McDonald's Loyalty Data Sparks Restaurant Debate

A WIRED reporter recently received a 515-page file detailing his McDonald's loyalty data, sparking online debate about restaurant data collection. The file contained his predicted visits, average order value, and a zero churn score, leading him to delete his data and stop eating there.

According to Ray Gallagher, SVP & GM of Olo Engage, the incident highlights a central question for restaurants with loyalty programs. The issue is not whether they possess such data, but whether they use it to make guests feel known or merely tracked.

Guest data is a transaction

Gallagher frames loyalty as a transaction where data is the currency spent by the guest. He argues this data is key for brands to make better business decisions, a view he says was echoed by some readers of the WIRED article who saw purchase tracking as a legitimate practice.

The reporter's extensive file essentially amounted to a receipt for years of purchases. In exchange for providing his order history, he received perks, points, and Monopoly prizes. Gallagher notes that the unsettling aspect for the reporter was seeing backend jargon like "attrition likelihood" laid bare, rather than the data collection itself. Stripped of technical terms, he contends that 515 pages of transaction history accumulated over years represents an ordinary amount of data necessary for effective marketing and forecasting.

Creating personalized loyalty at scale

Drawing on 15 years of restaurant experience, Gallagher recalls an analog version of loyalty where staff knew regulars' favorite drinks and booths. He states that recreating this personalized experience at scale once required costly analyst teams only major chains could afford.

Technology has changed that, he says. Modern guest engagement platforms can build a single profile per guest across all ordering channels and automatically segment them by recency, frequency, and monetary value of orders. This allows any operator, not only giants like McDonald's, to automatically generate win-back offers or personalized birthday rewards. Gallagher lists specific benefits a known guest should receive that a stranger cannot buy, including carry-over order preferences, labeled pickup orders thanking them for loyalty, early access to limited items, and unprompted comped items after a bad visit.

How guest data benefits the brand

Repeat guests are vital for restaurant sales, and their data directly informs forecasts, staffing plans, and marketing budgets. Gallagher argues that personalization built on this data transforms raw information into offers guests actually want.

This data also allows brands to prove personalization works. By holding out a control group that does not receive an offer, operators can see if the offer genuinely drove a visit or if the guest would have come anyway. Without this comparison, marketing spending is based on faith.

Gallagher concludes by urging loyalty program operators to ask what a known guest gets that a stranger cannot. He asserts that while guests provide vast amounts of data daily, the resources needed to create a personal system are no longer exclusive to the largest chains. When guests feel known, he says, the story written about a brand shifts from data collection to personalized service.