From Amazon to Apple: Modeling Online Retail Sales, Purchase Incidence and Visit Behavior

In this study we construct a multivariate stochastic model for website visit duration, page views, purchase incidence and the sale amount for online retailers. The model is constructed by composition from parametric distributions that account for consumer heterogeneity, and involves copula components. Our model is readily estimated using full maximum likelihood, allows for the strong nonlinear relationships between the sales and visit variables to be explored in detail, and can be used to construct sales predictions. We examine a number of top-ranked U.S. online retailers, and find that the visit duration and the number of pages viewed are both related to sales, but in very different ways for different products. Using Bayesian methodology we show how the model can be extended to account for latent household segments, further accounting for consumer heterogeneity. The model can also be adjusted to accommodate a more accurate analysis of online retailers like apple.com that sell products at a very limited number of price points. In a validation study across a range of different websites, we find that the purchase incidence and sales amount are both forecast more accurately using our stochastic model, when compared to regression, probit regression and a popular data-mining method.