From raw behavioral events to daily churn and LTV predictions in four steps.
No data team, no ML engineering, no months of setup. If you have a Shopify store, a Stripe subscription feed, or a web app with a login event, you can have live propensity scores within a week. Here is exactly what that looks like.
Connect your event data
Three ingestion paths. Pick the one that fits your stack today. You can add a second source later. The events that matter most for churn prediction are: logins, page depth, order completions, plan changes, email opens, and cancellation attempts. If your stack generates those, you have what the model needs.
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Define your segments
The rule builder works against any attribute in your unified customer profile: recency, frequency, monetary value, email engagement rate, feature adoption depth. You can define a segment in a few minutes without writing SQL. Once defined, the segment population updates continuously as new events arrive. It is never a static snapshot.
Before
1 segment: Active Customers
After
5 segments: High-Value Actives, Price-Sensitive, Lapsing, Recently Churned, Recovering
Get propensity scores and LTV forecasts
After 7 days of ingestion, Segmentvue trains the initial churn propensity model and 12-month LTV forecast for your tenant using your own behavioral history, not an industry average. Every customer profile receives a daily-updated score. The model continues improving as it observes more events: confidence bands tighten noticeably by day 30.
Sync scores to your activation stack
Segmentvue does not replace your email tool or CRM. It feeds them. Configure a sync rule: when a customer's churn propensity crosses a threshold, push them to a win-back list in your ESP, write the score to a CRM contact field, or fire a webhook to any downstream system. Your lifecycle automation then runs exactly as you designed it, but on daily-fresh data instead of last month's export.
A realistic setup timeline
We do not promise a churn model on day one. Here is what actually happens, and when.