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Analyzing Recurrent Customer Purchases and Unobserved Defections: a Bayesian Data Augmentation Scheme

Author

Listed:
  • Sharad Borle

    (Rice University)

  • Siddharth Shekhar Singh

    (Indian School of Business)

  • Dipak C. Jain

    (Chulalongkorn University)

  • Ashutosh Patil

    (University of Missouri)

Abstract

Understanding customer purchase behavior is important for firms’ customer relationship management (CRM) efforts. In certain contexts of firm-customer relationship (e.g., retailing and catalog marketing), a firm does not observe customer defections or termination of relationship. Thus, specifying and estimating models of customer lifetime purchases is more difficult in such contexts, specifically in analyzing two key issues, viz. how often will a customer purchase from the firm (purchase frequency) and how long will the customer continue purchasing from the firm (customer lifetime). In this paper, we use a Bayesian data augmentation scheme that overcomes the estimation constraints and allows the use of all available information on customers. Using data from a direct marketing company and also an online classifieds company, we demonstrate the flexibility of this scheme by estimating existing models of lifetime purchase behavior, along with a new proposed model. We show how different types of customer heterogeneity (i.e., observed, unobserved, and time varying) can be incorporated in these models, which is made possible due to the data augmentation.

Suggested Citation

  • Sharad Borle & Siddharth Shekhar Singh & Dipak C. Jain & Ashutosh Patil, 2016. "Analyzing Recurrent Customer Purchases and Unobserved Defections: a Bayesian Data Augmentation Scheme," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 3(1), pages 11-28, March.
    • Handle: RePEc:spr:custns:v:3:y:2016:i:1:d:10.1007_s40547-015-0059-7
    DOI: 10.1007/s40547-015-0059-7
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    References listed on IDEAS

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