Dropbox applies gradient-boosted ML to optimize subscription payment retry timing
Dropbox's payment retry system relied on about ten static hardcoded rule sets that decayed in performance over time and required complex, time-consuming manual customer segmentation and A/B testing to maintain.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Payment attempt fails
When a payment attempt for a customer fails, the payments platform makes a request to the predict module to get the next best time to charge.
The ML system outperforms the rule-based approach and the individual-customer model is deployed in production. Migrating to Predict Service reduced prediction latency from several minutes to under 300ms for 99% of predictions, and the system delivers an overall increase in payment charge success rates and reduction of collection time.
What failed first
An initial ML design using a separate model per payment attempt created unnecessary complexity and contradicted the goal of simplifying the billing system. Loading and running the model inside the Payments Platform directly caused significant bloat and prediction latencies averaging around two minutes.
Results
Time savedunder 300ms for 99 percent of them
Volumeoverall increase in payment charge success rates