个性化
排名(信息检索)
点选流向
计算机科学
个性化营销
杠杆(统计)
个性化搜索
收入
在线广告
移动设备
推荐系统
移动商务
机器学习
业务
万维网
数字营销
互联网
Web 2.0版
会计
Web API
企业对政府
营销投资回报率
作者
Shengjun Mao,Sanjeev Dewan,Yi‐Jen Ho
标识
DOI:10.1287/isre.2022.1156
摘要
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.
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