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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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