Production and Operations Management

Using Clickstream Data to Improve Flash Sales Effectiveness

Early View

Flash sales retailers organize online campaigns where products are sold for a short period of time at a deep discount. The demand in these events is very uncertain, but clickstream data can potentially help retailers with detailed information about the shopping process, thereby allowing them to manage such risks. For this purpose, we build a predictive model for shoppers’ sequential decisions about visiting a campaign, obtaining product information and placing a purchase, which we validate using a large data set from a leading flash sales firm. The proposed hierarchical approach mirrors the different stages of the shopping funnel and allows for a direct decomposition of its main sources of variation, from customers arrival to products purchase. We identify life‐cycle dynamics and heterogeneity across campaigns and products as the main sources of variation: these allow us to provide the best predictions from a statistical standpoint, which outperform machine learning alternatives in out‐of‐sample accuracy. Our model thus enables flash sales retailers to learn about the performance of new products in a few hours and to update prices so as to better match supply and demand forecast and improve profits. We simulate our forecasting and optimization procedures on several campaigns including thousands of products and show that our model can successfully separate popular and unpopular products and lift revenues significantly.

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Published features on are checked for statistical accuracy by a panel from the European Network for Business and Industrial Statistics (ENBIS)   to whom Wiley and express their gratitude. This panel are: Ron Kenett, David Steinberg, Shirley Coleman, Irena Ograjenšek, Fabrizio Ruggeri, Rainer Göb, Philippe Castagliola, Xavier Tort-Martorell, Bart De Ketelaere, Antonio Pievatolo, Martina Vandebroek, Lance Mitchell, Gilbert Saporta, Helmut Waldl and Stelios Psarakis.