LinkedIn lookalike audiences (now called predictive audiences) are one of the most misunderstood features in LinkedIn Campaign Manager.
In theory, they let you find new accounts that resemble your best customers.
In practice, most teams use them incorrectly.
They upload a thin seed list, turn on audience expansion, and end up showing ads to companies that look nothing like their ICP.
Experts have observed this pattern several times in ABM programs.
But it can be fixed 🙂
In this article, I will walk you through exactly how to build LinkedIn lookalike audiences for account-based marketing the right way.
In case you want a quick overview:
LinkedIn lookalike audiences let you create a new audience based on the characteristics of an existing audience you have already built.
You provide a source audience, which is typically a matched audience built from a company list or contact list, and LinkedIn’s algorithm identifies other professionals and companies on the platform that share similar attributes.
The algorithm considers factors like industry, company size, job function, seniority, skills, and engagement patterns.
LinkedIn then builds a new audience that is typically 2-15x larger than your seed audience.
You cannot control the exact expansion ratio, but you can control the quality of what goes in.
For ABM specifically, lookalike audiences serve a particular purpose.
They help you discover accounts that fit your ICP but were not on your original target account list.
Lookalikes can surface these accounts, but only if the seed list is strong.
As Bilal, GTM Engineer at Userpilot, puts it:
Start with your customer list. They are people who’ve already bought from you. That’s your truth.
This applies directly to how you should build the seed list for your lookalike audience.
Your customers are the signal LinkedIn needs to find more companies like them.
Building a lookalike audience in LinkedIn Campaign Manager takes about five minutes.
Building the right seed list to feed into it takes much longer and is where most teams fail.
Here is the full process.
Your seed audience is the foundation.
The quality of your lookalike audience is entirely dependent on what you put in here.
There are three main options:
For ABM, I recommend starting with your customer list.
If you are doing this for the first time, review our guide on running ABM on LinkedIn for the full context on how lookalike audiences fit into the broader campaign structure.
In LinkedIn Campaign Manager, go to Plan > Audiences > Create Audience > Matched Audience.
Upload your CSV file with company names and domains.
Here’s what the CSV template provided by LinkedIn looks like:
LinkedIn will match your list against its database.
Expect a 50-70% match rate for company lists and 30-60% for contact lists.
If your match rate is below 30%, your data quality needs work.
Pro tip: If anyone has fewer than 300 connections or 200 connections, don’t push them into your seed TAL. Contacts with very few connections are often inactive profiles, and including them adds noise to your seed audience.
And if you’re wondering how to check connections count at scale, Clay can help:
Similar workflows are available for company lists, too.
Once your matched audience is ready (this can take up to 48 hours), go to Plan > Audiences > Create Audience > Lookalike.
Select your matched audience as the source.
LinkedIn will generate the lookalike audience, which usually takes another 24-48 hours to populate.
This is the step most teams skip, and it is the most important one.
A raw lookalike audience will include companies outside your ICP due to them belonging to the wrong industries, being of the wrong sizes, or being in the wrong geographies.
You need to layer targeting filters on top: industry, company size, geography, and seniority at a minimum.
You should also exclude your existing customers (they are already customers, so do not spend ad budget on them) and any accounts in active sales conversations.
This is a place where ZenABM helps in a very practical way.
Its CRM sync, ABM stages, and company exclusions make it easier to suppress customers, open opportunities, or already-saturated accounts from your discovery campaigns, which is useful if you want to stop wasting budget on accounts your team is already working or has already penetrated heavily.
Most ABM teams make the same errors with lookalike audiences.
Here are the ones I see most frequently.
Lookalike audiences are a discovery tool, not a replacement for your target account list.
If your entire ABM strategy relies on LinkedIn’s algorithm to find the right companies, you have given up control over which accounts you are investing in.
Your primary campaigns should target your curated TAL.
Lookalike campaigns should be a smaller portion of your budget (10-15%).
A seed list of 20 companies does not give LinkedIn enough signal to build a useful lookalike.
I recommend a minimum of 100 companies in your seed list, and ideally 300 or more.
Also, the companies should be your best customers, not every account that ever signed up for a trial.
This is a classic trap.
When you use a lookalike audience in a campaign, LinkedIn may also suggest turning on audience expansion, which effectively makes LinkedIn’s algorithm expand beyond even the lookalike.
For ABM, this defeats the purpose entirely.
As Maximillian Herczeg, former LinkedIn employee and founder at Kamrat, warns: “Audience expansion – don’t use that. And LAN – don’t use it either.”
I cannot stress this enough.
Turn it off every time you set up a campaign.
After LinkedIn builds your lookalike, check the demographics.
Go to Campaign Manager > Demographics and look at the companies, industries, and job functions in the audience.
