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A Multiactivity Latent Attrition Model for Customer Base Analysis

Author

Listed:
  • David A. Schweidel

    (Goizueta Business School, Emory University, Atlanta, Georgia 30322)

  • Young-Hoon Park

    (Samuel Curtis Johnson Graduate School of Management, Cornell University, Ithaca, New York 14853)

  • Zainab Jamal

    (Hewlett-Packard Labs, Palo Alto, California 94304)

Abstract

Customer base analysis is a key element in customer valuation and can provide guidance for decisions such as resource allocation. Yet extant models often focus on a single activity, such as purchases from a retailer or donations to a nonprofit organization. These models do not consider other ways that an individual may engage with an organization, such as purchasing in multiple brands or contributing user-generated content. In this research, we propose a framework to generalize extant models for customer base analysis to multiple activities.Using the data from a website that allows users to purchase digital content and/or post digital content at no charge, we develop a flexible “buy ‘til you die” model to empirically examine how the two activities are related. Compared with benchmarks, our model more accurately forecasts the future behavior for both types of activities. In addition to finding evidence of coincidence between the activities while customers are “alive,” we find that the latent attrition processes are related. This suggests that conducting one type of activity is informative of whether customers are still alive to conduct another type of activity and, consequently, affects inferences of customer value.

Suggested Citation

  • David A. Schweidel & Young-Hoon Park & Zainab Jamal, 2014. "A Multiactivity Latent Attrition Model for Customer Base Analysis," Marketing Science, INFORMS, vol. 33(2), pages 273-286, March.
  • Handle: RePEc:inm:ormksc:v:33:y:2014:i:2:p:273-286
    DOI: 10.1287/mksc.2013.0832
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    References listed on IDEAS

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    Cited by:

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    4. Park, Chang Hee & Park, Young-Hoon & Schweidel, David A., 2014. "A multi-category customer base analysis," International Journal of Research in Marketing, Elsevier, vol. 31(3), pages 266-279.
    5. Park, Chang Hee, 2017. "Online Purchase Paths and Conversion Dynamics across Multiple Websites," Journal of Retailing, Elsevier, vol. 93(3), pages 253-265.
    6. Kappe, Eelco & Stadler Blank, Ashley & DeSarbo, Wayne S., 2018. "A random coefficients mixture hidden Markov model for marketing research," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 415-431.
    7. Jonathan Z. Zhang & Chun-Wei Chang, 2021. "Consumer dynamics: theories, methods, and emerging directions," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 166-196, January.
    8. Park, Chang Hee & Park, Young-Hoon & Schweidel, David A., 2018. "The effects of mobile promotions on customer purchase dynamics," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 453-470.
    9. Clarence Lee & Elie Ofek & Thomas J. Steenburgh, 2018. "Personal and Social Usage: The Origins of Active Customers and Ways to Keep Them Engaged," Management Science, INFORMS, vol. 64(6), pages 2473-2495, June.
    10. Kumar, Ashish, 2021. "An empirical examination of the effects of design elements of email newsletters on consumers’ email responses and their purchase," Journal of Retailing and Consumer Services, Elsevier, vol. 58(C).

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