Capturing Evolving Visit Behavior in Clickstream Data

Abstract

Many online retailers monitor visitor traffic as a measure of their storesâ success. However, summary measures such as the total number of visits per month provide little insight about individual-level shopping behavior. Additionally, behavior may evolve over time, especially in a changing environment like the Internet. Understanding the nature of this evolution provides valuable knowledge that can influence how a retail store is managed and marketed. This paper develops an individual-level model for store visiting behavior based on Internet clickstream data. We capture cross-sectional variation in store-visit behavior as well as changes over time as visitors gain experience with the store. That is, as someone makes more visits to a site, her latent rate of visit may increase, decrease, or remain unchanged as in the case of static, mature markets. So as the composition of the customer population changes (e.g., as customers mature or as large numbers of new and inexperienced Internet shoppers enter the market), the overall degree of visitor heterogeneity that each store faces may shift. We also examine the relationship between visiting frequency and purchasing propensity. Previous studies suggest that customers who shop frequently may be more likely to make a purchase on any given shopping occasion. As a result, frequent shoppers often comprise the preferred target segment. We find evidence supporting the fact that people who visit a store more frequently are more likely to buy. However, we also show that changes (i.e., evolution) in an individualâ s visit frequency over time provides further information regarding which customer segments are more likely to buy. Rather than simply targeting all frequent shoppers, our results suggest that a more refined segmentation approach that incorporates how much an individualâ s behavior is changing could more efficiently identify a profitable target segment.