If you see companies or industries that do not belong, add them as exclusions before spending any budget.
The goal is to catch bad matches before they eat into your ad spend.
Once the campaign is live, ZenABM’s company-level engagement view and job title analytics can make this review process more useful.
Instead of just seeing broad campaign output, you can inspect which companies and roles are actually engaging and decide whether those accounts deserve promotion, exclusion, or tighter filtering.
In a well-structured ABM program, your campaigns are segmented by account stage: cold accounts, warm accounts (engaged with your content or site), and hot accounts (in pipeline).
Lookalike audiences sit at the very top of this structure.
They are colder than cold because these are accounts you have not even vetted yet.
Here is how I structure it:
For detailed guidance on how to set up this kind of structure, see our post on how to structure LinkedIn ABM campaigns.
This is not an either/or decision.
Both methods serve different purposes in your ABM program.
Use account list targeting when:
Use lookalike audiences when:
The most effective approach is both: run your core ABM campaigns against your curated TAL, and run a smaller discovery campaign using lookalike audiences to continuously feed new accounts into the top of your ABM funnel.
For more on how to see which companies are engaging, check out our guide on seeing companies engaging with LinkedIn ads.
Lookalike audiences tend to be large (often in the hundreds of thousands).
This puts them in the broad audience category, where costs and conversion dynamics differ from narrow ABM targeting.
Based on our LinkedIn ABM Performance Benchmarks Report 2026, here is what to expect:
Since lookalike audiences sit in the broad-to-medium range, expect lower CPLs but also lower conversion rates.
This is fine.
The purpose of these campaigns is discovery, not direct conversion.
You are paying to learn which new accounts engage, and then promoting those accounts to your higher-converting curated TAL campaigns.
I recommend keeping 20-30% of your total ABM budget for remarketing audiences, and within your remaining prospecting budget, allocating no more than 10-15% to lookalike discovery campaigns.
The rest goes to your primary TAL campaigns.
This is also exactly the kind of scenario where ZenABM’s free ABM budget calculator is useful.
Do not measure lookalike audience campaigns the same way you measure your core ABM campaigns.
The purpose is different, so the metrics should be different.
Primary metrics for lookalike campaigns:
Secondary metrics:
If your lookalike campaigns are surfacing accounts that consistently pass your ICP filter, the campaigns are working.
If most of the engaged accounts turn out to be bad fits, your seed list needs improvement or your exclusion filters need tightening.
ZenABM makes this measurement layer much more operational.
Company-level engagement tracking shows which previously unknown accounts interacted with your ads, account scoring helps prioritize them, and custom webhooks or automated BDR assignment can push the best ones into follow-up workflows quickly.
If your team prefers to explore performance conversationally instead of digging through dashboards, Zena, ZenABM’s AI chatbot, can also help you pull LinkedIn ABM analytics in natural language, which is handy when you want a fast read on which lookalike-sourced accounts are actually worth keeping.
For more on understanding LinkedIn audience penetration and how to measure it, check out our dedicated guide.
LinkedIn lookalike audiences can be useful for ABM, but only when you treat them like a discovery layer, not a replacement for your curated target account list.
The real work is in seed quality, exclusions, budget discipline, and post-click account qualification.
That is where ZenABM can add real value beyond standard LinkedIn reporting, with features like company-level LinkedIn ad engagement, CRM sync, ABM stages, account scoring, first-party qualitative intent, job title analytics, company exclusions, and the ABM budget calculator that helps you plan a sane spend split before you launch.
Try ZenABM for free (37-day free trial) or book a demo now to know more!
Some frequently asked questions about LinkedIn lookalike audiences for ABM and their answers:
LinkedIn requires a minimum matched audience of 300 members to create a lookalike.
However, for ABM purposes, I recommend a seed list of at least 100 companies (which usually translates to 1,000+ matched members) to give the algorithm enough signal to find meaningful patterns.
Yes. A contact list lookalike finds people similar to your contacts.
A company list lookalike finds companies similar to your accounts.
For ABM, company list lookalikes tend to be more useful because the targeting stays at the account level.
I refresh the seed list quarterly, updating it with new customers won and removing any that churned.
When you update the seed list, LinkedIn recalculates the lookalike.
This keeps the audience current with your evolving ICP.
If your total addressable market is very small (under 500 companies), lookalike audiences are less useful because there simply are not many companies to discover.
In that case, you are better off investing time in manual research to find every possible account that fits your ICP.
Not as your first campaign.
Start with your curated TAL, validate that your messaging and targeting work, then layer in lookalike audiences once you have a baseline.
Running lookalikes before you have proven your core ABM campaigns work adds complexity without a foundation to build on.
Review the ABM strategy guide for the full recommended launch sequence.
