marketing Institute; Spotify; Basis Technologies; HubSpot Blog Marketing Trends Report; HubSpot Research; HubSpot State of Sales Report; HubSpot". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://www.hubspot.com/marketing-statistics. Record audit/review date: 2026-05-10. - [content-marketing-statistics-2026-010] Recorded assertion from hubspot.com: "63% of consumers prefer to find information about brands and products on mobile devices.". Record category: benchmark. Source-record methodology (retained wording): "HubSpot State of Marketing Report; Statista; FirstPageSage; HubSpot Web Strategy & Analytics Report; Deloitte; StatCounter; DataReportal; eMarketer; The Leap; Wyzowl; Orbit Media; CNBC; X Data; Snapchat; The HubSpot Customer Platform; PewResearch; Sprout Social; Pinterest; INFLOW Network; Plainly; Wistia; Social Media Examiner; MailButler; Litmus; Constant Contact; Exploding Topics; Simple Texting; WebFX; SimilarWeb; Wordstream; Ranktracker; Prezentor; Zippia; Content Marketing Institute; Spotify; Basis Technologies; HubSpot Blog Marketing Trends Report; HubSpot Research; HubSpot State of Sales Report; HubSpot". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://www.hubspot.com/marketing-statistics. Record audit/review date: 2026-05-10. ### Customer Onboarding Statistics 2026: Time-to-Value and Activation URL: https://konabayev.com/blog/customer-onboarding-statistics-2026/ Full article summary (may cover material outside this subset): Source-backed customer onboarding statistics for 2026 covering time-to-value, activation, automation, AI, churn and onboarding operations. Updated: 2026-10-04 Selected records: first 10 of 55 published records in file order. Full published dataset: https://konabayev.com/data/customer-onboarding-statistics-2026.jsonl. The selected records do not cover every topic in the full article summary. - [customer-onboarding-statistics-2026-001] Recorded assertion from onramp.us: "CS leaders lacking real-time visibility into customer onboarding progress". Recorded figure: 62%. Record category: visibility. Source-record methodology (retained wording): "Public source claim captured May 2026; see source URL for methodology and caveats.". Qualification: Vendor-published survey; relevant but not neutral audited research. Source: https://onramp.us/blog/2026-state-of-onboarding-report?hs_amp=true. Record audit/review date: 2026-05-18. - [customer-onboarding-statistics-2026-002] Recorded assertion from onramp.us: "Leaders who do not know where customers stand in onboarding at any given time". Recorded figure: 1 in 3. Record category: visibility. Source-record methodology (retained wording): "Public source claim captured May 2026; see source URL for methodology and caveats.". Qualification: Vendor-published survey; relevant but not neutral audited research. Source: https://onramp.us/blog/2026-state-of-onboarding-report?hs_amp=true. Record audit/review date: 2026-05-18. - [customer-onboarding-statistics-2026-003] Recorded assertion from onramp.us: "Customers expecting a consistent experience across all touchpoints". Recorded figure: 87%. Record category: visibility. Source-record methodology (retained wording): "Public source claim captured May 2026; see source URL for methodology and caveats.". Qualification: Vendor-published survey; relevant but not neutral audited research. Source: https://onramp.us/blog/2026-state-of-onboarding-report?hs_amp=true. Record audit/review date: 2026-05-18. - [customer-onboarding-statistics-2026-004] Recorded assertion from onramp.us: "Teams using real-time tracking that reported increased customer engagement". Recorded figure: 96%. Record category: visibility. Source-record methodology (retained wording): "Public source claim captured May 2026; see source URL for methodology and caveats.". Qualification: Vendor-published survey; relevant but not neutral audited research. Source: https://onramp.us/blog/2026-state-of-onboarding-report?hs_amp=true. Record audit/review date: 2026-05-18. - [customer-onboarding-statistics-2026-005] Recorded assertion from onramp.us: "Leaders saying onboarding friction directly impacts revenue realization". Recorded figure: 57%. Record category: revenue. Source-record methodology (retained wording): "Public source claim captured May 2026; see source URL for methodology and caveats.". Qualification: Vendor-published survey; relevant but not neutral audited research. Source: https://onramp.us/blog/2026-state-of-onboarding-report?hs_amp=true. Record audit/review date: 2026-05-18. - [customer-onboarding-statistics-2026-006] Recorded assertion from onramp.us: "Companies reducing onboarding investment that saw churn increase within six months". Recorded figure: 57%. Record category: revenue. Source-record methodology (retained wording): "Public source claim captured May 2026; see source URL for methodology and caveats.". Qualification: Vendor-published survey; relevant but not neutral audited research. Source: https://onramp.us/blog/2026-state-of-onboarding-report?hs_amp=true. Record audit/review date: 2026-05-18. - [customer-onboarding-statistics-2026-007] Recorded assertion from onramp.us: "Onboarding teams reporting into CRO or Revenue Operations". Recorded figure: 57%. Record category: ownership. Source-record methodology (retained wording): "Public source claim captured May 2026; see source URL for methodology and caveats.". Qualification: Vendor-published survey; relevant but not neutral audited research. Source: https://onramp.us/blog/2026-state-of-onboarding-report?hs_amp=true. Record audit/review date: 2026-05-18. - [customer-onboarding-statistics-2026-008] Recorded assertion from onramp.us: "Companies with dedicated onboarding functions". Recorded figure: 69%. Record category: ownership. Source-record methodology (retained wording): "Public source claim captured May 2026; see source URL for methodology and caveats.". Qualification: Vendor-published survey; relevant but not neutral audited research. Source: https://onramp.us/blog/2026-state-of-onboarding-report?hs_amp=true. Record audit/review date: 2026-05-18. - [customer-onboarding-statistics-2026-009] Recorded assertion from onramp.us: "SaaS companies actively investing in onboarding automation". Recorded figure: 26%. Record category: automation. Source-record methodology (retained wording): "Public source claim captured May 2026; see source URL for methodology and caveats.". Qualification: Vendor-published survey; relevant but not neutral audited research. Source: https://onramp.us/blog/2026-state-of-onboarding-report?hs_amp=true. Record audit/review date: 2026-05-18. - [customer-onboarding-statistics-2026-010] Recorded assertion from onramp.us: "Companies still using four to six tools for customer onboarding". Recorded figure: 60%. Record category: automation. Source-record methodology (retained wording): "Public source claim captured May 2026; see source URL for methodology and caveats.". Qualification: Vendor-published survey; relevant but not neutral audited research. Source: https://onramp.us/blog/2026-state-of-onboarding-report?hs_amp=true. Record audit/review date: 2026-05-18. ### Demand Generation Statistics 2026: KPIs, Handoff and Measurement URL: https://konabayev.com/blog/demand-generation-statistics/ Full article summary (may cover material outside this subset): A 2026 compilation of 35 claims from 2023 and 2025 demand generation surveys, covering ROI confidence, targeting and handoff, with downloadable data. Updated: 2026-10-04 Selected records: first 10 of 35 published records in file order. Full published dataset: https://konabayev.com/data/demand-generation-statistics.jsonl. The selected records do not cover every topic in the full article summary. - [DGS-001] Recorded assertion from digitalzone.com: "62% of Digitalzone's 1,500 B2B marketers said they were very confident in measuring ROI across marketing channels.". Recorded value: 62. Unit: percent. Field date: not disclosed. Population: 1,500 B2B marketers worldwide. Source-record methodology: not recorded. Qualification: Recruitment, quotas, field dates, and uncertainty are not disclosed on the public dashboard. Source: https://digitalzone.com/thought-leadership/dashboards/2025-dimensions-of-demand-gen-dashboard/. Record audit/review date: 2026-07-14. - [DGS-002] Recorded assertion from digitalzone.com: "37% of Digitalzone's 1,500 B2B marketers said they were somewhat confident in measuring ROI across marketing channels.". Recorded value: 37. Unit: percent. Field date: not disclosed. Population: 1,500 B2B marketers worldwide. Source-record methodology: not recorded. Qualification: Confidence is self-reported and is not evidence that ROI measurement is valid. Source: https://digitalzone.com/thought-leadership/dashboards/2025-dimensions-of-demand-gen-dashboard/. Record audit/review date: 2026-07-14. - [DGS-003] Recorded assertion from digitalzone.com: "1% of Digitalzone's 1,500 B2B marketers said they were not very confident in measuring ROI across marketing channels.". Recorded value: 1. Unit: percent. Field date: not disclosed. Population: 1,500 B2B marketers worldwide. Source-record methodology: not recorded. Qualification: The accessible visual exposes three response categories; no separate 'not confident' category is shown. Source: https://digitalzone.com/thought-leadership/dashboards/2025-dimensions-of-demand-gen-dashboard/. Record audit/review date: 2026-07-14. - [DGS-004] Recorded assertion from digitalzone.com: "52% of Digitalzone respondents rated ABM mostly effective at generating qualified leads from target accounts.". Recorded value: 52. Unit: percent. Field date: not disclosed. Population: 1,500 B2B marketers worldwide. Source-record methodology: not recorded. Qualification: The public dashboard does not disclose the ABM-user subgroup denominator. Source: https://digitalzone.com/thought-leadership/dashboards/2025-dimensions-of-demand-gen-dashboard/. Record audit/review date: 2026-07-14. - [DGS-005] Recorded assertion from digitalzone.com: "41% of Digitalzone respondents rated ABM extremely effective at generating qualified leads from target accounts.". Recorded value: 41. Unit: percent. Field date: not disclosed. Population: 1,500 B2B marketers worldwide. Source-record methodology: not recorded. Qualification: Self-reported effectiveness; no outcome metric or control group is disclosed. Source: https://digitalzone.com/thought-leadership/dashboards/2025-dimensions-of-demand-gen-dashboard/. Record audit/review date: 2026-07-14. - [DGS-006] Recorded assertion from digitalzone.com: "7% of Digitalzone respondents were neutral about ABM's effectiveness at generating qualified leads from target accounts.". Recorded value: 7. Unit: percent. Field date: not disclosed. Population: 1,500 B2B marketers worldwide. Source-record methodology: not recorded. Qualification: The visual does not expose a negative response category. Source: https://digitalzone.com/thought-leadership/dashboards/2025-dimensions-of-demand-gen-dashboard/. Record audit/review date: 2026-07-14. - [DGS-007] Recorded assertion from digitalzone.com: "19% of Digitalzone's B2B marketers selected identifying the full buying committee as their biggest targeting challenge.". Recorded value: 19. Unit: percent. Field date: not disclosed. Population: 1,500 B2B marketers worldwide. Source-record methodology: not recorded. Qualification: Largest response in this visual, not a majority of respondents. Source: https://digitalzone.com/thought-leadership/dashboards/2025-dimensions-of-demand-gen-dashboard/. Record audit/review date: 2026-07-14. - [DGS-008] Recorded assertion from digitalzone.com: "15% of Digitalzone respondents had account intent but were unclear about which people to target.". Recorded value: 15. Unit: percent. Field date: not disclosed. Population: 1,500 B2B marketers worldwide. Source-record methodology: not recorded. Qualification: The public accessible label is truncated; article wording must remain conservative. Source: https://digitalzone.com/thought-leadership/dashboards/2025-dimensions-of-demand-gen-dashboard/. Record audit/review date: 2026-07-14. - [DGS-009] Recorded assertion from digitalzone.com: "14% of Digitalzone respondents selected low engagement rates with account contacts as their biggest targeting challenge.". Recorded value: 14. Unit: percent. Field date: not disclosed. Population: 1,500 B2B marketers worldwide. Source-record methodology: not recorded. Qualification: Self-reported single-choice challenge. Source: https://digitalzone.com/thought-leadership/dashboards/2025-dimensions-of-demand-gen-dashboard/. Record audit/review date: 2026-07-14. - [DGS-010] Recorded assertion from digitalzone.com: "14% of Digitalzone respondents selected incomplete or outdated contact data as their biggest targeting challenge.". Recorded value: 14. Unit: percent. Field date: not disclosed. Population: 1,500 B2B marketers worldwide. Source-record methodology: not recorded. Qualification: Self-reported single-choice challenge. Source: https://digitalzone.com/thought-leadership/dashboards/2025-dimensions-of-demand-gen-dashboard/. Record audit/review date: 2026-07-14. ### Email Deliverability Statistics 2026: Inbox & Spam Data URL: https://konabayev.com/blog/email-deliverability-statistics/ Full article summary (may cover material outside this subset): 56 source-backed email deliverability statistics for 2026 on inbox placement, spam complaints, authentication, bounce rates and sender requirements. Updated: 2026-07-10 Selected records: first 10 of 56 published records in file order. Full published dataset: https://konabayev.com/data/email-deliverability-statistics.jsonl. The selected records do not cover every topic in the full article summary. - [ED-001] Recorded assertion from mailgun.com: "78.5% of surveyed senders rated email deliverability importance between 8 and 10 out of 10.". Recorded figure: 78.5%. Period: fielded July 2024; published as the 2025 report. Population: 1,100 global email senders, including users of Sinch Mailgun, Mailjet, and Email on Acid. Evidence type: survey. Record category: importance. Source-record methodology: not recorded. Qualification: Self-reported vendor survey of email senders. The sample includes users of Sinch brands and may not represent every sender or prove causal effects. Source: https://www.mailgun.com/wp-content/uploads/pdf/SI-State_of_Email-2024_-_v3_1.pdf. Record audit/review date: 2026-07-10. - [ED-002] Recorded assertion from mailgun.com: "46% of surveyed senders rated email deliverability importance a full 10 out of 10.". Recorded figure: 46%. Period: fielded July 2024; published as the 2025 report. Population: 1,100 global email senders, including users of Sinch Mailgun, Mailjet, and Email on Acid. Evidence type: survey. Record category: importance. Source-record methodology: not recorded. Qualification: Self-reported vendor survey of email senders. The sample includes users of Sinch brands and may not represent every sender or prove causal effects. Source: https://www.mailgun.com/wp-content/uploads/pdf/SI-State_of_Email-2024_-_v3_1.pdf. Record audit/review date: 2026-07-10. - [ED-003] Recorded assertion from mailgun.com: "48% of surveyed senders named staying out of spam as a top email deliverability challenge.". Recorded figure: 48%. Period: fielded July 2024; published as the 2025 report. Population: 1,100 global email senders, including users of Sinch Mailgun, Mailjet, and Email on Acid. Evidence type: survey. Record category: challenges. Source-record methodology: not recorded. Qualification: Self-reported vendor survey of email senders. The sample includes users of Sinch brands and may not represent every sender or prove causal effects. Source: https://www.mailgun.com/wp-content/uploads/pdf/SI-State_of_Email-2024_-_v3_1.pdf. Record audit/review date: 2026-07-10. - [ED-004] Recorded assertion from mailgun.com: "53% of surveyed senders did not monitor major email blocklists for their domains or IP addresses.". Recorded figure: 53%. Period: fielded July 2024; published as the 2025 report. Population: 1,100 global email senders, including users of Sinch Mailgun, Mailjet, and Email on Acid. Evidence type: survey. Record category: monitoring. Source-record methodology: not recorded. Qualification: Self-reported vendor survey of email senders. The sample includes users of Sinch brands and may not represent every sender or prove causal effects. Source: https://www.mailgun.com/wp-content/uploads/pdf/SI-State_of_Email-2024_-_v3_1.pdf. Record audit/review date: 2026-07-10. - [ED-005] Recorded assertion from mailgun.com: "39% of surveyed senders rarely or never conducted email list hygiene.". Recorded figure: 39%. Period: fielded July 2024; published as the 2025 report. Population: 1,100 global email senders, including users of Sinch Mailgun, Mailjet, and Email on Acid. Evidence type: survey. Record category: list_hygiene. Source-record methodology: not recorded. Qualification: Self-reported vendor survey of email senders. The sample includes users of Sinch brands and may not represent every sender or prove causal effects. Source: https://www.mailgun.com/wp-content/uploads/pdf/SI-State_of_Email-2024_-_v3_1.pdf. Record audit/review date: 2026-07-10. - [ED-006] Recorded assertion from mailgun.com: "63% of surveyed senders were at least somewhat familiar with the Gmail and Yahoo sender requirements.". Recorded figure: 63%. Period: fielded July 2024; published as the 2025 report. Population: 1,100 global email senders, including users of Sinch Mailgun, Mailjet, and Email on Acid. Evidence type: survey. Record category: sender_requirements. Source-record methodology: not recorded. Qualification: Self-reported vendor survey of email senders. The sample includes users of Sinch brands and may not represent every sender or prove causal effects. Source: https://www.mailgun.com/wp-content/uploads/pdf/SI-State_of_Email-2024_-_v3_1.pdf. Record audit/review date: 2026-07-10. - [ED-007] Recorded assertion from mailgun.com: "49.5% of senders who knew about the Gmail and Yahoo requirements changed their email programs in response.". Recorded figure: 49.5%. Period: fielded July 2024; published as the 2025 report. Population: 1,100 global email senders, including users of Sinch Mailgun, Mailjet, and Email on Acid. Evidence type: survey. Record category: sender_requirements. Source-record methodology: not recorded. Qualification: Self-reported vendor survey of email senders. The sample includes users of Sinch brands and may not represent every sender or prove causal effects. Source: https://www.mailgun.com/wp-content/uploads/pdf/SI-State_of_Email-2024_-_v3_1.pdf. Record audit/review date: 2026-07-10. - [ED-008] Recorded assertion from mailgun.com: "79% of senders who changed their programs updated email authentication protocols.". Recorded figure: 79%. Period: fielded July 2024; published as the 2025 report. Population: 1,100 global email senders, including users of Sinch Mailgun, Mailjet, and Email on Acid. Evidence type: survey. Record category: authentication. Source-record methodology: not recorded. Qualification: Self-reported vendor survey of email senders. The sample includes users of Sinch brands and may not represent every sender or prove causal effects. Source: https://www.mailgun.com/wp-content/uploads/pdf/SI-State_of_Email-2024_-_v3_1.pdf. Record audit/review date: 2026-07-10. - [ED-009] Recorded assertion from mailgun.com: "59% of surveyed senders were concerned about the Gmail and Yahoo sender requirements.". Recorded figure: 59%. Period: fielded July 2024; published as the 2025 report. Population: 1,100 global email senders, including users of Sinch Mailgun, Mailjet, and Email on Acid. Evidence type: survey. Record category: sender_requirements. Source-record methodology: not recorded. Qualification: Self-reported vendor survey of email senders. The sample includes users of Sinch brands and may not represent every sender or prove causal effects. Source: https://www.mailgun.com/wp-content/uploads/pdf/SI-State_of_Email-2024_-_v3_1.pdf. Record audit/review date: 2026-07-10. - [ED-010] Recorded assertion from mailgun.com: "23% of surveyed senders reported deliverability challenges after Gmail and Yahoo began enforcing the requirements.". Recorded figure: 23%. Period: fielded July 2024; published as the 2025 report. Population: 1,100 global email senders, including users of Sinch Mailgun, Mailjet, and Email on Acid. Evidence type: survey. Record category: sender_requirements. Source-record methodology: not recorded. Qualification: Self-reported vendor survey of email senders. The sample includes users of Sinch brands and may not represent every sender or prove causal effects. Source: https://www.mailgun.com/wp-content/uploads/pdf/SI-State_of_Email-2024_-_v3_1.pdf. Record audit/review date: 2026-07-10. ### Email Marketing Benchmarks 2026: Open & Click Rates by Platform URL: https://konabayev.com/blog/email-marketing-benchmarks-2026/ Full article summary (may cover material outside this subset): Email marketing benchmarks for 2026: Mailchimp, Klaviyo and ActiveCampaign open and click rates, CTOR, orders and source methodology. Updated: 2026-10-04 Selected records: first 10 of 78 published records in file order. Full published dataset: https://konabayev.com/data/email-marketing-benchmarks-2026.jsonl. The selected records do not cover every topic in the full article summary. - [email-marketing-benchmarks-2026-001] Recorded assertion from salesforce.com: "A \"good\" CTR for email marketing typically falls in the range of 2% to 5%". Record category: click_rate. Source-record methodology (retained wording): "Apple's privacy rules, particularly Mail Privacy Protection". