I requested a copy of my data from McDonald’s loyalty program

What gave the story weight was not the dossier itself but what that kind of data is used for. The sharpest takeaway was that consumer data and operational metrics tend to push companies toward “good enough” service and maximum extraction, not better products. People connected the article to what fast food now feels like in practice: fewer workers, dirty dining rooms, kiosk-first ordering, app-only discounts, and stores optimizing the numbers they are measured on instead of the experience customers actually want. McDonald’s was the example, but the conversation kept broadening to grocery stores, telecoms, and retail generally. That led to two concrete concerns. First, loyalty programs are a clean way to separate customers by price sensitivity. Regulars who install the app and hunt for deals get one price. Occasional customers pay list price. Over time the next step is obvious: individualized offers, weaker coupons for people who are likely to buy anyway, and eventually personalized pricing if regulation allows it. Second, even data that feels harmless in isolation becomes dangerous once it leaks, gets sold, or is joined with outside datasets. Many people were fine with McDonald’s using its own purchase history to sell more fries. They were not fine with that history flowing to insurers, data brokers, law enforcement, or any system that makes opaque decisions about risk, health, or eligibility. The mood was less panic than resignation. Few were shocked that a huge loyalty app runs customer lifetime value models. What bothered people was how normalized the whole stack has become. The app is the discount gate. The metrics are the management system. The service degrades because the dashboard says loyal customers keep showing up. Once that loop is in place, the business can look healthier on paper while feeling worse to everyone inside it.