How did COVID-19 change what people buy: Evidence from a supermarket chain

https://doi.org/10.1016/j.jretconser.2022.103010Get rights and content

Abstract

This research takes a retrospective view of the COVID-19 pandemic and attempts to accurately measure its impact on sales of different product categories in grocery retail. In total 150 product categories were analyzed using the data of a major supermarket chain in the Netherlands. We propose to measure the pandemic impact by excess sales – the difference of actual and expected sales. We show that the pandemic impact is twofold: (1) There was a large but brief growth at 30.6% in excess sales associated with panic buying across most product categories within a two-week period; and (2) People spending most of their time at home due to imposed restrictions resulted in an estimated 5.4% increase in total sales lasting as long as the restrictions were active. The pandemic impact on different product categories varies in magnitudes and timing. Using time series clustering, we identified eight clusters of categories with similar pandemic impacts. Using clustering results, we project that product categories used for cooking, baking or meal preparation in general will have elevated sales even after the pandemic.

Keywords

Consumer behavior
COVID-19 pandemic
Panic buying
Retail
Sales forecast
Time series clustering

Cited by (0)

Danas Zuokas, a Senior Data Scientist at NielsenIQ received his Ph.D. in Mathematics from the Vilnius University in Lithuania. He also has a bachelor's degree in Mathematics with minor degree in Business Management and a master's degree in Mathematical methods in Economics from the Vytautas Magnus University in Lithuania. Dr. Zuokas has 15 years of experience in applying various statistical and machine learning methods to solve problems in retail (customer segmentation, market basket analysis), telecommunication (churn prediction, customer lifetime value), and investment management (portfolio optimization) industries.

Evren Gul, a Data Science Advisor at NielsenIQ received his Master of Statistics and Ph.D. in Industrial Engineering with concentration in Statistics from the Georgia Institute of Technology. He also obtained bachelor's and master's degrees from the Middle East Technical University in Turkey. Dr. Gul has 10 years of experience in research and development of novel statistical methods for solving complex problems and is currently focused on solving retail merchandising problems. He is a recipient of the Statistical Partnerships Among Academe, Industry & Government (SPAIG) Award from the American Statistical Association in 2020 and the Lloyd S. Nelson Award from the American Society for Quality in 2021.

Alvin Lim, the Chief Science Officer of NielsenIQ received his Ph.D. in Mathematical Sciences from the Johns Hopkins University. He also has a Bachelor of Science in Mathematics and a Master Science in Applied Mathematics from the University of the Philippines, Diliman. Dr. Lim has over 25 years of research and development experience and specializes in the solution of pricing, promotion, product assortment, and marketing mix optimization problems in retail and B2B applications. He is an industry leader in the research, development and application of pricing, revenue management and marketing analytics.

View Abstract