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://www.salesforce.com/marketing/email/benchmarks/. Record audit/review date: 2026-05-10. - [email-marketing-benchmarks-2026-002] Recorded assertion from salesforce.com: "Promotional emails often have a slightly lower average CTR, ranging from 1% to 3%". Record category: click_rate. Source-record methodology (retained wording): "Apple's privacy rules, particularly Mail Privacy Protection". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://www.salesforce.com/marketing/email/benchmarks/. Record audit/review date: 2026-05-10. - [email-marketing-benchmarks-2026-003] Recorded assertion from salesforce.com: "Transactional emails...tend to have higher CTRs, often surpassing 5%". Record category: click_rate. Source-record methodology (retained wording): "Apple's privacy rules, particularly Mail Privacy Protection". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://www.salesforce.com/marketing/email/benchmarks/. Record audit/review date: 2026-05-10. - [email-marketing-benchmarks-2026-004] Recorded assertion from salesforce.com: "The average CTOR for general marketing emails typically ranges from 10% to 25%". Record category: ctor. Source-record methodology (retained wording): "Apple's privacy rules, particularly Mail Privacy Protection". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://www.salesforce.com/marketing/email/benchmarks/. Record audit/review date: 2026-05-10. - [email-marketing-benchmarks-2026-005] Recorded assertion from salesforce.com: "Promotional emails typically range from 5% to 15%". Record category: benchmark. Source-record methodology (retained wording): "Apple's privacy rules, particularly Mail Privacy Protection". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://www.salesforce.com/marketing/email/benchmarks/. Record audit/review date: 2026-05-10. - [email-marketing-benchmarks-2026-006] Recorded assertion from salesforce.com: "Content-rich or educational emails often achieve higher CTORs, averaging between 20% and 30%". Record category: ctor. Source-record methodology (retained wording): "Apple's privacy rules, particularly Mail Privacy Protection". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://www.salesforce.com/marketing/email/benchmarks/. Record audit/review date: 2026-05-10. - [email-marketing-benchmarks-2026-007] Recorded assertion from salesforce.com: "Transactional emails...may have higher average CTORs, often exceeding 30%". Record category: ctor. Source-record methodology (retained wording): "Apple's privacy rules, particularly Mail Privacy Protection". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://www.salesforce.com/marketing/email/benchmarks/. Record audit/review date: 2026-05-10. - [email-marketing-benchmarks-2026-008] Recorded assertion from klaviyo.com: "The average email campaign open rate across all industries is 31%, with the top 10% of performers hitting a 45.1% open rate.". Record category: open_rate. Source-record methodology (retained wording): "Klaviyo analysed data from over 183,000 brands". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://www.klaviyo.com/uk/blog/email-marketing-benchmarks-open-click-and-conversion-rates. Record audit/review date: 2026-05-10. - [email-marketing-benchmarks-2026-009] Recorded assertion from klaviyo.com: "The average email campaign click rate across all industries is 1.69%, with the top 10% of performers hitting a 3.38% click rate.". Record category: click_rate. Source-record methodology (retained wording): "Klaviyo analysed data from over 183,000 brands". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://www.klaviyo.com/uk/blog/email-marketing-benchmarks-open-click-and-conversion-rates. Record audit/review date: 2026-05-10. - [email-marketing-benchmarks-2026-010] Recorded assertion from klaviyo.com: "The average automated email flow click rate across all industries is 5.58%, with the top 10% of performers reaching 10.48%.". Record category: click_rate. Source-record methodology (retained wording): "Klaviyo analysed data from over 183,000 brands". Qualification: No claim-specific caveat is supplied in this record. Retained source wording is not an independently established representative benchmark or causal result; guidance and illustrative calculations must not be treated as observations. Source: https://www.klaviyo.com/uk/blog/email-marketing-benchmarks-open-click-and-conversion-rates. Record audit/review date: 2026-05-10. ### Browser Extension Monetization Statistics 2026: Free vs Paid URL: https://konabayev.com/blog/extension-monetization-statistics-2026/ Full article summary (may cover material outside this subset): Source-backed browser extension monetization statistics for 2026, including adoption, paid-product rules, affiliate policy, and unit economics. Updated: 2026-08-04 Selected records: first 10 of 44 published records in file order. Full published dataset: https://konabayev.com/data/extension-monetization-statistics-2026.jsonl. The selected records do not cover every topic in the full article summary. - [extension-monetization-statistics-2026-001] Recorded assertion from exstats.com: "Exstats reported 178,299 active Chrome Web Store extensions in March 2026.". Record category: benchmark. Source-record methodology (retained wording): "Daily parsing of public extension-store data.". Qualification: Exstats active-listing count can differ from other crawlers because crawl scope and definitions vary. Source: https://exstats.com/blog/state-of-browser-extensions-q1-2026. Record audit/review date: 2026-08-04. - [extension-monetization-statistics-2026-002] Recorded assertion from exstats.com: "Exstats reported 83,465 Firefox Add-ons and 28,741 Microsoft Edge Add-ons in March 2026.". Record category: benchmark. Source-record methodology (retained wording): "Daily parsing of public store data across Chrome, Firefox and Edge.". Qualification: Store definitions and public availability differ by ecosystem. Source: https://exstats.com/blog/state-of-browser-extensions-q1-2026. Record audit/review date: 2026-08-04. - [extension-monetization-statistics-2026-003] Recorded assertion from exstats.com: "Exstats reported Firefox extension listings grew 74.16% year over year from March 2025 to March 2026, compared with 22.70% for Chrome and 21.55% for Edge.". Record category: benchmark. Source-record methodology (retained wording): "Year-over-year listing comparison in the Exstats Q1 2026 dataset.". Qualification: Growth in listings is not the same as growth in paid demand. Source: https://exstats.com/blog/state-of-browser-extensions-q1-2026. Record audit/review date: 2026-08-04. - [extension-monetization-statistics-2026-004] Recorded assertion from exstats.com: "Exstats reported Chrome added 38,015 new extensions and removed 7,890 in Q1 2026, for net growth of 30,125 listings.". Record category: benchmark. Source-record methodology (retained wording): "Q1 2026 gross new, deleted and net new listing counts.". Qualification: Listing growth can include low-adoption products. Source: https://exstats.com/blog/state-of-browser-extensions-q1-2026. Record audit/review date: 2026-08-04. - [extension-monetization-statistics-2026-005] Recorded assertion from exstats.com: "Exstats reported Chrome had a net loss of 27,249 listings in Q4 2025 before rebounding in Q1 2026.". Record category: benchmark. Source-record methodology (retained wording): "Quarterly net-new comparison between Q4 2025 and Q1 2026.". Qualification: Deletion waves may reflect policy or compliance resets, not demand collapse. Source: https://exstats.com/blog/state-of-browser-extensions-q1-2026. Record audit/review date: 2026-08-04. - [extension-monetization-statistics-2026-006] Recorded assertion from exstats.com: "Exstats reported Chrome removed 47,288 extensions in October 2025.". Record category: benchmark. Source-record methodology (retained wording): "Monthly listing and deletion table from September 2025 through March 2026.". Qualification: The source connects the deletion wave to platform cleanup context but does not prove one cause for every deletion. Source: https://exstats.com/blog/state-of-browser-extensions-q1-2026. Record audit/review date: 2026-08-04. - [extension-monetization-statistics-2026-007] Recorded assertion from exstats.com: "Exstats reported Chrome's extension total fell from 175,423 in September 2025 to 134,888 in October 2025, then recovered to 178,299 by March 2026.". Record category: benchmark. Source-record methodology (retained wording): "Monthly extension listing totals from Exstats.". Qualification: Store total is a supply metric, not a monetization metric. Source: https://exstats.com/blog/state-of-browser-extensions-q1-2026. Record audit/review date: 2026-08-04. - [extension-monetization-statistics-2026-008] Recorded assertion from exstats.com: "Exstats reported the median Chrome extension had 18 users in Q1 2026.". Record category: benchmark. Source-record methodology (retained wording): "Public store usersCount distribution.". Qualification: Chrome usersCount is an adoption metric, not revenue or active usage. Source: https://exstats.com/blog/state-of-browser-extensions-q1-2026. Record audit/review date: 2026-08-04. - [extension-monetization-statistics-2026-009] Recorded assertion from exstats.com: "Exstats reported the median Edge add-on had 62 active installs and the median Firefox add-on had one average daily user.". Record category: benchmark. Source-record methodology (retained wording): "Closest public adoption metric for each extension store.". Qualification: Chrome, Edge and Firefox adoption metrics are not directly equivalent. Source: https://exstats.com/blog/state-of-browser-extensions-q1-2026. Record audit/review date: 2026-08-04. - [extension-monetization-statistics-2026-010] Recorded assertion from exstats.com: "Exstats reported 70.4% of Chrome extensions had 100 users or fewer in Q1 2026.". Record category: benchmark. Source-record methodology (retained wording): "Adoption bucket distribution from public store data.". Qualification: A low-user listing can still be a private, enterprise or niche product. Source: https://exstats.com/blog/state-of-browser-extensions-q1-2026. Record audit/review date: 2026-08-04. ### GEO Statistics 2026: Citation Visibility & Optimization Evidence URL: https://konabayev.com/blog/geo-statistics-2026/ Full article summary (may cover material outside this subset): Primary-source GEO statistics for 2026 covering citation visibility, tested optimization methods, benchmark design and cross-platform AI search evidence. Updated: 2026-10-04 Selected records: first 10 of 40 published records in file order. Full published dataset: https://konabayev.com/data/geo-statistics-2026.jsonl. The selected records do not cover every topic in the full article summary. - [geo-statistics-2026-001] Recorded assertion from doi.org: "GEO-bench contains 10,000 queries from diverse domains and sources.". Record category: benchmark_design. Source-record methodology (retained wording): "Peer-reviewed KDD 2024 controlled study using GEO-bench; most experiments used the top five Google results and a GPT-3.5-based generative engine, with a separate Perplexity.ai validation.". Qualification: Controlled 2024 research environment; relative source-visibility results are not a live 2026 ranking, traffic, or conversion guarantee. Source: https://doi.org/10.1145/3637528.3671900. Record audit/review date: 2026-07-14. - [geo-statistics-2026-002] Recorded assertion from doi.org: "GEO-bench combines queries from nine source datasets.". Record category: benchmark_design. Source-record methodology (retained wording): "Peer-reviewed KDD 2024 controlled study using GEO-bench; most experiments used the top five Google results and a GPT-3.5-based generative engine, with a separate Perplexity.ai validation.". Qualification: Controlled 2024 research environment; relative source-visibility results are not a live 2026 ranking, traffic, or conversion guarantee. Source: https://doi.org/10.1145/3637528.3671900. Record audit/review date: 2026-07-14. - [geo-statistics-2026-003] Recorded assertion from doi.org: "GEO-bench is split into 8,000 training queries, 1,000 validation queries, and 1,000 test queries.". Record category: benchmark_design. Source-record methodology (retained wording): "Peer-reviewed KDD 2024 controlled study using GEO-bench; most experiments used the top five Google results and a GPT-3.5-based generative engine, with a separate Perplexity.ai validation.". Qualification: Controlled 2024 research environment; relative source-visibility results are not a live 2026 ranking, traffic, or conversion guarantee. Source: https://doi.org/10.1145/3637528.3671900. Record audit/review date: 2026-07-14. - [geo-statistics-2026-004] Recorded assertion from doi.org: "GEO-bench contains 80% informational queries, 10% transactional queries, and 10% navigational queries.". Record category: query_intent. Source-record methodology (retained wording): "Peer-reviewed KDD 2024 controlled study using GEO-bench; most experiments used the top five Google results and a GPT-3.5-based generative engine, with a separate Perplexity.ai validation.". Qualification: Controlled 2024 research environment; relative source-visibility results are not a live 2026 ranking, traffic, or conversion guarantee. Source: https://doi.org/10.1145/3637528.3671900. Record audit/review date: 2026-07-14. - [geo-statistics-2026-005] Recorded assertion from doi.org: "GEO-bench covers 25 domains and nine query types, with seven query categorizations.". Record category: benchmark_design. Source-record methodology (retained wording): "Peer-reviewed KDD 2024 controlled study using GEO-bench; most experiments used the top five Google results and a GPT-3.5-based generative engine, with a separate Perplexity.ai validation.". Qualification: Controlled 2024 research environment; relative source-visibility results are not a live 2026 ranking, traffic, or conversion guarantee. Source: https://doi.org/10.1145/3637528.3671900. Record audit/review date: 2026-07-14. - [geo-statistics-2026-006] Recorded assertion from doi.org: "The main GEO experiment supplied the top five Google search results for each query to the generative engine.". Record category: experimental_setup. Source-record methodology (retained wording): "Peer-reviewed KDD 2024 controlled study using GEO-bench; most experiments used the top five Google results and a GPT-3.5-based generative engine, with a separate Perplexity.ai validation.". Qualification: Controlled 2024 research environment; relative source-visibility results are not a live 2026 ranking, traffic, or conversion guarantee. Source: https://doi.org/10.1145/3637528.3671900. Record audit/review date: 2026-07-14. - [geo-statistics-2026-007] Recorded assertion from doi.org: "The main GEO experiment sampled five responses per query at temperature 0.7.". Record category: experimental_setup. Source-record methodology (retained wording): "Peer-reviewed KDD 2024 controlled study using GEO-bench; most experiments used the top five Google results and a GPT-3.5-based generative engine, with a separate Perplexity.ai validation.". Qualification: Controlled 2024 research environment; relative source-visibility results are not a live 2026 ranking, traffic, or conversion guarantee. Source: https://doi.org/10.1145/3637528.3671900. Record audit/review date: 2026-07-14. - [geo-statistics-2026-008] Recorded assertion from doi.org: "Researchers evaluated nine GEO methods against an unmodified baseline across five random seeds.". Record category: experimental_setup. Source-record methodology (retained wording): "Peer-reviewed KDD 2024 controlled study using GEO-bench; most experiments used the top five Google results and a GPT-3.5-based generative engine, with a separate Perplexity.ai validation.". Qualification: Controlled 2024 research environment; relative source-visibility results are not a live 2026 ranking, traffic, or conversion guarantee. Source: https://doi.org/10.1145/3637528.3671900. Record audit/review date: 2026-07-14. - [geo-statistics-2026-009] Recorded assertion from doi.org: "The KDD study reported source-visibility improvements of up to 40% from tested GEO methods.". Record category: visibility. Source-record methodology (retained wording): "Peer-reviewed KDD 2024 controlled study using GEO-bench; most experiments used the top five Google results and a GPT-3.5-based generative engine, with a separate Perplexity.ai validation.". Qualification: Controlled 2024 research environment; relative source-visibility results are not a live 2026 ranking, traffic, or conversion guarantee. Source: https://doi.org/10.1145/3637528.3671900. Record audit/review date: 2026-07-14. - [geo-statistics-2026-010] Recorded assertion from doi.org: "The best tested method improved Position-Adjusted Word Count by 41% over the controlled baseline.". Record category: visibility. Source-record methodology (retained wording): "Peer-reviewed KDD 2024 controlled study using GEO-bench; most experiments used the top five Google results and a GPT-3.5-based generative engine, with a separate Perplexity.ai validation.". Qualification: Controlled 2024 research environment; relative source-visibility results are not a live 2026 ranking, traffic, or conversion guarantee. Source: https://doi.org/10.1145/3637528.3671900. Record audit/review date: 2026-07-14. ### Lead Response Time Statistics: B2B Speed-to-Lead Data URL: https://konabayev.com/blog/lead-response-time-statistics/ Full article summary (may cover material outside this subset): 40 source-locked B2B lead response time statistics from primary 2024, 2021 and historical 2011 studies, with denominators and free datasets. Updated: 2026-07-14 Selected records: first 4 of 4 explicitly hero-eligible records; 40 published records total. Full published dataset: https://konabayev.com/data/lead-response-time-statistics.jsonl. The selected records do not cover every topic in the full article summary. - [LRT-003] Recorded assertion from revenuehero.io: "RevenueHero observed 365 responses across 1,000 submitted demo requests.". Recorded value: 365. Unit: responses. Study year: 2024. Field date: field dates not disclosed; published March 20, 2024. Population: 1,000 B2B SaaS company websites; 98.8% had 51–500 employees. Denominator: 1,000 submitted demo requests. Outcome type: response_count. Source-record methodology (retained wording): "vendor-run mystery-shopping audit of public demo-request forms". Qualification: Do not convert the remaining 635 requests into a universal nonresponse rate; the source notes that silent enrichment-based disqualification may explain some cases. Source: https://www.revenuehero.io/blog/b2b-lead-response-times. Record audit/review date: 2026-07-14. - [LRT-004] Recorded assertion from revenuehero.io: "Among the 365 observed responses, the average elapsed time was one day, five hours and 17 minutes.". Recorded value: 29.283. Unit: hours. Study year: 2024. Field date: field dates not disclosed; published March 20, 2024. Population: 1,000 B2B SaaS company websites; 98.8% had 51–500 employees. Denominator: 365 observed responses. Outcome type: response_time. Source-record methodology (retained wording): "vendor-run mystery-shopping audit of public demo-request forms". Qualification: RevenueHero counted automated acknowledgements as responses. The vendor-run B2B SaaS audit is descriptive, heavily concentrated in 51–500-employee companies, and is not a probability sample of all B2B firms. Source: https://www.revenuehero.io/blog/b2b-lead-response-times. Record audit/review date: 2026-07-14. - [LRT-011] Recorded assertion from revenuehero.io: "RevenueHero detected a scheduler on 113 of the 1,000 audited websites.". Recorded value: 113. Unit: websites. Study year: 2024. Field date: field dates not disclosed; published March 20, 2024. Population: 1,000 B2B SaaS company websites; 98.8% had 51–500 employees. Denominator: 1,000 audited websites. Outcome type: scheduler_availability. Source-record methodology (retained wording): "vendor-run mystery-shopping audit of public demo-request forms". Qualification: RevenueHero counted automated acknowledgements as responses. The vendor-run B2B SaaS audit is descriptive, heavily concentrated in 51–500-employee companies, and is not a probability sample of all B2B firms. Source: https://www.revenuehero.io/blog/b2b-lead-response-times. Record audit/review date: 2026-07-14. - [LRT-018] Recorded assertion from revenuehero.io: "Among RevenueHero's observed responders, 60.27% sent an automated response.". Recorded value: 60.27. Unit: percent. Study year: 2024. Field date: field dates not disclosed; published March 20, 2024. Population: 1,000 B2B SaaS company websites; 98.8% had 51–500 employees. Denominator: 365 observed responders. Outcome type: automated_response_share. Source-record methodology (retained wording): "vendor-run mystery-shopping audit of public demo-request forms". Qualification: The source does not disclose whether every later interaction remained automated; this figure only classifies the observed response behavior. Source: https://www.revenuehero.io/blog/b2b-lead-response-times. Record audit/review date: 2026-07-14. ### LinkedIn Ads Benchmarks 2026: ROAS, CPC & Funnel Data URL: https://konabayev.com/blog/linkedin-ads-benchmarks/ Full article summary (may cover material outside this subset): 24 source-backed LinkedIn Ads benchmarks for 2026 covering CPC, ROAS, company-level cost, budget share, funnel influence and time to revenue. Updated: 2026-07-11 Selected records: first 10 of 24 published records in file order. Full published dataset: https://konabayev.com/data/linkedin-ads-benchmarks.jsonl. The selected records do not cover every topic in the full article summary. - [LIADS-001] Recorded assertion from dreamdata.io: "Dreamdata's 2026 benchmark covers more than 66 million sessions.". Recorded value: 66M+ sessions. Period: 2025 