Product intelligence
Raw event names become understanding. Banchurn clusters events into features by shared vocabulary and session co-occurrence, builds a Markov map of how customers move and where they drop off, and watches every event for spikes, drops, and brand-new behaviors with an EWMA / z-score baseline. AI is used only on the margin, to name a new feature or explain a fresh anomaly, never in the per-event path.
You have millions of events and no idea what they mean
A busy product emits a firehose of raw event names. Grouping them into features by hand is endless, mapping the real user journeys is guesswork, and spotting the one metric that quietly broke this morning is impossible at that scale.
The flow, end to end
Classify
Every event is tagged into a coarse taxonomy at ingest, cheaply and deterministically.
Group
Event names cluster into features by shared tokens, refined by which events fire together.
Map
Per-session sequences build a transition map over features, with drop-off points ranked.
Detect & explain
Each event's daily series is baselined; anomalies are flagged, then AI explains only the new ones.
What's in the box
Automatic feature grouping
Events cluster into named features by shared vocabulary and session co-occurrence: no taxonomy to maintain.
Flow mapping
A Markov transition map over features shows the real paths customers take, and where they exit.
Anomaly detection
An EWMA baseline plus z-score flags volume spikes, drops-to-zero, and other deviations per event.
Novelty detection
A brand-new behavior that just started appearing is surfaced as something worth a look.
AI on the margin
AI only names a new feature group and explains a fresh anomaly: cost scales with surprise, not traffic.
Anomalies → alerts
Fresh critical anomalies are pushed to your alerts inbox, deduplicated so re-runs never spam.
Heuristics scale with events. AI scales with anomalies.
Running an LLM over every event is the wrong layer: it's slow, costly, and non-deterministic for work simple math does better. So Banchurn does all the high-volume work with deterministic heuristics and calls AI only on the exceptions: naming a new feature, explaining a fresh anomaly. Ten times the traffic costs the same in AI.
- ✓Feature grouping, flows, and anomalies are pure, tested heuristics
- ✓AI touches only new groups and new anomalies: capped per run
- ✓Deterministic in the load-bearing path; no model flakiness
Features, journeys, and the one thing that changed today
You get a living map of your product without maintaining it: which events belong to which feature, how customers actually move between features, where they fall out and, the moment it happens, which event spiked, dropped to zero, or appeared for the first time, with a plain-language explanation of what likely changed.
- ✓Named feature groups, kept current automatically
- ✓Flow map with ranked drop-off points
- ✓Spike / drop / novelty anomalies, explained in words
Churn prediction
A per-customer risk score that learns your product's baseline, so at-risk customers surface before they leave.
Learn moreAnalytics
Funnels, retention curves, revenue, and insight dashboards: all over the event stream you're already sending.
Learn moreAutomations
A visual journey builder: trigger on any event or when a customer enters or leaves a lifecycle state, then send email or in-app, wait, branch, and act.
Learn moreStop losing customers you could have kept
Book a walkthrough and see churn prediction, account signals, segments, campaigns, destinations, and Autopilot working together, on one platform.