用智能代理优化跨渠道营销，提升用户参与度。
Agentic Personalisation of Cross-Channel Marketing Experiences
- 基于顺序决策框架，自动优化内容与推送时机。
- 在1.5亿用户上实现多产品功能参与度显著提升。
- 适合需要个性化营销的平台与增长团队使用。
消费者应用为向用户展示和传递各类内容提供了丰富机会，包括新功能或订阅的推广活动、持续性的互动提醒，以及个性化推荐等，覆盖邮件、推送通知和应用内界面。传统沟通协调方式高度依赖人工操作，难以实现内容、时间、频率和文案的高效个性化。本文将该任务建模为序列决策问题，旨在优化一个模块化决策策略，以最大化任意转化漏斗事件的增量参与度。方法采用差异中的差异（Difference-in-Differences）设计进行个体处理效应估计，并结合汤普森采样（Thompson sampling）平衡探索与利用。在多服务应用上的实验结果显示，该方法显著提升了多个产品功能的目标事件发生率，目前已在1.5亿用户中部署。
原文摘要 · Abstract (English)
Consumer applications provide ample opportunities to surface and communicate various forms of content to users. From promotional campaigns for new features or subscriptions, to evergreen nudges for engagement, or personalised recommendations; across e-mails, push notifications, and in-app surfaces. The conventional approach to orchestration for communication relies heavily on labour-intensive manual marketer work, and inhibits effective personalisation of content, timing, frequency, and copy-writing. We formulate this task under a sequential decision-making framework, where we aim to optimise a modular decision-making policy that maximises incremental engagement for any funnel event. Our approach leverages a Difference-in-Differences design for Individual Treatment Effect estimation, and Thompson sampling to balance the explore-exploit trade-off. We present results from a multi-service application, where our methodology has resulted in significant increases to a variety of goal events across several product features, and is currently deployed across 150 million users.
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