How a lookalike audience is built
A lookalike extends an audience through similarity. The process has three steps.
- Define the seed: choose the best customers — the VIP tier by RFM, buyers with high lifetime
value, converters from a specific campaign. - Run the algorithm: the system analyses the seed’s attributes — demographics, behaviour,
interests — and searches for similar people across a wider base. - Receive the extended audience: a list of new users the system considers similar to the seed.
Quality depends on two things: how representative the seed is, and how much data is available to
search across.
Applications in e-commerce
| Scenario | Seed | Goal |
|---|---|---|
| Extending ad reach | Buyers with above-median LTV | Find new customers with a similar profile |
| Converting new visitors | People who bought on the first visit | Identify similar visitors and personalize the homepage |
| Cross-category selling | Buyers of one category | Find similar people for an adjacent category offer |
Important: the narrower and cleaner the seed, the more precise the lookalike. A seed made of all
registered users produces a weak result — focus on specific behaviour.
Limits
A lookalike creates no new data; it extrapolates patterns from what is known. If the seed is biased
— only users from one region, for instance — the lookalike reproduces that bias. The seed also needs
refreshing: a customer base shifts, and a six-month-old seed can misrepresent current value.
When lookalikes are used in advertising, consent requirements for transferring an email base to an
ad platform apply and have to be checked against the relevant data protection regime.