Description

Customers don't churn overnight — value is created or lost as customers move between states: active → drifting → dormant → returning. States are a snapshot — transitions are the signal. Customer State Intelligence uses Hidden Markov Models to infer these transitions from first-party behavior and assign probabilities for where each customer is now — and where they're most likely to move next.

Using your first-party behavioral data, we model how customers transition between lifecycle states over time, quantifying both risk and opportunity as behavior changes. Rather than static labels, you get a forward-looking view of momentum: who is stabilizing, who is drifting, and who is recoverable — before traditional KPIs move.

The output is simple and operational: clear state definitions, transition probabilities, and decision-ready rules your CRM team can use for targeting, timing, and escalation. We build the models, wire them into your CRM/CDP, and keep them calibrated as behavior, seasonality, and strategy evolve.

Typical questions it answers

• Who is quietly drifting — and what's their probability of churn in the next weeks/months?

• Who is most likely to reactivate with the right nudge (and who isn't)?

• Where in the lifecycle are we losing value — and what moves customers forward?

• Which interventions reduce "state decay" without training customers to discounts?