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

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  • Anastasios Panagiotelis
  • Michael S. Smith
  • Peter J. Danaher

Abstract

In this study, we propose a multivariate stochastic model for Web site visit duration, page views, purchase incidence, and the sale amount for online retailers. The model is constructed by composition from carefully selected distributions and involves copula components. It 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. The model is readily estimated using maximum likelihood, making it an attractive choice in practice given the large sample sizes that are commonplace in online retail studies. 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 a finite mixture model to account for consumer heterogeneity via latent household segmentation. 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 Web sites, we find that the purchase incidence and sales amount are both forecast more accurately using our model, when compared to regression, probit regression, a popular data-mining method, and a survival model employed previously in an online retail study. Supplementary materials for this article are available online.

Suggested Citation

  • Anastasios Panagiotelis & Michael S. Smith & Peter J. Danaher, 2014. "From Amazon to Apple: Modeling Online Retail Sales, Purchase Incidence, and Visit Behavior," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(1), pages 14-29, January.
    • Handle: RePEc:taf:jnlbes:v:32:y:2014:i:1:p:14-29
    DOI: 10.1080/07350015.2013.835729
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    Cited by:

    1. Junming Liu & Weiwei Chen & Jingyuan Yang & Hui Xiong & Can Chen, 2022. "Iterative Prediction-and-Optimization for E-Logistics Distribution Network Design," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 769-789, March.
    2. Panagiotelis, Anastasios & Czado, Claudia & Joe, Harry & Stöber, Jakob, 2017. "Model selection for discrete regular vine copulas," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 138-152.
    3. Jian Huang & Qinyu Chen & Chengqing Yu, 2022. "A New Feature Based Deep Attention Sales Forecasting Model for Enterprise Sustainable Development," Sustainability, MDPI, vol. 14(19), pages 1-18, September.

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