Capturing Evolving Visit Behavior in Clickstream Data

Many online retailers monitor visitor traffic as a measure of their stores’ success. However, summary measures such as the number of hits per month provide little insight into individual consumers’ 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 web store is managed and marketed. This paper develop 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 consumers gain experience with the store. That is, as consumers make more visits to a site, their latent rate of visit may increase, decrease, or remain unchanged as in the case of static, mature markets. So as the composition of the consumer population changes (e.g., as consumers mature or as large numbers of new and inexperienced Internet shoppers enter the market), the overall degree of consumer heterogeneity that each store faces may shift. We also examine the relationship between visiting frequency and purchasing propensity. Previous studies suggest that consumers 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 individual’s visit frequency over time provides further information regarding which consumer 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 individual’s behavior is changing could more efficiently identify a profitable target segment.