Giving Sellers Peace of Mind Against a Bad Month
Scaled a return-cost insurance product to 20K sellers in 14 months with a 1% profit increase
Designed the pricing and targeting model for an insurance-style product that protected sellers from unpredictable return-rate costs.
Problem
Return costs varied wildly seller to seller. Some sellers quietly raised prices to cover losses, hurting their own competitiveness with no product to stabilize the cost instead.
Approach
Clustered sellers on revenue, return rate, category, and location, then collapsed that into a simple return-rate based fee tier sellers could understand, validated with P&L analysis per cohort.
Solution
Took the product from proof of concept to a fully running program with its own data pipeline and reporting, then handed it to ops to run at scale.
Success Metrics
20K
sellers enrolled
14 mo
0 → scale
1%
profit increase
Problem of small sellers
Case Study Details