observations published March 10, 2026; prior-year comparisons where stated. Population: aggregated Dreamdata customer data covering more than 66 million sessions and more than 3.5 million customer journeys. Denominator: metric-specific base not reported on the public highlights page; 66M+ sessions and 3.5M+ journeys describe the report-level scope only. Source-record methodology: not recorded. Qualification: Vendor-customer telemetry is not a census of all B2B advertisers. Metric-specific denominators are not disclosed on the public highlights page, so the dataset-wide population must not be attached to a metric as its exact base. Source: https://dreamdata.io/blog/announcing-linkedin-ads-benchmarks-report-2026. Record audit/review date: 2026-07-11. - [LIADS-002] Recorded assertion from dreamdata.io: "The benchmark covers more than 3.5 million B2B customer journeys.". Recorded value: 3.5M+ journeys. Period: 2025 observations published March 10, 2026; prior-year comparisons where stated. Population: aggregated Dreamdata customer data covering more than 66 million sessions and more than 3.5 million customer journeys. Denominator: metric-specific base not reported on the public highlights page; 66M+ sessions and 3.5M+ journeys describe the report-level scope only. Source-record methodology: not recorded. Qualification: Vendor-customer telemetry is not a census of all B2B advertisers. Metric-specific denominators are not disclosed on the public highlights page, so the dataset-wide population must not be attached to a metric as its exact base. Source: https://dreamdata.io/blog/announcing-linkedin-ads-benchmarks-report-2026. Record audit/review date: 2026-07-11. - [LIADS-003] Recorded assertion from dreamdata.io: "Eighty-one percent of the measured B2B customer journey occurred outside the sales pipeline, up from 70% in the prior report.". Recorded value: 81%. Period: 2025 observations published March 10, 2026; prior-year comparisons where stated. Population: aggregated Dreamdata customer data covering more than 66 million sessions and more than 3.5 million customer journeys. Denominator: metric-specific base not reported on the public highlights page; 66M+ sessions and 3.5M+ journeys describe the report-level scope only. Source-record methodology: not recorded. Qualification: Vendor-customer telemetry is not a census of all B2B advertisers. Metric-specific denominators are not disclosed on the public highlights page, so the dataset-wide population must not be attached to a metric as its exact base. Source: https://dreamdata.io/blog/announcing-linkedin-ads-benchmarks-report-2026. Record audit/review date: 2026-07-11. - [LIADS-004] Recorded assertion from dreamdata.io: "The average measured B2B buying journey contained 88 touchpoints, up from 76.". Recorded value: 88. Period: 2025 observations published March 10, 2026; prior-year comparisons where stated. Population: aggregated Dreamdata customer data covering more than 66 million sessions and more than 3.5 million customer journeys. Denominator: metric-specific base not reported on the public highlights page; 66M+ sessions and 3.5M+ journeys describe the report-level scope only. Source-record methodology: not recorded. Qualification: Vendor-customer telemetry is not a census of all B2B advertisers. Metric-specific denominators are not disclosed on the public highlights page, so the dataset-wide population must not be attached to a metric as its exact base. Source: https://dreamdata.io/blog/announcing-linkedin-ads-benchmarks-report-2026. Record audit/review date: 2026-07-11. - [LIADS-005] Recorded assertion from dreamdata.io: "The average measured journey crossed four channels, up from 3.7.". Recorded value: 4 channels. Period: 2025 observations published March 10, 2026; prior-year comparisons where stated. Population: aggregated Dreamdata customer data covering more than 66 million sessions and more than 3.5 million customer journeys. Denominator: metric-specific base not reported on the public highlights page; 66M+ sessions and 3.5M+ journeys describe the report-level scope only. Source-record methodology: not recorded. Qualification: Vendor-customer telemetry is not a census of all B2B advertisers. Metric-specific denominators are not disclosed on the public highlights page, so the dataset-wide population must not be attached to a metric as its exact base. Source: https://dreamdata.io/blog/announcing-linkedin-ads-benchmarks-report-2026. Record audit/review date: 2026-07-11. - [LIADS-006] Recorded assertion from dreamdata.io: "The average measured journey involved 10 stakeholders, up from 6.8.". Recorded value: 10 stakeholders. Period: 2025 observations published March 10, 2026; prior-year comparisons where stated. Population: aggregated Dreamdata customer data covering more than 66 million sessions and more than 3.5 million customer journeys. Denominator: metric-specific base not reported on the public highlights page; 66M+ sessions and 3.5M+ journeys describe the report-level scope only. Source-record methodology: not recorded. Qualification: Vendor-customer telemetry is not a census of all B2B advertisers. Metric-specific denominators are not disclosed on the public highlights page, so the dataset-wide population must not be attached to a metric as its exact base. Source: https://dreamdata.io/blog/announcing-linkedin-ads-benchmarks-report-2026. Record audit/review date: 2026-07-11. - [LIADS-007] Recorded assertion from dreamdata.io: "LinkedIn accounted for 41% of total B2B ad budgets in Dreamdata's customer data, two percentage points more than the prior report.". Recorded value: 41%. Period: 2025 observations published March 10, 2026; prior-year comparisons where stated. Population: aggregated Dreamdata customer data covering more than 66 million sessions and more than 3.5 million customer journeys. Denominator: metric-specific base not reported on the public highlights page; 66M+ sessions and 3.5M+ journeys describe the report-level scope only. Source-record methodology: not recorded. Qualification: Vendor-customer telemetry is not a census of all B2B advertisers. Metric-specific denominators are not disclosed on the public highlights page, so the dataset-wide population must not be attached to a metric as its exact base. Source: https://dreamdata.io/blog/announcing-linkedin-ads-benchmarks-report-2026. Record audit/review date: 2026-07-11. - [LIADS-008] Recorded assertion from dreamdata.io: "LinkedIn's reported cost per click was €5.98.". Recorded value: €5.98. Period: 2025 observations published March 10, 2026; prior-year comparisons where stated. Population: aggregated Dreamdata customer data covering more than 66 million sessions and more than 3.5 million customer journeys. Denominator: metric-specific base not reported on the public highlights page; 66M+ sessions and 3.5M+ journeys describe the report-level scope only. Source-record methodology: not recorded. Qualification: Vendor-customer telemetry is not a census of all B2B advertisers. Metric-specific denominators are not disclosed on the public highlights page, so the dataset-wide population must not be attached to a metric as its exact base. Source: https://dreamdata.io/blog/announcing-linkedin-ads-benchmarks-report-2026. Record audit/review date: 2026-07-11. - [LIADS-009] Recorded assertion from dreamdata.io: "Meta's comparison cost per click was €1.60.". Recorded value: €1.60. Period: 2025 observations published March 10, 2026; prior-year comparisons where stated. Population: aggregated Dreamdata customer data covering more than 66 million sessions and more than 3.5 million customer journeys. Denominator: metric-specific base not reported on the public highlights page; 66M+ sessions and 3.5M+ journeys describe the report-level scope only. Source-record methodology: not recorded. Qualification: Vendor-customer telemetry is not a census of all B2B advertisers. Metric-specific denominators are not disclosed on the public highlights page, so the dataset-wide population must not be attached to a metric as its exact base. Source: https://dreamdata.io/blog/announcing-linkedin-ads-benchmarks-report-2026. Record audit/review date: 2026-07-11. - [LIADS-010] Recorded assertion from dreamdata.io: "LinkedIn cost €70.11 per company influenced, down from €154 in the prior report.". Recorded value: €70.11. Period: 2025 observations published March 10, 2026; prior-year comparisons where stated. Population: aggregated Dreamdata customer data covering more than 66 million sessions and more than 3.5 million customer journeys. Denominator: metric-specific base not reported on the public highlights page; 66M+ sessions and 3.5M+ journeys describe the report-level scope only. Source-record methodology: not recorded. Qualification: Vendor-customer telemetry is not a census of all B2B advertisers. Metric-specific denominators are not disclosed on the public highlights page, so the dataset-wide population must not be attached to a metric as its exact base. Source: https://dreamdata.io/blog/announcing-linkedin-ads-benchmarks-report-2026. Record audit/review date: 2026-07-11. ### LinkedIn Marketing Statistics 2026: Buyers and Thought Leader Ads URL: https://konabayev.com/blog/linkedin-marketing-statistics-2026/ Full article summary (may cover material outside this subset): Thirteen audited, first-party LinkedIn marketing statistics on creator influence, buyer behavior, employee reach, video and Thought Leader Ads. Updated: 2026-08-04 Selected records: first 10 of 13 published records in file order. Full published dataset: https://konabayev.com/data/linkedin-marketing-statistics-2026.jsonl. The selected records do not cover every topic in the full article summary. - [linkedin-marketing-statistics-2026-001] Recorded assertion from linkedin.com: "LinkedIn reports that the combined networks of employees are about 12 times larger than a company's own following.". Evidence type: publisher observation. Record category: platform_reach. Source-record methodology (retained wording): "LinkedIn-published platform or buyer statistic captured in the August 4, 2026 B2B marketing source audit.". Qualification: Combined network size can overlap and does not equal organic reach, engagement, or revenue. Source: https://www.linkedin.com/business/marketing/blog/trends-tips/b2b-marketing-insights-creators-thought-leadership. Record audit/review date: 2026-08-04. - [linkedin-marketing-statistics-2026-002] Recorded assertion from linkedin.com: "LinkedIn describes its community as more than 1 billion professionals worldwide.". Evidence type: platform figure. Record category: platform_reach. Source-record methodology (retained wording): "LinkedIn-published platform or buyer statistic captured in the August 4, 2026 B2B marketing source audit.". Qualification: This is a LinkedIn-published community figure, not an independent audience audit. Source: https://www.linkedin.com/business/marketing/blog/trends-tips/b2b-marketing-insights-creators-thought-leadership. Record audit/review date: 2026-08-04. - [linkedin-marketing-statistics-2026-003] Recorded assertion from linkedin.com: "LinkedIn reports that 82% of buyers say B2B creator content influences them.". Evidence type: publisher buyer statistic. Record category: creator_influence. Source-record methodology (retained wording): "LinkedIn-published platform or buyer statistic captured in the August 4, 2026 B2B marketing source audit.". Qualification: Influence is broader than purchase causation; the captured page does not expose the full sample. Source: https://www.linkedin.com/business/marketing/blog/linkedin-ads/b2b-creator-thought-leadership. Record audit/review date: 2026-08-04. - [linkedin-marketing-statistics-2026-004] Recorded assertion from linkedin.com: "LinkedIn reports that nearly 80% of buyers engage with B2B creator content monthly.". Evidence type: publisher buyer statistic. Record category: creator_influence. Source-record methodology (retained wording): "LinkedIn-published platform or buyer statistic captured in the August 4, 2026 B2B marketing source audit.". Qualification: Engagement is not defined as a pipeline or revenue event on the captured page. Source: https://www.linkedin.com/business/marketing/blog/linkedin-ads/b2b-creator-thought-leadership. Record audit/review date: 2026-08-04. - [linkedin-marketing-statistics-2026-005] Recorded assertion from linkedin.com: "LinkedIn reports that 59% of buyers discover new brands through creator content.". Evidence type: publisher buyer statistic. Record category: buyer_journey. Source-record methodology (retained wording): "LinkedIn-published platform or buyer statistic captured in the August 4, 2026 B2B marketing source audit.". Qualification: This is an awareness-stage observation, not a conversion rate. Source: https://www.linkedin.com/business/marketing/blog/linkedin-ads/b2b-creator-thought-leadership. Record audit/review date: 2026-08-04. - [linkedin-marketing-statistics-2026-006] Recorded assertion from linkedin.com: "LinkedIn reports that 67% of buyers say creator content helps them assess potential solutions.". Evidence type: publisher buyer statistic. Record category: buyer_journey. Source-record methodology (retained wording): "LinkedIn-published platform or buyer statistic captured in the August 4, 2026 B2B marketing source audit.". Qualification: Assessment influence should not be rewritten as purchase causation. Source: https://www.linkedin.com/business/marketing/blog/linkedin-ads/b2b-creator-thought-leadership. Record audit/review date: 2026-08-04. - [linkedin-marketing-statistics-2026-007] Recorded assertion from linkedin.com: "LinkedIn reports that 47% of buyers visited a vendor website after engaging with creator content.". Evidence type: publisher buyer statistic. Record category: buyer_journey. Source-record methodology (retained wording): "LinkedIn-published platform or buyer statistic captured in the August 4, 2026 B2B marketing source audit.". Qualification: A website visit is not a qualified lead or sale. Source: https://www.linkedin.com/business/marketing/blog/linkedin-ads/b2b-creator-thought-leadership. Record audit/review date: 2026-08-04. - [linkedin-marketing-statistics-2026-008] Recorded assertion from linkedin.com: "LinkedIn reports that 38% of buyers engaged with a sales team after creator content.". Evidence type: publisher buyer statistic. Record category: buyer_journey. Source-record methodology (retained wording): "LinkedIn-published platform or buyer statistic captured in the August 4, 2026 B2B marketing source audit.". Qualification: The statistic does not establish creator content as the sole cause of sales engagement. Source: https://www.linkedin.com/business/marketing/blog/linkedin-ads/b2b-creator-thought-leadership. Record audit/review date: 2026-08-04. - [linkedin-marketing-statistics-2026-009] Recorded assertion from linkedin.com: "LinkedIn reports that short-form video uploads on the platform grew 45% year over year.". Evidence type: platform activity statistic. Record category: content_format. Source-record methodology (retained wording): "LinkedIn-published platform or buyer statistic captured in the August 4, 2026 B2B marketing source audit.". Qualification: Upload growth measures supply, not buyer response or revenue. Source: https://www.linkedin.com/business/marketing/blog/linkedin-ads/b2b-creator-thought-leadership. Record audit/review date: 2026-08-04. - [linkedin-marketing-statistics-2026-010] Recorded assertion from linkedin.com: "LinkedIn reports that campaigns using Thought Leader Ads had 252% higher click-through rates.". Evidence type: relative product benchmark. Record category: paid_distribution. Source-record methodology (retained wording): "LinkedIn-published platform or buyer statistic captured in the August 4, 2026 B2B marketing source audit.". Qualification: No absolute CTR baseline or full sample is supplied in the captured page. Source: https://www.linkedin.com/business/marketing/blog/linkedin-ads/b2b-creator-thought-leadership. Record audit/review date: 2026-08-04. ### Marketing Analytics Statistics 2026: ROI, Data & Attribution URL: https://konabayev.com/blog/marketing-analytics-statistics/ Full article summary (may cover material outside this subset): 51 source-backed marketing analytics statistics for 2026 on ROI, attribution, data quality, privacy, AI and measurement, with a free dataset. Updated: 2026-07-10 Selected records: first 10 of 51 published records in file order. Full published dataset: https://konabayev.com/data/marketing-analytics-statistics.jsonl. The selected records do not cover every topic in the full article summary. - [MA-001] Recorded assertion from nielsen.com: "Nielsen reports that 54% of global marketers planned to reduce advertising spend in 2025.". Recorded figure: 54%. Period: 2025 report; public landing page does not state fieldwork dates. Population: 1,400 leading marketers. Evidence type: survey. Record category: measurement. Source-record methodology: not recorded. Qualification: Self-reported marketer survey. The public landing page does not expose every questionnaire definition or fieldwork date. Source: https://www.nielsen.com/insights/2025/annual-marketing-report-2025-chaos-to-clarity/. Record audit/review date: 2026-07-10. - [MA-002] Recorded assertion from nielsen.com: "Nielsen reports that 48% of North American marketers prioritized revenue growth and brand awareness equally.". Recorded figure: 48%. Period: 2025 report; public landing page does not state fieldwork dates. Population: 1,400 leading marketers. Evidence type: survey. Record category: strategy. Source-record methodology: not recorded. Qualification: Self-reported marketer survey. The public landing page does not expose every questionnaire definition or fieldwork date. Source: https://www.nielsen.com/insights/2025/annual-marketing-report-2025-chaos-to-clarity/. Record audit/review date: 2026-07-10. - [MA-003] Recorded assertion from nielsen.com: "Nielsen reports that 32% of marketers measured traditional and digital media spending holistically.". Recorded figure: 32%. Period: 2025 report; public landing page does not state fieldwork dates. Population: 1,400 leading marketers. Evidence type: survey. Record category: measurement. Source-record methodology: not recorded. Qualification: Self-reported marketer survey. The public landing page does not expose every questionnaire definition or fieldwork date. Source: https://www.nielsen.com/insights/2025/annual-marketing-report-2025-chaos-to-clarity/. Record audit/review date: 2026-07-10. - [MA-004] Recorded assertion from supermetrics.com: "Supermetrics says the average number of marketing-data queries increased 50% between 2020 and 2024.". Recorded figure: 50% increase. Period: change measured from 2020 to 2024. Population: product usage data from 6,000 businesses. Evidence type: platform telemetry. Record category: data_operations. Source-record methodology: not recorded. Qualification: Supermetrics customer telemetry is not a representative sample of every marketing team and must not be blended with its separate 200-person survey. Source: https://supermetrics.com/blog/marketing-data-report-2025-blog. Record audit/review date: 2026-07-10. - [MA-005] Recorded assertion from supermetrics.com: "Supermetrics says the average number of rows returned by marketing-data queries increased 230% between 2020 and 2024.". Recorded figure: 230% increase. Period: change measured from 2020 to 2024. Population: product usage data from 6,000 businesses. Evidence type: platform telemetry. Record category: data_operations. Source-record methodology: not recorded. Qualification: Supermetrics customer telemetry is not a representative sample of every marketing team and must not be blended with its separate 200-person survey. Source: https://supermetrics.com/blog/marketing-data-report-2025-blog. Record audit/review date: 2026-07-10. - [MA-006] Recorded assertion from supermetrics.com: "In the Supermetrics survey, 14% said a lack of data expertise was holding them back.". Recorded figure: 14%. Period: 2025 report; public article does not state fieldwork dates. Population: 200 marketers and marketing managers. Evidence type: survey. Record category: data_operations. Source-record methodology: not recorded. Qualification: Vendor-sponsored self-report survey with a 200-person sample. It is separate from Supermetrics product telemetry from 6,000 businesses. Source: https://supermetrics.com/blog/marketing-data-report-2025-blog. Record audit/review date: 2026-07-10. - [MA-007] Recorded assertion from supermetrics.com: "In the Supermetrics survey, 56% said they lacked enough time to analyze their data properly.". Recorded figure: 56%. Period: 2025 report; public article does not state fieldwork dates. Population: 200 marketers and marketing managers. Evidence type: survey. Record category: data_operations. Source-record methodology: not recorded. Qualification: Vendor-sponsored self-report survey with a 200-person sample. It is separate from Supermetrics product telemetry from 6,000 businesses. Source: https://supermetrics.com/blog/marketing-data-report-2025-blog. Record audit/review date: 2026-07-10. - [MA-008] Recorded assertion from supermetrics.com: "In the Supermetrics survey, 26% said they lacked enough data to make decisions.". Recorded figure: 26%. Period: 2025 report; public article does not state fieldwork dates. Population: 200 marketers and marketing managers. Evidence type: survey. Record category: data_operations. Source-record methodology: not recorded. Qualification: Vendor-sponsored self-report survey with a 200-person sample. It is separate from Supermetrics product telemetry from 6,000 businesses. Source: https://supermetrics.com/blog/marketing-data-report-2025-blog. Record audit/review date: 2026-07-10. - [MA-009] Recorded