LinkedIn ABM 2026 – Performance Benchmarks Report & Top Performing LinkedIn Ads

I remember how much I struggled to find reliable Linkedin ABM benchmarks (well, check this full benchmarks report now!) when I was starting to work on my LinkedIn ABM strategy a couple of years ago. Many companies planning to launch their first Linkedin ABM campaigns (Account Based Marketing on LinkedIn) in 2026 will face the same struggle – launching without knowing what they *really* need to do to hit their revenue goals: 

• how many companies they should target, how much they should spend (monthly) per company 
• what budgets they need to spend, 
• which ads they should use (and how many) 
• what LinkedIn ad performance metrics benchmarks (CTRs, CPCs, CPMs – overall and per inventory type – image, video, TLA, DM, document ads, text ads etc.) they should achieve – let alone what ad content performs best.
  

So I looked at anonymised data of 211 ZenABM’s users – both quantitative (ad spend, number of accounts targeted, number of ads) and qualitative – which ads performed best and what they had in common (inventory type, to CTA and actual ad content) 

In this report, you will find actionable information about how companies that were running successful ABM campaigns with LinkedIn Ads in 2025 did in terms of ad spend, number of campaigns, and financial outcomes – influenced pipeline & pipeline per $ spent, as well as what was the content and type of their top performing ads.  The analysis is based on 211 companies across 29 countries, with a total ad spend of  $5,536,829.22 and 161,256 ads they were running in 2025.

The anonymised data has been pulled from ZenABM’s Campaign Performance Dashboards, which combines data from the users’ LinkedIn Campaign Manager API and their CRM, to provide detailed insights into LinkedIn ad campaigns performance and influence on pipeline and revenue.

You can try ZenABM for free for 37 days and get your own performance benchmarks for free, as ZenABM backfills 90 days of your ad performance data. It takes only 2 minutes to set up and a few minutes (depending on the number of LinkedIn campaigns run) to get your own report. The data was subsequently analysed using GPT-5.2. Extended Thinking model, Claude Sonnet 4-20250929 and Claude Opus 4.5. 

In this report, you will learn both quantitative and qualitative insights into LinkedIn ad performance

PART 1: Quantitative ABM and LinkedIn campaign performance metrics benchmarks

In this part of the report, you will find answers to the following questions: 

1. 1. How much were companies spending on their ABM campaigns in 2025?
   2. How many ads were companies running in 2025, on average?
   3. How many accounts were targeted (by month and overall) by the companies running LinkedIn ABM?
   4. What were the median ad performance metrics (CTR, CPC, CPM)?
      
   5. What was the median pipeline per $ spent (efficiency metric) and ROAS for companies running ABM Campaigns in 2025?
   6. What is the median Deal Open rate (= number of deals / number of companies targeted)?
   7. For each question, you’ll find a comparison of TOP Performers vs the average company + a breakdown by country, company size, and industry
      

Data you will find in the report: 

• Average & mean monthly ad spend by company size, country and industry 
• Number of ABM and LinkedIn ads run (by company size, country and industry) 
• Number of Target Accounts (= companies that ads were delivered to, with at least 3 ad impressions) by company size, country and industry 
• Number of Ads, LinkedIn Ads Sets (formerly Campaigns) and Campaigns (formerly Campaign Groups) run – again, overall, and split by country/industry/
• Ad Performance Metrics – Average LinkedIn Ads CPMs, CTRs (by company size, country and industry) 
• Efficiency (Performance) metrics & ROAS (Return on Ad Spend) – how much ABM/Linkedin influenced pipeline did the companies in the study generate, how much pipeline per $ spent did they get (campaign efficiency)  – overall and by company size, country and industry 
• WINNING company insights – the benchmarks from top-performing companies in terms of pipeline per $ spent, and takeaways for companies planning to run LinkedIn ABM. 

PART 2: Qualitative LinkedIn ABM  ad performance – which ads performed best (content, CTAs, and ad formats) 

This part of the report focuses on ad performance (measured by CTR and pipeline per $ spent on campaign level) by inventory type, and qualitative aspects of the “winning” ads:

• Ad content:
• • Visuals: Screenshots of interface? Photos of people? 
  • User-generated content? 
  • Testimonials? 
  • AI vs human-designed ads? 
• –  and CTAs (sign up, learn more etc.) 

Methodology 

This report is based on data from 211 companies running ABM campaigns through LinkedIn Ads in 2025, with a total ad spend of  $5,536,829.22 across 29 countries, running a total of 161,256 ads.

What is LinkedIn ABM? 

LinkedIn ABM is an Account Based Marketing strategy that relies predominantly on delivering the campaign content to the target accounts through LinkedIn ads. According to Kyle Poyar’s research, 20% of companies surveyed poined to ABM as their top-performing Growth Strategy in 2025: 

Why?

In my experience, LinkedIn ABM is a great strategy for companies that want more control and precision in their marketing activities – instead of “spraying and praying” that content published on their own platforms / parter sites will attract the right companies with the right propensity to pay, ABM allows you to proactively reach your ICPs in the ideal target accoutns.

What metrics should companies running ABM on LinkedIn track? 

Since LinkedIn allows more precise targeting for B2B than display advertising networks, more and more Saas companies are turning to running the Account Based Marketing programs on LinkedIn – hence the term “LinkedIn ABM”.

 In the past, most companies used to track leads generated from ads (which is a very ineffective way of evaluating the performance of Account (!) based campaigns!) and ROAS (which is a very lagging metric and does not allow to evaluate whether a campaign is working or not for often more than 6 months, and does not provide any indication as to what needs to be improved to make the campaigns work better). So Companies that are planning to run LinkedIn ABM Campaigns are often at a loss as to what metrics they should track to understand & improve their LinkedIn ABM campaign performance. 

This is exactly why we launched ZenABM – to build upon our experience with running LinkedIn ABM campaigns, and provide done-for-you, prescriptive analytics dashboards with all the right reports tracking the right efficiency and effectiveness metrics: 

• Funnel conversion rate and account penetration – % of target companies reached, % of companies moving into the awareness, interest, consideration, selection (deal open) and customer/ closed lost stages of the ABM funnel – overall, and per each ABM campaign:

  

• ABM campaign performance (ABM campaigns is a higher-order unit of measurement than “LinkedIn Campaign”, and usually is defined by one target audience = one company list) – including total pipeline generated and pipeline per dollar spent (efficiency metric).
• LinkedIn ad & Campaign performance – CTRs, CPCs, CPMs per LinkedIn ad (for different ad formats)
  

  

• Ad spend, total pipeline influenced (generated from companies that had enough impressions of the ads in the ABM campaigns), total LinkedIn ads influenced revenue: 
  

  

  
  

PART 1: Quantitative ABM and LinkedIn campaign performance 

Here’s an executive summary of the quantitative analysis of 211 ZenABM’s users across 29 countries – mostly B2B SaaS companies: 

1) How much were companies spending on LinkedIn ABM campaigns per month in 2025?

While the typical company spends a modest ~$2,700 monthly (median), the average spend ($8,788/mo) is pulled significantly higher ($12k+ in the US) by large enterprise budgets and aggressive spending in specific regions like the Netherlands ($35,260 mean, n = 9 companies)

• Typical company: median $2,693/mo, average $8,788/mo (average is pulled up by big spenders).
• By country (means): US $12,740/mo, UK $4,869/mo, Netherlands $35,260/mo.
• By employee size (means): 11–50 employees $4,892/mo vs 1k–5k employees $68,249/mo.
• Top performers (your trimmed Top-25% by pipeline/$ cohort): median $6,576/mo.

  

2) How many LinkedIn ABM ads were companies running, on average?

A typical company running LinkedIn ABM  (medians runs 312 ads, across 16 campaign groups, and 44 ad sets. The highest number of ads are run by companies in the US, UK and the Netherlands. Interestingly – Top performing companies are running fewer ads (but this doesn’t imply causality – as the mean is pulled heavily by average ACV rather than pipeline efficiency or ad creatives).

• Typical company (medians): 312 ads, 16 campaign groups, 44 ad sets.
• Overall (means): 724 ads, 26.5 campaign groups, 84.8 ad sets.
• By country (mean # ads): US 582, UK 647, Netherlands 4,011.
• By employee size (mean # ads): 11–50 employees 622 vs 1k–5k employees 7,297.
• Top performers (Top-25% by pipeline/$): median 114 ads, 10 campaign groups, 20 ad sets.

3) How many target accounts do companies running LinkedIn ABM typically reach?

The “typical company” running LinkedIn ABM campaigns runs ads to around 9,875 companies in total, and  6,423 target accounts per month.  The number of companies targeted scales heavily with company size rather than performance tier.

• Typical company: median 6,423 target accounts/mo, median 9,875 total.
• Averages: 4,398 target accounts/mo, 10,454 total.
• By country (average, target accounts/mo): US 4,136/mo, UK 5,326/mo.
• By employee size (average, target accounts/mo): 11–50 employees 4,370/mo vs 1k–5k employees 10,016/mo.
• Top performers (Top-25% by pipeline/$ cohort): median 9,071 total target accounts.

4) What were the median ad performance benchmarks (CTR, CPC, CPM)?

The “typical” company in the dataset running ABM ads on LinkedIn has CTRs of CTR 0.69%, CPC $11.04, CPM $78.30

• Overall medians: CTR 0.69%, CPC $11.04, CPM $78.30.
• Country medians (examples):
• • US: CTR 0.52%, CPC $8.99, CPM $62.67
  • UK: CTR 0.55%, CPC $9.16, CPM $56.62
  • Netherlands: CTR 0.72%, CPC $6.40, CPM $50.08
• Top performers (your trimmed Top-25% by pipeline/$ cohort): CTR 0.21%, CPC $21.91, CPM $56.62 (i.e., which indicate the top performance is skewed by high ACV rather than great ad performance!).

5) What was the median pipeline-per-$ and ROAS?

