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Clickstream Data and Inventory Management: Model and Empirical Analysis

Huang, T; Van Mieghem, JA; (2014) Clickstream Data and Inventory Management: Model and Empirical Analysis (Singhal, K, Trans.). Production and Operations Management , 23 (3) pp. 333-347. 10.1111/poms.12046. Green open access

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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.

Type: Article
Title: Clickstream Data and Inventory Management: Model and Empirical Analysis
Location: USA
Open access status: An open access version is available from UCL Discovery
DOI: 10.1111/poms.12046
Publisher version: http://dx.doi.org/10.1111/poms.12046
Additional information: © 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.
Keywords: click tracking; advance demand information; inventory theory and control; empirical research; dynamic programming; econometric analysis; big data;
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > UCL School of Management
URI: http://discovery.ucl.ac.uk/id/eprint/1381246
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