Premium
Clickstream Data and Inventory Management: Model and Empirical Analysis
Author(s) -
Huang Tingliang,
Van Mieghem Jan A.
Publication year - 2014
Publication title -
production and operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.279
H-Index - 110
eISSN - 1937-5956
pISSN - 1059-1478
DOI - 10.1111/poms.12046
Subject(s) - clickstream , counterfactual thinking , computer science , transaction data , purchasing , data set , matching (statistics) , the internet , data mining , business , artificial intelligence , marketing , database , statistics , world wide web , philosophy , database transaction , mathematics , epistemology , web api , web modeling
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.