Customer Churn Model
ChurnGuard — Customer Churn Predictor
Model Intelligence Report
The Client’s State Before Us:
A $5M annual revenue fashion accessories store had a “loyalty program” but no real churn intervention. 65% of customers never made a second purchase. They sent blanket 10%-off emails to the entire inactive list every month, burning margin without understanding who was worth saving and who was already gone.
The Diagnosis:
Their definition of churn was simply “no purchase in 6 months,” which was a lagging indicator of death. Our model analyzed purchase cadence, browsing behavior, support interactions, and email engagement. It predicted churn 60 days in advance with 88% precision. We discovered that customers who contacted support about shipping and then didn't browse again within 7 days had a 73% probability of churning permanently. 80% of their reactivation budget was being spent on customers predicted to churn with zero probability of returning — ghosts.
The Intervention:
We deployed the Customer Churn Prediction and Retention Analyzer, outputting a daily "Churn Risk List" segmented into High, Medium, and Low risk, directly integrated into their email tool (Klaviyo). We designed a three-tier intervention: High-risk got a personal outreach plus a "we miss you" gift (not a discount), Medium-risk got an early-access product drop, and Low-risk were suppressed to avoid damaging them with unnecessary offers.
- • Within 120 days, recovered $210,000 in revenue from customers previously considered “lost” and on the brink of permanent churn.
- • Second-purchase rate among high-risk group jumped from 4% to 19% using the targeted intervention.
- • Saved $6,200/month in wasted discount margin by cutting the generic blast emails to customers the model identified as unreachable.