A heuristic for identifying algorithmic pricing in low-resolution price data

Abstract

Existing methods for detecting algorithmic pricing rely on high-frequency data with sub-daily timestamps, yet academic researchers and competition authorities typically have access only to daily price snapshots. This paper develops a heuristic that identifies likely algorithmic pricers in such low-resolution data by combining two standardised metrics: the Average Rate of Price Changes (ARPC), capturing the frequency of price adjustments, and a Market Presence Ratio (MPR), capturing sustained retailer engagement. Requiring both metrics to exceed a threshold reduces false positives from promotional sellers and transient market participants. We apply the heuristic to a dataset of 10,365 unique retailer-category observations from the PriceSpy price comparison platform, covering seven national markets and 16 product categories over an 18-month period (July 2021–December 2022). Our results show that algorithmic pricing is prevalent across markets, with algorithmic pricers present in all 16 categories in the United Kingdom and in all seven countries for six categories. Prevalence is notably higher in consumer electronics than in household appliances. As a lower-bound estimate, approximately 4.5% of retailers are classified as algorithmic pricers at our baseline threshold. The heuristic provides a practical screening tool for competition authorities investigating algorithmic pricing adoption using commercially available data.