Optimizing for the wrong customer is a strategy, and it fails like one

Shake Shack’s French fry crisis is the clearest case study available for what happens when a company optimizes for the wrong audience. As Seth Godin tells it, Danny Meyer’s team switched from crinkle-cut to fresh-cut fries in pursuit of approval from New York cultural critics, the kind of voices that confer authenticity status in the food world. The outcome was almost uniformly bad: the fries tasted worse, the internet rebelled, staff hated the new preparation, and operational complexity rose with no compensating benefit.

Godin’s framing of the eventual reversal is instructive. Meyer, he says, eventually asked a clarifying question: do the fries exist to help a “non-C customer” feel good about eating at a Danny Meyer restaurant, or do they exist to further the story of what it means to come to Shake Shack? The phrasing is Godin’s own, and the contrast it draws is the point. Shake Shack ripped out the fresh-cut operation, returned to crinkle-cut, and, in Godin’s account, profits went up, productivity went up, and the company got back to what it was trying to do in the first place.

The mechanism behind the failure is worth naming. The cultural critics were a vocal, high-status audience whose approval felt meaningful. But their preferences had no relationship to the preferences of the people actually buying burgers. Treating critic approval as a proxy for customer satisfaction is a category error, and it cost the company on every axis that mattered operationally.

The bigger the account, the longer these deployments, the longer the feedback loops. Mark Cranney

Mark Cranney identifies a structural version of the same trap in enterprise sales. His formulation is spare but pointed: the bigger the account, the longer the deployments, and the longer the feedback loops. A company that chases large accounts therefore extends the time between a product decision and the signal that tells the team whether that decision was correct. Smaller, faster-cycling customer relationships compress that loop. The distortion is not just about revenue mix. It is about which customer’s reality shapes the product roadmap, and how long the team goes without knowing whether they read that reality correctly.

Sam Parr, who built a company that closed seven-figure advertising deals, supplies the sales-side variant. His working assumption going into those deals was that buyers would respond to return-on-investment arguments: spend this amount, make this amount back. That pitch, he says, consistently failed. What actually closed deals was relationship-building and a different kind of buyer logic entirely, the reassurance that a budget already allocated would be spent safely. The rational business-owner buyer Parr had imagined was not the buyer sitting across the table. The buyer in the room was making a career-safety calculation, not an ROI calculation.

These three cases share an architecture. In each, the seller or operator built a mental model of the customer they were serving, and that model was wrong in a specific, costly way. Godin’s Shake Shack was optimizing for critics rather than regulars. Cranney’s enterprise-focused teams were optimizing for accounts whose scale made them prestigious but whose feedback cycles made them poor guides. Parr was optimizing for a rational economic actor who does not appear to exist in B2B advertising in the form he assumed.

The correction, in each case, required asking a version of Meyer’s question: who is this actually for? That question sounds obvious. The Shake Shack example demonstrates that even sophisticated operators, running a well-regarded brand, can spend a meaningful period answering it incorrectly before the operational and financial consequences force clarity. The cost of the confusion is not abstract. It shows up in staff morale, in slower product feedback, and in sales cycles that stall because the pitch addresses a buyer who is not in the room.