Identify customers likely to lapse, estimate lifetime value, and recommend the best intervention for each policyholder or account, before the renewal window closes.
Illustrative figures, except the Vitallogix result, which is from a live deployment.
Score every policyholder or account for renewal risk.
Know which customers are worth investing to keep.
Choose the intervention with the highest expected ROI.
Focus agent time on the relationships that matter.
Track campaign uplift against a holdout.
Improve which interventions win over time.
Surface why a customer is at risk.
Skip intervention when ROI is low.
Reminders treat every customer the same. Renewal Intelligence predicts who is actually at risk, estimates their value, and recommends the specific action most likely to retain them profitably.
Yes. The same lapse-prediction, LTV, and best-action loop applies to policy renewals, bank relationships, and subscription accounts.
Actions are chosen on expected ROI, including a "no intervention" option when a customer would renew anyway, so you stop discounting customers you were never going to lose.
Treated customers are measured against a holdout, so you see realized renewal-rate and retention-ROI uplift, not just model scores.
Start with a roadmap that quantifies renewal-rate and retention-ROI upside on your data.
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