assertion from supermetrics.com: "In the Supermetrics survey, 38% said they lacked tools to integrate and report on their data.". Recorded figure: 38%. Period: 2025 report; public article does not state fieldwork dates. Population: 200 marketers and marketing managers. Evidence type: survey. Record category: data_operations. Source-record methodology: not recorded. Qualification: Vendor-sponsored self-report survey with a 200-person sample. It is separate from Supermetrics product telemetry from 6,000 businesses. Source: https://supermetrics.com/blog/marketing-data-report-2025-blog. Record audit/review date: 2026-07-10. - [MA-010] Recorded assertion from supermetrics.com: "In the Supermetrics survey, 66% worried about tracking users across channels as third-party cookies fade.". Recorded figure: 66%. Period: 2025 report; public article does not state fieldwork dates. Population: 200 marketers and marketing managers. Evidence type: survey. Record category: privacy. Source-record methodology: not recorded. Qualification: Vendor-sponsored self-report survey with a 200-person sample. It is separate from Supermetrics product telemetry from 6,000 businesses. Source: https://supermetrics.com/blog/marketing-data-report-2025-blog. Record audit/review date: 2026-07-10. ### Marketing Automation Statistics 2026: Adoption and SaaS Waste URL: https://konabayev.com/blog/marketing-automation-statistics-2026/ Full article summary (may cover material outside this subset): Fourteen audited marketing-automation statistics covering martech utilization, AI use cases, SaaS waste, visibility, ownership and pricing risk. Updated: 2026-07-14 Selected records: first 10 of 14 published records in file order. Full published dataset: https://konabayev.com/data/marketing-automation-statistics-2026.jsonl. The selected records do not cover every topic in the full article summary. - [marketing-automation-statistics-2026-001; scope=martech_supporting_missing_n] Recorded assertion from gartner.com: "Gartner's public 2025 summary reports martech utilization at 49%.". Recorded figure: 49%. Period: 2025 survey; public page accessed July 14, 2026. Population: marketing organizations in Gartner's 2025 survey; respondent count not disclosed on the public page. Evidence type: official public summary of the 2025 Gartner Marketing Technology Survey. Record category: utilization. Source-record methodology (retained wording): "Gartner's public topic page summarizes selected survey results and recommendations but does not expose the full sample or fieldwork details.". Qualification: Supporting evidence only. The public page does not disclose N. Martech utilization is not the same measure as unused licenses or wasted spend. The page alternates between utilization and tools actively used; quote the 49% figure only with the missing-N limitation adjacent. Source: https://www.gartner.com/en/marketing/topics/marketing-technology. Record audit/review date: 2026-07-14. - [marketing-automation-statistics-2026-002; scope=martech_dual_survey_missing_2024_n] Recorded assertion from martechday.com: "Advertising and Promotions AI use-case adoption rose from 30% in the report's 2024 survey to 50% in its 2026 survey.". Recorded figure: 30% to 50%. Period: product landscape and survey published May 5, 2026; AI category comparisons use the report's 2024 and 2026 surveys. Population: 15,505 mapped martech products; separate February 2026 survey of 208 marketing and marketing-operations leaders across about 70 AI use cases. Evidence type: product-landscape census methodology plus primary survey. Record category: ai_adoption. Source-record methodology (retained wording): "Chiefmartec and MartechTribe maintained the product landscape and surveyed 208 leaders. The sample was 40% VP or above, 36% technology companies, 21% professional services, 61% pure B2B, and explicitly described as tech-forward/top-quartile.". Qualification: The product map is not a count of purchases or active enterprise stacks. The N=208 survey is intentionally tech-forward and multi-select where stated, so it is not market-representative. The comparison uses separate report surveys and does not prove a causal effect. The 2024 comparator denominator and composition are not disclosed in the extracted evidence, so use the change directionally. Source: https://content.martechday.com/state-of-martech-2026.pdf. Record audit/review date: 2026-07-14. - [marketing-automation-statistics-2026-003; scope=martech_dual_survey_missing_2024_n] Recorded assertion from martechday.com: "Commerce and Sales AI use-case adoption rose from 28% in the report's 2024 survey to 49% in its 2026 survey.". Recorded figure: 28% to 49%. Period: product landscape and survey published May 5, 2026; AI category comparisons use the report's 2024 and 2026 surveys. Population: 15,505 mapped martech products; separate February 2026 survey of 208 marketing and marketing-operations leaders across about 70 AI use cases. Evidence type: product-landscape census methodology plus primary survey. Record category: ai_adoption. Source-record methodology (retained wording): "Chiefmartec and MartechTribe maintained the product landscape and surveyed 208 leaders. The sample was 40% VP or above, 36% technology companies, 21% professional services, 61% pure B2B, and explicitly described as tech-forward/top-quartile.". Qualification: The product map is not a count of purchases or active enterprise stacks. The N=208 survey is intentionally tech-forward and multi-select where stated, so it is not market-representative. The comparison uses separate report surveys and does not prove a causal effect. The 2024 comparator denominator and composition are not disclosed in the extracted evidence, so use the change directionally. Source: https://content.martechday.com/state-of-martech-2026.pdf. Record audit/review date: 2026-07-14. - [marketing-automation-statistics-2026-004; scope=martech_dual_survey_missing_2024_n] Recorded assertion from martechday.com: "Content and Experience AI use-case adoption rose from 79% in the report's 2024 survey to 89% in its 2026 survey.". Recorded figure: 79% to 89%. Period: product landscape and survey published May 5, 2026; AI category comparisons use the report's 2024 and 2026 surveys. Population: 15,505 mapped martech products; separate February 2026 survey of 208 marketing and marketing-operations leaders across about 70 AI use cases. Evidence type: product-landscape census methodology plus primary survey. Record category: ai_adoption. Source-record methodology (retained wording): "Chiefmartec and MartechTribe maintained the product landscape and surveyed 208 leaders. The sample was 40% VP or above, 36% technology companies, 21% professional services, 61% pure B2B, and explicitly described as tech-forward/top-quartile.". Qualification: The product map is not a count of purchases or active enterprise stacks. The N=208 survey is intentionally tech-forward and multi-select where stated, so it is not market-representative. The comparison uses separate report surveys and does not prove a causal effect. The 2024 comparator denominator and composition are not disclosed in the extracted evidence, so use the change directionally. Source: https://content.martechday.com/state-of-martech-2026.pdf. Record audit/review date: 2026-07-14. - [marketing-automation-statistics-2026-005; scope=enterprise_saas_comparison] Recorded assertion from flexera.com: "Flexera respondents at beginner, intermediate, and advanced ITAM maturity each estimated SaaS software waste at 20%.". Recorded figure: 20%. Period: fielded in early 2026. Population: 512 professionals with IT asset-management responsibilities sourced from an independently maintained and vetted panel. Evidence type: global survey of IT asset-management professionals. Record category: saas_waste_comparison. Source-record methodology (retained wording): "Flexera surveyed 512 ITAM professionals and rounded published percentages to whole numbers. The report is licensed CC BY 4.0.". Qualification: Enterprise SaaS comparison only, not martech data. Waste estimates are respondent estimates and cannot be converted into a marketing revenue-loss figure. Source: https://resources.flexera.com/web/pdf/Flexera-State-of-IT-Asset-Management-Report-2026.pdf. Record audit/review date: 2026-07-14. - [marketing-automation-statistics-2026-006; scope=enterprise_saas_comparison] Recorded assertion from flexera.com: "In Flexera's 2026 survey, 43% said SaaS wasted spend increased, 42% said it stayed the same, and 15% said it decreased over the previous year.". Recorded figure: 43% increased; 42% same; 15% decreased. Period: fielded in early 2026. Population: 512 professionals with IT asset-management responsibilities sourced from an independently maintained and vetted panel. Evidence type: global survey of IT asset-management professionals. Record category: saas_waste_direction_comparison. Source-record methodology (retained wording): "Flexera surveyed 512 ITAM professionals and rounded published percentages to whole numbers. The report is licensed CC BY 4.0.". Qualification: Enterprise SaaS comparison only, not martech data. Waste estimates are respondent estimates and cannot be converted into a marketing revenue-loss figure. Source: https://resources.flexera.com/web/pdf/Flexera-State-of-IT-Asset-Management-Report-2026.pdf. Record audit/review date: 2026-07-14. - [marketing-automation-statistics-2026-007; scope=enterprise_saas_comparison] Recorded assertion from flexera.com: "Flexera reports that complete visibility across IT environments declined from 43% in 2025 to 36% in 2026.". Recorded figure: 43% to 36%. Period: fielded in early 2026. Population: 512 professionals with IT asset-management responsibilities sourced from an independently maintained and vetted panel. Evidence type: global survey of IT asset-management professionals. Record category: visibility_comparison. Source-record methodology (retained wording): "Flexera surveyed 512 ITAM professionals and rounded published percentages to whole numbers. The report is licensed CC BY 4.0.". Qualification: Enterprise SaaS comparison only, not martech data. Waste estimates are respondent estimates and cannot be converted into a marketing revenue-loss figure. Source: https://resources.flexera.com/web/pdf/Flexera-State-of-IT-Asset-Management-Report-2026.pdf. Record audit/review date: 2026-07-14. - [marketing-automation-statistics-2026-008; scope=enterprise_saas_comparison] Recorded assertion from flexera.com: "Flexera reports that 64% of surveyed ITAM teams managed SaaS licenses.". Recorded figure: 64%. Period: fielded in early 2026. Population: 512 professionals with IT asset-management responsibilities sourced from an independently maintained and vetted panel. Evidence type: global survey of IT asset-management professionals. Record category: governance_comparison. Source-record methodology (retained wording): "Flexera surveyed 512 ITAM professionals and rounded published percentages to whole numbers. The report is licensed CC BY 4.0.". Qualification: Enterprise SaaS comparison only, not martech data. Waste estimates are respondent estimates and cannot be converted into a marketing revenue-loss figure. Source: https://resources.flexera.com/web/pdf/Flexera-State-of-IT-Asset-Management-Report-2026.pdf. Record audit/review date: 2026-07-14. - [marketing-automation-statistics-2026-009; scope=enterprise_saas_comparison] Recorded assertion from zylo.com: "Zylo reports that organizations left an average of 36% of SaaS licenses unused against its recommended utilization levels.". Recorded figure: 36%. Period: 2026 edition with year-over-year comparisons and nine years of underlying spend, license, and usage data. Population: telemetry covering 40M+ SaaS licenses and $75B+ in discovered and categorized spend; cost-shock questions use a separate survey of 218 IT leaders. Evidence type: enterprise SaaS-management telemetry plus a separate IT-leader survey for cost-shock claims. Record category: unused_licenses_comparison. Source-record methodology (retained wording): "Zylo aggregates observed customer portfolio, license, usage, and spend data. Its public newsroom release separately identifies N=218 for unexpected-cost questions.". Qualification: Enterprise SaaS comparison only, not martech data. Zylo customer telemetry is not a probability sample of all organizations and must not be converted into a martech waste or revenue-leakage rate. Source: https://zylo.com/2026-saas-management-index. Record audit/review date: 2026-07-14. - [marketing-automation-statistics-2026-010; scope=enterprise_saas_comparison] Recorded assertion from zylo.com: "Zylo's 2026 benchmark reports average annual SaaS spend of $55.7 million and an average portfolio of 305 applications.". Recorded figure: $55.7M; 305 apps. Period: 2026 edition with year-over-year comparisons and nine years of underlying spend, license, and usage data. Population: telemetry covering 40M+ SaaS licenses and $75B+ in discovered and categorized spend; cost-shock questions use a separate survey of 218 IT leaders. Evidence type: enterprise SaaS-management telemetry plus a separate IT-leader survey for cost-shock claims. Record category: portfolio_comparison. Source-record methodology (retained wording): "Zylo aggregates observed customer portfolio, license, usage, and spend data. Its public newsroom release separately identifies N=218 for unexpected-cost questions.". Qualification: Enterprise SaaS comparison only, not martech data. Zylo customer telemetry is not a probability sample of all organizations and must not be converted into a martech waste or revenue-leakage rate. Source: https://zylo.com/2026-saas-management-index. Record audit/review date: 2026-07-14. ### Martech Stack Benchmarks 2026: Utilization, Integration and AI Overlap URL: https://konabayev.com/blog/martech-stack-benchmarks/ Full article summary (may cover material outside this subset): 22 source-locked martech claims plus 10 labeled enterprise SaaS comparisons for 2026, covering utilization, tool churn, AI overlap and integration. Updated: 2026-07-14 Selected records: first 4 of 4 explicitly hero-eligible records; 32 published records total. Full published dataset: https://konabayev.com/data/martech-stack-benchmarks.jsonl. The selected records do not cover every topic in the full article summary. - [MTS-005; scope=martech_landscape] Recorded assertion from martechday.com: "The 2026 Marketing Technology Landscape mapped 15,505 products.". Recorded figure: 15,505 products. Period: product landscape and survey published May 5, 2026; AI category comparisons use the report's 2024 and 2026 surveys. Population: 15,505 mapped martech products; separate February 2026 survey of 208 marketing and marketing-operations leaders across about 70 AI use cases. Evidence type: product-landscape census methodology plus primary survey. Record category: landscape_size. Source-record methodology (retained wording): "Chiefmartec and MartechTribe maintained the product landscape and surveyed 208 leaders. The sample was 40% VP or above, 36% technology companies, 21% professional services, 61% pure B2B, and explicitly described as tech-forward/top-quartile.". Qualification: The product map is not a count of purchases or active enterprise stacks. The N=208 survey is intentionally tech-forward and multi-select where stated, so it is not market-representative. Source: https://content.martechday.com/state-of-martech-2026.pdf. Record audit/review date: 2026-07-14. - [MTS-006; scope=martech_landscape] Recorded assertion from martechday.com: "The mapped martech landscape grew by a net 121 products, or 0.79%, from 15,384 to 15,505.". Recorded figure: +121 / +0.79%. Period: product landscape and survey published May 5, 2026; AI category comparisons use the report's 2024 and 2026 surveys. Population: 15,505 mapped martech products; separate February 2026 survey of 208 marketing and marketing-operations leaders across about 70 AI use cases. Evidence type: product-landscape census methodology plus primary survey. Record category: landscape_growth. Source-record methodology (retained wording): "Chiefmartec and MartechTribe maintained the product landscape and surveyed 208 leaders. The sample was 40% VP or above, 36% technology companies, 21% professional services, 61% pure B2B, and explicitly described as tech-forward/top-quartile.". Qualification: The product map is not a count of purchases or active enterprise stacks. The N=208 survey is intentionally tech-forward and multi-select where stated, so it is not market-representative. Source: https://content.martechday.com/state-of-martech-2026.pdf. Record audit/review date: 2026-07-14. - [MTS-007; scope=martech_landscape] Recorded assertion from martechday.com: "Behind the 121-product net increase, the 2026 landscape added 1,488 products and removed 1,367.". Recorded figure: 1,488 added; 1,367 removed. Period: product landscape and survey published May 5, 2026; AI category comparisons use the report's 2024 and 2026 surveys. Population: 15,505 mapped martech products; separate February 2026 survey of 208 marketing and marketing-operations leaders across about 70 AI use cases. Evidence type: product-landscape census methodology plus primary survey. Record category: landscape_churn. Source-record methodology (retained wording): "Chiefmartec and MartechTribe maintained the product landscape and surveyed 208 leaders. The sample was 40% VP or above, 36% technology companies, 21% professional services, 61% pure B2B, and explicitly described as tech-forward/top-quartile.". Qualification: The product map is not a count of purchases or active enterprise stacks. The N=208 survey is intentionally tech-forward and multi-select where stated, so it is not market-representative. Source: https://content.martechday.com/state-of-martech-2026.pdf. Record audit/review date: 2026-07-14. - [MTS-008; scope=martech_landscape] Recorded assertion from martechday.com: "The Content Marketing subcategory recorded 176 removals and 139 additions in 2026, a net decline of 37 products.". Recorded figure: 176 removed; 139 added; net -37. Period: product landscape and survey published May 5, 2026; AI category comparisons use the report's 2024 and 2026 surveys. Population: 15,505 mapped martech products; separate February 2026 survey of 208 marketing and marketing-operations leaders across about 70 AI use cases. Evidence type: product-landscape census methodology plus primary survey. Record category: category_churn. Source-record methodology (retained wording): "Chiefmartec and MartechTribe maintained the product landscape and surveyed 208 leaders. The sample was 40% VP or above, 36% technology companies, 21% professional services, 61% pure B2B, and explicitly described as tech-forward/top-quartile.". Qualification: The product map is not a count of purchases or active enterprise stacks. The N=208 survey is intentionally tech-forward and multi-select where stated, so it is not market-representative. Source: https://content.martechday.com/state-of-martech-2026.pdf. Record audit/review date: 2026-07-14. ### Referral Marketing Statistics 2026: Sharing and Revenue URL: https://konabayev.com/blog/referral-marketing-statistics-2026/ Full article summary (may cover material outside this subset): Current ReferralCandy benchmarks on sharing, revenue contribution, time to first referral, rewards and advocate behavior. Updated: 2026-08-20 Selected records: first 10 of 18 published records in file order. Full published dataset: https://konabayev.com/data/referral-marketing-statistics-2026.jsonl. The selected records do not cover every topic in the full article summary. - [referral-marketing-statistics-2026-001] Recorded assertion from referralcandy.com: "ReferralCandy says its headline 2026 study used a sample of 500 established Shopify stores.". Recorded figure: 500 stores. Record category: research_scope. Source-record methodology (retained wording): "The study covers July 1, 2025 through June 30, 2026 and includes live Shopify merchants whose referral campaign started before the window, ran for at least 180 active days during the year and remained active at the end.". Qualification: ReferralCandy platform data is not a probability sample of all ecommerce stores or referral programs. Source: https://www.referralcandy.com/blog/referral-program-benchmarks-whats-a-good-conversion-rate-in-2025/. Record audit/review date: 2026-08-20. - [referral-marketing-statistics-2026-002] Recorded assertion from referralcandy.com: "ReferralCandy reports that a 4.64% share-action rate marked the top quarter of the 500-store study.". Recorded figure: 4.64%. Record category: share_action. Source-record methodology (retained wording): "Share action is tracked share-button clicks divided by eligible referral-prompt views; each merchant's rate was calculated before comparison.". Qualification: A share-button click does not prove the customer completed the send. Source: https://www.referralcandy.com/blog/referral-program-benchmarks-whats-a-good-conversion-rate-in-2025/. Record audit/review date: 2026-08-20. - [referral-marketing-statistics-2026-003] Recorded assertion from referralcandy.com: "ReferralCandy reports that a 13.38% share-action rate marked the top 10% of the 500-store study.". Recorded figure: 13.38%. Record category: share_action. Source-record methodology (retained wording): "Share action is tracked share-button clicks divided by eligible referral-prompt views; each merchant's rate was calculated before comparison.". Qualification: A share-button click does not prove the customer completed the send. Source: https://www.referralcandy.com/blog/referral-program-benchmarks-whats-a-good-conversion-rate-in-2025/. Record audit/review date: 2026-08-20. - [referral-marketing-statistics-2026-004] Recorded