When to use
Ideal for:
Use this play if you already run Meta lookalikes, or
Have a large total addressable market where Meta’s algorithm can explore without collapsing delivery.
Tools You’ll Need:
Primer
Builds seed and suppression audiences
Meta Ads
Runs lookalike and exclusion targeting
Meta Experiments Tool
Runs A/B tests between constrained vs unconstrained targeting
Meta CAPI
Sends back conversion data for model training
Primer Pixel
Provides performance and pipeline data
Launch Checklist
Audiences:
Minimum of suppression audiences built in Primer
CAPI:
sending real conversion signals
Ad sets:
lookalike and control ad sets created
Experiment tool:
configured for A/B test
Test:
Run ≥14 days or until 50 conversions per ad set
Meta’s algorithm is powerful — but lacks B2B signals. It optimizes for conversions but not quality company fit or role. Primer constrains it: you tell Meta exactly who’s in bounds (ICP) and who’s off limits (anti-ICP). You still get Meta’s reach, but within defined B2B guardrails.
Prerequisites:
Pixel firing
CAPI configured
Primer connected to Meta
Step 1
Build Seed and Suppression Audiences in Primer
Use high-quality converters or paying customers as the seed for your lookalike
Build an anti-ICP of irrelevant or low-value users (e.g., small companies, students, agencies).
Sync both to Meta.
Tip:
The suppression list is what makes this play work — it limits how far Meta can drift from your ICP.
Step 2
Create Lookalike & Constrained Ad Sets
Create a Lookalike Audience (1–3%) from your seed.
Apply your Primer suppression audience as an exclusion layer.
Duplicate the ad set and remove exclusions — this becomes your control.
Keep budgets, creative, and optimization events identical.
Tip:
The difference between the two ad sets reveals how much the suppression layer improves quality.
Step 3
A/B Test with Meta’s Experiment Tool
In Meta’s Experiments, split spend evenly between:
Ad Set A: Lookalike + Primer Suppression
Ad Set B: Lookalike Only (Native Meta)
Keep conversion event constant (e.g., demo booked or signup).
Warning:
If your audience or signal volume is low, ad sets may never exit learning mode. In that case, manually optimize based on early cost and conversion trends.
Principle
Impact
High-quality inclusions and exclusions shape learning
Primer tells Meta who’s in and who’s off limits, helping the algorithm focus on your real ICP.
The algorithm needs volume — but direction
Primer provides a large, structured audience so Meta’s machine learning can optimize within your ICP instead of wandering into irrelevant segments.
Step 4
Measure & Decide
Compare performance across:
- Reach
- CPL
- Cost per Qualified Lead (CPQL)
- Down-funnel conversion rates
What to expect
Don’t use lookalikes unless your Conversion API (CAPI) is active and sending reliable conversion data back to Meta. Without feedback loops, the algorithm can’t self-correct.
Metric
CPL
Meta Native
$82
Primer Constrained
$65
Change
–21%
Metric
Qualified Rate
Meta Native
28%
Primer Constrained
44%
Change
+57%
Metric
CPQL
Meta Native
$293
Primer Constrained
$148
Change
–49%
