AI-Driven Customer Retention Engine

The Problem

Our client had months of behavioral and transaction data but no way to know which customers were at risk of leaving until they'd already cancelled. Retention efforts were reactive, generic, and expensive — a blanket discount campaign sent to everyone, working on almost no one.

Our Approach

We ran the Ketarth Blueprint: audited existing CRM and transaction data, engineered a clean feature pipeline from account activity and support interactions, then trained a churn-prediction model validated against 18 months of historical outcomes. The model surfaced a daily risk-ranked list, feeding directly into the retention team's workflow instead of a static report nobody opened.

The Results

Within two quarters of go-live, churn dropped 22% and customer lifetime value rose 3.5x among the at-risk segment the model correctly flagged. Model accuracy held at 94% against holdout data, and the retention team now spends its budget on the customers who were actually going to leave.