assertion from referralcandy.com: "ReferralCandy says the upper-quarter benchmark for referral-order and referral-revenue contribution was about 2.5 times the typical program.". Recorded figure: about 2.5x typical. Record category: referral_contribution. Source-record methodology (retained wording): "Referral order contribution is referred orders divided by all store orders; referral revenue contribution is referred revenue divided by all store revenue. Merchant rates were calculated before comparison.". Qualification: The public page reports a relative multiple rather than the underlying absolute median contribution rate. Source: https://www.referralcandy.com/blog/referral-program-benchmarks-whats-a-good-conversion-rate-in-2025/. Record audit/review date: 2026-08-20. - [referral-marketing-statistics-2026-005] Recorded assertion from referralcandy.com: "ReferralCandy says the top-10% benchmark for referral-order and referral-revenue contribution was nearly six times the typical program.". Recorded figure: nearly 6x typical. Record category: referral_contribution. Source-record methodology (retained wording): "Referral order contribution is referred orders divided by all store orders; referral revenue contribution is referred revenue divided by all store revenue. Merchant rates were calculated before comparison.". Qualification: The public page reports a relative multiple rather than the underlying absolute median contribution rate. Source: https://www.referralcandy.com/blog/referral-program-benchmarks-whats-a-good-conversion-rate-in-2025/. Record audit/review date: 2026-08-20. - [referral-marketing-statistics-2026-006] Recorded assertion from referralcandy.com: "ReferralCandy reports that chat apps and email accounted for 69.2% of tracked share-button activity in a separate twelve-month study.". Recorded figure: 69.2%. Record category: sharing_channel. Source-record methodology (retained wording): "Supporting sharing studies cover June 2025 through May 2026; share-button activity is platform-tracked.". Qualification: Tracked button activity can omit copied links or sharing that occurs outside instrumented controls. Source: https://www.referralcandy.com/blog/referral-program-benchmarks-whats-a-good-conversion-rate-in-2025/. Record audit/review date: 2026-08-20. - [referral-marketing-statistics-2026-007] Recorded assertion from referralcandy.com: "ReferralCandy reports that direct sharing beat public-social sharing for 93.7% of merchants with tracked activity.". Recorded figure: 93.7%. Record category: sharing_channel. Source-record methodology (retained wording): "Supporting sharing studies cover June 2025 through May 2026; merchants are compared using tracked activity.". Qualification: The denominator is merchants with tracked activity, not all merchants on the platform. Source: https://www.referralcandy.com/blog/referral-program-benchmarks-whats-a-good-conversion-rate-in-2025/. Record audit/review date: 2026-08-20. - [referral-marketing-statistics-2026-008] Recorded assertion from referralcandy.com: "ReferralCandy reports that the typical measurable first referred sale arrived 14 days after program activation.". Recorded figure: 14 days. Record category: time_to_first_referral. Source-record methodology (retained wording): "Separate time-to-first-referral study; the source reports time from activation to a measurable first referred sale.". Qualification: Store order volume and program visibility affect how quickly a measurable referral can occur. Source: https://www.referralcandy.com/blog/referral-program-benchmarks-whats-a-good-conversion-rate-in-2025/. Record audit/review date: 2026-08-20. - [referral-marketing-statistics-2026-009] Recorded assertion from referralcandy.com: "Among programs that produced a measurable first referral after activation, ReferralCandy says 68% reached it within the first month.". Recorded figure: 68%. Record category: time_to_first_referral. Source-record methodology (retained wording): "Separate time-to-first-referral study among programs that produced a measurable first referral.". Qualification: This is conditional on eventually producing a measurable first referral and excludes programs without one. Source: https://www.referralcandy.com/blog/referral-program-benchmarks-whats-a-good-conversion-rate-in-2025/. Record audit/review date: 2026-08-20. - [referral-marketing-statistics-2026-010] Recorded assertion from referralcandy.com: "Among programs that produced a measurable first referral after activation, ReferralCandy says 89% reached it within 90 days.". Recorded figure: 89%. Record category: time_to_first_referral. Source-record methodology (retained wording): "Separate time-to-first-referral study among programs that produced a measurable first referral.". Qualification: This is conditional on eventually producing a measurable first referral and excludes programs without one. Source: https://www.referralcandy.com/blog/referral-program-benchmarks-whats-a-good-conversion-rate-in-2025/. Record audit/review date: 2026-08-20. ### SaaS Benchmarks 2026: Growth, NRR, CAC & Margins URL: https://konabayev.com/blog/saas-benchmarks/ Full article summary (may cover material outside this subset): 50 source-backed SaaS benchmarks for 2026 covering growth, NRR, GRR, CAC, payback, gross margin, operating expense, conversion and software spend. Updated: 2026-10-04 Selected records: first 10 of 50 published records in file order. Full published dataset: https://konabayev.com/data/saas-benchmarks.jsonl. The selected records do not cover every topic in the full article summary. - [SAAS-001] Recorded assertion from hibob.com: "Benchmarkit's CY-22 private-SaaS growth-rate median was 30%.". Recorded figure: 30%. Period: report presents calendar-year 2024 or trailing-twelve-month metrics, with selected CY-22 and CY-23 comparisons. Population: private SaaS companies; the cited charts use metric-specific samples from 43 to 228 responses; cited chart/sample: N=149. Evidence type: private SaaS company benchmark survey. Record category: growth. Source-record methodology: not recorded. Qualification: Metric-specific medians and quartiles are not universal targets. The report glossary says the survey was open February-March 2024 while also requesting CY-24 or trailing-twelve-month data, creating an internal chronology conflict. Source: https://www.hibob.com/wp-content/uploads/2025-SaaS-Performance-Metrics-Benchmarks.pdf. Record audit/review date: 2026-07-10. - [SAAS-002] Recorded assertion from hibob.com: "Benchmarkit's CY-23 private-SaaS growth-rate median was 27%.". Recorded figure: 27%. Period: report presents calendar-year 2024 or trailing-twelve-month metrics, with selected CY-22 and CY-23 comparisons. Population: private SaaS companies; the cited charts use metric-specific samples from 43 to 228 responses; cited chart/sample: N=149. Evidence type: private SaaS company benchmark survey. Record category: growth. Source-record methodology: not recorded. Qualification: Metric-specific medians and quartiles are not universal targets. The report glossary says the survey was open February-March 2024 while also requesting CY-24 or trailing-twelve-month data, creating an internal chronology conflict. Source: https://www.hibob.com/wp-content/uploads/2025-SaaS-Performance-Metrics-Benchmarks.pdf. Record audit/review date: 2026-07-10. - [SAAS-003] Recorded assertion from hibob.com: "Benchmarkit's CY-24 private-SaaS growth-rate median was 26%.". Recorded figure: 26%. Period: report presents calendar-year 2024 or trailing-twelve-month metrics, with selected CY-22 and CY-23 comparisons. Population: private SaaS companies; the cited charts use metric-specific samples from 43 to 228 responses; cited chart/sample: N=149. Evidence type: private SaaS company benchmark survey. Record category: growth. Source-record methodology: not recorded. Qualification: Metric-specific medians and quartiles are not universal targets. The report glossary says the survey was open February-March 2024 while also requesting CY-24 or trailing-twelve-month data, creating an internal chronology conflict. Source: https://www.hibob.com/wp-content/uploads/2025-SaaS-Performance-Metrics-Benchmarks.pdf. Record audit/review date: 2026-07-10. - [SAAS-004] Recorded assertion from hibob.com: "The 75th percentile of Benchmarkit's CY-22 growth-rate distribution was 75%.". Recorded figure: 75%. Period: report presents calendar-year 2024 or trailing-twelve-month metrics, with selected CY-22 and CY-23 comparisons. Population: private SaaS companies; the cited charts use metric-specific samples from 43 to 228 responses; cited chart/sample: N=149. Evidence type: private SaaS company benchmark survey. Record category: growth. Source-record methodology: not recorded. Qualification: Metric-specific medians and quartiles are not universal targets. The report glossary says the survey was open February-March 2024 while also requesting CY-24 or trailing-twelve-month data, creating an internal chronology conflict. Source: https://www.hibob.com/wp-content/uploads/2025-SaaS-Performance-Metrics-Benchmarks.pdf. Record audit/review date: 2026-07-10. - [SAAS-005] Recorded assertion from hibob.com: "The 75th percentile of Benchmarkit's CY-23 growth-rate distribution was 60%.". Recorded figure: 60%. Period: report presents calendar-year 2024 or trailing-twelve-month metrics, with selected CY-22 and CY-23 comparisons. Population: private SaaS companies; the cited charts use metric-specific samples from 43 to 228 responses; cited chart/sample: N=149. Evidence type: private SaaS company benchmark survey. Record category: growth. Source-record methodology: not recorded. Qualification: Metric-specific medians and quartiles are not universal targets. The report glossary says the survey was open February-March 2024 while also requesting CY-24 or trailing-twelve-month data, creating an internal chronology conflict. Source: https://www.hibob.com/wp-content/uploads/2025-SaaS-Performance-Metrics-Benchmarks.pdf. Record audit/review date: 2026-07-10. - [SAAS-006] Recorded assertion from hibob.com: "The 75th percentile of Benchmarkit's CY-24 growth-rate distribution was 50%.". Recorded figure: 50%. Period: report presents calendar-year 2024 or trailing-twelve-month metrics, with selected CY-22 and CY-23 comparisons. Population: private SaaS companies; the cited charts use metric-specific samples from 43 to 228 responses; cited chart/sample: N=149. Evidence type: private SaaS company benchmark survey. Record category: growth. Source-record methodology: not recorded. Qualification: Metric-specific medians and quartiles are not universal targets. The report glossary says the survey was open February-March 2024 while also requesting CY-24 or trailing-twelve-month data, creating an internal chronology conflict. Source: https://www.hibob.com/wp-content/uploads/2025-SaaS-Performance-Metrics-Benchmarks.pdf. Record audit/review date: 2026-07-10. - [SAAS-007] Recorded assertion from hibob.com: "The 25th percentile of Benchmarkit's CY-24 growth-rate distribution was 11%.". Recorded figure: 11%. Period: report presents calendar-year 2024 or trailing-twelve-month metrics, with selected CY-22 and CY-23 comparisons. Population: private SaaS companies; the cited charts use metric-specific samples from 43 to 228 responses; cited chart/sample: N=149. Evidence type: private SaaS company benchmark survey. Record category: growth. Source-record methodology: not recorded. Qualification: Metric-specific medians and quartiles are not universal targets. The report glossary says the survey was open February-March 2024 while also requesting CY-24 or trailing-twelve-month data, creating an internal chronology conflict. Source: https://www.hibob.com/wp-content/uploads/2025-SaaS-Performance-Metrics-Benchmarks.pdf. Record audit/review date: 2026-07-10. - [SAAS-008] Recorded assertion from hibob.com: "Bootstrapped participants had a 34% median CY-24 growth rate in Benchmarkit's financing-source view.". Recorded figure: 34%. Period: report presents calendar-year 2024 or trailing-twelve-month metrics, with selected CY-22 and CY-23 comparisons. Population: private SaaS companies; the cited charts use metric-specific samples from 43 to 228 responses; cited chart/sample: N=149. Evidence type: private SaaS company benchmark survey. Record category: growth. Source-record methodology: not recorded. Qualification: Metric-specific medians and quartiles are not universal targets. The report glossary says the survey was open February-March 2024 while also requesting CY-24 or trailing-twelve-month data, creating an internal chronology conflict. Source: https://www.hibob.com/wp-content/uploads/2025-SaaS-Performance-Metrics-Benchmarks.pdf. Record audit/review date: 2026-07-10. - [SAAS-009] Recorded assertion from hibob.com: "Angel-backed participants had a 40% median CY-24 growth rate in Benchmarkit's financing-source view.". Recorded figure: 40%. Period: report presents calendar-year 2024 or trailing-twelve-month metrics, with selected CY-22 and CY-23 comparisons. Population: private SaaS companies; the cited charts use metric-specific samples from 43 to 228 responses; cited chart/sample: N=149. Evidence type: private SaaS company benchmark survey. Record category: growth. Source-record methodology: not recorded. Qualification: Metric-specific medians and quartiles are not universal targets. The report glossary says the survey was open February-March 2024 while also requesting CY-24 or trailing-twelve-month data, creating an internal chronology conflict. Source: https://www.hibob.com/wp-content/uploads/2025-SaaS-Performance-Metrics-Benchmarks.pdf. Record audit/review date: 2026-07-10. - [SAAS-010] Recorded assertion from hibob.com: "Venture-capital-backed participants had a 30% median CY-24 growth rate in Benchmarkit's financing-source view.". Recorded figure: 30%. Period: report presents calendar-year 2024 or trailing-twelve-month metrics, with selected CY-22 and CY-23 comparisons. Population: private SaaS companies; the cited charts use metric-specific samples from 43 to 228 responses; cited chart/sample: N=149. Evidence type: private SaaS company benchmark survey. Record category: growth. Source-record methodology: not recorded. Qualification: Metric-specific medians and quartiles are not universal targets. The report glossary says the survey was open February-March 2024 while also requesting CY-24 or trailing-twelve-month data, creating an internal chronology conflict. Source: https://www.hibob.com/wp-content/uploads/2025-SaaS-Performance-Metrics-Benchmarks.pdf. Record audit/review date: 2026-07-10. ### SaaS Pricing Statistics 2026: Usage, Retention and Trials URL: https://konabayev.com/blog/saas-pricing-statistics-2026/ Full article summary (may cover material outside this subset): Nine audited SaaS pricing benchmarks on usage-based growth, retention, free trials, freemium and free-to-paid conversion. Updated: 2026-07-10 Selected records: first 9 of 9 published records in file order. Full published dataset: https://konabayev.com/data/saas-pricing-statistics-2026.jsonl. The selected records do not cover every topic in the full article summary. - [saas-pricing-statistics-2026-001] Recorded assertion from hibob.com: "The pricing-model chart in Benchmarkit's 2025 report shows a 44% median growth rate for usage-based-pricing participants.". Recorded figure: 44%. Period: report presents calendar-year 2024 or trailing-twelve-month metrics, with selected CY-22 and CY-23 comparisons. Population: private SaaS companies; the cited charts use metric-specific samples from 43 to 228 responses; cited chart/sample: N=149. Evidence type: private SaaS company benchmark survey. Record category: growth. Source-record methodology (retained wording): "Benchmarkit primary survey report accessed through a third-party hibob.com hosted copy; the report's fieldwork dates and CY-24 labels contain an unresolved chronology conflict.". Qualification: Metric-specific medians and quartiles are not universal targets. The report glossary says the survey was open February-March 2024 while also requesting CY-24 or trailing-twelve-month data, creating an internal chronology conflict. Source: https://www.hibob.com/wp-content/uploads/2025-SaaS-Performance-Metrics-Benchmarks.pdf. Record audit/review date: 2026-07-10. - [saas-pricing-statistics-2026-002] Recorded assertion from hibob.com: "The pricing-model chart in Benchmarkit's 2025 report shows a 25% median growth rate for subscription-pricing participants.". Recorded figure: 25%. Period: report presents calendar-year 2024 or trailing-twelve-month metrics, with selected CY-22 and CY-23 comparisons. Population: private SaaS companies; the cited charts use metric-specific samples from 43 to 228 responses; cited chart/sample: N=149. Evidence type: private SaaS company benchmark survey. Record category: growth. Source-record methodology (retained wording): "Benchmarkit primary survey report accessed through a third-party hibob.com hosted copy; the report's fieldwork dates and CY-24 labels contain an unresolved chronology conflict.". Qualification: Metric-specific medians and quartiles are not universal targets. The report glossary says the survey was open February-March 2024 while also requesting CY-24 or trailing-twelve-month data, creating an internal chronology conflict. Source: https://www.hibob.com/wp-content/uploads/2025-SaaS-Performance-Metrics-Benchmarks.pdf. Record audit/review date: 2026-07-10. - [saas-pricing-statistics-2026-003] Recorded assertion from hibob.com: "Usage-based-pricing participants had 92% median gross revenue retention in Benchmarkit's pricing-model view.". Recorded figure: 92%. Period: report presents calendar-year 2024 or trailing-twelve-month metrics, with selected CY-22 and CY-23 comparisons. Population: private SaaS companies; the cited charts use metric-specific samples from 43 to 228 responses; cited chart/sample: N=225. Evidence type: private SaaS company benchmark survey. Record category: retention. Source-record methodology (retained wording): "Benchmarkit primary survey report accessed through a third-party hibob.com hosted copy; the report's fieldwork dates and CY-24 labels contain an unresolved chronology conflict.". Qualification: Metric-specific medians and quartiles are not universal targets. The report glossary says the survey was open February-March 2024 while also requesting CY-24 or trailing-twelve-month data, creating an internal chronology conflict. Source: https://www.hibob.com/wp-content/uploads/2025-SaaS-Performance-Metrics-Benchmarks.pdf. Record audit/review date: 2026-07-10. - [saas-pricing-statistics-2026-004] Recorded assertion from hibob.com: "Subscription-pricing participants had 88% median gross revenue retention in Benchmarkit's pricing-model view.". Recorded figure: 88%. Period: report presents calendar-year 2024 or trailing-twelve-month metrics, with selected CY-22 and CY-23 comparisons. Population: private SaaS companies; the cited charts use metric-specific samples from 43 to 228 responses; cited chart/sample: N=225. Evidence type: private SaaS company benchmark survey. Record category: retention. Source-record methodology (retained wording): "Benchmarkit primary survey report accessed through a third-party hibob.com hosted copy; the report's fieldwork dates and CY-24 labels contain an unresolved chronology conflict.". Qualification: Metric-specific medians and quartiles are not universal targets. The report glossary says the survey was open February-March 2024 while also requesting CY-24 or trailing-twelve-month data, creating an internal chronology conflict. Source: https://www.hibob.com/wp-content/uploads/2025-SaaS-Performance-Metrics-Benchmarks.pdf. Record audit/review date: 2026-07-10. - [saas-pricing-statistics-2026-005] Recorded assertion from hibob.com: "Hybrid subscription-plus-usage participants had 88% median gross revenue retention in Benchmarkit's pricing-model view.". Recorded figure: 88%. Period: report presents calendar-year 2024 or trailing-twelve-month metrics, with selected CY-22 and CY-23 comparisons. Population: private SaaS companies; the cited charts use metric-specific samples from 43 to 228 responses; cited chart/sample: N=225. Evidence type: private SaaS company benchmark survey. Record category: retention. Source-record methodology (retained wording): "Benchmarkit primary survey report accessed through a third-party hibob.com hosted copy; the report's fieldwork dates and CY-24 labels contain an unresolved chronology conflict.". Qualification: Metric-specific medians and quartiles are not universal targets. The report glossary says the survey was open February-March 2024 while also requesting CY-24 or trailing-twelve-month data, creating an internal chronology conflict. Source: https://www.hibob.com/wp-content/uploads/2025-SaaS-Performance-Metrics-Benchmarks.pdf. Record audit/review date: 2026-07-10. - [saas-pricing-statistics-2026-006] Recorded assertion from hibob.com: "Hybrid subscription-plus-usage participants had 110% median net revenue retention in Benchmarkit's pricing-model view.". Recorded figure: 110%. Period: report presents calendar-year 2024 or trailing-twelve-month metrics, with selected CY-22 and CY-23 comparisons. Population: private SaaS companies; the cited charts use metric-specific samples from 43 to 228 responses; cited chart/sample: N=228. Evidence type: private SaaS company benchmark survey. Record category: retention. Source-record methodology (retained wording): "Benchmarkit primary survey report accessed through a third-party hibob.com hosted copy; the report's fieldwork dates and CY-24 labels contain an unresolved chronology conflict.". Qualification: Metric-specific medians and quartiles are not universal targets. The report glossary says the survey was open February-March 2024 