Account signals
Revenue renews at the account, so Banchurn scores the whole account, not just one user. Each account's people and behavior roll into a single score across four vectors (fit, usage, intent, and risk), and that score becomes a plain signal: PQA when an account is leaning in and ready to expand, PQR when it's slipping toward churn. It's product-qualified accounts for your expansion motion and an early-warning list for your save motion, computed the same deterministic way every time, with the evidence behind every call.
Account health lives in someone's head, not your data
A per-person churn score tells you one user is cooling, but the money renews at the account. Is the whole account trending toward a bigger seat count or toward a cancellation? That answer is scattered across dozens of users, a CRM nobody keeps current, and a gut feeling in the AE's head, so expansion gets missed and risk gets caught too late.
The flow, end to end
Aggregate
Each account's people, usage, and billing roll up from the same event stream every other module reads.
Score
Fit, usage, intent, and risk are each measured against your product's baseline, then combined into one score.
Classify
The account crosses into PQA (expand) or PQR (save) when its scores clear your thresholds: PQR wins ties.
Fan out
A crossing becomes an event: enroll an account journey, alert your team, or push it to Slack and your CRM.
What's in the box
Four-vector score
Fit, usage, intent, and risk combine into one account score: every input visible, nothing a black box.
PQA: expansion-ready
Accounts leaning in surface as product-qualified, so expansion reaches them while the intent is still live.
PQR: product-qualified risk
The retention-native inversion: accounts slipping toward churn, flagged early enough to actually save.
Evidence on every signal
Each crossing carries its top reasons (the features cooling, the seats filling, the intent climbing) in plain language.
Rolls up your people
Per-customer churn and engagement scores aggregate to the account, so health means the whole relationship.
Deterministic scoring
Every number is computed the same way each run: no model drift, so a signal is explainable and testable.
The same data reads two ways: expand, or save
The behavior that predicts expansion and the behavior that predicts churn come from one place: how the account actually uses your product. Banchurn scores both directions off the same four vectors, so the account leaning in and the account slipping out surface from one system: no second tool, no separate model, no data-team project to stand it up.
- ✓PQA and PQR from one four-vector account score
- ✓Expansion motion and save motion off the same signal
- ✓PQR wins ties: risk is never hidden by opportunity
Every signal shows its work
Because the score is deterministic, each PQA or PQR carries the evidence that produced it (the features going cold, the seats filling up, the intent states climbing) as a ranked, plain-language list on an account timeline. Your AE opens the account and sees why it lit up, not just that it did.
- ✓Top reasons attached to every signal crossing
- ✓Fit / usage / intent / risk each broken out on the account
- ✓A timeline of crossings, enrollments, and deliveries per account
Churn prediction
A per-customer risk score that learns your product's baseline, so at-risk customers surface before they leave.
Learn moreIntegrations & destinations
Push segments and account signals to Slack, HubSpot, ad audiences, and webhooks: activate your data wherever your team already works.
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.