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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 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.

Suggested Citation

  • Anastasios Panagiotelis & Michael S. Smith & Peter J Danaher, 2013. "From Amazon to Apple: Modeling Online Retail Sales, Purchase Incidence and Visit Behavior," Monash Econometrics and Business Statistics Working Papers 5/13, Monash University, Department of Econometrics and Business Statistics.
  • Handle: RePEc:msh:ebswps:2013-5
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    File URL: http://business.monash.edu/econometrics-and-business-statistics/research/publications/ebs/wp05-13.pdf
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    References listed on IDEAS

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    1. Murray D. Smith, 2003. "Modelling sample selection using Archimedean copulas," Econometrics Journal, Royal Economic Society, vol. 6(1), pages 99-123, June.
    2. Zhang, Xibin & King, Maxwell L. & Hyndman, Rob J., 2006. "A Bayesian approach to bandwidth selection for multivariate kernel density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 3009-3031, July.
    3. Francis Vella, 1998. "Estimating Models with Sample Selection Bias: A Survey," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 127-169.
    4. Zhang, Xibin & Brooks, Robert D. & King, Maxwell L., 2009. "A Bayesian approach to bandwidth selection for multivariate kernel regression with an application to state-price density estimation," Journal of Econometrics, Elsevier, vol. 153(1), pages 21-32, November.
    5. Cameron,A. Colin & Trivedi,Pravin K., 2008. "Microeconometrics," Cambridge Books, Cambridge University Press, number 9787111235767, March.
    6. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 361-393.
    7. van Hasselt, Martijn, 2011. "Bayesian inference in a sample selection model," Journal of Econometrics, Elsevier, vol. 165(2), pages 221-232.
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    Cited by:

    1. 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.

    More about this item

    Keywords

    Online purchasing; panel data; copulas; marketing models;

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