McDonald's lawsuit tests whether pooled-data pricing advice counts as franchise price-fixing

Both sides of the case rely on the same fact. "Independent businesses must set their prices independently," the lawsuit said ^[7]. McDonald's defense rests on that same independence ^[11]. The plaintiffs contend that routing guidance through one central system fed by nonpublic data weakens the rivalry that should exist between McDonald's locations, because owners who might price on their own are nudged toward common figures ^[6].

The guidance is detailed. Reuters reported that the engine continually analyzes millions of daily transactions ^[5] and produces recommendations for individual products ^[4]. A franchisee who takes the number is pricing off sales data from other restaurants that, by the plaintiffs' account, is not public ^[2].

Suppose a court treats the owner's freedom to refuse as decisive. Then the suit fails early, and McDonald's argument that recommendation tools and analytics are widespread across industries ^[12] ends up protecting everyone who runs one. If the claim survives, discovery turns on how often franchisees priced at the recommended figure. The published accounts of the complaint and the company's statement do not include that rate. A settlement would leave the question open for the next defendant.

The one store-level comparison in the reporting points away from uniform prices. Reuters data showed a Big Mac at $5.69 at one corporate location in Fresno, California, and $6.89 at another about two miles away ^[17]. The difference is $1.20 ^[21], about 21% of the lower price ^[22]. Reuters could not establish whether the software caused it ^[18]. A system that, as Reuters described it, weighs regional buying patterns and how sensitive customers are to price in each market ^[13] would tend to pull nearby prices apart.

If prices diverge, the plaintiffs' stronger argument is about how high prices are set. Lark Turner, a lawyer for the plaintiff, said McDonald's is "leveraging its troves of data and its franchised system to nickel-and-dime consumers down to the last French fry" ^[14]. A recommender that finds each store's local ceiling could remove the rivalry between two restaurants two miles apart without making their prices match. (That version is harder to plead as price-fixing, and harder for McDonald's to answer by pointing at dispersion.)

I think the acceptance rate decides how far this reaches. If owners routinely depart from the recommendation, the risk to other franchisors with shared tools is small. In that case McDonald's is right that it hands out advice and leaves the pricing to owners. If owners mostly take it, the theory applies to any franchisor whose owners price from a shared recommender built on each other's data.