Competition in Retail Pricing Algorithms

Algorithms don't just come from the suggestion of AI methods,  we worked with many, over many years.   Here an attempt to infer retail pricing strategies from data, and implications.  Note increased data from online.  Also note the implication of action and reaction,  what actions are being caused by competitive actions.   Can be taken all the way to game theory, which we examined as well.

Competition in Pricing Algorithms
by Zach Y. Brown and Alexander MacKay

The adoption of pricing technology can lead to higher prices, by increasing the frequency of price changes and/or encoding pricing strategies in algorithms. This raises new antitrust questions for policymakers, as firms do not need to coordinate or collude to raise prices.
Author Abstract

Increasingly, retailers have access to better pricing technology, especially in online markets. Through pricing algorithms, firms can automate their response to rivals’ prices. What are the implications for price competition? We develop a model in which firms choose algorithms, rather than prices. Even with simple (i.e., linear) algorithms, competitive equilibria can have higher prices than in the standard simultaneous Bertrand pricing game. Using hourly prices of over-the-counter drugs from five major online retailers, we document evidence that these retailers possess different pricing technologies. In addition, we find pricing patterns consistent with competition in pricing algorithms. A simple calibration of the model suggests that pricing algorithms lead to meaningful increases in markups, especially for firms with superior pricing technology.

Paper Information
Full Working Paper Text (pdf)
Working Paper Publication Date: November 2019
HBS Working Paper Number: HBS Working Paper #20-067
Faculty Unit(s): Strategy