while also requesting CY-24 or trailing-twelve-month data, creating an internal chronology conflict. Source: https://www.hibob.com/wp-content/uploads/2025-SaaS-Performance-Metrics-Benchmarks.pdf. Record audit/review date: 2026-07-10. - [saas-pricing-statistics-2026-007] Recorded assertion from chartmogul.com: "In ChartMogul's 2026 survey, 57% of products led with a free trial, 26% with freemium, and 7% with a reverse trial.". Recorded figure: 57% / 26% / 7%. Period: Report published in January 2026; observation calendar not established in the retained report.. Population: 200 B2B software products. Evidence type: survey of B2B software products. Record category: entry_motion. Source-record methodology: not recorded. Qualification: The survey defines free-to-paid conversion as a lead or free signup becoming a paying customer within six months. Entry motion, card requirement, price, and product type change the result. Editorial clarification October 3, 2026: the 2026 report publication year is not an observation-calendar claim. The six-month free-to-paid interval defines elapsed conversion follow-up, not a verified calendar period. No new source acquisition. Source: https://chartmogul.com/reports/saas-conversion-report/. Record audit/review date: 2026-07-10. - [saas-pricing-statistics-2026-008] Recorded assertion from chartmogul.com: "A 14-day trial was the most common trial length, used by 62% of surveyed products.". Recorded figure: 62%. Period: Report published in January 2026; observation calendar not established in the retained report.. Population: 200 B2B software products. Evidence type: survey of B2B software products. Record category: trial_length. Source-record methodology: not recorded. Qualification: The survey defines free-to-paid conversion as a lead or free signup becoming a paying customer within six months. Entry motion, card requirement, price, and product type change the result. Editorial clarification October 3, 2026: the 2026 report publication year is not an observation-calendar claim. The six-month free-to-paid interval defines elapsed conversion follow-up, not a verified calendar period. No new source acquisition. Source: https://chartmogul.com/reports/saas-conversion-report/. Record audit/review date: 2026-07-10. - [saas-pricing-statistics-2026-009] Recorded assertion from chartmogul.com: "The median free-to-paid conversion rate across ChartMogul's 200 surveyed B2B software products was 8% within six months.". Recorded figure: 8%. Period: Report published in January 2026; observation calendar not established in the retained report.. Population: 200 B2B software products. Evidence type: survey of B2B software products. Record category: conversion. Source-record methodology: not recorded. Qualification: The survey defines free-to-paid conversion as a lead or free signup becoming a paying customer within six months. Entry motion, card requirement, price, and product type change the result. Editorial clarification October 3, 2026: the 2026 report publication year is not an observation-calendar claim. The six-month free-to-paid interval defines elapsed conversion follow-up, not a verified calendar period. No new source acquisition. Source: https://chartmogul.com/reports/saas-conversion-report/. Record audit/review date: 2026-07-10. ### Sales Enablement Statistics 2026: Adoption, AI and ROI URL: https://konabayev.com/blog/sales-enablement-statistics-2026/ Full article summary (may cover material outside this subset): Source-backed sales enablement statistics for 2026 covering AI, platforms, content, training, coaching, productivity and ROI signals. Updated: 2026-10-04 Selected records: first 10 of 112 published records in file order. Full published dataset: https://konabayev.com/data/sales-enablement-statistics-2026.jsonl. The selected records do not cover every topic in the full article summary. - [sales-enablement-statistics-2026-001] Recorded assertion from highspot.com: "Highspot reports that its 2025 State of Sales Enablement Report draws on 350 go-to-market professionals across enablement, sales, marketing and operations.". Record category: ratio. Source-record methodology (retained wording): "Highspot says the 11th edition of its State of Sales Enablement Report draws on 350 go-to-market professionals across enablement, sales, marketing and operations teams, collected in February to March 2025 across 21 countries and 61 industries.". Qualification: Vendor-sponsored GTM enablement survey; use for respondent-reported GTM, AI, training, coaching and platform adoption benchmarks, not audited revenue causality. Source: https://www.highspot.com/state-of-sales-enablement-2025/. Record audit/review date: 2026-05-13. - [sales-enablement-statistics-2026-002] Recorded assertion from highspot.com: "Highspot reports that the 2025 study captured perspectives from 21 countries and 61 industries.". Record category: benchmark. Source-record methodology (retained wording): "Highspot says the 11th edition of its State of Sales Enablement Report draws on 350 go-to-market professionals across enablement, sales, marketing and operations teams, collected in February to March 2025 across 21 countries and 61 industries.". Qualification: Vendor-sponsored GTM enablement survey; use for respondent-reported GTM, AI, training, coaching and platform adoption benchmarks, not audited revenue causality. Source: https://www.highspot.com/state-of-sales-enablement-2025/. Record audit/review date: 2026-05-13. - [sales-enablement-statistics-2026-003] Recorded assertion from highspot.com: "Highspot reports that its 2025 State of Sales Enablement research was conducted from February to March 2025.". Record category: benchmark. Source-record methodology (retained wording): "Highspot says the 11th edition of its State of Sales Enablement Report draws on 350 go-to-market professionals across enablement, sales, marketing and operations teams, collected in February to March 2025 across 21 countries and 61 industries.". Qualification: Vendor-sponsored GTM enablement survey; use for respondent-reported GTM, AI, training, coaching and platform adoption benchmarks, not audited revenue causality. Source: https://www.highspot.com/state-of-sales-enablement-2025/. Record audit/review date: 2026-05-13. - [sales-enablement-statistics-2026-004] Recorded assertion from highspot.com: "Highspot found that 55% of organizations are unable to effectively drive their go-to-market initiatives.". Record category: benchmark. Source-record methodology (retained wording): "Highspot says the 11th edition of its State of Sales Enablement Report draws on 350 go-to-market professionals across enablement, sales, marketing and operations teams, collected in February to March 2025 across 21 countries and 61 industries.". Qualification: Vendor-sponsored GTM enablement survey; use for respondent-reported GTM, AI, training, coaching and platform adoption benchmarks, not audited revenue causality. Source: https://www.highspot.com/state-of-sales-enablement-2025/. Record audit/review date: 2026-05-13. - [sales-enablement-statistics-2026-005] Recorded assertion from highspot.com: "Highspot found that 29% of companies still rely on multiple disconnected go-to-market tools.". Record category: benchmark. Source-record methodology (retained wording): "Highspot says the 11th edition of its State of Sales Enablement Report draws on 350 go-to-market professionals across enablement, sales, marketing and operations teams, collected in February to March 2025 across 21 countries and 61 industries.". Qualification: Vendor-sponsored GTM enablement survey; use for respondent-reported GTM, AI, training, coaching and platform adoption benchmarks, not audited revenue causality. Source: https://www.highspot.com/state-of-sales-enablement-2025/. Record audit/review date: 2026-05-13. - [sales-enablement-statistics-2026-006] Recorded assertion from highspot.com: "Highspot found that 90% of organizations are either currently using AI to support go-to-market efforts or planning to start.". Record category: benchmark. Source-record methodology (retained wording): "Highspot says the 11th edition of its State of Sales Enablement Report draws on 350 go-to-market professionals across enablement, sales, marketing and operations teams, collected in February to March 2025 across 21 countries and 61 industries.". Qualification: Vendor-sponsored GTM enablement survey; use for respondent-reported GTM, AI, training, coaching and platform adoption benchmarks, not audited revenue causality. Source: https://www.highspot.com/state-of-sales-enablement-2025/. Record audit/review date: 2026-05-13. - [sales-enablement-statistics-2026-007] Recorded assertion from highspot.com: "Highspot found that 57% of executives expect their go-to-market teams to grow this year.". Record category: benchmark. Source-record methodology (retained wording): "Highspot says the 11th edition of its State of Sales Enablement Report draws on 350 go-to-market professionals across enablement, sales, marketing and operations teams, collected in February to March 2025 across 21 countries and 61 industries.". Qualification: Vendor-sponsored GTM enablement survey; use for respondent-reported GTM, AI, training, coaching and platform adoption benchmarks, not audited revenue causality. Source: https://www.highspot.com/state-of-sales-enablement-2025/. Record audit/review date: 2026-05-13. - [sales-enablement-statistics-2026-008] Recorded assertion from highspot.com: "Highspot found that 27% more companies use a few core go-to-market tools that are well integrated and managed.". Record category: benchmark. Source-record methodology (retained wording): "Highspot says the 11th edition of its State of Sales Enablement Report draws on 350 go-to-market professionals across enablement, sales, marketing and operations teams, collected in February to March 2025 across 21 countries and 61 industries.". Qualification: Vendor-sponsored GTM enablement survey; use for respondent-reported GTM, AI, training, coaching and platform adoption benchmarks, not audited revenue causality. Source: https://www.highspot.com/state-of-sales-enablement-2025/. Record audit/review date: 2026-05-13. - [sales-enablement-statistics-2026-009] Recorded assertion from highspot.com: "Highspot reports that organizations are using two fewer tools on average for their go-to-market efforts compared with the prior year.". Record category: benchmark. Source-record methodology (retained wording): "Highspot says the 11th edition of its State of Sales Enablement Report draws on 350 go-to-market professionals across enablement, sales, marketing and operations teams, collected in February to March 2025 across 21 countries and 61 industries.". Qualification: Vendor-sponsored GTM enablement survey; use for respondent-reported GTM, AI, training, coaching and platform adoption benchmarks, not audited revenue causality. Source: https://www.highspot.com/state-of-sales-enablement-2025/. Record audit/review date: 2026-05-13. - [sales-enablement-statistics-2026-010] Recorded assertion from highspot.com: "Highspot reports that businesses with well-integrated enablement tech stacks are 42% more likely to increase sales productivity.". Record category: benchmark. Source-record methodology (retained wording): "Highspot says the 11th edition of its State of Sales Enablement Report draws on 350 go-to-market professionals across enablement, sales, marketing and operations teams, collected in February to March 2025 across 21 countries and 61 industries.". Qualification: Vendor-sponsored GTM enablement survey; use for respondent-reported GTM, AI, training, coaching and platform adoption benchmarks, not audited revenue causality. Source: https://www.highspot.com/state-of-sales-enablement-2025/. Record audit/review date: 2026-05-13. ### SEO Automation Statistics 2026: AI Search Clicks and Crawl Monitoring URL: https://konabayev.com/blog/seo-automation-statistics-2026/ Full article summary (may cover material outside this subset): Twenty audited SEO automation statistics and observations covering AI-summary exposure, click behavior, query patterns and controlled crawl monitoring. Updated: 2026-07-14 Selected records: first 10 of 20 published records in file order. Full published dataset: https://konabayev.com/data/seo-automation-statistics-2026.jsonl. The selected records do not cover every topic in the full article summary. - [seo-automation-statistics-2026-001] Recorded assertion from pewresearch.org: "Pew analyzed tracked browsing data from 900 U.S. adults.". Record category: research_scope. Source-record methodology (retained wording): "Pew analysis of tracked March 2025 browsing from 900 U.S. adults and 68,879 unique Google searches; result pages were recollected April 7-17, 2025.". Qualification: U.S. panel and Google only; regenerated result pages may differ from what a participant saw in March, and next actions were inferred from timestamps and URLs. Source: https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/. Record audit/review date: 2026-07-14. - [seo-automation-statistics-2026-002] Recorded assertion from pewresearch.org: "Pew analyzed 68,879 unique Google searches, of which 12,593 produced an AI summary in the recollected results.". Record category: research_scope. Source-record methodology (retained wording): "Pew analysis of tracked March 2025 browsing from 900 U.S. adults and 68,879 unique Google searches; result pages were recollected April 7-17, 2025.". Qualification: U.S. panel and Google only; regenerated result pages may differ from what a participant saw in March, and next actions were inferred from timestamps and URLs. Source: https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/. Record audit/review date: 2026-07-14. - [seo-automation-statistics-2026-003] Recorded assertion from pewresearch.org: "Pew found that 58% of panel participants conducted at least one March 2025 Google search that produced an AI summary in the recollected results.". Record category: exposure. Source-record methodology (retained wording): "Pew analysis of tracked March 2025 browsing from 900 U.S. adults and 68,879 unique Google searches; result pages were recollected April 7-17, 2025.". Qualification: U.S. panel and Google only; regenerated result pages may differ from what a participant saw in March, and next actions were inferred from timestamps and URLs. Source: https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/. Record audit/review date: 2026-07-14. - [seo-automation-statistics-2026-004] Recorded assertion from pewresearch.org: "AI summaries appeared for 18% of the Google searches in Pew's dataset.". Record category: exposure. Source-record methodology (retained wording): "Pew analysis of tracked March 2025 browsing from 900 U.S. adults and 68,879 unique Google searches; result pages were recollected April 7-17, 2025.". Qualification: U.S. panel and Google only; regenerated result pages may differ from what a participant saw in March, and next actions were inferred from timestamps and URLs. Source: https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/. Record audit/review date: 2026-07-14. - [seo-automation-statistics-2026-005] Recorded assertion from pewresearch.org: "Pew found a traditional-result click on 8% of visits with an AI summary versus 15% of visits without one.". Record category: click_behavior. Source-record methodology (retained wording): "Pew analysis of tracked March 2025 browsing from 900 U.S. adults and 68,879 unique Google searches; result pages were recollected April 7-17, 2025.". Qualification: U.S. panel and Google only; regenerated result pages may differ from what a participant saw in March, and next actions were inferred from timestamps and URLs. Source: https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/. Record audit/review date: 2026-07-14. - [seo-automation-statistics-2026-006] Recorded assertion from pewresearch.org: "Pew found that users clicked a source link inside an AI summary on 1% of visits to pages with a summary.". Record category: click_behavior. Source-record methodology (retained wording): "Pew analysis of tracked March 2025 browsing from 900 U.S. adults and 68,879 unique Google searches; result pages were recollected April 7-17, 2025.". Qualification: U.S. panel and Google only; regenerated result pages may differ from what a participant saw in March, and next actions were inferred from timestamps and URLs. Source: https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/. Record audit/review date: 2026-07-14. - [seo-automation-statistics-2026-007] Recorded assertion from pewresearch.org: "Pew found that users ended their browsing session after 26% of Google pages with an AI summary versus 16% of pages without one.". Record category: click_behavior. Source-record methodology (retained wording): "Pew analysis of tracked March 2025 browsing from 900 U.S. adults and 68,879 unique Google searches; result pages were recollected April 7-17, 2025.". Qualification: U.S. panel and Google only; regenerated result pages may differ from what a participant saw in March, and next actions were inferred from timestamps and URLs. Source: https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/. Record audit/review date: 2026-07-14. - [seo-automation-statistics-2026-008] Recorded assertion from pewresearch.org: "Around two-thirds of all Google searches in Pew's study ended with the user staying on Google or leaving without clicking a result link.". Record category: click_behavior. Source-record methodology (retained wording): "Pew analysis of tracked March 2025 browsing from 900 U.S. adults and 68,879 unique Google searches; result pages were recollected April 7-17, 2025.". Qualification: U.S. panel and Google only; regenerated result pages may differ from what a participant saw in March, and next actions were inferred from timestamps and URLs. Source: https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/. Record audit/review date: 2026-07-14. - [seo-automation-statistics-2026-009] Recorded assertion from pewresearch.org: "Wikipedia, YouTube, and Reddit collectively represented 15% of sources in AI summaries and 17% of sources in standard Google results in Pew's study.". Record category: citation_mix. Source-record methodology (retained wording): "Pew analysis of tracked March 2025 browsing from 900 U.S. adults and 68,879 unique Google searches; result pages were recollected April 7-17, 2025.". Qualification: U.S. panel and Google only; regenerated result pages may differ from what a participant saw in March, and next actions were inferred from timestamps and URLs. Source: https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/. Record audit/review date: 2026-07-14. - [seo-automation-statistics-2026-010] Recorded assertion from pewresearch.org: "Government websites represented 6% of sources in AI summaries versus 2% of sources in standard Google results in Pew's study.". Record category: citation_mix. Source-record methodology (retained wording): "Pew analysis of tracked March 2025 browsing from 900 U.S. adults and 68,879 unique Google searches; result pages were recollected April 7-17, 2025.". Qualification: U.S. panel and Google only; regenerated result pages may differ from what a participant saw in March, and next actions were inferred from timestamps and URLs. Source: https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/. Record audit/review date: 2026-07-14. ### Vibe Coding Statistics 2026: Security Benchmarks URL: https://konabayev.com/blog/vibe-coding-statistics/ Full article summary (may cover material outside this subset): 24 primary-source vibe coding statistics for 2026 comparing functional correctness, secure implementations, agent frameworks and mitigation results. Updated: 2026-07-11 Selected records: first 10 of 24 published records in file order. Full published dataset: https://konabayev.com/data/vibe-coding-statistics.jsonl. The selected records do not cover every topic in the full article summary. - [VIBE-001] Recorded assertion from arxiv.org: "SusVibes contains 200 real-world repository-level feature tasks.". Recorded value: 200 tasks. Period: arXiv version 1, December 2025. Population: SusVibes benchmark of 200 Python repository-level feature tasks covering 77 CWE categories. Denominator: 200 SusVibes tasks for the main pass@1 results unless a claim states a conditional functionally-correct subset. Source-record methodology: not recorded. Qualification: Preprint results are benchmark-specific pass@1 outcomes. Agent framework, model, prompt, task set, runtime, and security-test definitions prevent direct generalization to all vibe-coded software. Source: https://arxiv.org/html/2512.03262v1. Record audit/review date: 2026-07-11. - [VIBE-002] Recorded assertion from arxiv.org: "The benchmark covers 77 Common Weakness Enumeration categories.". Recorded value: 77 CWEs. Period: arXiv version 1, December 2025. Population: SusVibes benchmark of 200 Python repository-level feature tasks covering 77 CWE categories. Denominator: 200 SusVibes tasks for the main pass@1 results unless a claim states a conditional functionally-correct subset. Source-record methodology: not recorded. Qualification: Preprint results are benchmark-specific pass@1 outcomes. Agent framework, model, prompt, task set, runtime, and security-test definitions prevent direct generalization to all vibe-coded software. Source: https://arxiv.org/html/2512.03262v1. Record audit/review date: 2026-07-11. - [VIBE-003] Recorded assertion from arxiv.org: "SusVibes spans 10 real-world application domains.". Recorded value: 10 domains. Period: arXiv version 1, December 2025. Population: SusVibes benchmark of 200 Python repository-level feature tasks covering 77 CWE categories. Denominator: 200 SusVibes tasks for the main pass@1 results unless a