Why Your Lookalike Audiences Underperform: The Seed Data Problem in Gaming UA

You launched a lookalike campaign on Meta. The setup looked right — 1% lookalike, US targeting, optimized for purchases. But the CPIs came back 30-40% above your target, and the install-to-payer rate was half what you expected.

The problem wasn't the algorithm. It was the seed.

Lookalike audiences are only as good as the data you feed them

Meta's lookalike engine is genuinely good at finding similar users. The issue is what "similar" means when your seed audience is built from platform interest categories.

When you target "interested in puzzle games" on Meta, you're reaching people who:

• Clicked on a puzzle game ad once
• Liked a gaming page three years ago
• Watched 3 seconds of a match-3 video in their feed
• Got categorized by an algorithm that inferred "gaming interest" from tangential behavior

Now build a lookalike from that group. Meta will dutifully find millions of users who look like people who clicked an ad once. Not people who play puzzle games daily and spend money inside them.

That's the seed data problem.

What behavioral seed data actually looks like

Compare the interest-based seed to a behavioral one:

Interest-based seed (Meta's "interested in puzzle games"):

• Signal: Ad clicks, page likes, video views
• Recency: Unknown — could be years old
• Spend behavior: None — no purchase data
• App-level granularity: None — "gaming" is one category

Behavioral seed (actual in-app data across 170,000+ apps):

• Signal: App installs, session frequency, in-app purchases
• Recency: Active within defined time windows
• Spend behavior: Scored by actual transaction data
• App-level granularity: Puzzle, casual, casino, card — each scored independently

When you give Meta a seed of 122M users who demonstrably play puzzle games and spend money inside them, the lookalike algorithm has something real to work with. "Find me more people like these actual spenders" is a fundamentally different instruction than "find me more people like these ad clickers."

The math on seed quality

A typical gaming UA team tests 3-5 lookalike audiences per month. Each test burns $2,000-$10,000 before you have statistically significant results. If your seeds are polluted with low-intent users, you're not just wasting the test budget — you're wasting the 2-3 weeks it takes to get a read.

Multiply that by 12 months: a team running on weak seeds burns $50,000-$100,000/year on tests that were doomed from the start.

The fix isn't more tests. It's better seeds.

How gaming publishers are fixing this

The shift happening in gaming UA right now is from platform-native audiences (interest categories, engagement-based custom audiences) to external behavioral data.

Here's what that looks like in practice:

1. Start with a behavioral segment — not "interested in gaming" but "active puzzle game players scored by in-app spend behavior." Pre-built, ready to upload.
2. Upload as a Custom Audience — one-click push to Meta, TikTok, Snapchat, or Google. Use it as a direct targeting audience or as a lookalike seed.
3. Test against your existing lookalikes — run behavioral seeds alongside your current seeds. Let the CPI and ROAS data decide.

This isn't theoretical. Platforms like Onmo Signal provide 273 pre-built gaming audience segments — 122M puzzle players, 37M casino players, 25M card game players — all scored by actual behavioral data across 300M+ device IDs.

What to look for in a behavioral data provider

Not all audience data is equal. Before you swap seeds, check:

• Data source: Is it based on actual app usage and transactions, or inferred from surveys and panels?
• Recency: How fresh is the data? Stale behavioral data is only slightly better than interest categories.
• Granularity: Can you get puzzle-specific segments, or is everything lumped into "gaming"?
• Scale: Does the provider have enough device coverage for your target geography? US needs 100M+ devices to be useful.
• Ease of use: Can you upload directly to your ad accounts, or do you need a managed service?

The bottom line

Your lookalike algorithm isn't broken. Your seed data is.

If you're a gaming publisher spending $10K+/month on UA and your lookalikes are built from Meta interest categories, you're giving the algorithm the wrong starting point.

Behavioral audience data — actual app usage, actual spend behavior, actual device-level signals — gives the algorithm what it needs to find real players, not ad clickers.

Onmo Signal has 273 pre-built gaming segments ready to upload as Custom Audiences. It's free right now — no minimums, no contracts. Test a behavioral seed against your current one and let the data decide.

Try it at signal.onmoai.com