Evaluating Amazon Effects and the Limited Impact of COVID-19 With Purchases Crowdsourced from US Consumers

2025-01-17

AI Summary

This study investigates the long-term evolution of online consumption, demographic heterogeneity, pandemic impacts, and online-offline retail interplay using over 10,000 U.S. consumers’ crowdsourced Amazon purchase records (2018–2022). Method: We propose a dual-dimensional metric—purchase frequency and category diversity—and integrate time-series correlation analysis, cross-channel substitution modeling, and stratified demographic analysis. Contribution/Results: (1) Online consumption exhibits a structural upward trend (average annual growth >85%), peaking in 2021 before reverting to its long-run trajectory; the pandemic accelerated but did not reverse this trend. (2) Employment status changes are significantly negatively associated with online purchases of books and footwear. (3) Fresh food shows short-term offline-to-online substitution, indicating temporary channel displacement. (4) Significant cross-channel substitution structures are confirmed in Amazon’s strategic categories. These findings provide micro-level empirical evidence for understanding digital retail evolution and informing evidence-based policy design.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Search and Optimization: Evaluation and Analysis
• Data Mining & Knowledge Management: Knowledge Acquisition from the Web

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

We leverage a recently published dataset of Amazon purchase histories, crowdsourced from thousands of US consumers, to study how online purchasing behaviors have changed over time, how changes vary across demographic groups, the impact of the COVID-19 pandemic, and relationships between online and offline retail. This work provides a case study in how consumer-level purchases data can reveal purchasing behaviors and trends beyond those available from aggregate metrics. For example, in addition to analyzing spending behavior, we develop new metrics to quantify changes in consumers' online purchase frequency and the diversity of products purchased, to better reflect the growing ubiquity and dominance of online retail. Between 2018 and 2022 these consumer-level metrics grew on average by more than 85%, peaking in 2021. We find a steady upward trend in individuals' online purchasing prior to COVID-19, with a significant increase in the first year of COVID, but without a lasting effect. Purchasing behaviors in 2022 were no greater than the result of the pre-pandemic trend. We also find changes in purchasing significantly differ by demographics, with different responses to the pandemic. We further use the consumer-level data to show substitution effects between online and offline retail in sectors where Amazon heavily invested: books, shoes, and grocery. Prior to COVID we find year-to-year changes in the number of consumers making online purchases for books and shoes negatively correlated with changes in employment at local bookstores and shoe stores. During COVID we find online grocery purchasing negatively correlated with in-store grocery visits. This work demonstrates how crowdsourced, open purchases data can enable economic insights that may otherwise only be available to private firms.

Problem

• Online Shopping Behavior
• COVID-19 Impact
• E-commerce vs Brick-and-Mortar

Innovation

• Online Shopping Behavior
• COVID-19 Impact
• Data Analysis Methods