• Across companies with pipeline attribution present (medians):
• • Influenced pipeline: $13,819/mo (median)
  • Pipeline per $: 5.21
  • ROAS: 1.62
• Top performers (your trimmed Top-25% by pipeline/$ cohort):
• • $106,500/mo influenced pipeline (median) → $1,278,000/yr annualized (median)
  • Pipeline per $: 15.20 (median)
  • ROAS: 2.79 (median)

6) What is the median Deal Open Rate?

• Overall median deal open rate: 0.58% (deals opened ÷ target accounts).
• Top performers (your trimmed Top-25% by pipeline/$ cohort): 0.66% (median).

Key LinkedIn ABM Metrics:

Below, you will find the key LinkedIn ABM metrics – overall, by Country, Company Size & Industry:

How does budget spend, number of companies targeted, number of LinkedIn ads, and ad performance metrics (average CTRs, CPCs, CPMs etc.) influence your ABM results (pipeline + efficiency – pipeline per $ spent) 

Below are the correlations between ABM performance (total pipeline produced and pipeline per $ spent = efficiency) and execution variables, plus CPM by ad spend tier.

Performance metrics used

• Influenced pipeline per month ($/mo) = TOTAL LinkedIn influenced pipeline / active months
• Pipeline per $ (efficiency) = Pipeline per $

Important: These correlations are computed on the subset of companies where the performance metric is populated (N = 33 for most pairs; N = 30 where “# LinkedIn ads” is missing).

Key takeaways – impact of budget, target account list size, number of ads  & LinkedIn ads performance metrics on ABM results 

• If you want more pipeline, budget matters most.

Monthly ad spend has the strongest relationship with pipeline influenced per month (ρ 0.47, r 0.57).
Do this: if your goal is more pipeline volume, plan to increase (or keep steady) monthly spend.

• Targeting more accounts is the second largest predictor of pipeline success (although not as powerful as spend).

Don’t increase your ABM budget without increasing your target account lists (i.e. don’t spend that extra budget on just increasing your bids to reach your existing list faster/ boost account penetration). Larger lists perform better for increasing monthly pipeline (bigger target account lists correlated positively with total pipeline(total ρ 0.37, per month ρ 0.35)), especially if paired with:

• more LinkedIn ad sets (ρ 0.39)
  
  
  Do this: after you set budget, grow pipeline by adding ad sets and expanding the target account list.

“More ads” alone doesn’t predict better results.

 Number of ads has only a weak link with pipeline/month (ρ 0.31, r 0.06).

This is probably because having too many ads in one campaign results in poor budget distribution, and not enough reach and frequency of the ads to really “tell the story” your campaign was intended to.

 Do this: don’t treat “ads launched” as success. Use more ads only when it supports testing and personalization.

CTR isn’t a good success metric for LinkedIn ABM pipeline.

Surprisingly, CTRs basically doesn’t move with pipeline/month (ρ -0.14) and doesn’t explain efficiency either. However – this is most likely skewed by the high ACV the top performers have – so they don’t have to worry about ad performance per se. 

Cheap clicks don’t equal more pipeline.

 CPC doesn’t clearly relate to pipeline/month (ρ 0.29, r -0.02).

Do this: don’t pause campaigns just because CPC is high. In ABM, higher CPC can still be fine if you’re reaching the right buyers.

CPM isn’t telling you much about performance here.

CPM is basically unrelated to pipeline/month (ρ 0.05) and efficiency (ρ -0.07).

Do this: use CPM mainly for planning reach, not for deciding whether a campaign “works.”

Efficiency (Pipeline per $) doesn’t improve just by scaling mechanics.

 Pipeline/$ has near-zero relationships with spend, targeting volume, # campaigns, # ads, CTR, CPC, CPM (most are between -0.13 and +0.09).
Do this: to improve efficiency, focus on things like: 

• better account list quality (fit + intent)
• clearer message + offer
• better landing page + follow-up (speed-to-lead, outbound to engaged accounts)

There’s a tiny “bigger = slightly less efficient” pattern.

 Spend vs Pipeline/$ is slightly negative (ρ -0.13) — very weak, but it shows up.
Do this: when you scale budget, do it in steps and watch Pipeline/$ by segment, so efficiency doesn’t drop.

Simple takeaway:
Spend + coverage = more pipeline.
Efficiency = better targeting + better messaging + better conversion + better follow-up.

Correlations vs Influenced pipeline per month ($/mo)

(Reported as Spearman rho + Pearson r)

Interpretation: in this view, pipeline volume scales most with monthly spend (moderate positive correlation). Variables like campaigns/ad sets and targeting volume show weak-to-moderate positive relationships with monthly pipeline volume, but notably smaller than spend.

How does LinkedIn ABM Ad Spend Tiers correlate with TOTAL influenced pipeline?

Annualized pipeline ($/yr) = (TOTAL LinkedIn influenced pipeline ÷ active months) × 12

What the medians show: higher spend tiers generally align with higher median annualized pipeline, with the biggest jump starting in the $10k–$20k/mo tier.

Correlation: LinkedIn ABM spend tier ↔ Pipeline per $ (efficiency)

Treating spend tier as an ordered variable (low → high):

• Spearman rho: -0.12
• Pearson r: -0.25

So, in this subset, moving up spend tiers is not associated with higher efficiency (if anything it’s slightly negative, but weak).

Pipeline per $ by monthly ad spend tier (medians)

What this table shows (light interpretation):

• The highest-spend tier does not have the highest median efficiency.
• The < $2k/mo tier has the highest median Pipeline/$, but also the widest spread (big variability), which is why the tier-level correlation is near zero.

Correlations vs Pipeline per $ (efficiency)

Interpretation: efficiency (Pipeline/$) has near-zero relationships with spend, targeting volume, campaign counts, ads count, CTR/CPC/CPM in this subset. The (slightly) negative signs for spend/targeting (more spend and more companies targeted = lower efficiency) are present, but very weak. 

What should you do to “win” at LinkedIn ABM? 

Here are data-backed “what to aim for” targets you can lift straight into a demand gen / ABM report. (All benchmarks are per-company, then aggregated. Where I reference correlations, it’s the N=33 pipeline-attributed subset you shared.)

Executive summary: the “do this” targets (to drive more pipeline from your ABM campaigns)

If you’re a demand gen manager planning a LinkedIn ABM motion:

• Spend: start at ~$2.7k/mo, plan for ~$6.9k/mo if you want an upper-quartile program, and ~$20k/mo if you want aggressive scale.
• Target accounts: start at ~2.5k/month, scale to ~6.4k/month for upper quartile, and ~10k/month for aggressive scale.
• Build volume: baseline ~250 ads / ~12 campaign groups / ~30 ad sets; scale roughly to the P75 bundle (~620 / ~20 / ~54) as budget grows.
• KPI guardrails: CTR ~0.7% baseline (~1.4% strong), CPC typically $5–$13, CPM typically ~$40–$80.
• Efficiency targets: aim for Pipeline/$ ≥ ~7 baseline, ≥ ~22 strong; Deal open rate ~0.35–0.40% baseline, ~0.8%+ strong; ROAS baseline ~1.6, strong ~2.5–3.0.

1) How much should I spend per month?

Recommendation (budget targets): pick your tier based on how aggressive you want to be.

Correlation-backed implication (pipeline volume):

• Spend is the strongest “volume lever” for influenced pipeline/month (Spearman 0.47 / Pearson 0.57 in your table).
• That means: if you want more pipeline volume, the most reliable change is moving from ~$2.7k/mo → $6.9k/mo → $20k/mo.

2) How many accounts should I target?

These are based on Monthly average # of companies targeted.

Prescriptive targets:

• Baseline ABM reach: target ~2.5k accounts/month
• Stronger ABM reach (upper quartile): ~6.4k/month
• Aggressive scale: ~10k/month

Correlation-backed note (pipeline volume):

• Targeting volume has only a weak-to-moderate relationship with pipeline volume (ρ 0.35–0.37 in your table), and basically no relationship with efficiency (Pipeline/$ ~0).
  So: scale targeting for volume, but don’t expect it to “fix” Pipeline/$.

3) How many Linkedin ads / campaigns should I run for my ABM Campaigns?

Use these as execution targets (not “success” KPIs).

Prescriptive target-setting:

• If you’re spending around the median ($2.7k/mo), plan for roughly ~250 ads / ~12 campaign groups / ~30 ad sets.
• If you’re spending around P75 ($6.9k/mo), plan for ~620 ads / ~20 groups / ~54 ad sets.
• If you’re spending around P90 ($20k/mo), plan for ~1.4k ads / ~39 groups / ~124 ad sets.

Correlation-backed note:

• # Ad sets has a somewhat positive relationship with pipeline/month (ρ 0.39), but it’s still materially weaker than spend.
• # Ads is weaker again (ρ 0.31, Pearson 0.06). Translation: more ads ≠ guaranteed more pipeline.

4) What ABM Linkedin ad performance metrics should I aim for? (CTR, CPC, CPM)

Overall benchmark targets

Prescriptive (what to aim for):

• CTR: aim to be at least around ~0.7% as a baseline; ~1.4% is “upper quartile”.
• CPC: “typical” is around $5–$13 (median → P75). (Higher isn’t automatically bad in ABM.)
• CPM: ~$40 is median; ~$80 is already upper quartile.

CTR 

• CTR vs pipeline/month: Spearman -0.14, Pearson 0.05 (basically no positive relationship, slightly negative on rank-correlation)
• CTR vs Pipeline/$: Spearman -0.07, Pearson 0.08 (near-zero)

So the prescriptive takeaway is:

• Don’t set a “high CTR” goal as your primary ABM objective.
  Use CTR as a creative/account coverage diagnostic, but judge success with pipeline/month + Pipeline/$.

Clicks DON’T correlate with pipeline, but Impressions DO Correlate with Pipeline!

Spearman ρ = 0.445 (p=0.009) – Statistically significant at p<0.01 level This is a moderate positive correlation, making impressions the 5th strongest driver of total pipeline.

