Is Your Algorithmic Pricing A Lawsuit Waiting To Happen?

Summary

Algorithmic pricing engines promise dynamic, customer-specific pricing powered by AI — a tempting tool for marketing teams. But they raise real antitrust red flags when they ingest competitors’ confidential data or create a de facto information-exchange network. Regulators and plaintiffs have targeted services that act as hubs for competitively sensitive information, resulting in major settlements (for example, the RealPages/Greystar industry settlement). At the same time, courts have sometimes dismissed claims where there is no evidence of an explicit understanding among competitors (the Cendyn case). The article explains legal concepts like “give to get” and “hub-and-spoke” conspiracies, and offers practical steps for managers to reduce legal risk: insist on transparency about data sources, avoid bundled opaque services, document decisions, and separate analytics from pricing execution where possible.

Key Points

Algorithmic pricing can be lawful when it uses only a company’s own data and publicly available information.
Risk rises when vendors use competitors’ confidential data or operate on a “give to get” model that effectively shares sensitive information.
Authorities view certain arrangements as hub-and-spoke conspiracies if there is a collective understanding (the “rim”) to coordinate pricing.
Recent enforcement and settlements (e.g. RealPages/Greystar) show the financial and reputational stakes for landlords and vendors.
Court precedent (e.g. Ninth Circuit dismissal of the Cendyn suit) means simply matching posted prices (parallel pricing) is not automatically illegal.
Practical mitigations: demand vendor transparency, keep a paper trail, separate data analysis from pricing execution, avoid inscrutable package deals, and ensure contracts don’t force compliance with algorithmic recommendations.

Context and Relevance

This article is important for CEOs, CFOs, heads of pricing, legal counsel and marketing chiefs using or evaluating dynamic-pricing tools. As more firms adopt AI-driven pricing, regulators and plaintiffs are focusing on whether those systems enable competitors to exchange competitively sensitive information — a core antitrust concern. The piece sits at the intersection of technology adoption and regulatory risk: it ties current enforcement trends to practical governance steps organisations can take to reduce exposure.

Why should I read this?

Short version: if you’re using or buying dynamic pricing tech, do not wing it. This is the sort of legal headache that can cost millions and wreck reputations. Read it to know which questions to ask, what clauses to insist on in contracts, and how to make sure your fancy algorithm doesn’t become a lawsuit magnet.