LinkedIn Ads Benchmarks 2026: ROAS, CPC & Funnel Data

Direct Answer: LinkedIn Ads Benchmarks at a Glance

Dreamdata’s 2026 customer telemetry puts LinkedIn CPC at €5.98, cost per company influenced at €70.11, and ROAS at 121%. LinkedIn represented 41% of total B2B ad budgets in the analyzed customer data. Its influence also remained visible deep in the measured funnel: 24.2% of MQL-stage sessions, 30.2% at SQL, and 28.3% at new business.

These numbers do not describe every LinkedIn advertiser. Dreamdata aggregated more than 66 million sessions and 3.5 million B2B customer journeys from its own customers. The public report highlights do not publish the exact metric-specific base, geography, industry mix, attribution model, or dispersion for every row. Use the benchmarks as a company-level B2B reference, not a guaranteed platform average.

Data period: 2025 observations, published March 2026. Last verified: July 11, 2026. For broader platform data, see LinkedIn marketing statistics. This page is paid-media and revenue-measurement only.

Cite This Report

Canonical URL: https://konabayev.com/blog/linkedin-ads-benchmarks/

Recommended citation: Tugelbay Konabayev, “LinkedIn Ads Benchmarks 2026: ROAS, CPC & Funnel Data,” Konabayev.com, July 11, 2026, https://konabayev.com/blog/linkedin-ads-benchmarks/

Machine-readable versions:

For one figure, cite its stable fragment, for example, https://konabayev.com/blog/linkedin-ads-benchmarks/#liads-013 for the 121% ROAS result. The article publishes neutral paraphrases and original tables; it does not reproduce the vendor’s charts or gated report.

Ten Most Citable LinkedIn Ads Benchmarks

Claim	Benchmark	Decision context
LIADS-007	LinkedIn received 41% of total B2B ad budget.	Channel allocation
LIADS-008	LinkedIn CPC: €5.98.	Click-level efficiency
LIADS-010	Cost per company influenced: €70.11, down from €154.	Account-level efficiency
LIADS-013	LinkedIn ROAS: 121%, up from 113%.	Revenue return
LIADS-016	Top-performing-customer LinkedIn ROAS: 279%.	Distribution, not a median
LIADS-017	LinkedIn share of MQL-stage sessions: 24.2%.	Early funnel influence
LIADS-018	LinkedIn share of SQL-stage sessions: 30.2%.	Sales-qualified influence
LIADS-019	LinkedIn share at new business: 28.3%.	Closed-revenue influence
LIADS-020	First impression to revenue: 281 days.	Measurement window
LIADS-022	First ad engagement to revenue: 212 days.	Measurement window

LinkedIn CPC and Company-Level Cost Benchmarks

The click looks expensive; the company-level comparison looks different. Dreamdata reported a €5.98 LinkedIn CPC versus €1.60 for Meta. But LinkedIn cost €70.11 per company influenced, below Google Search at €110.37 and Meta at €128.70 in the same published comparison.

Claim	Channel	Metric	Result
LIADS-009	Meta	CPC	€1.60
LIADS-011	Meta	Cost per company influenced	€128.70
LIADS-012	Google Search	Cost per company influenced	€110.37

Do not convert these figures into a universal CPL. A person, lead, account, opportunity, and company influenced are different units. Before benchmarking a campaign, reproduce the source’s counting rule in your own dashboard and preserve currency, attribution window, and qualified-company definition.

LinkedIn ROAS Benchmarks

LinkedIn was the only channel in the public comparison above 100% ROAS. The report listed 121% for LinkedIn, 67% for Google Search, and 51% for Meta. Among the report’s top-performing customers, LinkedIn reached 279%.

Claim	Channel	Reported ROAS
LIADS-014	Google Search	67%
LIADS-015	Meta	51%

ROAS is highly sensitive to attribution and revenue recognition. Dreamdata measures customer journeys across channels; an ad-platform dashboard may use a shorter window and give a different answer. Compare channels only inside one consistent attribution model, then test sensitivity using first-touch, last-touch, and multi-touch views.