What Drives Linkedin ABM Pipeline (Ranked by Strength):

1. Deals Opened (ρ = 0.593) *** – Most predictive
2. Total Ad Spend (ρ = 0.566) *** – Budget is key
3. Monthly Ad Spend (ρ = 0.511) ** – Consistent spending matters
4. Total Engagements (ρ = 0.480) ** – Likes/comments/shares work
5. Total Impressions (ρ = 0.445) ** – Brand awareness drives pipeline ⭐
6. LinkedIn Ad Sets (ρ = 0.422) * – More segmentation helps
7. Companies Targeted (ρ = 0.395) * – Larger lists help
8. Total Clicks (ρ = 0.349) * – Weakest significant driver

What DOESN’T Drive Linkedin ABM  Pipeline:

• ❌ CTR (ρ = -0.170, p=0.34) – Actually negative!
• ❌ CPM (ρ = 0.112, p=0.54) – No relationship
• ❌ Number of Ads (ρ = 0.186, p=0.33) – More ads ≠ more results

5) What efficiency targets should I aim for? (Pipeline/$, ROAS, Deal open rate)

These are best read on the pipeline-attributed subset (N=33).

Pipeline/$ targets (this one is stable)

• In that N=33 set: median Pipeline/$ = 6.89, P75 = 21.83, mean = 38.96 (mean is skewed by big outliers)

Prescriptive:

• Baseline efficiency target: Pipeline/$ ≥ ~7
• Strong efficiency target (top quartile): Pipeline/$ ≥ ~22

Deal open rate targets (deals opened ÷ target accounts)

• In the N=33 set: median 0.37%, P75 0.81%
• Whole dataset is lower (median 0.20%) because many companies aren’t opening many deals relative to large account counts.

Prescriptive:

• Baseline deal open rate: ~0.35–0.40%
• Strong: ~0.8%+

ROAS targets (be careful with the mean)

In N=33, ROAS has extreme outliers:

• Median ROAS = 1.65
• Mean ROAS is distorted; even a trimmed mean is ~6.0

Prescriptive:

• Baseline: ROAS ≥ ~1.6
• Strong: ~2.5–3.0 (this lines up with the “top performer” ROAS medians you’ve been using elsewhere)

6) Segment-specific “starting points” (spend + targeting)

By country (top 5 by sample size in this dataset, excluding “Unknown”)

Prescriptive: If you want “typical” targets for your geo, use the median spend + targeting. If you’re planning for scale, use the mean (and expect skew).

By company size (employee brackets)

Prescriptive: for most SaaS ABM teams, the “practical” planning bands come from 11–500 employees rows (more stable sample sizes). The 1k–5k tier behaves like a different universe.

By industry (top 5 by sample size, excluding “Unknown”)

Key LinkedIn ABM Metrics by Country, Industry and Company size

1) Monthly ABM spend — what budgets looked like in 2025

Typical vs Top performers (context)

• Typical company (overall): median $2,693/mo, mean $8,788/mo
• Top performers (your cohort): median $6,576/mo

By country 

By company size (employee brackets)

By industry (Top 5 by sample size; “Unknown” excluded)

2) How many ads were companies running? (volume + structure)

Typical vs Top performers (context)

• Typical company (overall medians, from dataset): 249 ads, 14.5 campaign groups, 28.5 ad sets
• Top performers (your cohort): 114 ads, 10 campaign groups, 20 ad sets

By country

By company size (employee brackets)

By industry

3) How many target accounts were reached?

Typical vs Top performers (context)

• Typical company (overall medians): 2,518 target accounts/mo, 5,842 total
• Top performers (your cohort): 9,071 total target accounts (median)

By country

By company size (employee brackets)

By industry

4) CTR, CPC, CPM — ad performance benchmarks

Typical vs Top performers (context)

• Typical company (overall medians): CTR 0.69%, CPC $5.53, CPM $40.15
• Top performers (your cohort): CTR 0.21%, CPC $21.91, CPM $56.62

By country

By company size (employee brackets)

By industry

5) Efficiency: Pipeline per $ and ROAS (plus influenced pipeline per month)

Typical vs Top performers (context)

• Typical company (your earlier benchmark): Pipeline/$ 5.21, ROAS 1.62, influenced pipeline $13,819/mo (median)
• Top performers (your cohort): Pipeline/$ 15.20, ROAS 2.79, influenced pipeline $106,500/mo (median)

Note: pipeline attribution is only present for a subset of companies in the dataset; segment tables below reflect only companies with these fields populated.

By country

By company size (employee brackets)

By industry

6) Deal Open Rate (Deals opened ÷ Target accounts)

Typical vs Top performers (context)

• Typical company (your benchmark): 0.58% (median)
• Top performers (your cohort): 0.66% (median)

By country

By company size (employee brackets)

By industry

Report Details: Company Demographics

• Average and median company size 
• Average and median company employee number 
• Company industries – split by %
• Company countries – split by %

Average & median company sizes 

• Average company size (employees): 420
• Median company size (employees): 43 (=most companies in the study were around this size)

Employee size bands (share of companies with employee count available):

Company industries (split by %)

Coverage

• Industry available for 180 / 199 companies

Top industries

Company countries (split by %)

Coverage

• Country available for 174 / 211 companies

Companies with TOTAL Ad Spend present: 199

Total ad spend (across those 199): $5,536,829.22

• Typical monthly spend (n=199): Mean monthly ad spend $8,788/mo. In practice, most teams sit under $6,918/mo, while “scaled” ABM budgets start showing up around $19,989+/mo and can go as high as $147,682/mo.
• Larger companies spend more:
• • The highest-spend company tier is 1k–5k employees at $68,249/mo mean and $70,159/mo median. 
  • Mid-market brackets drop sharply: 201–500 at $10,512 mean / $6,915 median, 
  • 51–200 at $8,697 / $4,507,
  •  and 11–50 at $4,892 / $2,175.
• Ad Spend by Country:
  
  US-based teams set the “budget ceiling”:
  
  The US is the largest segment and also the biggest spender: $12,741/mo mean and $3,998/mo median.
  
  The next biggest markets in this dataset are Unknown ($7,514 / $2,875) and UK ($4,869 / $2,469) – and together these three account for 59.3% of companies with spend, so they heavily shape what “average” looks like.
• Ad Spend by Industry:
  
  Software Development is the most reliable industry baseline: It’s the biggest segment and therefore the most stable benchmark: $7,804/mo mean and $3,237/mo median.
  
  Next-largest buckets are smaller and more volatile: IT Services & Consulting at $6,696 / $1,690, and Technology, Information & Internet at $7,878 / $2,502.
• Financial Services is a “whale” vertical: Financial Services shows very high budgets on average ($47,521/mo mean) but a much lower “typical” budget ($5,208/mo median). That gap is a strong signal the vertical includes a few very large ABM programs rather than uniformly high spend across all FinTech teams.
• Average & Median Monthly Ad spend by country:

Average Monthly LinkedIn ABM Ad spend by industry:

Average Monthly LinkedIn ABM ad spend by company size:

Average & Median monthly ad spend by employee bracket: 

Number of ABM and LinkedIn Campaigns ABM by company size, industry and country

Number of Campaigns run by country, industry, and Company Size

1) Country – United States leads the pack, with UK and…the Netherlands following closely

• United States teams are the clear baseline: with 59 companies, they average 31.8 LinkedIn Campaign Groups and 104.1 LinkedIn Campaigns per team.
• United Kingdom teams run fewer campaign groups (22.9) but a much higher number of LinkedIn Campaigns (172.4), indicating heavier segmentation inside fewer structures.
• Netherlands stands out among mid-sized samples (9 companies) with 229.7 LinkedIn Campaigns on average — one of the highest execution volumes in the dataset.
• Smaller-country segments (India, Austria, Australia) show high campaign counts relative to group count, but with limited sample sizes they should be read as directional.

2) Company size – larger companies operate on a larger scale not only in terms of ad spend, but also volume – they are running more campaigns

• 1–10 employees: ~17 campaign groups and 34 campaigns — relatively simple setups.
• 11–50 employees: ~20 groups and 98 campaigns — a sharp jump in execution volume.
• 51–200 employees: ~29 groups and 98 campaigns — similar campaign volume to the prior bracket, but more structural segmentation.
• 201–500 employees: ~38 groups and 154 campaigns — complexity increases meaningfully with scale.
• 1k–5k employees: ~36 groups and 387 campaigns — fewer structures than expected, but very high campaign volume.

• 10k+ employees: ~148 groups and 293 campaigns — extreme segmentation inside LinkedIn, even with few companies in this bracket.

3) Industry – Agencies (Advertising Services) obviously run the most campaigns; FinTech, IT (Services and Consluting) and SaaS (Software Development) follow in terms of volume

• Software Development (largest segment) averages 24.5 campaign groups and 76.9 campaigns, making it the most stable reference point.
• Advertising Services is the most execution-heavy: 273.3 campaigns on average with only 20.1 groups — very high throughput per structure.
• IT Services & IT Consulting teams run many groups (48.0) and 103.1 campaigns, signaling broad, multi-stream LinkedIn programs.
• Financial Services and Marketing Services both skew high on campaign volume (200.4 and 147.0 campaigns, respectively).
• The “Unknown” industry bucket shows consistently high activity (31.4 groups, 95.4 campaigns), behaving more like a power-user segment than a long tail.

Average Number of ABM and LinkedIn Campaigns by country 

Average Number of ABM and LinkedIn Campaigns by Industry:

Average Number of ABM and LinkedIn Campaigns by company size (brackets) 

Linkedin ABM Campaign Setup: 

• Number of Companies targeted
• Number of Ads/ LinkedIn Ad Sets/ LinkedIn Campaigns Run
• Influence Threshold
• Number of Intents Tracked
• Total ABM Budget
• Monthly ABM Budget 

Quick executive takeaways:

The “typical” company targets ~4.4k accounts/month on average (and ~10.5k lifetime).

• Teams run on average ~26 campaign groups and ~100 ad sets, but nearly ~900 ads — suggesting many teams iterate heavily at the ad/creative layer.
• Intent tracking coverage is low in the dataset (only 54 companies have values), so treat intent averages as directional, not a core benchmark.
• Average budgets align with what we already saw: ~$27.8k total spend and ~$8.8k/month average monthly ABM budget.

