Build tiered Meta lookalike audiences from your best customers
Checks the seed audience, previews 1%, 3% and 5% lookalikes with non-overlapping tiers, sizes them against the country audience and sets up a paused 2-week tier test.
When to use it
When you want to reach new people who resemble your best customers on Meta and need a clean lookalike setup you can test properly.
What you get
A seed audience check, previews of 1%, 3% and 5% non-overlapping lookalikes with reach estimates, and a paused 2-week tier test, all created only after you approve.
Does it change anything in my account?
Only with your approval. The assistant first shows a preview of every change and waits for your go-ahead.
Works with
Meta Ads
Fill in before sending
[ad account name][source audience name][country][min age][max age][daily budget per ad set][campaign name]
I want to prospect on Meta using people who resemble our real customers, not just page engagers.
1. Find ad account [ad account name] with list_meta_ad_accounts and list its custom audiences with list_meta_audiences. Show the type, approximate size and last update for each one, as far as the data includes them.
2. Evaluate [source audience name] as the seed. Meta needs at least 100 people from one country in the seed, and well above that gives better results. Use the approximate size if it is returned; the per-country match count for [country] is not exposed here, so ask me to confirm it from the audience's details in Ads Manager. Also judge from its type and name whether it reflects customers or qualified leads rather than all website visitors. If it is too small or too broad, recommend a better seed from the list.
3. Meta builds each lookalike from 0% up to the chosen ratio, so a 3% list already contains the 1% list. Propose three lookalikes in [country] at 1% (ratio 0.01), 3% (0.03) and 5% (0.05), with clear names, and plan the tiers so they do not overlap at ad set level: the 1 to 3% ad set targets the 3% list and excludes the 1% list, and the 3 to 5% ad set targets the 5% list and excludes the 3% list.
4. Get the total Meta audience in [country] with meta_reach_estimate (countries=[country], age [min age] to [max age]) and use it with each ratio to give a rough reach estimate per tier. Say clearly that this is an approximation; the real size appears in list_meta_audiences once Meta finishes building the list. Also check with list_meta_adsets whether any existing ad set already targets a lookalike from the same seed. If the ad set data does not show its custom audiences, say so instead of guessing, and ask me to check in Ads Manager.
5. Show the three lookalikes as a preview with create_meta_lookalike_audience using confirm=false, and wait for my approval before creating anything.
After creation, suggest a 2-week test: one ad set per tier with the exclusions above (custom_audience_ids and excluded_custom_audience_ids), the same creative and an equal daily budget of [daily budget per ad set]. Ask me whether to use existing campaign [campaign name] (find it with list_meta_campaigns) or a new one; if new, preview create_meta_campaign with status PAUSED and no campaign budget, so each ad set keeps its own equal budget. Preview each ad set with create_meta_adset using confirm=false and status PAUSED, and create nothing until I approve. Once created, run meta_delivery_estimate on each ad set to sanity-check the estimated audience and results.
When the test ends, pull results per ad set with meta_campaign_insights at adset level. Judge the winner on cost per qualified lead, not on CTR: Meta reports leads, not lead quality, so ask me for the qualified lead count per ad set from our CRM and calculate it from that.
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