The pattern that describes how a system remembers what it knows about a person across sessions and how that memory decays or refreshes. Increasingly important for AI-orchestrated experiences where the system needs to recall prior context to behave consistently. Distinct from raw event history because memory implies an editorial layer: what's worth remembering, what's worth surfacing, what should fade.
The capability a stack needs to run this pattern, and the vendors that provide it, on Martech Stack Builder.
Recognize tenure and usage milestones with a touch that reads as personal rather than as a template with a date merged in
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 adjacent products in the rhythm of how the original item is actually used, instead of dumping every cross-sell into one email
Give the AI a memory of each customer that is curated and decaying, not a raw event firehose it cannot use and should not keep
Combine realized revenue with a predicted future, and carry the model's uncertainty into the decisions LTV drives, so the bid that depends on it knows what it is standing on
Get the unified spend, exposure, and conversion data clean enough for the model to produce decisions you can act on, not directional outputs that need a footnote
Time the reorder reminder to the customer's own consumption cadence, not a category-wide average that is wrong for most of them