Number of Ads/ LinkedIn Ad Sets/ LinkedIn Campaigns Run by Company Size

Here’s the overall breakdown (average + median) for ads, campaigns/ad sets, and campaign groups:

Number of LinkedIn ABM Ads

Creative volume scales sharply with company size (step-change at mid-market):

• 1–10 employees: 40 ads avg
• 11–50: 316
• 51–200: 973
• 201–500: 1,582
• 1k–5k: 4,501 (highest)
  
  This isn’t linear growth — it’s a structural shift once teams move from “a few experiments” to always-on, multi-audience ABM where ads need rotation and refresh to sustain delivery.

Smaller teams aren’t “under-testing” – they’re running a different motion:

Averages under ~300–1,000 ads typically indicate fewer parallel plays (often 1 ICP segment, 1–2 personas, limited retargeting layers). That’s normal for SMB ABM and it’s the right constraint until the team has enough spend and account coverage to learn fast.

Enterprise-like teams concentrate more volume into ads than programs:

The 1k–5k bracket runs the most ads (4,501 avg) even though earlier we saw ABM program counts don’t rise proportionally. Interpretation: scaled teams often run fewer core programs, but each program has high creative depth across personas/segments/angles and continuous refresh.

Country leaders (by creative volume) point to where ABM is most “mature” structurally:

• Netherlands: 1,775 ads avg (n=9)
• US: 1,098 (n=59)
• UK: 804 (n=30)
  The US is the most reliable benchmark because of sample size; Netherlands looks “high maturity” but should be treated as directional due to smaller n.

Vertical differences are huge — agencies + regulated industries drive the biggest ad libraries:

• Marketing Services: 2,622 ads avg
• Financial Services: 2,537
• Advertising Services: 1,357
  Compared to Software Development at 657, these categories run much deeper creative libraries, usually because they support multiple client programs (agencies) or require heavier segmentation + message testing (FinServ).

Baseline benchmark for B2B SaaS ABM readers:
If you’re a typical B2B software team, Software Development (n=67) is your best reference point: ~657 ads per company on average. Use this as the “default expectation” and then calibrate up/down by company size and region.

Data-quality note that matters for interpretation:
There’s a large Unknown industry bucket (31 companies) with very high average (1,710 ads) — this can inflate global comparisons. For reporting, anchor benchmarks on clearly classified segments (e.g., Software Development, US/UK).

Number of Linkedin Ads Run – by  Country, Company size, Employee count (quartiles), and Industry.

Below is the full breakdown of the number of LinkedIn ads run (“Number of linkedin ads”), showing average + median by Country, Company size, Employee count (quartiles), and Industry.

Quick read:

• Bigger orgs run more ads: median ads jump from 106–132 (≤50 employees) → 589 (51–200) → 1,182 (201–500) → 3,024 (1k–5k).
• By country, the largest samples show medians of US: 309, UK: 254, Unknown: 149.
• By industry, Software Development is the biggest base segment with median 154 ads, while IT Services & IT Consulting has a higher median (589).
  
  
  Executive summary: Number of LinkedIn ads run (avg + median)
• The distribution is extremely skewed (medians tell the “typical” story; averages reflect a few very ad-heavy teams).
• • Example: in the US, the median is 309 ads while the average is 1,074 — meaning most teams run a few hundred ads, but a handful run thousands, pulling the mean up.
  • Same pattern in the UK (median 254, avg 970) and Unknown (median 149, avg 435).
• Company size is the cleanest driver: bigger orgs run materially more ads.
• • 1–10: median 132 ads (avg 301)
  • 11–50: median 106 (avg 406) — median slightly lower than the smallest bracket, but still “~100-ish” typical volume
  • 51–200: median 589 (avg 823) — this is the first bracket where typical execution jumps into the high hundreds
  • 201–500: median 1,182 (avg 1,020) — “~1k ads” becomes normal here
  • 1k–5k: median 3,024 (avg 6,122) — thousands of ads is standard at this size
  • 10k+: avg/median 11,375 (but only 2 companies with ads values — treat as directional)
• Light interpretation: once you get past ~50 employees, teams tend to support more parallel streams (segments, personas, creatives, languages, regions), which shows up as a step-change in ad volume.
• Employee quartiles show the same scaling effect (and confirm it’s not just the bracket labels).
• • Q1–Q2 medians are ~106–132 ads
  • Q3 median is 254
  • Q4 median is 589, while the average jumps to 2,439 (again: a few very large ad libraries)
• Country pattern: US/UK are “high-volume baselines”; some smaller countries show very high averages that look like outlier behavior rather than a typical norm.
• • “Benchmark-stable” medians: US 309, UK 254, Unknown 149
  • Netherlands has a very high avg (4,011) and a high median (537) across 9 companies, suggesting systematically larger ad libraries there than most countries in the set.
  • Countries with 1–3 companies can look extreme (e.g., Canada avg/median 1,301 on 2 companies with ad values) — those are best read as examples.
• Industry pattern: Software Development is the baseline; services-heavy categories tend to run larger ad libraries.
• • Software Development (largest segment): median 154 (avg 613) → solid “default” benchmark.
  • IT Services & IT Consulting: median 589 (avg 1,035) → consistently higher ad volume than software product companies.
  • Advertising Services: median 185 (avg 1,212) → typical isn’t massively higher than software, but some teams run huge libraries (mean shoots up).
  • Financial Services: median 58 (avg 1,140) → the biggest skew signal: most are small libraries, a few are enormous.

One-line takeaway: Medians scale sharply with org size (hundreds → thousands of ads), while averages are dominated by a small number of “ad factories,” especially visible in the US/UK and in service-heavy industries.

1) Number of ads by Country

2) Number of LinkedIn ABM ads by Company size (brackets)

3) Number of LinkedIn ABM ads by Employee count (quartiles)

4) Number of ads by Industry

Total LinkedIn ABM Budget

Annual ABM Budget = (TOTAL Ad Spend ÷ Number of active months) × 12

Usable companies: 199 / 211

Executive summary – Annual ABM Budget

• Overall annualized (monthly budget *12)  budget level (per company):
• Median: $32,314 / year
• Average: $105,454 / year
• Company size is the clearest budget separator (annualized medians):
• 1k–5k employees (N usable=5): $842,023 median | $818,986 avg
• 10k+ employees (N usable=2): $1,424,641 median | $1,424,641 avg
• 201–500 (N=13): $82,969 median | $126,136 avg
• 51–200 (N=61): $54,078 median | $104,359 avg
• 11–50 (N=67): $26,092 median | $58,701 avg
• 1–10 (N=27): $12,419 median | $79,020 avg
• Countries with the biggest budgets (annualized):
• United States (N=55): $47,967 median | $152,880 avg
• Netherlands (N=9): $50,755 median | $423,122 avg
• United Kingdom (N=28): $29,619 median | $58,423 avg
• Poland (N=9): $18,184 median | $29,782 avg
• Unknown (N=35): $34,499 median | $90,158 avg
• Industries with the biggest budgets (annualized):
• Financial Services (N=6): $62,491 median | $570,241 avg
• Software Development (N=65): $38,833 median | $94,743 avg
• Advertising Services (N=15): $37,022 median | $104,669 avg
• IT Services & IT Consulting (N=16): $20,294 median | $83,458 avg
• Technology, Information & Internet (N=15): $30,023 median | $94,530 avg

Annual LinkedIn ABM Budget — overall (average + median)

Annual LinkedIn ABM Budget by company size bracket

Annual LinkedIn ABM Budget by employee number quartiles

Annual ABM Budget by country

Annual ABM Budget by industry

LinkedIn Campaign Performance Benchmarks:

Average number of ad impressions, engagements and clicks, + Average number of ad impressions, engagements and clicks per campaign, per company reached.

Executive summary (impressions, engagements, clicks – TOTAL)

• Totals per company are heavily skewed (mean ≫ median):
• • Impressions: median 156,102 vs average 779,738 (≈ 5.0× gap)
  • Engagements: median 2,256 vs average 12,688 (≈ 5.6× gap)
  • Clicks: median 1,220 vs average 6,228 (≈ 5.1× gap)
• “Per ABM campaign” performance is also skewed (and has smaller coverage): (only companies with ABM campaign counts)
• • Impressions / ABM campaign: median 108,314 vs average 451,626 (≈ 4.2×)
  • Engagements / ABM campaign: median 1,410 vs average 12,042 (≈ 8.5×)
  • Clicks / ABM campaign: median 818 vs average 5,151 (≈ 6.3×)
• “Per targeted company” (per account) is much tighter, but still skewed:
• • Impressions / targeted company: median 27.3 vs average 57.1 (≈ 2.1×)
  • Engagements / targeted company: median 0.37 vs average 0.92 (≈ 2.5×)
  • Clicks / targeted company: median 0.20 vs average 0.47 (≈ 2.4×)
• Company size is the biggest separator for total volume:
• • 1k–5k employees has median 2,643,559 impressions, 37,316 engagements, 16,042 clicks per company (N=5 with impressions).
  • Compare: 11–50 employees has median 110,395 impressions, 1,575 engagements, 996 clicks (N=67 with impressions).
• Countries (by median total impressions, among larger segments):
• • Germany: median 363,513 impressions (N=8)
  • Netherlands: median 297,908 impressions (N=9)
  • US: median 128,847 impressions (N=54)
  • UK: median 131,600 impressions (N=28)
• Industries (by median total impressions, among larger segments):
• • Financial Services: median 491,350 impressions (N=6)
  • Software Development: median 289,352 impressions (N=64)

Overall (all companies)

Executive summary (per company per month)

• Typical monthly volume (whole dataset):
• • Impressions/mo: median 71,424 | avg 247,979
  • Engagements/mo: median 873 | avg 4,219
  • Clicks/mo: median 515 | avg 2,126
• The distribution is skewed (means are much higher than medians):
• • 75% of companies are at ≤ 209,387 impressions/mo, ≤ 3,878 engagements/mo, ≤ 1,819 clicks/mo.
  • Top 10% are at ≥ 589,141 impressions/mo, ≥ 10,376 engagements/mo, ≥ 5,498 clicks/mo.
  • Max observed: 4,815,349 impressions/mo, 58,020 engagements/mo, 33,783 clicks/mo.
• Company size drives monthly volume (median):
• • 1k–5k employees: 1,321,780 impressions/mo, 18,658 engagements/mo, 8,021 clicks/mo (N=5)
  • 11–50 employees: 53,424 impressions/mo, 746 engagements/mo, 354 clicks/mo (N=67)
• Countries (median impressions/mo, among larger segments):
• • Netherlands: 148,954 (N=9)
  • Sweden: 126,966 (N=5)
  • Germany: 101,322 (N=8)
  • UK: 89,752 (N=28)
  • US: 55,535 (N=54)
• Industries (median impressions/mo, among larger segments):
• • Financial Services: 245,675 impressions/mo (N=6) — and the avg is 1,095,980, so this bucket contains very large outliers.
  • Software Development: 127,584 impressions/mo (N=64)

Overall totals – LinkedIn ABM impressions, engagements, clicks – per company

Overall per company per month –LinkedIn ABM impressions, engagements, clicks 

By company size (employee brackets)

By employee number (quartiles in this dataset)

By country

By industry

Average CTR, Average CPC, Average CPM of LinkedIn ABM Ads

Quick read: medians are CTR 0.69%, CPC $5.53, CPM $40.18; averages are higher (skew).

