Solution Finder
What is your customer trying to achieve? Pick the problem closest to what they actually said — this translates it into the right methodology, data requirements, and Lab.
You said: “Find more people like our best customers”
Lookalike Audience Modelling
AudienceWhy this fits
Lookalike Audience Modelling takes a seed of known good customers and scores a wider population for similarity.
What 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 required
- 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.
Implementation approaches
Vendors
Risks & limitations
Risks
- A poorly defined seed audience (e.g. mixing customer types) produces a noisy lookalike model
- Overly tight similarity thresholds can shrink the audience below useful scale
Limitations
- Correlation-based similarity does not guarantee the lookalike audience will convert
- Lookalike quality degrades as the seed audience ages without refresh