Learning from Clickstream Data in Online Retail

Posted: 29 Aug 2016

See all articles by Dorothee Honhon

Dorothee Honhon

University of Texas at Dallas

Bharadwaj Kadiyala

University of Utah - Department of Operations and Information Systems

Canan Ulu

Georgetown University - Robert Emmett McDonough School of Business

Date Written: August 27, 2016

Abstract

E-tailers now have the resources to record the sequence of clicks their online customers make, also known as their clickstream. Such detailed browsing information reveals customer preferences and, as such, can be used to improve product assortment and display decisions over time. In this paper, we consider a Bayesian framework to study how an e-tailer can draw inferences about customer preferences using clickstream and sales data, for utilitarian and hedonic product categories. For a given product category, the e-tailer dynamically determines the set of products to display on his website, via clickable pictures, and which products among the displayed ones to make available for purchase. We show that it may be optimal for the e-tailer to display "phantom" products, which are clickable, but later shown as unavailable for purchase. Under some conditions on the customers' clicking behavior on the website, we prove the optimality of an "offer less" strategy which is reminiscent of the well-known "stock more" result in multi-period newsvendor problem with demand learning. We also discuss the implications of our results on when the e-tailer should communicate product availability information with customers. Finally, we quantify the benefits which accrue from learning from clickstream data for the e-tailer. These benefits are substantial, especially for product categories for which customers do not have well-established preferences and go through an exploratory browsing process before making a purchase.

Suggested Citation

Honhon, Dorothee and Kadiyala, Bharadwaj and Ulu, Canan, Learning from Clickstream Data in Online Retail (August 27, 2016). Georgetown McDonough School of Business Research Paper No. 2830877. Available at SSRN: https://ssrn.com/abstract=2830877 or http://dx.doi.org/10.2139/ssrn.2830877

Dorothee Honhon

University of Texas at Dallas ( email )

2601 North Floyd Road
Richardson, TX 75083
United States

Bharadwaj Kadiyala (Contact Author)

University of Utah - Department of Operations and Information Systems ( email )

1645 E Campus Center Drive
University of Utah
Salt Lake City, UT 84112
United States

Canan Ulu

Georgetown University - Robert Emmett McDonough School of Business ( email )

3700 O Street, NW
Washington, DC 20057
United States

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