Executive summary (CTR, CPC, CPM)

• Baseline benchmarks (whole dataset):
• • CTR: median 0.69%, average 1.26% (N=197)
  • CPC: median $5.53, average $10.70 (N=197)
  • CPM: median $40.18, average $74.53 (N=198)
• Company size signal (bigger orgs don’t automatically have worse efficiency):
• • 1k–5k employees (N=5) shows the highest average CTR at 3.56% (median 1.05%) and lowest CPC at $3.96 (median $4.22), with median CPM $44.50.
  • Core SMB/mid-market brackets sit closer to the dataset median:
  • • 11–50 (N=67): median CTR 0.65%, CPC $5.12, CPM $41.40
    • 51–200 (N=60): median CTR 0.78%, CPC $6.26, CPM $38.22
• Employee quartiles (median CTR improves slightly in the largest quartile):
• • Q4 (>105 employees, N=43): median CTR 0.84% vs Q1 (≤19, N=45) median 0.63%.
  • Median CPC stays in a tight band ($5.15–$6.26) across Q1–Q4, while median CPM rises in Q4 ($59.02).
• Country patterns (based on larger segments):
• • US (N=55): median CTR 0.92%, CPC $5.81, CPM $57.79 (higher CPM than UK).
  • UK (N=28): median CTR 0.68%, CPC $5.55, CPM $34.67 (lower CPM).
  • Poland (N=9): highest median CTR among the main segments at 1.71%, with median CPC $4.29 and CPM $36.47.
  • Netherlands (N=9): median CTR 1.05%, CPC $5.63, CPM $45.20.
  • Several countries are N≤2 and should be treated as “examples”, not stable benchmarks.
• Industry patterns (where the dataset is actually thick enough to benchmark):
• • Software Development (N=65) is the best baseline: median CTR 0.74%, CPC $4.93, CPM $38.01.
  • Advertising Services (N=15) has the strongest CTR: median 1.73% (avg 1.84%) with median CPC $5.58 and CPM $53.89.
  • IT Services & IT Consulting (N=16) is more expensive: median CPC $8.26 and median CPM $59.02 (median CTR 0.84%).
  • Financial Services (N=6) is the highest-CPM bucket: average CPM $131.70 (median $44.85), while median CTR (0.75%) and median CPC ($6.78) are not extreme — meaning the cost pressure shows up mainly in CPM, and the average is pulled up by a subset of expensive accounts.

By company size (brackets)

By employee number (quartiles)

By country

By industry

LinkedIn ABM Performance & efficiency Benchmarks – TOTAL Influenced Pipeline,  Pipeline Per $ Spent, ROAS, Deal Open Rate (deals/companies targeted), ACV, Deals Won % 

Executive summary (medians; segment tables sorted by Median Pipeline per $)

• Overall medians: $95.6k influenced pipeline, 6.89 pipeline per $, 3.47 ROAS, 0.20% deal open rate, $77.1k ACV, 5.86% deals-won rate (coverage varies by metric: pipeline N=33; ROAS/ACV N=21; won-rate N=15).
• Company size (median efficiency): the top median Pipeline per $ among brackets with meaningful coverage is 11–50 employees at 9.98 (N=9 pipeline), followed by 51–200 at 7.63 (N=14), then 1–10 at 4.42 (N=5).
• • The highest median influenced pipeline among size brackets is 1k–5k at $900k (N=3).
• Employee quartiles (median efficiency): Q3 (43–105) has the highest median Pipeline per $ = 9.98 (N=7); Q4 (>105) follows at 7.63 (N=19).
• Countries (medians in the largest segments):
• • US (N=7 pipeline): median Pipeline per $ 6.89, median pipeline $462.4k, median deal open rate 0.19%.
  • Unknown (N=9): median Pipeline per $ 6.39, median pipeline $95.6k, median deal open rate 0.38%.
  • UK (N=7): median Pipeline per $ 4.84, median pipeline $22.0k, median ROAS 18.02 (ROAS N=6).
• Industries (where N is biggest):
• • Unknown industry (N=10): median Pipeline per $ 7.85, median pipeline $207.8k, median ROAS 3.29.
  • Software Development (N=13): median Pipeline per $ 6.89, median pipeline $103.0k, median ROAS 11.68, median deals-won rate 14.71% (won-rate N=4).

Overall influenced pipeline (median)

By company size (smallest → largest)

By country 

By employee number (quartiles)

By industry (only industries with ≥3 companies with usable Pipeline/$ data)

Dataset-wide median efficiency is 6.89 pipeline per $, with a $95.6k median influenced pipeline and 3.47 median ROAS.

Efficiency by company size rises notably through mid-market: 11–50 has 9.98 median pipeline/$; 51–200 has 7.63; 201–500 is 25.39 (where available).

Among the only countries with >3 companies worth benchmarking on Pipeline/$, the US shows the highest median pipeline volume ($462.4k) while UK shows the highest median ROAS (18.02).

Deal Open Rate: 

The deal open rate (in other words, the % of total companies targeted that the companies were able to open pipeline from = that reached the “Selecting” funnel stage)  is calculated by dividing the number of deals opened ÷ TOTAL number of companies targeted.

Overall (companies with usable deal open rate): 49

 Mean deal open rate (overall): 0.53%

Mean deal open rate by country

Light interpretation: US and UK dominate the usable sample (22/49), with mean deal-open rates around 0.4–0.5%.

Mean deal open rate by company size bracket

Light interpretation: within brackets with real volume, 51–200 is the highest mean (0.50%) vs 11–50 (0.36%).

Mean deal open rate by employee count (quartiles)

Light interpretation: the top employee quartile (77+) shows a higher mean (0.72%) than the other known quartiles (~0.31–0.45%).

Top performers vs the rest (mean deal open rate)

Top performers definition used: Top decile by ROAS OR top decile by Pipeline per $ (union).

Light interpretation: top performers have a higher mean deal-open rate (0.72%) than the rest (0.51%) in the rows where deal-open-rate is available.

LinkedIn ABM Campaign Top Performers:

This section looks at the “Habits of the top-performing LinkedIn ABM companies” – “top performing” as defined by: 

• Annualised pipeline generated (average monthly pipeline x 12 months) 
• Pipeline per $ spent (campaign efficiency) 
• ROAS (return on ad spent)

To make the results more representative, we removed outliers from the dataset – companies that fell two standard deviations (SDs) above or below the mean. This resulted in 32 companies with adspend and pipeline per $ spent data available. 

We analysed the “Top Performers” in terms of: 

1) Top performers by annualized LinkedIn ABM pipeline generated

Light interpretation (data-only): this group runs much higher monthly budgets and larger ad libraries, and produces the highest pipeline volume (even though CTR is not especially high).

2) Top performers by Pipeline per $ spent

This group achieves higher efficiency (Pipeline/$) with lower monthly spend and a smaller ad library; their median deal open rate is higher than the “pipeline volume leaders” segment.

3. Top performers by LinkedIn ABM ROAS 

Median influenced pipeline per month ($/mo): $7,200

Median annualized influenced pipeline ($/yr): $86,400

Median pipeline per $: 6.11

Median monthly spend ($/mo): $1,179
Median impressions per month: 23,877
Median engagements per month: 567

Median clicks per month: 190
Median CTR (%): 0.91%

Median CPC ($): $9.87

Median CPM ($): $49.39
Median # LinkedIn ads: 49
Median # Campaign Groups: 14

Median # LinkedIn Campaigns (ad sets): 20

Median companies targeted (total): 5,135
Median deal open rate (deals / companies targeted): 0.66%
Median ROAS: 17.85

Median influence threshold: 10

Median intents tracked: — (not populated for these top-ROAS rows)

Median employees: 18

Top Countries: United Kingdom

Top Industries: Software Development

• ROAS threshold (75th percentile): 13.09
• Top performers by ROAS: 2 companies

Median profile — Top ROAS performers

Light interpretation (data-only)

• Pipeline/$ median (4.85) is not higher than in the highest ROAS group – which indicated ROAS 

LinkedIn ABM Correlations: 

In this part of the report, we’re looking at the correlations between ABM campaign performance (as expressed by average monthly pipeline influenced, and pipeline per dollar spent (= efficiency) and: 

• Monthly ABM Ad Spend 
• Number of ABM Companies targeted – in total 
• Number of ABM Companies targeted – in per month 
• Number of LinkedIn Campaign Groups and Campaigns run  
• Number of ads run
• Median CTR
• Median CPC 
• Median CPM

Spend & efficiency: 

• CPM by ad spend tier

Below = same analysis, but excluding “ACV outliers” defined as: ACV > 1.3 × dataset median ACV.

