Browsing the Aisles or Browsing the App? How Online Grocery Shopping is Changing What We Buy

This paper studies the impact of the online grocery retail channel on the variety and composition of shopping baskets. We use data from around one million shopping trips that capture both offline (brick-and-mortar) and online (Instacart) shopping behavior. We use unsupervised machine learning algorithms that are agnostic to the channel type to infer what constitutes a regular restocking shopping trip for each household. Our empirical analysis reveals that shopping basket variety, as measured by the number of categories purchased, is lower for online shopping trips and that the composition differs from offline trips in identifiable ways. We find that Instacart shopping baskets typically have 19.6% fewer fresh vegetable items and fewer items from impulse purchase categories that include candy (6.6%), bakery desserts (5.7%), and savory snacks (4.1%). Importantly, these fresh vegetables and impulse purchases are not picked up via alternative or additional shopping trips within a seven-day period. We show that these purchasing patterns are unlikely to be driven by price or assortment differences across the two channels or stock-outs due to Covid-19. Finally, we find that within a given household, the Instacart baskets are significantly more similar to each other than offline baskets, potentially suggesting a past-orders-shortcut mechanism behind our results