Publication Type

Journal Article

Version

publishedVersion

Publication Date

8-2023

Abstract

This paper investigates the systematic differences between online and offline grocery shopping baskets using data from approximately two million brick-and-mortar and Instacart trips. We apply unsupervised machine learning algorithms agnostic to the shopping channel to identify what constitutes a typical food shopping trip for each household. We find that food shopping basket variety is significantly lower for online shopping trips as measured by the number of unique food categories and items purchased. Within a given household, the Instacart baskets are more similar to each other as compared with offline baskets with twice as many overlapping items between successive trips to the same retailer. These results suggest a potential link between online grocery shopping environments and heightened consumer inertia, which may lead to stronger brand loyalty and pose challenges for new entrants in establishing a customer base. Furthermore, Instacart baskets have 13% fewer fresh vegetables and 5%–7% fewer impulse purchases, such as candy, bakery desserts, and savory snacks, which are not compensated for by alternative or additional shopping trips. We discuss the implications of these systematic shopping basket differences for competition, product management, retailers, consumers, and online platforms.

Keywords

Digitization, Food Marketing, Omnichannel Retail, Grocery Industry, Variety

Discipline

Marketing

Areas of Excellence

Digital transformation

Publication

Marketing Science

Volume

43

Issue

5

First Page

506

Last Page

522

ISSN

0732-2399

Identifier

10.1287/mksc.2022.0292

Publisher

Institute for Operations Research and Management Sciences

Additional URL

https://doi.org/10.1287/mksc.2022.0292

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