The copyright line in this article was changed on 11 August 2014 after online publication.
Clickstream Data and Inventory Management: Model and Empirical Analysis
Version of Record online: 19 JUL 2013
© 2013 The Authors. Production and Operations Management published by Wiley Periodicals, Inc. on behalf of Production and Operations Management Society
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Production and Operations Management
Volume 23, Issue 3, pages 333–347, March 2014
How to Cite
Huang, T. and Van Mieghem, J. A. (2014), Clickstream Data and Inventory Management: Model and Empirical Analysis. Production and Operations Management, 23: 333–347. doi: 10.1111/poms.12046
- Issue online: 14 MAR 2014
- Version of Record online: 19 JUL 2013
- Manuscript Accepted: DEC 2012
- Manuscript Received: JAN 2012
- click tracking;
- advance demand information;
- inventory theory and control;
- empirical research;
- dynamic programming;
- econometric analysis;
- big data
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.