• Dataset median ACV (where present) = $77,097
• Outlier cutoff = $100,226
• Removed 9 companies (only those with ACV present and above the cutoff)

Correlations – how different metrics correlate with Pipeline Generated and Pipeline per $ spent

Below are the correlations between ABM performance and execution variables, plus CPM by ad spend tier.

Performance metrics used

• Influenced pipeline per month ($/mo) = TOTAL LinkedIn influenced pipeline / active months
• Pipeline per $ (efficiency) = Pipeline per $

Important: These correlations are computed on the subset of companies where the performance metric is populated (N = 33 for most pairs; N = 30 where “# LinkedIn ads” is missing).

Correlations vs Influenced pipeline per month ($/mo)

(Reported as Spearman rho + Pearson r)

Interpretation: in this view, pipeline volume scales most with monthly spend (moderate positive correlation). Variables like campaigns/ad sets and targeting volume show weak-to-moderate positive relationships with monthly pipeline volume, but notably smaller than spend.

Correlations vs Pipeline per $ (efficiency)

Interpretation: efficiency (Pipeline/$) has near-zero relationships with spend, targeting volume, campaign counts, ads count, CTR/CPC/CPM in this subset. The (slightly) negative signs for spend/targeting (more spend and more companies targeted = lower efficiency) are present, but very weak. 

PART 2: LinkedIn ABM Ad Content Analysis 

This part of the report is based on quantitative and qualitative analysis 2,828 ads run in 2025 by 7 B2B SaaS companies. This part of the report covers selected ad formats that were represented in the data set: Singe image ads (1676), video ads (423), carousel ads (310), and TLAs (215). 

LinkedIn ABM Ad performance by inventory type – key metrics (CTR/ CTR to landing page, CPC, CPM, Dwell time)

LinkedIn ABM Ads –  split by inventory type:

LinkedIn ABM Ad Spend by inventory type:

Global Spend (USD + Converted EUR) Total Spend: $611,365.30

• Image Ads (inlcuding TLAs): $378,230.22 (61.87%)
• Video Ads: $193,915.51 (31.72%)
• Document Ads: $17,910.93 (2.93%)
• Carousel Ads: $16,703.14 (2.73%)
• Other / Unclassified: $4,605.50 (0.75%)

LinkedIn Single Image ads benchmarks:

 Linkedin Video ads benchmarks: 

LinkedIn Thought Leader Ad Benchmarks: 

LinkedIn ABM Carousel ads benchmarks (overall)

(Uses the ad-level rows in the carousel exports; card rows handled separately below.)

Carousel card-level performance (overall)

(Weighted CTR = total card clicks / total card impressions per card position.)

 LinkedIn ABM Document Ads benchmarks

Since there were only 9 Document Ads in the data set, the document ad benchmarks (overall) are directional only:

Top Performing Ads  – Quantitative LinkedIn Ad Analysis:

Top Performing Ad formats – LinkedIn Performance & Engagement: 

• Single image ads 
• Video ads 
• Thought Leader Ads 
• Carousel Ads
• Document Ads 
• Text Ads 
• Lead Gen Form ads
• Conversation Ads

1 Format mix benchmarks (share of spend / share of impressions)

Report:

• % spend by format (table)
• % impressions by format
• % clicks by format

2) Performance & Engagement per Ad format

Impressions, Engagements, Clicks, CTR (= Clicks to Landing Page), CPM & Dwell Time by ad format 

• Impressions 
• Engagements
• Clicks
• CTR 
• CPM
• Dwell Time
  

Lead Gen Form ads Benchmarks

Lead Gen Form ads — overall performance (avg + median)

Lead-form specific benchmarks

Top Performing LinkedIn ABM Ads – Qualitative Analysis

Analysis of 2,828 LinkedIn Ads | December 2025

Executive Summary

This report analyzes quantitative performance data from 2,828 B2B SaaS LinkedIn ads across 6 major formats, combined with qualitative visual analysis of 463+ top-performing ads. Key Finding: Thought Leader Ads (TLAs) deliver the most cost-effective landing page traffic at $3.06 per click (77% cheaper than single image ads), while video ads significantly underperform despite receiving 31.72% of total budget allocation. Strategic Opportunity: Reallocating budget from video to TLAs and applying proven creative patterns to image ads can yield 2-3× more landing page traffic for the same spend.

Format Performance Overview

Comprehensive Performance Table

Comprehensive Format Analysis

1. Thought Leader Ads (TLAs) – The Performance Leader

Performance Metrics:

• Overall CTR: 2.68% (median) | 3.40% (average)
• CTR to Landing Page: 0.29% (median) | 0.43% (average)
• CPC (all clicks): $0.48 (weighted) | $2.29 (median)
• CPC to Landing Page: $3.06 (weighted)
• CPM: $49.37 (median) | $116.74 (average)
• Dwell Time: 6.63s (median) — 82% higher than image ads
• Sample size: 119 TLAs

The TLA Economics:

What You’re Buying with TLAs:

• 84.3% of clicks = engagement with the post (likes, comments, shares, “see more”, profile views)
• 15.7% of clicks = actual landing page visits
• Bonus: High social proof (visible engagement signals credibility)

ROI Comparison ($1,000 budget):

The TLA Advantage:

• Same $1,000 budget yields 4.3× more landing page clicks
• Plus: ~1,750 engagement clicks for social proof
• Plus: 20% more reach (lower CPM)

Strategic Use Cases: ✅ Best for:

• Maximum landing page traffic on limited budget
• Building personal/company authority while driving conversions
• Social proof generation (visible engagement + LP traffic)
• Top + mid-funnel combo campaigns

✅ Optimization Opportunity:

• Current median CTR to LP: 0.29%
• Applying winner formula (specific offers, real people, strong CTAs)
• Target: 0.5-0.8% CTR to LP
• Expected: +72-176% more LP clicks at same cost

When TLAs Work Best:

• Founder/expert has credible LinkedIn presence
• Content marketing + performance hybrid strategy
• Budget efficiency is priority (low-cost LP traffic)
• Building brand authority while driving conversions

2. Single Image Ads 

Performance Metrics:

• CTR: 0.42% (median) | 0.48% (average)
• CPC: $13.23 (median) | $16.34 (average)
• CPM: $59.15 (median) | $72.94 (average)
• Dwell Time: 3.64s (median) | 4.19s (average)
• Sample size: 936 non-TLA single image ads

Performance Distribution:

• Above median (≥0.42% CTR): 463 ads (49.5%)
• Top performers (≥2.00% CTR): 14 ads (1.5%)
• Top 1.5% achieve 5× median CTR

CTR Bracket Breakdown:

Visual Pattern Analysis (Top vs Bottom Performers):

Cost-Effectiveness Potential:

Strategic Use Cases: ✅ Best for:

• Direct response campaigns with clear offers
• A/B testing creative variations at scale
• When you control 61.87% of budget (massive impact potential)
• Campaigns requiring 100% LP click-through (vs TLA’s 15.7%)

✅ High-Impact Opportunity:

• 50.5% of image ads are below median
• Small creative improvements = massive ROI
• Winner formula application can move 0.42% → 1.5-2.0% CTR

3. Video Ads — The Budget Misallocation

Performance Metrics:

• CTR: 0.24% (median) | 0.37% (average)
• CPC: $15.61 (median) | $20.66 (average)
• CPM: $38.94 (median) | $48.87 (average)
• View-Through Rate: 39.5% (median) — 60.5% don’t finish watching
• Watch Time: 5.86s (median) | 6.54s (average)
• Dwell Time: 3.91s
• Sample size: 342 video ads

The Video Problem:

ROI Comparison ($1,000 budget):

Why Video Underperforms:

1. Low completion rates (39.5% view-through) = wasted impressions
2. Higher CPC despite lower CPM (engagement doesn’t convert to clicks)
3. LinkedIn feed behavior = users scroll past video quickly
4. Production cost = higher creative investment for worse results

Limited Use Cases Where Video Works:

• Retargeting warm audiences (website visitors, TLA engagers)
• Complex product demonstrations requiring motion
• Executive thought leadership (CEO/founder personal brand)
• Brand awareness campaigns (not performance marketing)

Strategic Recommendation:

• Current allocation: 31.72%
• Recommended: 5-8% (retargeting only)
• Reallocate 24-27% to TLAs and optimized image ads
• Expected impact: +40-60% more landing page traffic from reallocation

4. Carousel Ads – The Underutilized Format

Performance Metrics:

• CTR: 0.32% (median) | 0.37% (average)
• CPC: $13.30 (median) | $19.60 (average)
• CPM: $45.28 (median) | $58.02 (average)
• Dwell Time: 4.56s (median) — 25% higher than images
• Budget Allocation: Only 2.73% ⚠️
• Sample size: 44 carousel ads

Card-Level Performance Pattern:

The Carousel Opportunity: Users who swipe past Card 1 are highly engaged qualified leads with exponentially higher CTR. Current vs Potential:

Winning Carousel Formula: Card 1 (Pattern Interrupt): Humor, provocative question, bold claim

• Goal: Earn the swipe (76% of viewers stop here)

Card 2-3 (Value Delivery): Benefits, testimonials, case study results

• Viewers here are 2-5× more engaged

Card 4-5 (Conversion): Specific offer + strong CTA

• Viewers here are 9-25× more likely to click

Example Optimized Sequence:

Strategic Use Cases: ✅ Best for:

• Multi-step storytelling (problem → agitation → solution)
• Showcasing multiple product benefits sequentially
• Case studies with before/after results
• Educational content with clear progression

Why Currently Underused:

• Only 2.73% budget allocation
• Requires more creative production (4-7 images vs 1)
• Most advertisers don’t understand card-level engagement dynamics

Recommendation:

• Increase from 2.73% → 10-15% of budget
• Focus on optimized card sequences
• Expected: +60-100% more LP clicks vs current carousel performance

5. Lead Gen Form Ads – High Cost Per Lead

Performance Metrics:

