人工与智能协作提升营销效果，智能系统可长期维持优化成果
Sustained Impact of Agentic Personalisation in Marketing: A Longitudinal Case Study
- 用自主代理系统自动推送个性化营销内容
- 被动期仍保持显著高于基线的用户参与度提升
- 适合需要长期稳定营销效果的企业应用
在消费者应用中，客户关系管理（CRM）传统上依赖人工优化静态规则型消息策略。尽管自适应和自主学习系统有望实现可扩展的个性化，但人机协同对长期性能提升的影响仍不明确。本文通过一项为期11个月的纵向案例研究，分析了一个利用代理架构为大规模用户群个性化营销信息的真实场景。比较了两个阶段：主动期由营销人员直接策划内容、受众和策略；随后是被动期，代理系统基于固定组件库自主运行。结果显示，虽主动管理带来最高的参与度提升，但自主代理在被动期仍成功维持了显著正向提升。这表明人类干预驱动初始策略探索，而自主代理可实现性能增益的规模化延续与保持。
原文摘要 · Abstract (English)
In consumer applications, Customer Relationship Management (CRM) has traditionally relied on the manual optimisation of static, rule-based messaging strategies. While adaptive and autonomous learning systems offer the promise of scalable personalisation, it remains unclear to what extent ``human-in-the-loop'' oversight is required to sustain performance uplift over time. This paper presents a longitudinal case study analysing a real-world consumer application that leverages agentic infrastructure to personalise marketing messaging for a large-scale user base over an 11-month period. We compare two distinct periods: an active phase where marketers directly curated content, audiences, and strategies -- followed immediately by a passive phase where agents operated autonomously from a fixed library of components. Our results demonstrate that whilst active human management generates the highest relative lift in engagement metrics, the autonomous agents successfully sustained a positive lift during the passive period. These findings suggest a symbiotic model where human intervention drives strategic initialisation and discovery, yet autonomous agents can ensure the scalable retention and preservation of performance gains.
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