From Accuracy to Diversity in Product Recommendations : Relationship Between Diversity and Customer Retention

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

14 Scopus Citations
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Author(s)

  • Sung-Hyuk Park
  • Sang Pil Han

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)51-71
Number of pages21
Journal / PublicationInternational Journal of Electronic Commerce
Volume18
Issue number2
Publication statusPublished - 2013

Abstract

Recommending diverse products to consumers is a new strategy for the next generation of recommender systems. However, no existing studies have empirically identified the impact of product diversity on consumer behavior. The aim of this study is to explain how product category diversity affects customer retention rates. To answer this research question, we examine how the number of product categories purchased by consumers is related to customer retention rates at a large digital content distributor. We use panel data consisting of product characteristics, purchase transactions, and customer retention rates from the company. Through segment-level and individual-level panel data analyses, we find that purchase quantity is positively associated with customer retention rates, and that variety of purchased digital content categories is positively associated with customer retention rates. That is, customers who have purchased digital content from multiple categories are more likely to stay longer than those who purchased digital content from a single category or from fewer categories. Put differently, as a complement to the conventional wisdom that just recommending products with similar features that a customer values highly (i.e., similar content from the same category) is important, our results imply that recommending products with different features (i.e., different content across different categories) is also important.

Research Area(s)

  • Cross-selling, customer retention, econometrics, product diversity, recommender systems, LIFETIME VALUE, LONG TAIL, SYSTEMS, SALES, IMPACT