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

TY - JOUR

T1 - Understanding consumer behavior during and after a pandemic:

T2 - Implications for customer lifetime value prediction models

AU - Tudoran, Ana Alina

AU - Thomsen , Charlotte

AU - Thomasen , Sophie

PY - 2024/3

Y1 - 2024/3

N2 - 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.

AB - 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.

KW - Customer lifetime value, stochastic models, Gamma-Gamma spending model, online retail grocery, COVID-19

KW - COVID-19

KW - Stochastic models

KW - Customer lifetime value

KW - Online retail grocery

KW - Gamma-Gamma spending model

U2 - 10.1016/j.jbusres.2024.114527

DO - 10.1016/j.jbusres.2024.114527

M3 - Journal article

SN - 0148-2963

VL - 174

JO - Journal of Business Research

JF - Journal of Business Research

M1 - 114527

ER -