Evaluating Amazon Effects and the Limited Impact of COVID-19 With Purchases Crowdsourced from US Consumers
- URL: http://arxiv.org/abs/2501.10596v1
- Date: Fri, 17 Jan 2025 23:03:56 GMT
- Title: Evaluating Amazon Effects and the Limited Impact of COVID-19 With Purchases Crowdsourced from US Consumers
- Authors: Alex Berke, Dana Calacci, Alex, Pentland, Kent Larson,
- Abstract summary: We leverage a recently published dataset of Amazon purchase histories, crowdsourced from thousands of US consumers.
We 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.
- Score: 42.80166440735519
- License:
- 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.
Related papers
- Protecting User Privacy in Online Settings via Supervised Learning [69.38374877559423]
We design an intelligent approach to online privacy protection that leverages supervised learning.
By detecting and blocking data collection that might infringe on a user's privacy, we can restore a degree of digital privacy to the user.
arXiv Detail & Related papers (2023-04-06T05:20:16Z) - Macroscopic properties of buyer-seller networks in online marketplaces [55.41644538483948]
We analyze two datasets containing 245M transactions that took place on online marketplaces between 2010 and 2021.
We show that transactions in online marketplaces exhibit strikingly similar patterns despite significant differences in language, lifetimes, products, regulation, and technology.
arXiv Detail & Related papers (2021-12-16T18:00:47Z) - Characterization of Frequent Online Shoppers using Statistical Learning
with Sparsity [54.26540039514418]
This work reports a method to learn the shopping preferences of frequent shoppers to an online gift store by combining ideas from retail analytics and statistical learning with sparsity.
arXiv Detail & Related papers (2021-11-11T05:36:39Z) - SizeFlags: Reducing Size and Fit Related Returns in Fashion E-Commerce [3.324876873771105]
We introduce SizeFlags, a probabilistic Bayesian model based on weakly annotated large-scale data from customers.
We demonstrate the strong impact of the proposed approach in reducing size-related returns in online fashion over 14 countries.
arXiv Detail & Related papers (2021-06-07T11:43:40Z) - Debate on Online Social Networks at the Time of COVID-19: An Italian
Case Study [4.176752121302988]
We analyze how the interaction patterns around popular influencers in Italy changed during the first six months of 2020.
We collected a large dataset for this group of public figures, including more than 54 million comments on over 140 thousand posts.
We also analyze the user sentiment through the psycholinguistic properties of comments, and the results testified the rapid boom and disappearance of topics related to the pandemic.
arXiv Detail & Related papers (2021-06-02T08:25:19Z) - OPAM: Online Purchasing-behavior Analysis using Machine learning [0.8121462458089141]
We present a customer purchasing behavior analysis system using supervised, unsupervised and semi-supervised learning methods.
The proposed system analyzes session and user-journey level purchasing behaviors to identify customer categories/clusters.
arXiv Detail & Related papers (2021-02-02T17:29:52Z) - Face Off: Polarized Public Opinions on Personal Face Mask Usage during
the COVID-19 Pandemic [77.34726150561087]
A series of policy shifts by various governmental bodies have been speculated to have contributed to the polarization of face masks.
We propose a novel approach to accurately gauge public sentiment towards face masks in the United States during COVID-19.
We find two key policy-shift events contributed to statistically significant changes in sentiment for both Republicans and Democrats.
arXiv Detail & Related papers (2020-10-31T18:52:41Z) - Online-to-Offline Advertisements as Field Experiments [0.17877823660518105]
We study the difference in offline behavior between customers who received online advertisements and regular customers.
We then find a long-run effect of this externality of advertising that a certain portion of the customers invited to the offline shops revisit these shops.
arXiv Detail & Related papers (2020-10-18T22:04:56Z) - Face to Purchase: Predicting Consumer Choices with Structured Facial and
Behavioral Traits Embedding [53.02059906193556]
We propose to predict consumers' purchases based on their facial features and purchasing histories.
We design a semi-supervised model based on a hierarchical embedding network to extract high-level features of consumers.
Our experimental results on a real-world dataset demonstrate the positive effect of incorporating facial information in predicting consumers' purchasing behaviors.
arXiv Detail & Related papers (2020-07-14T06:06:41Z) - How do online consumers review negatively? [1.52292571922932]
Using 1, 450, 000 negative reviews from JD.com, the largest B2C platform in China, the behavioral patterns from temporal, perceptional and emotional perspectives are explored.
Consumers from lower levels express more intensive negative feelings, especially on product pricing and seller attitudes.
The value of negative reviews from higher-level consumers is unexpectedly highlighted because of less emotionalization and less biased narration.
arXiv Detail & Related papers (2020-04-28T12:54:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.