How Do You Find Lookalike Companies in Apollo?

How do you find lookalike companies in Apollo?

Start with a short list of your best customers, run them through Apollo's lookalike feature, then narrow the results with filters for size, location, and industry. Review the list by hand before anything goes into a sequence. The quality of the seed accounts decides the quality of everything Apollo returns.

This tutorial is for one situation: a B2B team with a handful of strong customers that wants more accounts like them, fast, without building a full Clay table. Apollo's lookalike tools are a quick way to turn what you already know about good customers into a target list.

Apollo's own Academy play for this is clear about the starting point. It recommends coming up with a list of three to five companies that are your best customers. Everything below builds on that advice, with the steps I would add so the list is ready for real outreach.

Step 1: How do you choose the right seed accounts?

Pick three to five customers who are profitable, renewed or expanded, closed without heavy discounting, and look like the market you want more of. Avoid outliers, such as a famous logo that bought for unusual reasons. Lookalike tools copy the pattern you feed them, including the parts you did not mean to copy.

Write down why each seed made the list. Common reasons are the same team size, the same tool stack, the same buying trigger, or the same role signing the deal. Those reasons become your filters in later steps, and they help you judge whether Apollo's suggestions really match.

If your best customers fall into two different groups, run them separately. Mixing a fifty-person agency and a two-thousand-person enterprise in one seed list asks the tool to find something in between, which usually matches neither. My guide on picking an ICP when two segments both buy helps if you are unsure which group to start with.

Step 2: How do you run the lookalike search?

Open Apollo's company search and use the lookalike option to enter your seed companies. Apollo's help center documents the exact location of the filter and any limits on how many companies you can enter at once, so check it for the current steps. Run the search and save the raw results to a new list.

Expect some noise in the raw results. A lookalike search depends on the data the tool holds about each company, and no company database is perfect. That is fine at this stage. The goal of the first pass is a wide, rough list that the next steps will narrow down.

Name the list clearly, with the date and the seed group, such as lookalikes, mid-market agencies, October. When you run the play again later, you will want to compare lists, and good names save time.

If you prefer a repeatable version, Apollo's Academy describes a lookalikes play that can run automatically each week, with optional steps such as AI research or adding contacts to a sequence. Start manual for the first run so you understand what the results look like before you automate anything.

Step 3: Which filters should you add to the results?

Add filters that match the reasons you wrote down in step one. Apollo's play suggests criteria like company size, location, industry, and AI research fields. Size and location are usually the most important, because they decide whether the account can buy and whether your team can serve it.

Be careful with industry filters. Many tools assign industry labels loosely, and a strong prospect can sit under an unexpected category. Use industry to exclude obvious mismatches rather than to require an exact match, and rely on your own review to catch the rest.

Add exclusions too. Remove current customers, open opportunities, competitors, and partners. If you can, import your CRM's existing accounts into Apollo or check against them before outreach, so a rep never cold-emails a company that already pays you.

Step 4: How do you review the list before outreach?

Review every account on a short list by hand, and a sample on a long one. Open the website, check what the company actually sells, and confirm it matches your seed reasons. Mark each account as strong, maybe, or no. Lookalike results are suggestions, not decisions, and a quick review prevents embarrassing outreach.

A useful rule is to stop and rethink if more than a third of a sample is clearly wrong. That usually means the seed accounts were too mixed or the filters too loose. Adjust the seeds and run the search again rather than pushing a weak list into a sequence.

While reviewing, note what the strong matches have in common that you did not expect. Sometimes a pattern appears, such as a specific tool in their stack or a recent hiring push. Those patterns are worth adding as filters or signals for the next run.

Step 5: How do you find the right people at each account?

For accounts marked strong, search for people in the roles that signed or championed your seed deals. Use job title and seniority filters, and look for one decision maker and one likely user at each account. Two contacts per account is usually enough for a first touch.

Keep the persona narrow. If your best deals were signed by heads of revenue operations, start there, not with every senior title in the company. A narrow persona makes the message sharper and the replies more useful.

Verify contact data before sending. Use Apollo's own verification status, and for important accounts, check the person's current role on LinkedIn. People change jobs often, and a message to someone who left the company wastes the account's first impression.

Step 6: How do you move the list into your CRM and outreach?

Import strong accounts and their contacts into your CRM with a source property that says lookalike and names the seed group. Then add them to a sequence written for that segment. Tracking the source lets you compare lookalike accounts against other lists on meetings booked and pipeline created.

Write the first message around the pattern you found, not around the fact that they look like your customers. Something specific about their situation, such as a recent change in their team or a problem common to companies of their size, works far better than a generic pitch.

If your sequence includes a manual research step before the first email, keep it. I explained why in building an Apollo sequence with a manual research step. Lookalike lists are a starting point, and a minute of research per account often decides whether the first email gets read.

What should you do next?

Pick three to five best customers today and write one sentence on why each belongs on the list. Run a lookalike search in Apollo, filter by size and location, exclude existing customers, and review twenty accounts by hand. If most look right, find two contacts each and start a small, tracked sequence.

After a month, compare the lookalike list against your other outbound lists. If it books more meetings, schedule the weekly version. If it does not, look at your seed accounts first, because the pattern you fed the tool is usually where the problem started.

If you want help building lookalike targeting, enrichment, and routing across Apollo, Clay, and HubSpot, reach out. Designing outbound systems that find the right accounts is a big part of my go-to-market work. Let's chat.