Behavior Model.
It learns how your members actually behave — not how a segment says they should.
Segments describe members from the outside. The model learns them from their own actions.
A compact behavioral model, trained per brand on your members' own event stream — visits, purchases, points, opens, clicks, dozens of governed event types. From that sequence it learns each member's behavioral fingerprint and keeps a live read on what tends to come next: purchase or lapse, and when. One model per brand. Never pooled.
One member's event stream
Fingerprint
who behaves like whom
What's next
purchase vs. lapse odds
When
due window · engaged hour
one model per brand · trained only on your members · never pooled
Why a model
Attributes tell you who someone is. Behavior tells you what they'll do.
Rules go stale
“Bought 3× in 90 days” was a good rule the day you wrote it. Members drift; the rule doesn't. A learned model updates with every event instead of waiting for someone to re-tune thresholds.
Averages hide the person
A segment average says members like this buy every 40 days. This member buys every 12 — or 80. Per-member predictions replace one-size-fits-the-segment guesses.
Lookalikes need more than demographics
“Same age, same city” finds people who look similar. A behavioral fingerprint finds people who act similar — the same rhythm of visits, categories and responses. That's what predicts value.
What it produces
Three live reads on every member.
Behavioral fingerprint
A compact representation of how this member behaves, learned from their whole event history. It's what lets the platform answer “who else behaves like your best members?” — the engine behind lookalike audiences and behavioral similarity.
Next-event probabilities
For each member: how likely the next event is a purchase, and how likely they're lapsing instead — refreshed as new behavior lands, surfaced on the member profile and available to segments and the agents that decide.
Per-member timing
Each member's own purchase rhythm — when the next purchase is due, with an honest window — and the hour of day they actually open and click, feeding send-time optimization.
The honesty gate
The model has to earn its job — on your data.
Every brand's model is tested against a transparent baseline on that brand's own held-out data — behavior the model never saw in training. Only if it genuinely predicts better does it go live. Until then, the platform runs on glass-box math — it never fakes a model it doesn't have.
And the test never stops: the model keeps being re-verified as your base evolves. If it stops earning its place, downstream features fall back automatically. No silent degradation, no black-box drift.
Privacy, machine-enforced
A model of your members that respects your members.
Yours alone
One model per brand, trained only on that brand's members. Your model never learns from anyone else's base — or teaches theirs.
Forgets on request
When a member is erased, their data leaves the model too — purging and retraining are part of the deletion machinery itself, not a manual afterthought.
Governed events only
The model trains on a governed catalog of behavioral events — not on free text, not on message content, and never on data a member asked you to restrict.
The other half of the world model runs this one forward — Digital Twin →
The model's reads surface on the member profile, in segments, and in the agents' reasoning.
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