Predicting Customer Churn Before It Happens

Losing 23% of loyalty members annually with no early warning system.

• No mechanism to identify at-risk customers before they left
• Marketing budget split equally across all customers regardless of churn risk
• Customer surveys only surfaced issues 3 months after dissatisfaction set in
• Retention team was reactive — chasing cancellations instead of preventing them

Built a predictive churn model integrated directly with their existing CRM.

• Trained a model on purchase frequency, recency, basket composition, and support ticket data
• Integrated weekly churn-score updates into the existing CRM system
• Created automated re-engagement campaigns triggered when churn scores crossed defined thresholds
• Designed a dashboard for the retention team with daily flagged accounts and recommended actions

The model transformed a reactive retention team into a proactive one, preserving $1.2M in customer lifetime value within the first year.

“We went from reacting to cancellations to preventing them. The model flagged customers we never would have caught.”

— VP of Operations

Timeline

6 weeks discovery and data integration, 8 weeks model build and CRM integration, ongoing optimization