AI Retention Systems That Stop Churn Before It Happens

We build models using product usage events (logins, feature adoption, session frequency), engagement metrics (email open/click rates, in-app notifications), support interactions (ticket volume, CSAT scores), billing events (failed payments, plan downgrades), and relationship signals (CSM touchpoint frequency). The more signals available, the more accurate the model.

Depending on the business, we typically detect meaningful churn signals 3–8 weeks before cancellation. This window is sufficient for automated intervention sequences to re-engage the account before they make the decision to cancel.

Early-stage interventions (when churn risk first elevates) convert at 35–55%. Late-stage interventions (account already inactive) convert at 12–25%. Win-back campaigns for churned customers convert at 8–18%. The earlier the intervention, the higher the conversion rate — which is why predictive detection matters.

Yes — for usage-based businesses, we adapt the health scoring model to track consumption trends (declining usage trajectories, dropped usage in key features) rather than seat-based signals. The intervention logic triggers earlier to prevent usage erosion before it reaches the billing threshold.

We classify churn reasons from exit surveys, last activity patterns, and support history, then route churned accounts to different win-back sequences: price-objection flows, competitor-loss flows, product-gap flows, and timing-based flows (customers who left before a key feature launched). Each sequence uses different messaging and incentive logic.

Yes — we integrate with both platforms and can either build health scoring within them or feed scores from our model into their existing CSM workflow triggers. We also work with simpler stacks (HubSpot + Mixpanel) for companies not yet using dedicated CS platforms.