Specialty Apparel Retailer · Retail & E-Commerce · Nashville, TN · 12 weeks
Churn prediction model identified $3
A specialty apparel retailer with 1.8 million loyalty members couldn’t identify which customers were likely to churn until after they stopped buying. We developed a churn prediction model that flagged at-risk members 90 days in advance. This allowed the retailer to run targeted win-back campaigns that recovered $3.1 million in annual loyalty revenue.
Challenge
The retailer considered a member churned only after 12 months of inactivity. This delay meant they missed any chance to act early. There was no system to identify at-risk members, no way to intervene, and no clear tracking of the loyalty program’s actual ROI beyond just counting members.
Outcome
The model flagged 94,000 high-risk members in its first run. We launched targeted win-back campaigns and recovered 68% of those who received outreach, securing $3.1M in retained annual loyalty revenue. This project also set up the first reliable way to measure loyalty program ROI.
Results
- $3.1M Annual loyalty revenue recovered
- 68% High-risk member recovery rate
- 94,000 At-risk members identified in first scoring run
- 90 days Early warning window before predicted churn
We used to wait a full year of no activity before counting someone as churned, which meant we were always behind. Thinklytics built a model that flags customers likely to leave 90 days early. That helped us save $3.1 million in the first year.