Clickstream Data and Inventory Management: Model and Empirical Analysis

Forthcoming, Production and Operations Management

31 Pages Posted: 27 May 2011 Last revised: 19 Aug 2014

Date Written: Dec 23, 2011

Abstract

We consider firms that feature their products on the Internet but take orders offline. Click and order data are disjoint on such non-transactional websites and their matching is error-prone. Yet, their time separation may allow the firm to react and improve its tactical planning. We introduce a dynamic decision support model that augments the classic inventory planning model with additional clickstream state variables. Using a novel data set of matched online clickstream and offline purchasing data, we identify statistically significant clickstream variables and empirically investigate the value of clickstream tracking on non-transactional websites to improve inventory management. We show that the noisy clickstream data is statistically significant to predict the propensity, amount, and timing of offline orders. A counterfactual analysis shows that using the demand information extracted from the clickstream data can reduce the inventory holding and backordering cost by 3% to 5% in our data set.

Keywords: Inventory Theory and Control, Dynamic Programming, Econometric Analysis, Structural Estimation,

JEL Classification: C70

Suggested Citation

Huang, Tingliang and Van Mieghem, Jan Albert, Clickstream Data and Inventory Management: Model and Empirical Analysis (Dec 23, 2011). Forthcoming, Production and Operations Management, Available at SSRN: https://ssrn.com/abstract=1851046 or http://dx.doi.org/10.2139/ssrn.1851046

Tingliang Huang (Contact Author)

Haslam College of Business, University of Tennessee ( email )

Haslam College of Business
Stokely Management Center
Knoxville, TN 37000
United States

Jan Albert Van Mieghem

Northwestern University - Kellogg School of Management ( email )

2001 Sheridan Road
Evanston, IL 60208
United States

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