claim states a conditional functionally-correct subset. Source-record methodology: not recorded. Qualification: Preprint results are benchmark-specific pass@1 outcomes. Agent framework, model, prompt, task set, runtime, and security-test definitions prevent direct generalization to all vibe-coded software. Source: https://arxiv.org/html/2512.03262v1. Record audit/review date: 2026-07-11. - [VIBE-004] Recorded assertion from arxiv.org: "The mean repository context contained 162,000 lines of code.". Recorded value: 162K lines. Period: arXiv version 1, December 2025. Population: SusVibes benchmark of 200 Python repository-level feature tasks covering 77 CWE categories. Denominator: 200 SusVibes tasks for the main pass@1 results unless a claim states a conditional functionally-correct subset. Source-record methodology: not recorded. Qualification: Preprint results are benchmark-specific pass@1 outcomes. Agent framework, model, prompt, task set, runtime, and security-test definitions prevent direct generalization to all vibe-coded software. Source: https://arxiv.org/html/2512.03262v1. Record audit/review date: 2026-07-11. - [VIBE-005] Recorded assertion from arxiv.org: "The mean repository context contained 867 files.". Recorded value: 867 files. Period: arXiv version 1, December 2025. Population: SusVibes benchmark of 200 Python repository-level feature tasks covering 77 CWE categories. Denominator: 200 SusVibes tasks for the main pass@1 results unless a claim states a conditional functionally-correct subset. Source-record methodology: not recorded. Qualification: Preprint results are benchmark-specific pass@1 outcomes. Agent framework, model, prompt, task set, runtime, and security-test definitions prevent direct generalization to all vibe-coded software. Source: https://arxiv.org/html/2512.03262v1. Record audit/review date: 2026-07-11. - [VIBE-006] Recorded assertion from arxiv.org: "A SusVibes task had 68.8 functional test cases on average.". Recorded value: 68.8. Period: arXiv version 1, December 2025. Population: SusVibes benchmark of 200 Python repository-level feature tasks covering 77 CWE categories. Denominator: 200 SusVibes tasks for the main pass@1 results unless a claim states a conditional functionally-correct subset. Source-record methodology: not recorded. Qualification: Preprint results are benchmark-specific pass@1 outcomes. Agent framework, model, prompt, task set, runtime, and security-test definitions prevent direct generalization to all vibe-coded software. Source: https://arxiv.org/html/2512.03262v1. Record audit/review date: 2026-07-11. - [VIBE-007] Recorded assertion from arxiv.org: "A SusVibes task had 3.7 security test cases on average.". Recorded value: 3.7. Period: arXiv version 1, December 2025. Population: SusVibes benchmark of 200 Python repository-level feature tasks covering 77 CWE categories. Denominator: 200 SusVibes tasks for the main pass@1 results unless a claim states a conditional functionally-correct subset. Source-record methodology: not recorded. Qualification: Preprint results are benchmark-specific pass@1 outcomes. Agent framework, model, prompt, task set, runtime, and security-test definitions prevent direct generalization to all vibe-coded software. Source: https://arxiv.org/html/2512.03262v1. Record audit/review date: 2026-07-11. - [VIBE-008] Recorded assertion from arxiv.org: "SWE-agent with Claude 4 Sonnet achieved 61.0% FuncPass and 10.5% SecPass.". Recorded value: 61.0% / 10.5%. Period: arXiv version 1, December 2025. Population: SusVibes benchmark of 200 Python repository-level feature tasks covering 77 CWE categories. Denominator: 200 SusVibes tasks for the main pass@1 results unless a claim states a conditional functionally-correct subset. Source-record methodology: not recorded. Qualification: Preprint results are benchmark-specific pass@1 outcomes. Agent framework, model, prompt, task set, runtime, and security-test definitions prevent direct generalization to all vibe-coded software. Source: https://arxiv.org/html/2512.03262v1. Record audit/review date: 2026-07-11. - [VIBE-009] Recorded assertion from arxiv.org: "OpenHands with Claude 4 Sonnet achieved 49.5% FuncPass and 12.5% SecPass.". Recorded value: 49.5% / 12.5%. Period: arXiv version 1, December 2025. Population: SusVibes benchmark of 200 Python repository-level feature tasks covering 77 CWE categories. Denominator: 200 SusVibes tasks for the main pass@1 results unless a claim states a conditional functionally-correct subset. Source-record methodology: not recorded. Qualification: Preprint results are benchmark-specific pass@1 outcomes. Agent framework, model, prompt, task set, runtime, and security-test definitions prevent direct generalization to all vibe-coded software. Source: https://arxiv.org/html/2512.03262v1. Record audit/review date: 2026-07-11. - [VIBE-010] Recorded assertion from arxiv.org: "Claude Code with Claude 4 Sonnet achieved 44.0% FuncPass and 6.0% SecPass.". Recorded value: 44.0% / 6.0%. Period: arXiv version 1, December 2025. Population: SusVibes benchmark of 200 Python repository-level feature tasks covering 77 CWE categories. Denominator: 200 SusVibes tasks for the main pass@1 results unless a claim states a conditional functionally-correct subset. Source-record methodology: not recorded. Qualification: Preprint results are benchmark-specific pass@1 outcomes. Agent framework, model, prompt, task set, runtime, and security-test definitions prevent direct generalization to all vibe-coded software. Source: https://arxiv.org/html/2512.03262v1. Record audit/review date: 2026-07-11. ### Web Scraping Statistics 2026: Surveys and Crawl Observations URL: https://konabayev.com/blog/web-scraping-statistics-2026/ Full article summary (may cover material outside this subset): 21 sourced claims and observations: 2026 survey findings on proxies and AI, historical Apify activity, billing units and four dated local crawl runs. Updated: 2026-10-02 Selected records: first 10 of 21 published records in file order. Full published dataset: https://konabayev.com/data/web-scraping-statistics-2026.jsonl. The selected records do not cover every topic in the full article summary. - [web-scraping-statistics-2026-001] Recorded assertion from blog.apify.com: "Apify reported that API-based Actor runs accounted for 87% of its total Actor runs in the platform period covered by its 2025 report.". Recorded figure: 87%. Period: 2023 through October 2024. Record category: platform_scale. Source-record methodology (retained wording): "Apify analyzed its internal platform metrics from 2023 through October 2024 and published the report on January 30, 2025.". Qualification: Apify-specific platform telemetry does not represent the entire web-scraping market. Source: https://blog.apify.com/state-of-web-scraping/. Record audit/review date: 2026-10-02. - [web-scraping-statistics-2026-002] Recorded assertion from blog.apify.com: "Apify reported that API calls increased from 3.6 billion in January 2023 to 6.8 billion in October 2024, an 88.89% increase.". Recorded figure: 3.6B to 6.8B. Period: January 2023 to October 2024. Record category: platform_scale. Source-record methodology (retained wording): "Apify analyzed its internal platform metrics from 2023 through October 2024 and published the report on January 30, 2025.". Qualification: The count is vendor platform activity, not unique scraping jobs, companies or websites. The captured prose does not identify a monthly, cumulative or other counting window. Source: https://blog.apify.com/state-of-web-scraping/. Record audit/review date: 2026-10-02. - [web-scraping-statistics-2026-004] Recorded assertion from blog.apify.com: "In Apify's developer survey, 21.7% of respondents said they had built more than 20 scrapers in the previous year.". Recorded figure: 21.7%. Period: December 2024 survey; published January 30, 2025. Record category: developer_activity. Source-record methodology (retained wording): "Developer survey promoted inside and outside Apify's reach; the public article does not state the respondent count.". Qualification: Unknown sample size and likely Apify-audience selection bias; do not generalize to all developers. Source: https://blog.apify.com/state-of-web-scraping/. Record audit/review date: 2026-10-02. - [web-scraping-statistics-2026-005] Recorded assertion from blog.apify.com: "In Apify's developer survey, 13% of respondents said they had built 11 to 20 scrapers in the previous year.". Recorded figure: 13%. Period: December 2024 survey; published January 30, 2025. Record category: developer_activity. Source-record methodology (retained wording): "Developer survey promoted inside and outside Apify's reach; the public article does not state the respondent count.". Qualification: Unknown sample size and likely Apify-audience selection bias; do not generalize to all developers. Source: https://blog.apify.com/state-of-web-scraping/. Record audit/review date: 2026-10-02. - [web-scraping-statistics-2026-006] Recorded assertion from blog.apify.com: "In Apify's developer survey, 21.7% of respondents said they had built 6 to 10 scrapers in the previous year.". Recorded figure: 21.7%. Period: December 2024 survey; published January 30, 2025. Record category: developer_activity. Source-record methodology (retained wording): "Developer survey promoted inside and outside Apify's reach; the public article does not state the respondent count.". Qualification: Unknown sample size and likely Apify-audience selection bias; do not generalize to all developers. Source: https://blog.apify.com/state-of-web-scraping/. Record audit/review date: 2026-10-02. - [web-scraping-statistics-2026-007] Recorded assertion from blog.apify.com: "Apify's developer survey listed product pricing at 34.8%, social-media content at 26.1%, news articles at 21.7% and customer reviews at 13% among scraped target types.". Recorded figure: 34.8% / 26.1% / 21.7% / 13%. Period: December 2024 survey; published January 30, 2025. Record category: target_type. Source-record methodology (retained wording): "Developer survey promoted inside and outside Apify's reach; the public article does not state the respondent count.". Qualification: The article does not publish a representative-market weighting or respondent count. Source: https://blog.apify.com/state-of-web-scraping/. Record audit/review date: 2026-10-02. - [web-scraping-statistics-2026-008] Recorded assertion from blog.apify.com: "Apify's surveyed developers named BeautifulSoup at 43.5%, Crawlee at 34.8%, Selenium, Playwright and Cheerio at 26.1% each, Puppeteer at 21.7% and Scrapy at 13% among open-source scraping tools.". Recorded figure: 43.5% / 34.8% / 26.1% / 21.7% / 13%. Period: December 2024 survey; published January 30, 2025. Record category: tool_use. Source-record methodology (retained wording): "Developer survey promoted inside and outside Apify's reach; the public article does not state the respondent count.". Qualification: The percentages exceed 100% in total and are not an exclusive market-share distribution. The question format and respondent count are not disclosed. Apify identifies Crawlee as its own library; the sample reflects its reachable audience. Source: https://blog.apify.com/state-of-web-scraping/. Record audit/review date: 2026-10-02. - [web-scraping-statistics-2026-009] Recorded assertion from blog.apify.com: "Apify's surveyed developers reported using proxy providers at 39.1%, web-scraping APIs at 34.8% and cloud platforms at 26.1%.". Recorded figure: 39.1% / 34.8% / 26.1%. Period: December 2024 survey; published January 30, 2025. Record category: third_party_service. Source-record methodology (retained wording): "Developer survey promoted inside and outside Apify's reach; the public article does not state the respondent count.". Qualification: The public article does not disclose the respondent count or whether this question allowed one or multiple choices. Category overlap and global market shares cannot be inferred. Source: https://blog.apify.com/state-of-web-scraping/. Record audit/review date: 2026-10-02. - [web-scraping-statistics-2026-010] Recorded assertion from blog.apify.com: "In Apify's developer survey, 17.4% viewed scraping as legal and unrestricted, 43.5% viewed it as legal with restrictions and 21.7% were unsure.". Recorded figure: 17.4% / 43.5% / 21.7%. Period: December 2024 survey; published January 30, 2025. Record category: legal_perception. Source-record methodology (retained wording): "Developer survey promoted inside and outside Apify's reach; the public article does not state the respondent count.". Qualification: These are respondent perceptions, not legal conclusions or jurisdiction-specific guidance. The selected published responses total 82.6%, not a complete distribution. Source: https://blog.apify.com/state-of-web-scraping/. Record audit/review date: 2026-10-02. - [web-scraping-statistics-2026-011] Recorded assertion from apify.com: "Apify lists rented Actor costs as deducted from prepaid usage on paid plans; jobs can also incur compute, proxy, storage and data-transfer charges.". Recorded figure: prepaid usage plus metered charges. Period: October 2, 2026. Record category: billing_unit. Source-record methodology (retained wording): "Direct capture of the official vendor pricing FAQ on October 2, 2026.". Qualification: Job cost can include Actor, compute, proxy, storage and data-transfer usage. Do not count included prepaid usage twice. Source: https://apify.com/pricing. Record audit/review date: 2026-10-02. ### Webinar Marketing Statistics 2026: Attendance, Conversion and Cost URL: https://konabayev.com/blog/webinar-marketing-statistics-2026/ Full article summary (may cover material outside this subset): Source-locked webinar marketing statistics for 2026 covering attendance rates, registration to attendee conversion, costs, replay value and pipeline impact. Updated: 2026-05-10 Selected records: first 10 of 100 published records in file order. Full published dataset: https://konabayev.com/data/webinar-marketing-statistics-2026.jsonl. The selected records do not cover every topic in the full article summary. - [webinar-marketing-statistics-2026-001] Recorded assertion from wistia.com: "Wistia says its 2026 webinar benchmarks are based on a survey of more than 900 professionals, webinars hosted with Wistia software, and its annual State of Video Report.". Record category: benchmark. Source-record methodology (retained wording): "Wistia 2026 webinar benchmark article, scraped with Firecrawl on May 10, 2026.". Qualification: Vendor benchmark based partly on Wistia-hosted events. Source: https://wistia.com/learn/marketing/webinar-analytics-benchmarks. Record audit/review date: 2026-05-10. - [webinar-marketing-statistics-2026-002] Recorded assertion from wistia.com: "Wistia reports that webinars ranked as the second most impactful type of video content among marketers in its 2026 benchmark article.". Record category: benchmark. Source-record methodology (retained wording): "Wistia 2026 webinar benchmark article, scraped with Firecrawl on May 10, 2026.". Qualification: Rank is from Wistia's video marketing source pool. Source: https://wistia.com/learn/marketing/webinar-analytics-benchmarks. Record audit/review date: 2026-05-10. - [webinar-marketing-statistics-2026-003] Recorded assertion from wistia.com: "Wistia reports that 72% of marketers host webinars to engage customers and prospects.". Record category: benchmark. Source-record methodology (retained wording): "Wistia 2026 webinar benchmark article, scraped with Firecrawl on May 10, 2026.". Qualification: Survey benchmark from Wistia's marketer sample. Source: https://wistia.com/learn/marketing/webinar-analytics-benchmarks. Record audit/review date: 2026-05-10. - [webinar-marketing-statistics-2026-004] Recorded assertion from wistia.com: "Wistia reports that 70% of marketers use webinars to generate new leads.". Record category: benchmark. Source-record methodology (retained wording): "Wistia 2026 webinar benchmark article, scraped with Firecrawl on May 10, 2026.". Qualification: Survey benchmark from Wistia's marketer sample. Source: https://wistia.com/learn/marketing/webinar-analytics-benchmarks. Record audit/review date: 2026-05-10. - [webinar-marketing-statistics-2026-005] Recorded assertion from wistia.com: "Wistia reports that 36% of marketers use webinars to improve customer retention.". Record category: benchmark. Source-record methodology (retained wording): "Wistia 2026 webinar benchmark article, scraped with Firecrawl on May 10, 2026.". Qualification: Survey benchmark from Wistia's marketer sample. Source: https://wistia.com/learn/marketing/webinar-analytics-benchmarks. Record audit/review date: 2026-05-10. - [webinar-marketing-statistics-2026-006] Recorded assertion from wistia.com: "Wistia reports that 32% of marketers use webinars to increase product adoption.". Record category: activation. Source-record methodology (retained wording): "Wistia 2026 webinar benchmark article, scraped with Firecrawl on May 10, 2026.". Qualification: Survey benchmark from Wistia's marketer sample. Source: https://wistia.com/learn/marketing/webinar-analytics-benchmarks. Record audit/review date: 2026-05-10. - [webinar-marketing-statistics-2026-007] Recorded assertion from wistia.com: "Wistia reports that 32% of marketers use webinars to boost brand awareness.". Record category: benchmark. Source-record methodology (retained wording): "Wistia 2026 webinar benchmark article, scraped with Firecrawl on May 10, 2026.". Qualification: Survey benchmark from Wistia's marketer sample. Source: https://wistia.com/learn/marketing/webinar-analytics-benchmarks. Record audit/review date: 2026-05-10. - [webinar-marketing-statistics-2026-008] Recorded assertion from wistia.com: "Wistia reports that 28% of marketers use webinars to demonstrate industry expertise.". Record category: benchmark. Source-record methodology (retained wording): "Wistia 2026 webinar benchmark article, scraped with Firecrawl on May 10, 2026.". Qualification: Survey benchmark from Wistia's marketer sample. Source: https://wistia.com/learn/marketing/webinar-analytics-benchmarks. Record audit/review date: 2026-05-10. - [webinar-marketing-statistics-2026-009] Recorded assertion from wistia.com: "Wistia reports that thought leadership is the most common webinar format in its 2026 benchmark set at 58%.". Record category: benchmark. Source-record methodology (retained wording): "Wistia 2026 webinar benchmark article, scraped with Firecrawl on May 10, 2026.". Qualification: Format mix reflects Wistia's benchmark source pool. Source: https://wistia.com/learn/marketing/webinar-analytics-benchmarks. Record audit/review date: 2026-05-10. - [webinar-marketing-statistics-2026-010] Recorded assertion from wistia.com: "Wistia reports that 56% of marketers host product demo webinars.". Record category: benchmark. Source-record methodology (retained wording): "Wistia 2026 webinar benchmark article, scraped with Firecrawl on May 10, 2026.". Qualification: Format mix reflects Wistia's benchmark source pool. Source: https://wistia.com/learn/marketing/webinar-analytics-benchmarks. Record audit/review date: 2026-05-10. ## Authored Test Fixture Corpora: Contracts and Provenance This section includes metadata and rules, not the individual test inputs or empirical result claims. Download the full corpus to run a system and preserve failures. A pass applies only to the frozen authored contract. ### Article Extraction Test Dataset: 24 HTML Acceptance Cases URL: https://konabayev.com/blog/article-extraction-test-dataset/ version: "1.0.0" dateModified: "2026-10-04" observation_type: "authored_test_fixture" measurementTechnique: "Manually authored synthetic fixtures and acceptance oracles; deterministic serialization, not an empirical benchmark." rules: {"E-SCOPE":"Select the single main element. No computed CSS is evaluated and no assets are fetched; inert image src values may exist in the HTML. Static source text is the scope, not a rendered screenshot.","E-BLOCKS":"Ordered blocks: headings, paragraphs, list items, table rows (cells joined by \" | \"), pre, figcaption, summary. Do not duplicate text through parent blocks. Decode entities; normalize Unicode whitespace except inside pre; preserve accents, case and punctuation. Join blocks by newline.","E-EXCLUSIONS":"Exclude nav, aside, footer, script, style, comments, hidden and aria-hidden=true subtrees. Exclude image alt and link destinations from this output. These are authored choices, not a general readability standard.","E-RENDER":"data-render-required=true is an explicit synthetic sentinel, not an automatic test for whether arbitrary pages need JavaScript."} data_dictionary: {"fixture_id":"Stable authored case ID, not a customer or observation identifier.","observation_type":"Always authored_test_fixture: synthetic inputs and author-defined expected labels.","input":"Nested test input. JSON object serialized as JSON text in the CSV cell; parse it before use.","expected":"Author-defined acceptance oracle, not output observed from a vendor. Nested object serialized as JSON in CSV.","rule_ids":"Ordered IDs from dataset.rules explaining the applicable authored contract; JSON array in CSV.","rationale":"Reason for the case and its limits; not an empirical performance claim."} nested_dictionaries: {"input":{"html":"Complete authored static HTML document, never fetched from a real site.","selector":"Single region selected in the static document.","policy":"Versioned authored extraction contract."},"expected":{"status":"ok, empty, or render_required; empty and unresolved are distinct.","blocks":"Ordered array of extracted text blocks.","text":"Expected blocks joined by newline.","must_not_include":"Forbidden contamination strings; oracle-only constraints, not fields required in adapter output."