The pattern that groups visitors into editorial cohorts at request time based on observed behavior rather than declared preferences. Cohorts are defined upstream by the team (intent categories, lifecycle stages, persona-like buckets) and a runtime classifier assigns each visitor to one or more. Distinct from inferred attribute generation because the cohorts are deliberately authored as activation units rather than emerging from a model's clustering.
The capability a stack needs to run this pattern, and the vendors that provide it, on Martech Stack Builder.
Aggregate the signals from every contact in the account into one account score, because the buying committee is buying together and scoring leads in isolation misses the picture that matters
Audit the personalization models for systematic bias in who they exclude, accelerate, or under-serve, so the model's behavior is defensible per protected category and per business-sensitive segment
Group users by when they signed up or first acted, then watch each group's engagement curve, so a quiet quarter doesn't disguise a real shift in who is sticking
Surface contextual conversion prompts at the moments most likely to convert the willing-to-pay player, without breaking the free experience for everyone else who is the reason the game has an audience at all
Tune the paywall against actual reader engagement, balancing subscription conversion against the reach and the trust that depend on readers being able to get to the content sometimes
Land visitors arriving on a specific search query on a category page shaped to that query's intent, not a generic category index
Remove customers from segments whose conditions they no longer meet, before the next campaign fires against an audience full of people the segment is wrong about