Using Clickstream Data to Improve Flash Sales Effectiveness

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.