RenewalRadar
AI predicts which accounts are likely to leave at renewal and suggests the play to keep them, before the increase lands cold.
The problem
An agency worked renewals in date order, which meant the team often reached a valuable account a week before expiration, after the client had already started shopping.
There was no early signal for which accounts were at risk, so the same effort went to clients who were never going to leave and to ones who were already gone.
What we built
RenewalRadar scores every upcoming renewal for churn risk, ranks the book by premium at risk, and suggests a retention play for each account. The team works the accounts worth a call first, with time to act before the renewal lands.
- •Churn risk score for every renewing account, with the drivers behind it
- •Pipeline ranked by premium at risk and days to renewal
- •Suggested retention play matched to what is driving each account risk
- •Account view with premium history, loss ratio, and service activity
- •Book health dashboard with retention trend and risk distribution by segment
A closer look
How it works
Each account is scored well before its renewal date, using rate change, service history, tenure, and competitive exposure. The pipeline puts the highest premium at risk at the top, with a suggested play for each one.
AI predicts the risk and recommends the approach. Producers decide how to work the account and log what they do. The model learns from what actually retains.
The outcome
Producers reach at-risk accounts early enough to act, not after the client has shopped.
Retention effort goes where it changes the outcome instead of spreading evenly.
Leadership can see book health and premium at risk across the whole portfolio.
Ready when you are
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