• CTR: 0.45% (median) | 0.45% (average)
• CPC: $12.33 (median) | $21.59 (average)
• CPM: $75.59 (median) | $102.57 (average)
• CTR to Landing Page: 0.00% (by design — no LP click)
• Lead Form Completion Rate: 2.256% (weighted)
• Cost per Lead: $811.09 (weighted)
• Dwell Time: 3.18s
• Sample size: 163 Lead Gen Form ads

The Lead Gen Economics:

ROI Comparison ($10,000 budget):

When Lead Gen Forms Work: ✅ High-Ticket B2B ($50K+ ACV)

• $811 CPL = 1.6% of deal value = acceptable

✅ Event Registrations

• Lower friction (just name/email)
• Completion rates improve to 5-10%+

✅ Newsletter/Content Downloads

• Minimal commitment required
• Completion rates can reach 8-15%

When Lead Gen Forms Fail: ❌ Low-Ticket Products (<$10K ACV)

• $811 CPL = 8%+ of deal value = unsustainable

❌ Complex B2B Sales

• Need landing page to educate/nurture first
• In-platform forms lack context and trust-building

❌ Early-Stage Startups

• Can’t afford $811 per lead for market discovery
• Better to optimize LP conversion funnel

Better Alternative for Most Advertisers: TLA or Image Ad → Optimized Landing Page → Form

Strategic Recommendation:

• Use Lead Gen Forms only for: High-ticket (>$50K ACV), events, newsletters
• For all other use cases: Landing page conversion = 67-96% lower CPL

6. Document Ads – Insufficient Data

Performance Metrics (Directional — Only 9 ads):

• CTR: 0.42% (median) | 1.58% (average — skewed by outliers)
• CTR to Landing Page: 0.00% (median)
• CPC: $11.06 (median) | $39.95 (average — big outliers)
• CPM: $60.95 (median) | $130.91 (average)
• Dwell Time: 3.13s (median)

Sample size too small for reliable benchmarks. Theoretical Use Cases:

• Long-form content distribution (whitepapers, reports, guides)
• When PDF download is the conversion goal (not LP visit)
• Educational/thought leadership campaigns

Not recommended due to lack of performance data in this dataset.

LinkedIn ABM Ads Visual Pattern Analysis: The Winner Formula

Top Elements in High-Performing Ads (≥2.00% CTR)

Anti-Patterns in Low-Performing Ads (<median CTR)

The Winner Formula (Apply to All Formats)

1. Lead with Specific Offer (65% pattern in top performers)

• Use “FREE” prominently
• Add time limit/urgency (“Ends Dec 31”, “10 spots left”)
• Show concrete value (“$10 pipeline per $1 spent”, “3 days vs 3 months”)

2. Feature Real Humans (47% pattern in top performers)

• Your team or customers (not stock photos)
• Include names, titles, credentials
• Show emotion and authenticity

3. Use Pattern Interruption (35% pattern in top performers)

• Humor/memes where brand-appropriate
• Bold statements that challenge industry norms
• Unexpected visuals that stop the scroll

4. Make CTA Unmissable (59% pattern in top performers)

• High-contrast button
• Action-oriented copy (“Get Free Demo”, “Claim Offer”, “Kickstart Now”)
• Above-the-fold placement

5. Strategic Visual Hierarchy (41% pattern in top performers)

• Process diagrams > dense UI screenshots
• Data visualization with context
• Before/after comparisons
• Guide the eye with annotations

Avoid at All Costs:

• ❌ Logo as hero image (25% in low performers vs 6% in top)
• ❌ Stock photography (35% in low performers vs 12% in top)
• ❌ Text-heavy layouts (40% in low performers vs 12% in top)
• ❌ Generic “Learn More” CTAs (80% in low performers)

Strategic Budget Allocation Recommendations

Recommended Allocation

Scenario A: Aggressive Performance Optimization

Expected Results (90 days):

• Overall CTR to LP: 0.35% → 0.7-1.0% (+100-186%)
• Average CPC to LP: $11.50 → $6-8 (-30-48%)
• Landing Page Traffic: +120-180%
• Cost per LP Visit: -35-50%

Scenario B: Balanced Growth (Lower Risk)

Expected Results (90 days):

• Overall CTR to LP: 0.35% → 0.5-0.65% (+43-86%)
• Average CPC to LP: $11.50 → $8-10 (-13-30%)
• Landing Page Traffic: +50-90%
• Cost per LP Visit: -20-35%

Single image ads

In this section, we analysed Top performing single image ads by CTR. I defined “top performing” as having CTRs > the median benchmark for single image ads (0.42%).

Out of the 936 single image ads, 463 ads were above the median benchmark. 

I then grouped into 5 CTR brackets:

1. low_above_median (0.42%–<0.55%) — 201
2. mid_above_median (0.55%–0.80%) — 173
3. high_above_median (0.81%–1.00%) — 40
4. very_high_above_median (1.10%–<2.00%) — 35
5. extremely_high_above_median (2.00%+) — 14

Here’s a qualitative analysis of the top performing image ads (with CTRs higher than the median CTR for single image ads in the data set = 0.42%):

Single Image Ads – Key Findings Summary

📊 Comparison Table

🎯 Top Patterns That Drive HIGH CTR

1. Specific Offers (65% → 10% dramatic drop)

• Top performers feature “FREE”, time-limited deals, concrete value props
• This is the #1 differentiator between high and low performers

2. Real People (47% in top tier)

• Authentic team members and customers (NOT stock photos)
• Shows emotion and personality
• Stock photography increases to 35% in low performers (inverse correlation)

3. Humor & Pattern Interruption (35% → 2% decline)

• Memes, unexpected visuals, provocative messaging
• Challenges status quo and breaks through ad blindness

4. Strong CTAs (59% → 20% decline)

• Prominent, contrasting buttons with action-oriented language
• Low performers bury CTAs or use weak “Learn More”

5. Strategic Diagrams (41% effective use)

• Process workflows and before/after comparisons
• NOT dense UI screenshots without context

❌ Anti-Patterns That KILL CTR

1. Logo-Centric Design (6% → 25% increase)

• Corporate branding over benefit messaging
• Large logo as hero image

2. Stock Photography (12% → 35% increase)

• Generic diverse teams smiling at laptops
• Creates distrust and ad blindness

3. Text-Heavy Layouts (12% → 40% increase)

• Dense paragraphs, small fonts, multiple competing messages

4. Generic Minimalism (18% → 45% increase)

• Too much white space without focal point
• No compelling reason to click

5. Screenshot Overload (35% → 55% increase)

• Dense UI without context or annotations
• Feature-heavy product screenshots

Visual Analysis of Extremely High CTR Ads (2.00%+)

Based on my analysis of all 17 ads, here are the common visual elements:

Key Patterns Found:

1. Photos of Real People – 35% (6 ads)

• Multiple ads showing two professionals (Black Friday deal series)
• Engineers/workers in industrial settings with hard hats
• Split-screen people collaborating
• Real, authentic human faces build trust and connection

2. Screenshot/Interface Mockups – 18% (3 ads)

• Userpilot product interface screenshots
• Dashboard/analytics displays
• Shows the actual product in action
• Helps viewers visualize the solution

3. Diagram/Flowchart – 35% (6 ads)

• Process flow diagrams (outbound operation flows)
• Technical integration diagrams (Connect → Transform → Deliver)
• Funnel diagrams
• Challenge-Solution-Testimonial layouts
• Visual explanation of how things work

4. Specific LIMITED-TIME Offer/Deal – 41% (7 ads)

• “Black Friday Deal” corner banner (multiple variations)
• “FREE” prominently displayed
• Time-limited offers (“valid until 5 December 2025”)
• Price comparisons (“3 months vendor = $50K, 3 days in-house = Free”)
• Creates urgency and FOMO

5. Strong, Clear CTA Button – 47% (8 ads)

• “Kickstart your ABM & Outbound for FREE!”
• “FREE IMPLEMENTATION”
• “Get a Demo”
• “Learn More”
• READ FULL BLOG”
• Bright contrasting colors (neon green/yellow, blue)

6. Testimonial/Quote – 18% (3 ads)

• Customer testimonials with 5-star ratings
• Named person with title and company
• Negative review of competitor (Pendo)
• Social proof builds credibility

7. Bold, Provocative Statement – 12% (2 ads)

• “CANS AREN’T GREEN. BOTTLES EITHER. HERE’S THE TRUTH.”
• Uses emojis (skull, warning signs) for attention
• Pattern interrupt / myth-busting angle

8. Partnership/Co-branding – 41% (7 ads)

• Multiple logos shown together (ZenABM × Sales Captain × Clay × HeyReach)
• “Black Friday Deal” series
• Builds credibility through associations

9. Dark Background with Bright Text – 35% (6 ads)

• Black or dark backgrounds
• Neon green/yellow highlighted text
• High contrast for readability
• Creates modern, premium feel

10. Problem-Solution Framework – 29% (5 ads)

• “Tired of vendors holding your machine data hostage?”
• Shows pain point + solution
• Challenge → Solution format
• Comparison layouts (vendor vs in-house)

Top Performing Combinations:

Most Common Stack (appears in ~35% of ads):

• FREE offer + Real people photos + Strong CTA + Dark background

Second Most Common (appears in ~29% of ads):

• Diagram/flowchart + Specific offer + Partnership logos

Unique Standouts:

• Only 1 ad used a meme/provocative visual approach (sustainability cans/bottles with skull emoji)
• Zero ads used stock photos – all used custom graphics, real photos, or interface screenshots
• Zero ads used video thumbnails – all static images

Top performing image ads by CTR – examples

LinkedIn TLA Content Analysis: Patterns & Anti-Patterns

Executive Summary

Analyzed 108 TLAs across performance tiers. The data reveals clear content patterns that separate top performers from failures. Key Finding: Top-performing TLAs use 1st person “I” voice (65%), place links at the bottom (75%), average 1,000-1,500 characters, and open with specific, relatable pain points rather than generic statements.