Funnel Influence and Time to Revenue

The report’s strongest operational lesson is that one-month measurement is too short for its observed B2B journeys. The average path contained 88 touchpoints, four channels, and 10 stakeholders. The first LinkedIn impression preceded revenue by 281 days on average; first conversion and first engagement were almost identical at 214 and 212 days.

Claim	Benchmark
LIADS-001	Dataset scope: 66M+ sessions.
LIADS-002	Dataset scope: 3.5M+ customer journeys.
LIADS-003	81% of the measured journey happened outside the sales pipeline, up from 70%.
LIADS-004	Average journey: 88 touchpoints, up from 76.
LIADS-005	Average journey: 4 channels, up from 3.7.
LIADS-006	Average journey: 10 stakeholders, up from 6.8.
LIADS-021	First ad conversion to revenue: 214 days.

The two-day conversion-versus-engagement gap does not prove that form fills are worthless. It shows that the selected customer journeys did not reward a simplistic assumption that conversion is always a much later or stronger account signal. Track impression, engagement, conversion, account qualification, opportunity creation, and revenue as separate timestamps.

Conversions API Results

The public highlights attribute a 20% reduction in CPA and 31% increase in attributed revenue to sending offline pipeline and revenue data through LinkedIn’s Conversions API.

Claim	Reported result
LIADS-023	CPA decreased 20%.
LIADS-024	Attributed revenue increased 31%.

The public page does not disclose the exact base, design, or control group for these two integration outcomes. Treat them as vendor-observed implementation evidence, not a causal benchmark. A proper rollout should record the pre-integration baseline, matching rate, event latency, modeled-versus-observed conversions, and campaign changes during the test.

How to Benchmark Your LinkedIn Ads

A useful benchmark compares the same objective, format, audience, geography, attribution window and business outcome. A global average without those coordinates can make a healthy campaign look weak or an inefficient one look strong.

Use three layers instead of one blended target:

Media efficiency: CPM, CTR, CPC and frequency by format, geography and audience size.
Account progression: qualified companies influenced, MQL-to-SQL movement, decision-maker coverage and opportunity creation.
Revenue: pipeline, closed-won revenue, ROAS and time from first signal to revenue.

Lock each definition before comparing. For CPC, specify chargeable-click rules. For cost per company, decide whether “influenced” requires one impression, an engagement, or a qualified session. For ROAS, state whether revenue is sourced, influenced, or fractionally attributed. Use medians and percentile bands when possible; one average can hide large campaign and segment differences.

For a related planning view, use B2B marketing budget benchmarks, multi-touch attribution for B2B, and B2B sales benchmarks.

Build a Comparable Campaign Cohort

Start by creating a comparison cohort before calculating an average. Separate brand awareness, website visits, lead generation, video views, event promotion and account-based campaigns. Their bidding systems optimize for different events, and a cheap engagement campaign is not a valid comparator for a pipeline campaign.

Within an objective, segment by ad format. Single-image ads, video, document ads, conversation ads and lead-gen forms create different click and conversion behavior. Keep audience size and targeting method visible. A narrow list of named enterprise accounts usually has higher auction costs and lower volume than a broad job-function audience, but it may create more qualified companies.

Geography and currency are equally important. The €5.98 source value should not be pasted into a US-dollar dashboard without a dated conversion rule. More importantly, auction intensity and reachable audience vary by country. A global blended CPC can move because the geographic mix changed even when every local campaign stayed stable.

Finally, lock the time cohort. Attribute a deal to the campaign cohort that could reasonably influence it, rather than comparing this month’s spend with this month’s closed revenue. The 281-day impression-to-revenue benchmark illustrates why a calendar-month ratio can mismatch acquisition and outcome periods.

Diagnose CPC, CPL and ROAS Together

No single LinkedIn metric identifies the cause of underperformance. Use the relationship between media, account and revenue metrics to decide what to investigate next.

If CPM rises while CTR is stable, the auction or audience may have become more expensive. If CPM is stable and CTR falls, inspect creative relevance, frequency, placement and audience-message fit. If CPC is acceptable but qualified-company cost rises, the landing experience or targeting may be attracting individual clicks from the wrong accounts.

If lead cost looks efficient but opportunity cost is weak, inspect qualification and the definition of a conversion. A content download can be a legitimate early signal without being a sales-ready lead. Route it into account-level engagement and nurture instead of forcing it into the same conversion class as a demo request.

