Suggest that the State Administration for Market Regulation investigate whether McDonald's China operations—Golden Arches (China) Co., Ltd.—have violated the Anti-Monopoly Law by repeatedly raising retail prices of menu items and entering into price-fixing monopoly agreements with franchisees.

In November 2025, the national consumer price index fell 0.1% month-on-month and rose only slightly by 0.7% year-on-year. Admittedly, under the Price Law, pricing decisions fall within an operator’s business autonomy—provided they comply with the law. However, under Article 18(1) of the current Anti-Monopoly Law, operators are prohibited from fixing the minimum resale price imposed on their trading counterparts—or from fixing the resale price itself. Article 18: Operators are prohibited from entering into the following monopoly agreements with their trading counterparts: (1) Fixing the price at which goods are resold to third parties; (2) Restricting the minimum price at which goods are resold to third parties; (3) Other monopoly agreements as designated by the State anti-monopoly enforcement authority. For agreements described in Items (1) and (2) above, prohibition shall not apply if the operator can demonstrate that such agreement does not have the effect of eliminating or restricting competition. Prohibition shall also not apply if the operator can prove that its market share in the relevant market falls below the threshold stipulated by the State anti-monopoly enforcement authority and satisfies other conditions prescribed by that authority. WeChat Official Account: “Shao Geng’s Cold Reflections on Competition Law” — Anti-Monopoly Law (2022 Revision) Within mainland China’s McDonald’s branded restaurant chain, some outlets are company-owned and directly controlled by Golden Arches (China) Co., Ltd.—the entity operating McDonald’s China business—while others are independently operated franchisees authorized under licensing agreements to use the McDonald’s brand, related intellectual property, and proprietary recipes. These franchisees are not subsidiaries under Golden Arches’ direct control but independent operators. Therefore, when Golden Arches collaborates with such franchisees, it remains fully subject to Article 18(1) of the Anti-Monopoly Law. For reference, the Beijing Municipal Administration for Market Regulation’s 2022 administrative penalty decision against Beijing Kerui Alliance Education Technology Co., Ltd. for a monopoly agreement case explicitly affirmed that franchising industries must likewise abide by the Anti-Monopoly Law. No currently effective regulations or departmental rules governing franchise operations stipulate that fixed resale pricing is an essential component of a unified franchise business model. … The primary operating costs for franchisees—rent and staff wages—vary significantly depending on local economic development levels, commercial district locations, and individual experience. … Market supply and demand are shaped by factors such as the number of competing businesses in the same area and the structure of local consumer demand—even within the same city, conditions may differ drastically across districts. Market prices should naturally emerge from vigorous competition among operators; yet the price-control system implemented by the investigated party deprives franchisees—their status as independent operators—of autonomous pricing rights. Its purpose and actual effect contradict both the legislative intent of fairness and voluntariness enshrined in the Regulations on Commercial Franchise Management, harm franchisee interests, and disrupt fair market competition. WeChat Official Account: “Shao Geng’s Cold Reflections on Competition Law” — Annotated Version | Beijing Municipal Administration for Market Regulation (2022) Administrative Penalty Decision on Beijing Kerui Alliance Education Technology Co., Ltd. Monopoly Agreement Case | Franchising | Resale Price Maintenance Moreover, because Golden Arches (China) Co., Ltd. simultaneously operates its own chain of company-owned restaurants in the food service industry, these outlets often compete—or potentially compete—with nearby McDonald’s franchise stores.

How well does McDonald’s really know you? — The story behind the AI algorithms.

You think you’re spontaneous—but your spontaneity has already been modeled. IV. 0% Is Not a Reward—It’s a Punishment In growth modeling, the field labeled “churn probability” serves only one purpose: budget allocation. Each quarter, the company has a finite amount it can spend on customer retention. Which customers receive coupons, how large those discounts are, and when they’re issued—all must be prioritized. Customers with high churn probability rank first—they’re most likely to leave imminently, so investing money to retain them makes sense. Where do those with 0% churn probability rank? At the very bottom—or not on the list at all. This is my interpretation—not written explicitly in any official document. But this is precisely my job, and this is the standard, intended use of this field. To the algorithm, that journalist belongs to the easiest category to manage. No intervention needed—he’ll come back anyway. V. What Truly Determines Your Classification Isn’t “How Loyal You Are,” But “How Likely You Are to Change” The above framework is still relatively coarse. Teams that operate more sophisticated models no longer focus solely on churn probability itself—they focus instead on *incrementality*. This approach is called incremental modeling, and it classifies users into four categories: Type Characteristics Should We Issue a Coupon? Category 1 Will come even without a coupon ❌ Wasteful—money thrown away Category 2 Will come *only if* given a coupon ✅ This is where budget should go Category 3 Won’t come even with a coupon ❌ Wasted effort Category 4 Becomes *less* likely to come after receiving a coupon ❌ Spending money to make things worse Budget should be allocated exclusively to Category 2. Who belongs to Category 1? That journalist—the “die-hard loyal user” with 0% churn probability. He was going to visit anyway. By discounting his $13.49 order by 20%, you merely collect $10.79 instead of $13.49—a $2.70 loss on revenue that would have occurred regardless. Category 3 is pure waste. Category 4 is worse: some people, upon receiving a reminder coupon, suddenly recall how long it’s been since their last visit—or feel annoyed by the intrusion—and immediately block the brand. So the question shifts from “Who is most loyal?” to “Whose behavior will this coupon actually change?” That journalist firmly belongs to Category 1. He is the system’s favorite—and the one it needs to invest in least. VI. The Logic We’ve Been Taught Since Childhood Is Inverted Here I know many readers find this unsettling. Because we’ve been taught since childhood: loyal customers deserve preferential treatment. A restaurant owner recognizes your face and gives you an extra spoonful—not because he’s calculating, but because he hopes you’ll return. That’s human warmth. But coupons in membership systems aren’t about warmth—they’re budget items requiring strict ROI accountability. The metric is clear: how much *additional*, otherwise-unlikely spending does this dollar generate? The more stable your behavior, the smaller the incremental gain—and thus, the less justified the expenditure. The more loyal you are, the less valuable you become in the algorithm’s eyes. That sounds harsh—but it’s derived from calculations, not personal bias. VII. You Can Request This Data in China—And You Should Article 45 of the Personal Information Protection Law states: Individuals have the right to consult and copy their personal information held by personal information handlers, who must provide such information promptly. The law is explicit. As for what data domestic membership systems will actually deliver—its format, completeness, and processing time—I lack verifiable case examples to cite, so I cannot guarantee outcomes. But this right exists—and is worth exercising. If you successfully obtain your data, remember what I said: Flip straight to the last few pages. The hundreds of pages of transaction history before that aren’t particularly revealing—they simply record what you already know you did.

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