Estimating the Long-Term Impact of Major Events on Consumption Patterns: Evidence from COVID-19

Abstract

We propose a general and flexible methodology for inferring the time-varying effects of a discrete event on consumer behavior. Our method enables analysis of events that span the target population being analyzed, where there is no contemporaneous "control group" and/or it is not possible to measure treatment status, by comparing the purchasing behavior of cohorts acquired at different times. Our method applies non-parametric age-period-cohort (APC) models, commonly used in sociology but with limited adoption in marketing, in conjunction with a predictive model of the counterfactual no-event baseline (i.e., an event study model). We use this method to infer how the COVID-19 pandemic has affected 12 online and offline consumption categories. Our results suggest that the pandemic initially drove significant spending lifts at e-commerce businesses at the expense of brick-and-mortar alternatives. After two years, however, these changes have largely reverted. We observe significant heterogeneity across categories, with more persistent changes in subscription-based categories and more transient changes in categories based on discretionary purchases, especially those of durable goods.

Keywords: customer relationship management; COVID-19; age-period-cohort model; persistence; causal inference; forecasting