Date of this Version
This paper develops a model of conversion behavior (i.e., converting store visits into purchases) that predicts each customer's probability of purchasing based on an observed history of visits and purchases. We offer an individual-level probability model that allows for different forms of customer heterogeneity in a very flexible manner. Specifically, we decompose an individual's conversion behavior into two components: one for accumulating visit effects and another for purchasing threshold effects. Each component is allowed to vary across households as well as over time. Visit effects capture the notion that store visits can play different roles in the purchasing process. For example, some visits are motivated by planned purchases, while others are associated with hedonic browsing (akin to window shopping); our model is able to accommodate these (and several other) types of visit-purchase relationships in a logical, parsimonious manner. The purchasing threshold captures the psychological resistance to online purchasing that may grow or shrink as a customer gains more experience with the purchasing process at a given website. We test different versions of the model that vary in the complexity of these two key components and also compare our general framework with popular alternatives such as logistic regression. We find that the proposed model offers excellent statistical properties, including its performance in a holdout validation sample, and also provides useful managerial diagnostics about the patterns underlying online buyer behavior.
Originally published in Management Science © 2004 INFORMS
This is a pre-publication version. The final version is available at http://dx.doi.org/10.1287/mnsc.1040.0153
stochastic models, e-commerce, online purchasing conversion, buyer behavior
Moe, W. W., & Fader, P. S. (2004). Dynamic Conversion Behavior at E-Commerce Sites. Management Science, 50 (3), 326-335. http://dx.doi.org/10.1287/mnsc.1040.0153
Behavioral Economics Commons, Business Administration, Management, and Operations Commons, Business Analytics Commons, Business Intelligence Commons, E-Commerce Commons, Marketing Commons, Sales and Merchandising Commons, Technology and Innovation Commons
Date Posted: 15 June 2018
This document has been peer reviewed.