Algorithmic pricing in horizontal merger review: An initial assessment

With the rapid progress in  Machine Learning (ML) and Artificial Intelligence (AI) in the last decade, sophisticated algorithmic pricing has become common.¹ Increasingly, more firms have in-house data science teams that leverage AI technologies to optimize prices and make other strategic decisions. There are also many third-party providers of AI-powered pricing algorithms for different applications (e.g., Competera, Eversight, Intelligence Node, Perfect Price, Remi, and Wise Athena, to name a few).

Pricing algorithms, especially those powered by AI, can automatically set and frequently update the prices of many products. With sufficient computational power, such algorithms could leverage detailed data on consumer characteristics and behavior, competitor prices, economic indicators/events, and other information that influences customers’ willingness to pay, to predict the demand for firms’ products. At least in theory, firms could also use AI to try to predict competitors’ responses to the firm’s prices, which could be built into the pricing algorithm.

While the possibility of algorithmic price discrimination and algorithmic collusion in conduct cases has been extensively discussed in the global antitrust community in recent years,² there has been much more limited discussion in the context of mergers. In this article, we aim to fill this gap by discussing some potential implications of algorithmic pricing on market definition, unilateral effects, coordinated effects, and remedies. Specifically, we discuss the following topics and related questions:

• Market definition. How to account for algorithm enhanced market/customer segmentation and identify relevant antitrust markets when prices are set by a “blackbox” algorithm.³
• Unilateral effects. How to use merging parties’ pricing algorithms to conduct merger simulations.
• Coordinated effects. How the recent scholarship can inform analysis of potential coordinated effects in merger investigations.
• Remedies. Why data compatibility and collusion risk are important considerations when analyzing the divestiture of a merging parties’ pricing algorithm.