Matched product data is collected from the leading online grocers in the U.S. The same exact products are identified in scanner data. The paper documents pricing strategies within and across online (and offline) retailers. First, online retailers exhibit substantially less uniform pricing than offline retailers. Second, online price differentiation across competing chains in narrow geographies is higher than offline retailers. Third, variation in offline elasticities, shipping distance, pricing frequency, and local demo- graphics are utilized to explain price differentiation. Surprisingly, pricing technology (across time) magnifies price differentiation (across locations). This evidence motivates a high-frequency study to unpack the patterns of algorithmic pricing. The data shows that algorithms: personalize prices at the delivery zipcode level, update prices very frequently and in tiny magnitudes, reduce price synchronization, exhibit lower menu costs, constantly explore the price grid, and often match competitors’ prices.
Authors
- Acknowledgements & Disclosure
- Data and codes to reproduce the results will be publicly available. Aparicio: daparicio AT iese.edu, IESE Business School; Metzman: zmetzman AT mit.edu, MIT; Rigobon: rigobon AT mit.edu, MIT and NBER. The authors thank Matthew Gentzkow and Duncan Simester for detailed discussions. The authors also thank Emek Basker, Michael Baye, Alberto Cavallo, Glenn Ellison, Ricard Gil, Avi Goldfarb, Madhav Kumar, Jessie Liu, Alex MacKay, Preston McAfee, Filippo Mezzanotti, Mateo Montenegro, Sarah Moshary, Leonard Nakamura, Thomas Otter, Ariel Pakes, Elena Pastorino, Ananya Sen, Ben Shiller, Hal Varian, and seminar participants at the Spring 2021 NBER Economics of Digitization, for helpful comments. Nestor Santiago Perez provided outstanding research assistance. Authors’ own analyses calculated (or derived) based in part on data from Nielsen Consumer LLC and marketing databases provided through the NielsenIQ Datasets at the Kilts Center for Marketing Data Center at The University of Chicago Booth School of Business. The conclusions drawn from the NielsenIQ data are those of the researchers and do not reflect the views of Nielsen. Nielsen is not responsible for, had no role in, and was not involved in analyzing and preparing the results reported herein. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
- DOI
- https://doi.org/10.3386/w28639
- Published in
- United States of America
