Clickstream data and inventory management: Model and empirical analysis

Tingliang Huang, Jan A. Van Mieghem

Research output: Contribution to journalArticlepeer-review

75 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)333-347
Number of pages15
JournalProduction and Operations Management
Issue number3
StatePublished - Mar 2014


  • advance demand information
  • big data
  • click tracking
  • dynamic programming
  • econometric analysis
  • empirical research
  • inventory theory and control

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Management of Technology and Innovation


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