A high-performance turnkey system for customer lifetime value prediction in retail brands
本文介绍一个部署在企业客户数据平台上的客户终身价值预测系统，通过编码嵌入、多阶段流失-价值建模和集成学习，在12个零售品牌上持续超越基准表现，适合需要快速部署通用预测模型的品牌方。
Abstract Customer lifetime value (CLV) modeling underpins modern marketing analytics, enabling the development of tailored customer relationship management strategies based on the predicted future value of their customers. As part of Amperity’s enterprise customer data platform (CDP), we deploy and maintain a CLV prediction system that caters to a rapidly growing list of brands across various industries, purchase behaviors, and scales. Given the impracticality of developing bespoke models for each brand, our solution must be adaptive, generalizable, and high-performing ”out of the box”. Furthermore, our platform demands daily prediction updates to facilitate prompt marketing decisions. This paper introduces a turnkey CLV prediction system that achieves state-of-the-art performance across a diverse set of brands. This system has several contributions: 1) the use of encodings and embeddings to incorporate signals from high-cardinality data; 2) a multi-stage churn-CLV modeling framework that augments additional flexibility in adjusting churn probabilities, subsequently reducing CLV prediction errors while maintaining a synergistic learning process; 3) a feature-weighted ensemble of both generative and discriminative models to accommodate diverse underlying purchase patterns. Empirical results show that our enhanced model consistently surpasses benchmark performances for twelve retail brands across six evaluation intervals from June 2020 to September 2022.