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Understanding consumer behavior during and after a Pandemic: Implications for customer lifetime value prediction models

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  • Tudoran, Ana Alina
  • Hjerrild Thomsen, Charlotte
  • Thomasen, Sophie

Abstract

Our study uses a cohort analysis to investigate Customer Lifetime Value (CLV) for customer cohorts acquired before and during the COVID-19 pandemic. The research estimates CLV in a continuous-time setting of customer transactions within the online grocery sector. Stochastic models are combined with the Gamma-Gamma spending model to predict CLV at individual and aggregate levels. The findings reveal the satisfactory fit of the models at both individual and aggregate levels. Combined with the Gamma-Gamma model, the MBG/NBD model stands out as the top performer, accurately classifying over 60 % of the best-CLV customers (top 10 % and 20 %). Cohort-based analyses outperform overall sample models in terms of out-of-sample errors. Furthermore, CLV prediction models differ between the customer cohorts analyzed. The models for the pre-COVID-19 cohort underestimate the cumulative CLV, whereas models for the COVID-19 cohort overestimate it. These discrepancies can relate to the shifting behavior of the COVID-19 and pre-COVID-19 customer cohorts.

Suggested Citation

  • Tudoran, Ana Alina & Hjerrild Thomsen, Charlotte & Thomasen, Sophie, 2024. "Understanding consumer behavior during and after a Pandemic: Implications for customer lifetime value prediction models," Journal of Business Research, Elsevier, vol. 174(C).
    • Handle: RePEc:eee:jbrese:v:174:y:2024:i:c:s0148296324000316
    DOI: 10.1016/j.jbusres.2024.114527
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    1. Mele, Emanuele & Filieri, Raffaele & De Carlo, Manuela, 2023. "Pictures of a crisis. Destination marketing organizations’ Instagram communication before and during a global health crisis," Journal of Business Research, Elsevier, vol. 163(C).
    2. Sheth, Jagdish, 2020. "Impact of Covid-19 on consumer behavior: Will the old habits return or die?," Journal of Business Research, Elsevier, vol. 117(C), pages 280-283.
    3. Jaeung Sim & Daegon Cho & Youngdeok Hwang & Rahul Telang, 2022. "Frontiers: Virus Shook the Streaming Star: Estimating the COVID-19 Impact on Music Consumption," Marketing Science, INFORMS, vol. 41(1), pages 19-32, January.
    4. Raj, Alok & Mukherjee, Abheek Anjan & de Sousa Jabbour, Ana Beatriz Lopes & Srivastava, Samir K., 2022. "Supply chain management during and post-COVID-19 pandemic: Mitigation strategies and practical lessons learned," Journal of Business Research, Elsevier, vol. 142(C), pages 1125-1139.
    5. W-K Ching & M K Ng & K-K Wong & E Altman, 2004. "Customer lifetime value: stochastic optimization approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(8), pages 860-868, August.
    6. Meyer, Brent H. & Prescott, Brian & Sheng, Xuguang Simon, 2022. "The impact of the COVID-19 pandemic on business expectations," International Journal of Forecasting, Elsevier, vol. 38(2), pages 529-544.
    7. Kirk, Colleen P. & Rifkin, Laura S., 2020. "I'll trade you diamonds for toilet paper: Consumer reacting, coping and adapting behaviors in the COVID-19 pandemic," Journal of Business Research, Elsevier, vol. 117(C), pages 124-131.
    8. Alok Raj & Abheek Anjan Mukherjee & Ana Beatriz Lopes de Sousa Jabbour & Samir K. Srivastava, 2022. "Supply chain management during and post-COVID-19 pandemic: Mitigation strategies and practical lessons learned," Post-Print hal-04275089, HAL.
    9. Jan Roelf Bult & Tom Wansbeek, 1995. "Optimal Selection for Direct Mail," Marketing Science, INFORMS, vol. 14(4), pages 378-394.
    10. Dwivedi, Yogesh K. & Hughes, D. Laurie & Coombs, Crispin & Constantiou, Ioanna & Duan, Yanqing & Edwards, John S. & Gupta, Babita & Lal, Banita & Misra, Santosh & Prashant, Prakhar & Raman, Ramakrishn, 2020. "Impact of COVID-19 pandemic on information management research and practice: Transforming education, work and life," International Journal of Information Management, Elsevier, vol. 55(C).
    11. Patrick Bachmann & Markus Meierer & Jeffrey Näf, 2021. "The Role of Time-Varying Contextual Factors in Latent Attrition Models for Customer Base Analysis," Marketing Science, INFORMS, vol. 40(4), pages 783-809, July.
    12. David C. Schmittlein & Robert A. Peterson, 1994. "Customer Base Analysis: An Industrial Purchase Process Application," Marketing Science, INFORMS, vol. 13(1), pages 41-67.
    13. Makoto Abe, 2009. "“Counting Your Customers” One by One: A Hierarchical Bayes Extension to the Pareto/NBD Model," Marketing Science, INFORMS, vol. 28(3), pages 541-553, 05-06.
    14. Piñeiro-Chousa, Juan & López-Cabarcos, M. Ángeles & Pérez-Pico, Ada M. & Caby, Jérôme, 2023. "The influence of Twitch and sustainability on the stock returns of video game companies: Before and after COVID-19," Journal of Business Research, Elsevier, vol. 157(C).
    15. Bas Donkers & Peter Verhoef & Martijn Jong, 2007. "Modeling CLV: A test of competing models in the insurance industry," Quantitative Marketing and Economics (QME), Springer, vol. 5(2), pages 163-190, June.
    16. Albert C. Bemmaor & Nicolas Glady, 2012. "Modeling Purchasing Behavior with Sudden "Death": A Flexible Customer Lifetime Model," Management Science, INFORMS, vol. 58(5), pages 1012-1021, May.
    17. David C. Schmittlein & Donald G. Morrison & Richard Colombo, 1987. "Counting Your Customers: Who-Are They and What Will They Do Next?," Management Science, INFORMS, vol. 33(1), pages 1-24, January.
    18. Reutterer, Thomas & Platzer, Michael & Schröder, Nadine, 2021. "Leveraging purchase regularity for predicting customer behavior the easy way," International Journal of Research in Marketing, Elsevier, vol. 38(1), pages 194-215.
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