You do not have to behave like a customer to be targeted like one.
Sometimes it is enough to resemble customers who came before you.
That is the logic behind lookalike, similar, and predictive audiences.
An advertiser starts with a source group: buyers, leads, subscribers, site visitors, or some other known audience.
The platform then looks for other people who share useful characteristics with that group.
The seed audience teaches the model what to search for
Google’s current Demand Gen documentation describes Lookalike segments as groups of people who share characteristics with members of an existing seed list. Advertisers can use customer lists, website visitors, app users, and other first-party sources to help find new customers who resemble people they already know. See Google Ads’ Demand Gen audiences overview.
LinkedIn uses a related concept called predictive audiences. Its current documentation says the system combines a source such as a contact list, conversion audience, lead form, or retargeting group with LinkedIn’s AI to generate a new audience predicted to perform similar actions. See LinkedIn’s predictive audience documentation.
The terminology changes.
The underlying move is similar.
Behavior from Group A helps decide who belongs in Group B.
The new person may never have volunteered the defining interest
Suppose a business uploads a list of customers who bought expensive camping equipment.
The advertising platform may identify patterns among those customers and find other users who statistically resemble them.
Those new users may never have visited the retailer.
They may never have searched for a tent.
They may never have said they enjoy camping.
Their eligibility can come from similarities discovered in data available to the platform.
That is why lookalike profiling is different from ordinary retargeting.
Retargeting says:
This person interacted with us before.
Lookalike modeling says:
This person resembles people who interacted with us before.
Resemblance is not intent
Statistical similarity can be commercially useful without being a personal truth.
Two people may share age, geography, browsing patterns, device usage, media habits, professional traits, or other attributes while wanting completely different things.
A model optimized for conversions does not need to prove that a new prospect has the same motive as the seed audience.
It only needs the resemblance to improve campaign performance often enough to be useful.
That distinction matters when interpreting the profile.
A person being placed into a lookalike audience does not establish that they hold the interests, needs, politics, health status, financial situation, or intentions of the seed group.
Profiling can propagate through other people
This is one of the stranger properties of modern advertising.
Your own behavior is not the only behavior that can shape what systems infer about you.
Other people’s conversion histories can become training examples that affect whether you are selected.
The Surveillance Economy therefore does not merely watch individuals.
It compares them.
A profile can be influenced by what you did.
A predictive profile can be influenced by what people who resemble you did.