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

Sung-Hyuk Park*, Sang Pil Han

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

24 Citations (Scopus)

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.

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

Funding

An earlier version of this paper was presented at the 2012 International Conference on Electronic Commerce. The authors are grateful to the special issue editors, Karl Reiner Lang and Ting Li, and the anonymous reviewers for their valuable suggestions and extremely helpful comments. Any remaining errors are the authors'. The work described in this paper was partially supported by a research fund from Recobell.

Research Keywords

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

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