If account progression is healthy but ROAS is weak, check sales-cycle maturity, average contract value, win rate and attribution. A cohort may not have had enough time to close. Conversely, a high short-window ROAS can be overstated when an attribution model gives an ad full credit for demand that began elsewhere.

A diagnostic dashboard should therefore display CPM, CTR, CPC, qualified-company cost, opportunity rate, pipeline, revenue, time to opportunity and time to revenue for the same cohort. Add median and percentile bands so one unusually large contract does not define the entire channel.

Use the Benchmarks in Forecasting

Forecast from your own funnel first and use external numbers as sensitivity checks. Begin with reachable audience, expected frequency, CPM or CPC, landing conversion, company qualification, opportunity creation, win rate, contract value and cycle length. Preserve each assumption as a separate cell.

Then compare the implied company-level cost and ROAS with the Dreamdata benchmark source. If your model requires every assumption to beat the published top-performer result, the forecast is fragile. If it works under conservative conversion and longer-cycle assumptions, it is more useful for budget decisions.

Run three scenarios rather than one promise. The downside case should include higher auction cost, creative fatigue and slower revenue. The base case should use your own mature cohort. The upside case can use a better qualified-company rate or win rate, but should not quietly change several assumptions at once.

Record the date and source beside every external input. On refresh, distinguish a real benchmark change from a definition change. A move from cost per lead to cost per company is not an efficiency improvement by itself; it is a different measurement unit.

What This Dataset Does Not Contain

The public highlights are strong on company and revenue outcomes but incomplete for a full media benchmark library. The form-gated report advertises detailed CTR, CPM, cost-per-contact and segment breakdowns, but those values are not exposed on the public page. This article does not backfill them from secondary roundups.

The source also does not provide confidence intervals, medians, industry distributions, account-size bands or metric-level sample counts in the highlights. The 66-million-session population describes the report as a whole. It must not be represented as the exact denominator behind every CPC, ROAS or stage-influence figure.

The result is deliberately narrower than many “all LinkedIn benchmarks” pages. Every included number can be traced to the public primary source, while unavailable rows remain unavailable. That boundary is more useful for citation and decision-making than a longer table assembled from incompatible agencies and anonymous campaign exports.

It also keeps future updates auditable: new rows can be appended only when their public source, unit, period and population are known.

Methodology and Limitations

All 24 claims come from Dreamdata’s public 2026 report highlights and landing-page methodology. Firecrawl was used to capture the public source. The full downloadable report is form-gated, so metrics available only inside that asset were not reconstructed from secondary roundups.

The public source describes aggregated customer data rather than a randomized market sample. It does not publish metric-level sample sizes, country weights, industry distribution, dispersion, or every operational definition on the highlights page. The article therefore preserves channel, unit, period, and source context and does not calculate a synthetic “average LinkedIn ad.”

FAQ

What is the average LinkedIn Ads CPC in 2026?

Dreamdata reported €5.98 in its 2026 report using 2025 observations. Geography, auction, objective, audience and format can move CPC substantially, so keep those dimensions beside your comparison.

What is a good LinkedIn Ads ROAS?

The source reported 121% overall and 279% among its top-performing customers. These are attribution-system outputs, not universal targets. Compare your result only after aligning the revenue and attribution definitions.

How long should a LinkedIn attribution window be for B2B?

Dreamdata observed 281 days from first ad impression to revenue, 214 days from first conversion, and 212 days from first engagement. Preserve the full journey in your warehouse even if the ad platform uses a shorter reporting window.

Is LinkedIn more expensive than Meta?

At the click level in this source, yes: €5.98 versus €1.60. At the company-influenced level, LinkedIn was lower: €70.11 versus €128.70. The conclusion changes with the unit.

Should LinkedIn be measured by leads?

Leads are one intermediate event. For complex B2B purchases, include qualified accounts, opportunities, revenue, stakeholder coverage and time to revenue.

How often should LinkedIn Ads benchmarks be refreshed?

Review campaign cohorts monthly, but refresh the external comparison when Dreamdata publishes a methodologically comparable annual edition. Preserve the old period so a source or definition change is not mistaken for market movement.