McDonald’s Loyalty Program Generates Detailed Customer Profiles
McDonald’s has put together an extensive 515-page dossier on one of its loyalty program members, using years of fast food buying data to estimate customer behavior. This information predicts how frequently a customer might visit, how much they are likely to spend, and their possible menu choices.
Reece Rogers, a journalist at Wired, found a fascinating range of digital insights after requesting the personal data stored by McDonald’s. His file featured predictive analytics reminiscent of “Minority Report,” estimating that he would visit McDonald’s 2.16 times in the next six weeks and spend about $13.49 each time.
According to the calculations, the fast-food chain expected Mr. Rogers to spend around $29.15 over those six weeks.
From his past purchases, the profile identified Large Diet Coke as his top choice, followed by Spicy Snack Wrap and Grinch McShaker Fries. His eating habits were categorized in particular ways, like “meal-driven afternoon snacks” or “quick lunches when I’m pressed for time.”
Interestingly, the document gave him a zero score for “likelihood of customer attrition,” implying that the algorithm found little reason to believe he would stop visiting McDonald’s anytime soon.
Along with a thorough history of his transactions and accrued loyalty points, the file also detailed promotional offers he had received. Even his interactions with the chain’s Monopoly sweepstakes were tracked meticulously.
A spokesperson for McDonald’s assured Wired that the company takes data privacy and security seriously, stating they implement significant measures to safeguard customer information. They explained that, similar to other digital loyalty programs, they use historical purchase data to provide a more engaging experience, offering the most relevant promotions and communications.
Rogers acknowledged he expected some level of data monitoring with his loyalty program enrollment but was taken aback by the sheer volume of aggregated information feeding into predictive algorithms.
After reviewing the materials, Rogers approached McDonald’s Privacy Rights Center to request the deletion of all information the company had collected about him. He also decided to stop visiting McDonald’s, perhaps as a way to challenge the algorithm’s predictions.
Experts are not surprised by the depth of the documents, given what McDonald’s has previously revealed about its data strategies. Stephanie T. Nguyen, a senior fellow at Columbia Law, noted that the company’s detailed privacy policy explains how it tracks customers’ locations, browsing habits, app usage, and social media interactions.
Moreover, companies like McDonald’s have openly stated they utilize this data to train AI models and create detailed customer profiles. Nguyen found the predictive capabilities particularly intriguing, suggesting that they can determine consumer preferences, psychological trends, and behaviors.
Joseph Turow, a professor emeritus at the University of Pennsylvania’s Annenberg School, called the 515-page file “mind-boggling” and remarked on the implications of AI-driven analytics. He emphasized that this example reflects a broader trend toward hyper-personalized marketing, where many consumers remain unaware of the extensive profiling and data gathering occurring behind the scenes.
Turow warned that this issue should concern people everywhere, suggesting that similar practices could be unfolding in numerous retail outlets across the country. He referred to this shift in marketing as “extreme personalization,” where companies aim to tailor individual relationships with customers.
