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.

客户终身价值 (CLV) 建模是现代营销分析的基础，能够根据客户的预测未来价值制定定制的客户关系管理策略。作为 Amperity 企业客户数据平台 (CDP) 的一部分，我们部署和维护 CLV 预测系统，以满足各个行业、购买行为和规模的快速增长的品牌列表。鉴于为每个品牌开发定制模型是不切实际的，我们的解决方案必须具有适应性、通用性和高性能“开箱即用”。此外，我们的平台需要每日更新预测，以促进及时的营销决策。本文介绍了一种交钥匙 CLV 预测系统，该系统在多个品牌中实现了最先进的性能。该系统有几个贡献：1）使用编码和嵌入来合并来自高基数数据的信号；2) 多阶段流失-CLV 建模框架，增强了调整流失概率的额外灵活性，从而减少了 CLV 预测误差，同时保持协同学习过程；3）生成模型和判别模型的特征加权集合，以适应不同的潜在购买模式。实证结果表明，我们的增强模型在 2020 年 6 月至 2022 年 9 月的六个评估区间内始终超过 12 个零售品牌的基准表现。