杠杆(统计)
服装
产品(数学)
业务
广告
营销
计算机科学
几何学
数学
考古
机器学习
历史
作者
Xiang Wan,Anuj Kumar,Xitong Li
标识
DOI:10.1287/isre.2020.0560
摘要
Practitioner’s Abstract Online platforms/retailers widely use collaborative filtering (CF)-based generic product recommendations to improve sales. These systems recommend products to a consumer based on the product co-views and co-purchases by other consumers on the website but do not leverage the consumer’s browsing data. Based on a field study on a U.S. fashion apparel and home goods retailer’s website, we show that informing generic CF recommendations to individual consumers’ browsing history can generate substantial additional sales. Specifically, we show that it is optimal to offer generic CF recommendations to a consumer if the consumer has not carted a product and recommend products he or she has seen in the previous sessions (retargeted recommendations) if he or she has carted a product. Our simulation results show that such recommendations could result in a 3% increase in total sales compared with conventional generic CF recommendations. Online platforms/retailers with detailed consumer browsing data can implement such recommendations to achieve higher sales.
科研通智能强力驱动
Strongly Powered by AbleSci AI