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.
Approach
We developed a churn model using purchase recency, frequency, monetary value, and engagement data from email, app, and in-store activity. Each week, we scored all 1.8 million members and identified those likely to churn within 90 days. We integrated these scores into the marketing platform to trigger targeted retention campaigns automatically.
Outcome
The model flagged 94,000 high-risk members in its first run. We launched targeted win-back campaigns and won back 68 of every 100 members 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.
Spotting Issues Early So We Don’t Get Hit With Big Delays
We scrapped the old 12-month inactivity check and jumped onto a 90-day churn prediction model instead. Every week, we dug into customer behaviors that shouted “at risk” and scored members accordingly. This gave the retailer fast heads-ups so they could jump in and stop churn before it even started.
How We Got Automation Up and Actually Saw What Made a Difference
Here’s what we did: we fed the churn scores directly into the marketing system. So the moment someone looked like they were about to bail, we’d fire off a win-back campaign immediately. We also kept a control group for each campaign. That way, we could track exactly how many folks we brought back and establish the real ROI of the loyalty program.
Results
- $3.1M Annual loyalty revenue recovered
- 68 of 100 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.