Part 1: Patterns for MAXIMUM LANDING PAGE CLICKS

Performance Benchmarks

• Top 20 TLAs: 0.81% – 3.42% LP CTR (avg 1.18%)
• Median: 0.35% LP CTR
• Bottom 20: 0.04% – 0.15% LP CTR (avg 0.09%)

🎯 Winning Patterns

1. Tone of Voice: 1st Person “I” Dominates

• 65% use 1st person “I” voice (vs 30% in bottom performers)
• 20% use “we” (customer testimonials)
• 0% corporate brand voice

Example (3.42% LP CTR):

“I’ve seen so many businesses still running operations on Excel spreadsheets. It works, until it doesn’t.”

2. Link Placement: Bottom 75%

• 75% place link in bottom 25% of text (vs 25% in bottom performers)
• Build value FIRST, ask for click LAST
• Never interrupt the story with a link

3. Topic: Personal Transformation Stories (40%)

• Excel → Better tool (highest performer at 3.42% LP CTR)
• ABM mistakes → Learnings (1.85% LP CTR)
• Industry pain → Solution (Pharma stories: 1.39-1.46% LP CTR)

4. Hook Strategy: Pain or Vulnerability (65%)

Top Hooks:

• “I’ve seen so many businesses…” (3.42% LP CTR)
• I made all the ABM mistakes…” (1.85% LP CTR)
• “Some of the biggest problems in Pharma aren’t loud…” (1.46% LP CTR)
• “Every finance team I talk to says…” (1.39% LP CTR)

5. Offer Type: Specific Free Trials Win (25%)

• “37-day free trial” appears in 5 top performers
• FREE ungated resources (35%)
• Case studies/reports (20%)
• NOT webinars (0% in top 20, 20% in bottom 20)

6. CTA: Direct Commands at Bottom (50%)

• “Want to find out more? Head to [link]”
• “Read the full case study 👇 [link]”
• NOT: Vague “Check it out” or “Learn more”

7. Text Length: 1,000-1,500 Characters (55%)

• Long enough to establish value
• Short enough to maintain attention
• NOT: <500 chars (no value) OR >2,000 chars (loses reader)

8. Social Proof: Specific Metrics (45%)

• “$650,000 in pipeline in 90 days”
• “$12 in pipeline per $ spent”
• “50% faster consolidation”
• “€1 million in revenue in 10 days”

9. Formatting: 100% Use Line Breaks

• Short paragraphs (1-2 sentences max)
• Line breaks every 2-3 lines
• Minimal emojis (35% use 1-3: 👍, 👇, 👉)
• ✅ checkmarks for benefit lists
• NO hashtag spam

10. Structure: Problem-Solution Framework

Winning Arc:

1. Hook (50-100 chars): Relatable pain
2. Backstory (300-500 chars): Personal experience
3. Problem Detail (200-300 chars): Specific issue
4. Solution (300-400 chars): Product + 3-4 benefits
5. Social Proof (100-200 chars): Metrics/customer name
6. CTA (50-100 chars): Direct command + link

Part 2: Patterns for MAXIMUM ENGAGEMENT (CTR + Dwell Time)

Key Differences from LP-Optimized TLAs

Shorter Text (60% <1,000 chars)

• Engagement: 600-1,000 chars
• LP clicks: 1,000-1,500 chars

More Provocative Hooks (30%)

• “2 years ago I thought LinkedIn ads were a complete waste of money” (9.43% overall CTR)
• “People have been sneak-dissing me…” (7.87% overall CTR)

25% Have NO Link

• Pure engagement posts generate discussion
• Example: “It’s been exactly 3 months since we launched our ABM campaign…” (17.39% overall CTR, 0% LP CTR)

More Emojis (50%)

• Emojis drive reactions but reduce professional clicks
• Use 3-5 emojis for engagement vs 1-3 for LP clicks

No Direct CTA (35%)

• Conversation starters vs action drivers
• Open-ended questions invite debate

High-Engagement Elements:

• Personal vulnerability (admitting mistakes)
• Specific dollar amounts ($650k pipeline)
• “Us vs them” positioning
• Controversial opinions

Part 3: ❌ ANTI-PATTERNS (What Kills LP Clicks)

What Bottom 20 TLAs Do Wrong

1. Corporate “We” Voice (45%)

• vs 20% in top performers
• Feels impersonal and sales-y

2. Generic Opening Statements (40%)

Bottom Performers:

• “Running an agency is a constant balancing act” (0.04% LP CTR)
• “Project planning is no easy task” (1.03% LP CTR)
• “Are agencies going to disappear?” (0.07% LP CTR webinar)

3. Link in Middle of Text (40%)

• vs 20% in top performers
• Interrupts value delivery

4. Webinar CTAs (20%)

• Consistently 0.07-0.08% LP CTR
• People won’t register for webinars via TLA

5. Overly Long Tangential Stories (20%)

“The Accountant Story” Pattern:

• Appears 4 times in bottom 20
• 2,000+ characters
• Story takes 60% before product mention
• 0.06-0.12% LP CTR

6. Vague Offers (30%)

• “Learn more”
• “Check it out”
• “Transform your decision-making”

7. Bracketed Links (20%)

• “[Check out the report here – link]”
• Reduces perceived clickability
• 0% in top performers, 20% in bottom

8. Hashtag Spam (25%)

• 5-7 hashtags at end
• Looks desperate
• 5% in top vs 25% in bottom

9. Future-Focused Without Pain (15%)

• “As we stand on the cusp of 2026…”
• “Data delays are soon to be a relic…”
• Start with pain NOW, not future vision

10. Survey Data Instead of Results (25%)

• “We surveyed 180 agencies…”
• NOT compelling; use customer outcomes

Comparison Table: Top vs Bottom Performers

📋 LinkedIn ABM PRESCRIPTIVE RECOMMENDATIONS

Formula #1: Maximize Landing Page Clicks

THE EXACT FORMULA: Tone: 1st person “I” (founder/expert voice) Length: 1,000-1,500 characters Structure:

[HOOK] (50-100 chars)

Relatable pain or vulnerability

“I’ve seen so many businesses…”

[BACKSTORY] (300-500 chars)

Personal experience with problem

“For years, our financials looked solid…”

[TURNING POINT] (200-300 chars)

“Turns out, we were…”

[SOLUTION + BENEFITS] (300-400 chars)

3-4 specific outcomes with ✅

👍 Benefit 1

👍 Benefit 2

👍 Benefit 3

[SOCIAL PROOF] (100-200 chars)

“$650k pipeline in 90 days”

OR “[Customer Name] built with [Product]”

[CTA + LINK] (50-100 chars)

“Want to find out more? Head to [link]”

Offer: “37-day free trial” OR “FREE ungated [resource]” Formatting:

• Short paragraphs (1-2 sentences)
• Line breaks every 2-3 lines
• 1-3 emojis total (👍, 👇, 👉)
• ✅ for benefits
• 0-2 hashtags max

Formula #2: Maximize Engagement

THE ENGAGEMENT FORMULA: Tone: 1st person “I” with vulnerability or contrarian take Length: 600-1,000 characters (shorter) Hook: Provocative

• “2 years ago I thought [popular thing] was a waste…”
• “People have been sneak-dissing me…”

Structure:

• Bold hook
• Quick before/after story
• Specific $ results
• Opinion/learning
• Optional link (or NO link for pure engagement)

Formatting:

• 3-5 emojis
• Numbered lists
• Question endings (invite discussion)

Formula #3: What to AVOID

❌ NEVER:

1. Generic corporate voice – “Running an agency is a balancing act”
2. Link in middle – Build value first
3. Webinar CTAs – 0.07% LP CTR consistently
4. 2,000+ char tangential stories – “Accountant story” pattern
5. Bracketed links – [Check here – link]
6. Hashtag spam – 5+ hashtags
7. Dense paragraphs – No line breaks = skip
8. Future vision hooks – “As we stand on the cusp of 2026…”
9. Survey data – “We surveyed 180…” (not compelling)
10. Vague CTAs – “Learn more”, “Check it out”

Ready-to-Use Template

High LP CTR Template

[HOOK – Specific Pain]

I’ve seen so many [target audience] still [painful current state].

It works, until it doesn’t. It’s just not built for [goal].

[BACKSTORY]

For years, our [business area] looked [good on surface].

[Metric] up. [Metric] stable. But I couldn’t shake the feeling

we were missing something.

Turns out, we were.

[PROBLEM DETAIL]

Our [old system] was [specific problem].

It masked [negative outcome]. We were [inefficient process]

just to get close to the truth.

That kind of [problem characteristic] doesn’t scale.

[SOLUTION]

That’s why we moved to [Product/Solution].

Now, [outcome is automatic]:

👍 [Specific benefit 1 with metric]

👍 [Specific benefit 2 with outcome]

👍 [Specific benefit 3 with transformation]

[SOCIAL PROOF]

[Customer Name/Your company] achieved [specific metric: “$650k pipeline in 90 days”]

[CTA]

Want to find out more? Head to [link] 👇

Expected Performance: 1.0-3.0% LP CTR

Key Success Metrics

TOP 20 LP CTR:

• Average LP CTR: 1.18%
• Average Overall CTR: 8.32%
• Average Length: 1,183 characters
• Average Dwell: 7.2 seconds

BOTTOM 20 LP CTR:

• Average LP CTR: 0.09% (13× worse)
• Average Overall CTR: 1.35%
• Average Length: 1,043 characters
• Average Dwell: 6.4 seconds

INSIGHT: Length alone doesn’t determine performance. VALUE DENSITY and STRUCTURE matter more than word count.


# LinkedIn Thought Leader Ad (TLA) Content Analysis

## Analysis Date: December 28, 2025

—

## Executive Summary

This analysis examines 60 LinkedIn Thought Leader Ads across three performance tiers:

– **Top 20 by CTR to Landing Page** (0.81% – 3.42% LP CTR)

– **Top 20 by Overall CTR** (0.24% – 17.39% overall engagement)

– **Bottom 20 by CTR to Landing Page** (0.04% – 0.15% LP CTR)

**Key Finding:** The highest-performing TLAs for driving landing page traffic use **personal narrative storytelling** with **clear problem-solution frameworks**, place links in the **bottom 25% of text**, and maintain a **1st person “I” voice**. They average **1000-1500 characters** and include **visible social proof with specific metrics**