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A Partially Hidden Markov Model of Customer Dynamics for CLV Measurement

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  • Romero, Jaime
  • van der Lans, Ralf
  • Wierenga, Berend

Abstract

Customer lifetime value (CLV) measurement is challenging as it requires forecasting customers' future purchases. Existing stochastic CLV models for this purpose generally make the following assumptions: 1) purchase behavior of customers can be described by purchase frequency and the average monetary value of transactions, 2) customers keep the same purchase behavior pattern over time, 3) purchase frequency and monetary value are independent, and 4) customers are active during a limited period of time after which they permanently defect. We develop a new stochastic model that relaxes these four assumptions. First, in addition to the number of transactions and its monetary values, we also model purchase incidence decisions (i.e. whether or not to purchase). Second, our partially hidden Markov truncated–NBD-GG (PHM/TNBD-GG) model allows dynamic purchase patterns, dependence between purchase frequency and monetary value, and customers to become active after a few periods of temporary inactivity. Validation of our model on two datasets demonstrates that if assumptions 1 to 4 of existing stochastic models are violated our model produces more accurate forecasts of future customer behavior.

Suggested Citation

  • Romero, Jaime & van der Lans, Ralf & Wierenga, Berend, 2013. "A Partially Hidden Markov Model of Customer Dynamics for CLV Measurement," Journal of Interactive Marketing, Elsevier, vol. 27(3), pages 185-208.
    • Handle: RePEc:eee:joinma:v:27:y:2013:i:3:p:185-208
    DOI: 10.1016/j.intmar.2013.04.003
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    2. James Agarwal & Wayne DeSarbo & Naresh K. Malhotra & Vithala Rao, 2015. "An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 2(1), pages 19-40, March.
    3. Valendin, Jan & Reutterer, Thomas & Platzer, Michael & Kalcher, Klaudius, 2022. "Customer base analysis with recurrent neural networks," International Journal of Research in Marketing, Elsevier, vol. 39(4), pages 988-1018.
    4. Rahul Madhavan & Ankit Baraskar, 2017. "A generalized Bayesian framework for the analysis of subscription based businesses," Papers 1704.05729, arXiv.org.
    5. Chen, Xian & Bai, Shuotian & Wei, Yongqin & Zhao, Yanhui & Yan, Peng & Jiang, Hai, 2023. "Passenger engagement dynamics in ride-hailing services: A heterogeneous hidden Markov approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 171(C).
    6. 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.
    7. Gui Liberali & Alina Ferecatu, 2022. "Morphing for Consumer Dynamics: Bandits Meet Hidden Markov Models," Marketing Science, INFORMS, vol. 41(4), pages 769-794, July.
    8. Juram Kim & Changyong Lee, 2017. "Stochastic service life cycle analysis using customer reviews," The Service Industries Journal, Taylor & Francis Journals, vol. 37(5-6), pages 296-316, April.
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