Clickstream data and inventory management: : model and empirical analysis

• Autores: Tingliang Huang, Jan A. Van Mieghem
• Localización: Production and Operations Management, ISSN-e 1937-5956, Vol. 23, Nº. 3, 2014, págs. 333-347
• Idioma: inglés
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• Resumen

  • 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.