Customer question
“How can I find more people like my best customers?”
What this solution does
You show the model your best customers, and it goes and finds more people who look statistically similar, so you're not just guessing who to target next.
Specifically, it answers: Given a set of high-value existing customers, who in a broader population shares similar characteristics and is therefore a good prospecting target?
How it works
A seed audience of known good customers is analysed for shared attributes, then a scoring model ranks a much larger population by similarity to that seed, producing a selectable lookalike audience at different size thresholds.
Data requirements
- Seed audience of known high-value or converting customers
- A larger reference population to score against
- Shared attributes across both groups (demographic, behavioural, transactional)
Discovery questions
How is 'best customer' defined for this seed audience?
The quality of the lookalike model depends entirely on how well the seed represents the value the customer actually wants more of.
How large is the seed audience?
Very small seed audiences produce unstable, unreliable similarity models.
What similarity threshold trade-off matters most — precision or reach?
Tighter thresholds are more precise but smaller; looser thresholds scale further but dilute quality.
Which platform or data environment will the lookalike be activated in?
Determines whether a platform-native lookalike tool is sufficient or a custom model is needed.
Build an experiment
Generate synthetic data
Data readiness
Run model / simulation
Results
Business ⟷ Technical interpretation
Selecting the similarityThreshold threshold produces a lookalike audience of audienceSize people, trading some precision for reach compared to a tighter threshold.
Shown as a template — the highlighted tokens are filled in once the model has been run.
What would you do next?
- Test multiple threshold levels in a live campaign before committing full budget
- Refresh the seed audience periodically to avoid model drift
Solution & vendor landscape
Implementation approaches
LiveRamp
First Party Data / Identity · Data Collaboration / Clean Rooms
Experian Marketing Services
Audience / Data
TransUnion (TruAudience)
Audience / Data
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