Churn prediction
A daily-updated risk score per subscriber, with the contributing signals shown plainly: billing failures, support touches, engagement drop-off (have they opened the portal in the last 30 days), product-fit signals (have they swapped items multiple times in a row). The score is the input to your retention playbook. Set your own risk thresholds to trigger save-offers or check-in emails. Thresholds are configurable as the model calibrates on your data.
Save-offer drafting
When a subscriber clicks cancel and gives a reason, an LLM drafts the save-offer copy based on their order history, their reason, and your offer playbook. A subscriber canceling for “too expensive” with 14 successful orders gets a different draft than a subscriber canceling for “not using it” in their second cycle. You approve the playbook; we generate the per-customer copy.
Dunning copy optimization
Subject lines and body copy on dunning emails are A/B tested at the cohort level. The winner becomes the new default, automatically. Recovery rate, not open rate, is the objective. Dunning copy optimization is designed to add incremental recovery on top of the decline-aware engine. Measured impact will be published later.
What we don’t do
- No chatbots. Subscribers want answers, not a chat window.
- No autopilot wizards. The merchant approves the playbook. Always.
- No black box. Every model decision shows its contributing signals.
- No training on your data. Models are trained on aggregated, anonymized industry data, not on your individual subscriber records.
Who this is for
Churn prediction ships on Growth. Save-offer drafting and dunning optimization ship on Scale.