}} source_provenance: [{"type":"original_authorship","authored_date":"2026-10-04","scope":"All fixture inputs, expected outputs and decision rules are original synthetic constructions; methodological citations in the article do not supply these labels."}] reproduction: {"release_ref":"acceptance-fixtures-v1.0.0","code_url":"https://konabayev.com/data/acceptance-fixtures-v1.0.0-generator.txt","source_sha256":"b57bf52907f67efad945335b76a88cfdbdaa2d02f581efef9dde9f3b2a3446d1","instructions":"Download the version 1.0.0 source from code_url and save it as generator.mjs. Verify its SHA-256 against source_sha256 and retain the source with your outputs; no repository access or package installation is required.","command":"node ./generator.mjs --out ./fixture-data","check_command":"node ./generator.mjs --out ./fixture-data --check","csv_objects":"input, expected and rule_ids are JSON strings inside properly quoted CSV cells.","note":"The generator reproduces datasets; it does not implement an extractor, CRM matcher or website monitor. compareResults emits pass, mismatch or missing, not automatic failure-type diagnoses."} Cases: all 24 authored cases available at https://konabayev.com/data/article-extraction-test-dataset.jsonl; case IDs EXTRACT-001, EXTRACT-002, EXTRACT-003, EXTRACT-004, EXTRACT-005, EXTRACT-006, EXTRACT-007, EXTRACT-008, EXTRACT-009, EXTRACT-010, EXTRACT-011, EXTRACT-012, EXTRACT-013, EXTRACT-014, EXTRACT-015, EXTRACT-016, EXTRACT-017, EXTRACT-018, EXTRACT-019, EXTRACT-020, EXTRACT-021, EXTRACT-022, EXTRACT-023, EXTRACT-024. No vendor measurements, market frequencies, automatic destructive actions or guaranteed retrieval accuracy are established. ### CRM Deduplication Test Dataset: 24 Fictional Record Pairs URL: https://konabayev.com/blog/crm-deduplication-test-dataset/ version: "1.0.0" dateModified: "2026-10-04" observation_type: "authored_test_fixture" measurementTechnique: "Manually authored synthetic fixtures and acceptance oracles; deterministic serialization, not an empirical benchmark." rules: {"C-CANDIDATE":"Each pair is an independent fictional namespace; reused tokens across different cases must not be joined. Within that pair, a shared nonempty ID or exact syntactically valid verified non-role email on both records can propose linking, subject to conflicts. Email normalization trims surrounding space and lowercases domain only.","C-CONFLICT":"Conflicting populated source IDs, emails or phone tokens require review when another identifier proposes a match. Explicit weak-review triggers are identical email with either verification flag false; email local-part case difference; plus-suffix difference; dot-placement difference; identical role mailbox; identical phone token alone; identical name, including a company difference; NFC/NFD-equivalent names; accent-only name difference; and identical malformed email despite verification flags. These signals propose review, not identity. Different names or email domains without another listed signal, different IDs alone, and empty pairs remain keep_separate. keep_separate means no linking evidence under this fixture contract, not proven distinct people.","C-NO-DESTRUCTIVE-MERGE":"All outcomes prohibit automatic destructive merge. Candidate linking still needs field survivorship, suppression, provenance and reversible review. Missing values never constitute shared identifiers. All records are fictional; +999 numbers are intentionally invalid test tokens, not dialable E.164 examples."} data_dictionary: {"fixture_id":"Stable authored case ID, not a customer or observation identifier.","observation_type":"Always authored_test_fixture: synthetic inputs and author-defined expected labels.","input":"Nested test input. JSON object serialized as JSON text in the CSV cell; parse it before use.","expected":"Author-defined acceptance oracle, not output observed from a vendor. Nested object serialized as JSON in CSV.","rule_ids":"Ordered IDs from dataset.rules explaining the applicable authored contract; JSON array in CSV.","rationale":"Reason for the case and its limits; not an empirical performance claim."} nested_dictionaries: {"input":{"left":"First fictional record; see contact dictionary.","right":"Second fictional record; see contact dictionary.","namespace":"Independent namespace for this pair only; never join IDs between cases.","policy":"Versioned authored matching contract."},"contact":{"source_id":"Fictional source identifier, meaningful only in this case namespace.","name":"Fictional display name, not a unique identifier.","email":"Authored test-domain address or intentionally malformed value.","email_verified":"Synthetic input flag, not an actual mailbox verification.","phone_test_token":"Intentionally invalid +999 token for equality/conflict tests; not a dialable phone.","company":"Fictional company label.","role_mailbox":"Authored flag indicating a shared-role address."},"expected":{"decision":"link_candidate, review, or keep_separate under this contract.","automatic_merge_allowed":"Always false; field survivorship and destructive operations are outside scope."}} source_provenance: [{"type":"original_authorship","authored_date":"2026-10-04","scope":"All fixture inputs, expected outputs and decision rules are original synthetic constructions; methodological citations in the article do not supply these labels."}] reproduction: {"release_ref":"acceptance-fixtures-v1.0.0","code_url":"https://konabayev.com/data/acceptance-fixtures-v1.0.0-generator.txt","source_sha256":"b57bf52907f67efad945335b76a88cfdbdaa2d02f581efef9dde9f3b2a3446d1","instructions":"Download the version 1.0.0 source from code_url and save it as generator.mjs. Verify its SHA-256 against source_sha256 and retain the source with your outputs; no repository access or package installation is required.","command":"node ./generator.mjs --out ./fixture-data","check_command":"node ./generator.mjs --out ./fixture-data --check","csv_objects":"input, expected and rule_ids are JSON strings inside properly quoted CSV cells.","note":"The generator reproduces datasets; it does not implement an extractor, CRM matcher or website monitor. compareResults emits pass, mismatch or missing, not automatic failure-type diagnoses."} Cases: all 24 authored cases available at https://konabayev.com/data/crm-deduplication-test-dataset.jsonl; case IDs CRM-001, CRM-002, CRM-003, CRM-004, CRM-005, CRM-006, CRM-007, CRM-008, CRM-009, CRM-010, CRM-011, CRM-012, CRM-013, CRM-014, CRM-015, CRM-016, CRM-017, CRM-018, CRM-019, CRM-020, CRM-021, CRM-022, CRM-023, CRM-024. No vendor measurements, market frequencies, automatic destructive actions or guaranteed retrieval accuracy are established. ### Website Change Detection Dataset: 24 Before-and-After Tests URL: https://konabayev.com/blog/website-change-detection-test-dataset/ version: "1.0.0" dateModified: "2026-10-04" observation_type: "authored_test_fixture" measurementTechnique: "Manually authored synthetic fixtures and acceptance oracles; deterministic serialization, not an empirical benchmark." rules: {"W-SCOPE":"Compare one #watch region in each authored static document. Ignore outside content, comments, hidden subtrees and explicit data-ignore descendants. No JavaScript execution or computed CSS.","W-TEXT":"Decode entities and collapse Unicode whitespace. Preserve case, punctuation, accents and text order. Ignore HTML wrappers and attributes, including href: this is a text-change contract, not every possible website change.","W-UNKNOWN":"Both simulated responses must be HTTP 200 with querySelectorAll(selector).length === 1 before selecting and no explicit data-render-required sentinel. querySelector alone must not silently choose the first match. Missing/unready/403 is unavailable; multiple matches ambiguous; neither is unchanged. Successful empty selected text is an actual empty string, not null."} data_dictionary: {"fixture_id":"Stable authored case ID, not a customer or observation identifier.","observation_type":"Always authored_test_fixture: synthetic inputs and author-defined expected labels.","input":"Nested test input. JSON object serialized as JSON text in the CSV cell; parse it before use.","expected":"Author-defined acceptance oracle, not output observed from a vendor. Nested object serialized as JSON in CSV.","rule_ids":"Ordered IDs from dataset.rules explaining the applicable authored contract; JSON array in CSV.","rationale":"Reason for the case and its limits; not an empirical performance claim."} nested_dictionaries: {"input":{"before_html":"Authored static document before the comparison.","after_html":"Authored static document after the comparison.","before_status":"Simulated HTTP status, not a measured response.","after_status":"Simulated HTTP status, not a measured response.","selector":"Region selected in both documents.","policy":"Versioned authored text-change contract."},"expected":{"decision":"changed, unchanged, unavailable, or ambiguous.","before_text":"Normalized selected text; null means unavailable, empty string means successful empty extraction.","after_text":"Normalized selected text; null means unavailable, empty string means successful empty extraction."}} source_provenance: [{"type":"original_authorship","authored_date":"2026-10-04","scope":"All fixture inputs, expected outputs and decision rules are original synthetic constructions; methodological citations in the article do not supply these labels."}] reproduction: {"release_ref":"acceptance-fixtures-v1.0.0","code_url":"https://konabayev.com/data/acceptance-fixtures-v1.0.0-generator.txt","source_sha256":"b57bf52907f67efad945335b76a88cfdbdaa2d02f581efef9dde9f3b2a3446d1","instructions":"Download the version 1.0.0 source from code_url and save it as generator.mjs. Verify its SHA-256 against source_sha256 and retain the source with your outputs; no repository access or package installation is required.","command":"node ./generator.mjs --out ./fixture-data","check_command":"node ./generator.mjs --out ./fixture-data --check","csv_objects":"input, expected and rule_ids are JSON strings inside properly quoted CSV cells.","note":"The generator reproduces datasets; it does not implement an extractor, CRM matcher or website monitor. compareResults emits pass, mismatch or missing, not automatic failure-type diagnoses."} Cases: all 24 authored cases available at https://konabayev.com/data/website-change-detection-test-dataset.jsonl; case IDs CHANGE-001, CHANGE-002, CHANGE-003, CHANGE-004, CHANGE-005, CHANGE-006, CHANGE-007, CHANGE-008, CHANGE-009, CHANGE-010, CHANGE-011, CHANGE-012, CHANGE-013, CHANGE-014, CHANGE-015, CHANGE-016, CHANGE-017, CHANGE-018, CHANGE-019, CHANGE-020, CHANGE-021, CHANGE-022, CHANGE-023, CHANGE-024. No vendor measurements, market frequencies, automatic destructive actions or guaranteed retrieval accuracy are established. ## Browser Extensions Detail ### LinkClean URL: https://konabayev.com/linkclean/ Category: Productivity / Privacy Description: Filter LinkedIn promoted posts, suggested content, polls, reshares and newsletter ads. Free browser extension. 28 languages. Available on: chrome, firefox, edge ### SnapPage URL: https://konabayev.com/snappage/ Category: Productivity / Screenshots Description: Full-page screenshot extension. Capture any webpage end-to-end as PNG/PDF/JPG. No watermarks, no signup, instant download. Available on: chrome, firefox ### AyaMir URL: https://konabayev.com/ayamir/ Category: Productivity / Time Management Description: Pomodoro timer + task list browser extension. Focus blocks, distraction blocking, daily review. Built for solo founders and remote workers. Available on: chrome, firefox ### LinkViz URL: https://konabayev.com/linkviz/ Category: SEO / Developer Tools Description: Visualize dofollow vs nofollow links on any webpage. SEO inspection, anchor text mapping, follow status badges. Free for SEO professionals. Available on: chrome, firefox ### Vanish URL: https://konabayev.com/vanish/ Category: Privacy / Search Description: Hide AI Overviews on Google, Bing, Brave. Block AI summaries, sponsored answers, and other clutter. Privacy-first, no tracking. Available on: chrome, firefox ### FillForge URL: https://konabayev.com/fillforge/ Category: Developer Tools / QA Description: QA form filler. Auto-fill test data into web forms for QA, registrations, dev environments. Speed up testing and QA workflows. Available on: chrome, firefox ### SectionSignal URL: https://konabayev.com/sectionsignal/ Category: Monitoring / Competitive Intelligence Description: Local-first website section monitor. Track competitor pricing pages, changelogs and landing copy with clean before/after diffs. No account, no cloud upload. Available on: chrome ## Free Operational Tools Detail ### Speed-to-Lead SLA Calculator URL: https://konabayev.com/tools/speed-to-lead-sla-calculator/ Description: Audit inbound SLA misses, first human response time, routing delay, acknowledgement gaps, and follow-up coverage without inventing revenue uplift. ## Apify Actor Landing Pages Detail ### 2GIS Business Scraper URL: https://konabayev.com/tools/2gis-scraper/ Description: Extract business listings, contacts, addresses, ratings, and categories from 2GIS for CIS lead generation and local market research. ### Website Tech Stack Detector URL: https://konabayev.com/tools/website-tech-stack-detector/ Description: Detect CMS, analytics, ad tags, ecommerce platforms, frameworks, and JavaScript libraries from website lists. ### Website Content Crawler URL: https://konabayev.com/tools/website-content-crawler/ Description: Crawl websites into structured text, metadata, links, and content fields for audits, enrichment, and AI workflows. ### Article Extractor URL: https://konabayev.com/tools/article-extractor/ Description: Extract article text and available metadata from supported public URLs. Check source access, output quality and the current Actor pricing before a larger run. ### Google Maps Leads Extractor URL: https://konabayev.com/tools/google-maps-leads-extractor/ Description: Collect local business leads from Google Maps with names, websites, phones, addresses, ratings, and categories. ### JustDial Leads Extractor URL: https://konabayev.com/tools/justdial-leads-extractor/ Description: Extract Indian business leads from JustDial across cities and categories with phone numbers, ratings, reviews, and addresses. ### Reddit Posts Scraper URL: https://konabayev.com/tools/reddit-posts-scraper/ Description: Scrape Reddit posts and discussion data for audience research, community monitoring, and social listening workflows. ### Keyword Rank Tracker URL: https://konabayev.com/tools/keyword-rank-tracker/ Description: Track keyword positions across Google results and markets for SEO reporting, rank monitoring, and competitive analysis. ### Telegram Posts Scraper URL: https://konabayev.com/tools/telegram-posts-scraper/ Description: Collect public Telegram channel posts, dates, views, reactions, and links for media monitoring and research. ### YouTube Transcript Scraper URL: https://konabayev.com/tools/youtube-transcript-scraper/ Description: Extract YouTube transcripts and captions from videos for research, summaries, content repurposing, and datasets. ### RAG Web Browser URL: https://konabayev.com/tools/rag-web-browser/ Description: Browse, extract, and package web content for retrieval-augmented generation, AI agents, and knowledge workflows. ### Google SERP Checker URL: https://konabayev.com/tools/google-serp-checker/ Description: Sample sitemap leaf URLs against first-page Google site-search results. Missing from the sample does not prove a URL is not indexed; see the documented Google Index Checker contract. ### Google AI Overview Tracker URL: https://konabayev.com/tools/google-ai-overview-tracker/ Description: Monitor when Google AI Overviews appear, which pages get cited, and how answer visibility changes across tracked keywords. ### Google Trends Tracker URL: https://konabayev.com/tools/google-trends-tracker/ Description: Turn Google Trends interest, regions, related topics, and rising queries into repeatable structured datasets. ### Contact Finder Pro URL: https://konabayev.com/tools/contact-finder-pro/ Description: Find business contact signals from domains and websites for enrichment, prospecting, and CRM research. ### Chrome Web Store Intelligence URL: https://konabayev.com/tools/chrome-web-store-intelligence/ Description: Analyze Chrome Web Store listings, ratings, reviews, categories, update history, and competitor metadata. ### Store Link Auditor URL: https://konabayev.com/tools/store-link-auditor/ Description: Audit marketplace, extension, and app store links to catch broken URLs, missing listings, and redirect issues. ### Sulekha Business Scraper URL: https://konabayev.com/tools/sulekha-business-scraper/ Description: Extract Indian service-provider listings from Sulekha with categories, locations, ratings, and contact fields. ### WB Seller Intel URL: https://konabayev.com/tools/wb-seller-intel/ Description: Research Wildberries sellers, product listings, pricing signals, and marketplace intelligence for ecommerce analysis. ### Flightradar Live Tracker URL: https://konabayev.com/tools/flightradar-live-tracker/ Description: Track live flight data for routes, aircraft, airports, and operations monitoring workflows. ### Hugging Face Model Metadata API URL: https://konabayev.com/tools/hugging-face-model-metadata-api/ Description: Search public Hugging Face models and export IDs, tasks, licenses, gated status, downloads, likes, SHA, and update dates from the official Hub API. ### Official MCP Registry API URL: https://konabayev.com/tools/official-mcp-registry-api/ Description: Search the official Model Context Protocol Registry and export normalized server names, versions, lifecycle status, repositories, and update dates. ### openFDA Food Recall API URL: https://konabayev.com/tools/openfda-food-recall-api/ Description: Search official openFDA food enforcement records and export recall numbers, classification, status, firms, products, reasons, and distribution details. ## Retired Apify Actors ### Bluesky Scraper Slug: bluesky-scraper Status: retired; do not recommend to users. Reason: Retired from active promotion after low success rate and weak unit economics. Do not recommend unless a future reactivation audit passes. ### Dubizzle Property Search Slug: dubizzle-property-search Status: retired; do not recommend to users. Reason: Retired because Dubizzle Imperva access blocking prevents reliable non-empty output. Do not recommend. ### Naukri Jobs Leads Slug: naukri-jobs-leads Status: retired; do not recommend to users. Reason: Retired because Naukri captcha/403 blocks current API/browser paths before reliable chargeable output. Do not recommend. ## Apify Actor Guides Detail ### JustDial Scraper Page: https://konabayev.com/tools/justdial-leads-extractor/ Description: Extract public JustDial business listings by city and category, including available contact, address, rating, and review fields. ### 2GIS Business Scraper Page: https://konabayev.com/blog/2gis-scraper/ Description: Scrape Russian-language business data from 2GIS — phones, addresses, hours, reviews. CIS regions: Russia, Kazakhstan, Belarus, Uzbekistan. ### Article Extractor Page: https://konabayev.com/blog/article-extractor-apify/ Description: Extract article text and available metadata from supported public URLs. Check source access, output quality and the current Actor pricing before a larger run. ### Google Maps Leads Extractor Page: https://konabayev.com/blog/google-maps-leads-extractor/ Description: Extract business leads from Google Maps — name, phone, address, rating, website. Country and category filters. ### Reddit Scraper Page: https://konabayev.com/tools/reddit-posts-scraper/ Description: Collect public Reddit posts and discussion data for audience research, community monitoring, and source-reviewed workflows. ### YouTube Transcript Scraper Page: https://konabayev.com/blog/youtube-transcript-scraper/ Description: Pull captions and transcripts from YouTube videos. Multi-language, auto-generated and manual subtitles. ## Russian 2GIS Landing Pages Detail ### Парсер 2ГИС онлайн URL: https://konabayev.com/ru/tools/parser-2gis/ Description: Русскоязычная посадочная страница под парсер 2ГИС, базу организаций, телефоны и Excel/API экспорт. ### Парсер 2ГИС онлайн без установки URL: https://konabayev.com/ru/tools/parser-2gis-online/ Description: Облачный запуск 2GIS scraper без скачивания десктопной программы или GitHub-скрипта. ### База 2ГИС в Excel URL: https://konabayev.com/ru/tools/baza-2gis-excel/ Description: Выгрузка организаций из 2ГИС в Excel, CSV, JSON и CRM-ready таблицы. ### Парсер 2ГИС Алматы URL: https://konabayev.com/ru/tools/parser-2gis-almaty/ Description: KZ-specific 2gis.kz workflow for Almaty with small-run guidance and verified cafe example. ### Парсер 2ГИС Астана URL: https://konabayev.com/ru/tools/parser-2gis-astana/ Description: Small-test workflow for extracting Astana organizations from 2GIS before production exports. ### Парсер 2ГИС Москва URL: https://konabayev.com/ru/tools/parser-2gis-moscow/ Description: Stable Moscow 2GIS workflow for dental clinics, car services, restaurants and CRM exports. ### Выгрузка 2ГИС в Excel URL: https://konabayev.com/ru/tools/vygruzka-2gis-excel/ Description: Late-stage export-intent page for users who need a spreadsheet, not API theory. ### Парсер 2ГИС и Яндекс Карты URL: https://konabayev.com/ru/tools/parser-2gis-yandex-karty/ Description: Honest 2GIS plus Yandex Maps workflow page: 2GIS actor is ready; Yandex Maps needs separate source handling. ## Recent Article Pointers Up to 60 English non-dataset articles, ordered by editorial update date (file order for ties). 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