Personalized Ranking at a Mobile App Distribution Platform
研究利用消费者点击流数据中的偏好信息，构建个性化应用展示排名的结构框架，同时考虑用户效用和每行动成本，实验表明个性化排名比当前做法提升收入16.73%。
Personalization is an emerging digital strategy to engage users across different business domains. It is defined as the capability to match content, products, and services to individual users based on the knowledge of their past behaviors and revealed preferences. It has shown its great potential across a variety of contexts, including search engines, recommender systems, targeted marketing, and more. In this study, we examine personalization on third-party mobile app platforms, which account for a $36 billion market in 2021. We develop a comprehensive structural framework for the personalized ranking of app impressions, leveraging revealed preferences embedded in consumer clickstream data. To improve platform revenues, the framework jointly accounts for consumer utility and cost per action margin. A series of policy experiments highlights the value of personalization to various extent. Remarkably, personalized rankings at the individual level outperform the current practice by 16.73%. This cost-efficient approach showcases how platforms can leverage routine consumer clickstream data to personalize the ranking of app impressions, thereby more effectively monetizing mobile app distribution.