用因果模型精准选客，兼顾预算和体验，提升营销收益
Guardrailed Uplift Targeting: A Causal Optimization Playbook for Marketing Strategy
- 结合因果效应估计与业务约束优化目标人群
- 在线测试显示收益和留存均优于传统方法
- 适合需要精细化营销的电商平台或会员体系
本文提出一种营销决策框架，通过整合异质处理效应估计与明确的业务约束（如预算、收入保护、客户体验），实现客户精准触达。首先利用 uplift 学习器估计条件平均处理效应（CATE），再求解带约束的分配问题，决定对谁推送何种优惠。该框架适用于留存提醒、活动奖励及消费门槛设置等场景。经离线仿真与线上 A/B 测试验证，其表现持续优于倾向性得分和静态基线，为规模化因果定向提供可复用的方法论。
原文摘要 · Abstract (English)
This paper introduces a marketing decision framework that optimizes customer targeting by integrating heterogeneous treatment effect estimation with explicit business guardrails. The objective is to maximize revenue and retention while adhering to constraints such as budget, revenue protection, and customer experience. The framework first estimates Conditional Average Treatment Effects (CATE) using uplift learners, then solves a constrained allocation problem to decide whom to target and which offer to deploy. It supports decisions in retention messaging, event rewards, and spend-threshold assignment. Validated through offline simulations and online A/B tests, the approach consistently outperforms propensity and static baselines, offering a reusable playbook for causal targeting at scale.
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