The pricing strategies of online grocery retailers

被引：12

作者：

Aparicio, Diego ^{[1
]}

Metzman, Zachary ^{[2
]}

Rigobon, Roberto ^{[2
,3
]}

机构：

[1] IESE Business Sch, Barcelona, Spain

[2] MIT, Cambridge, MA USA

[3] NBER, Cambridge, MA USA

来源：

QME-QUANTITATIVE MARKETING AND ECONOMICS | 2024年 / 22卷 / 01期

关键词：

Algorithmic pricing; Price dispersion; Price discrimination; Online groceries; Scanner data; Amazon; Walmart; L1; L2; M3; O3; DISPERSION EVIDENCE; EMPIRICAL-ANALYSIS; INTERNET; PRICES; COMMERCE; DISCRIMINATION; COLLUSION; MARKETS;

D O I：

10.1007/s11129-023-09273-w

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

This paper documents the differences in pricing strategies between online and offline (brick-and-mortar) channels. We collect price data for identical products from leading online grocery retailers in the United States and complement it with offline data for the same products from scanner data. Our findings reveal a consistent pattern: online retailers exhibit higher price dispersion than their offline counterparts. More specifically, online grocers employ price algorithms that amplify price discrimination in three key dimensions: (1) over time (through frequent price changes), (2) across locations (by charging varying prices based on delivery zipcodes), and (3) across sellers (by setting dispersed prices for identical products across rival retailers).

引用

页码：1 / 21

页数：21

共 56 条

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