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ML-based Payment Renewal Optimization

1 post · May 2021

What this trail covers

Dropbox developed and deployed a machine learning system to optimize payment renewal attempts, reducing involuntary churn and improving customer retention. This system moves beyond static, rule-based retry logic to dynamically predict the best times to charge customers during renewal failures, leading to increased payment success rates and a more efficient billing process. The ML approach also simplifies the overall billing system complexity.

All Dropbox trails
  1. Optimizing payments with machine learningThis post details the application of machine learning, specifically gradient boosted ranking models and a multi-armed bandit approach, to optimize payment renewal attempts. It describes the transition from manual, rule-based retry logic to a system that predicts optimal charge times based on customer segments, payment failure types, and account usage patterns. The implementation involved training models, serving predictions via the Predict Service to reduce latency and system complexity, and A/B testing the end-to-end optimized renewal process.