The logic of behavioural segmentation

The underlying idea is simple: behaviour is the best predictor of future behaviour. Someone who has
looked at winter jackets three times in a week is very likely shopping for a jacket. No demographic
attribute carries that signal.

Behavioural segments are built from a combination of:

  • Frequency of the action — how many times it happened
  • Recency — when it last happened
  • Context — in which categories, from which devices

The core behavioural segments in e-commerce

Segment Criterion Use
New users 0 purchases, first visit Onboarding pop-up, bestsellers
Active buyers 2+ purchases in 90 days Cross-sell, upsell
Dormant Bought before, no visits in 60+ days Win-back campaign, reactivation email
Abandoned cart Added, did not buy Triggered pop-up / email within 24 hours
Category browsers 3+ views in one category with no purchase Personalized banner, recommendations

Real-time vs batch segmentation

How fast segments refresh is decisive for triggered scenarios:

User adds an item to the cart (event at T=0)
→ CDP updates the cart-without-purchase segment (T+0 sec)
→ pop-up is shown on the next page view (T+5 sec)
→ if segments refresh in batch (once a day) — the pop-up is ~24 hours late

Important: for abandoned-cart and exit-intent triggers, real-time segment updates are not an
optimisation — they are a functional requirement.

Behavioural segments vs the affinity profile

A behavioural segment is a discrete group (added to cart in the last 24 hours). An affinity profile
is a continuous vector of preferences (interest in Footwear 0.87, in Electronics 0.12).

The two are used together: the segment decides the moment of the communication, the affinity decides
its content. A user in the returned-after-a-gap segment is shown content from exactly the categories
they have the highest affinity for.