Crowd-assisted Search for Price Discrimination in E-Commerce: First results

TL;DR

This study uses crowd-sourcing via a browser extension to systematically detect and analyze instances of online price discrimination, revealing significant price variations across different retailers and user vantage points.

Contribution

It introduces a scalable crowd-sourcing approach to identify and quantify online price discrimination, expanding previous smaller-scale studies with a larger dataset and systematic analysis.

Findings

Prices vary by 10%-30% across retailers.

Some cases show price differences up to a factor of 2.

Price gaps are not explained by currency, shipping, or taxes.

Abstract

After years of speculation, price discrimination in e-commerce driven by the personal information that users leave (involuntarily) online, has started attracting the attention of privacy researchers, regulators, and the press. In our previous work we demonstrated instances of products whose prices varied online depending on the location and the characteristics of perspective online buyers. In an effort to scale up our study we have turned to crowd-sourcing. Using a browser extension we have collected the prices obtained by an initial set of 340 test users as they surf the web for products of their interest. This initial dataset has permitted us to identify a set of online stores where price variation is more pronounced. We have focused on this subset, and performed a systematic crawl of their products and logged the prices obtained from different vantage points and browser configurations. By analyzing this dataset we see that there exist several retailers that return prices for the same product that vary by 10%-30% whereas there also exist isolated cases that may vary up to a multiplicative factor, e.g., x2. To the best of our efforts we could not attribute the observed price gaps to currency, shipping, or taxation differences.