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Dynamic Conversion Behavior at E-Commerce Sites

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Author Info

  • Wendy W. Moe

    ()
    (Department of Marketing, McCombs School of Business, University of Texas at Austin, 1 University Station, B6700, Austin, Texas 78712)

  • Peter S. Fader

    ()
    (Department of Marketing, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

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    Abstract

    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.

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    File URL: http://dx.doi.org/10.1287/mnsc.1040.0153
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 50 (2004)
    Issue (Month): 3 (March)
    Pages: 326-335

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    Handle: RePEc:inm:ormnsc:v:50:y:2004:i:3:p:326-335

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    Related research

    Keywords: stochastic models; e-commerce; online purchasing conversion; buyer behavior;

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    Cited by:
    1. Mihai TICHINDELEAN, 2013. "Models Used for Measuring Customer Engagement," Expert Journal of Marketing, Sprint Investify, vol. 1(1), pages 38-49.
    2. Lynn Wu & Erik Brynjolfsson, 2013. "The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales," NBER Chapters, in: Economics of Digitization National Bureau of Economic Research, Inc.
    3. Benjamin Reed Shiller, 2013. "First Degree Price Discrimination Using Big Data," Working Papers 58, Brandeis University, Department of Economics and International Businesss School, revised Sep 2013.
    4. Garrow, Laurie A. & Hotle, Susan & Mumbower, Stacey, 2012. "Assessment of product debundling trends in the US airline industry: Customer service and public policy implications," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(2), pages 255-268.

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