I requested a copy of my data from McDonald’s loyalty program
I requested a copy of my data from McDonald’s loyalty program
我向麦当劳忠诚度计划索取了我的个人数据副本
When I first downloaded the McDonald’s app years ago, I signed up for its loyalty program hoping to get better deals. Why not get some cheap french fries? While I understood that this would entail some type of data tracking, I didn’t grasp how the information would be used to algorithmically predict my next purchase.
多年前，当我第一次下载麦当劳应用程序时，我注册了它的忠诚度计划，希望能获得更优惠的价格。毕竟，谁不想吃点便宜的薯条呢？虽然我明白这必然涉及某种形式的数据追踪，但我并没有意识到这些信息会被如何利用，通过算法来预测我的下一次消费。
As a California resident, I have the legal right to access the data a company stores about me. So, I decided to request that information from McDonald’s to better understand what one of the major fast-food companies is collecting about its customers. A few days after I visited the McDonald’s Privacy Rights Center site and requested access, I received a 515-page file in my inbox with the iconic golden arches stamped across the top.
作为加州居民，我有权依法获取公司存储的关于我的数据。因此，我决定向麦当劳索取这些信息，以便更好地了解这家大型快餐巨头究竟在收集客户的哪些数据。在我访问麦当劳隐私权中心网站并提交申请几天后，我的收件箱里收到了一份长达 515 页的文件，文件顶部印着标志性的金色拱门图标。
When customers sign up for loyalty programs, like the one offered through the McDonald’s app, they might not fully realize the extent to which their data is being aggregated and used in predictive models.
当客户注册忠诚度计划（例如麦当劳应用程序提供的计划）时，他们可能并未完全意识到自己的数据被汇总并用于预测模型的程度。
“McDonald’s secret sauce is really commercial surveillance,” says Jeff Chester, executive director at the Center for Digital Democracy, a group that advocates for consumer protections. Privacy experts I spoke with said this level of detail may feel invasive, but it’s fairly standard for how large companies in the US run their loyalty programs.
“麦当劳的‘秘制酱料’其实就是商业监控，”数字民主中心（Center for Digital Democracy）执行董事杰夫·切斯特（Jeff Chester）表示，该组织致力于倡导消费者保护。我采访的隐私专家表示，这种详细程度可能会让人感到被侵犯，但这在美国大公司运营忠诚度计划的方式中相当普遍。
“This report contains specific pieces of personal information about you that were identified by searching McDonald’s systems which contain information about our customers,” read the report’s intro. It was lengthy and difficult to parse, so I used a generative AI tool to extract key information before verifying details with the original document and reaching out to privacy experts.
“本报告包含通过搜索麦当劳客户信息系统所识别出的关于您的特定个人信息，”报告的引言中写道。这份报告篇幅冗长且难以解析，因此我使用生成式 AI 工具提取了关键信息，随后与原始文档核对细节，并咨询了隐私专家。
| What McDonald’s Calls It | What My Data Says | What That Means |
|---|---|---|
| 麦当劳术语 | 我的数据内容 | 含义解读 |
| Estimated Number of Visits During the Next 6 Weeks | 2.16 | How many times McDonald’s expects me to return before mid-September. |
| 未来 6 周预计到店次数 | 2.16 次 | 麦当劳预计我在 9 月中旬前会光顾的次数。 |
| Estimated Average Order Spend During the Next 6 Weeks | $13.49 | What McDonald’s expects me to spend per order. |
| 未来 6 周预计平均客单价 | 13.49 美元 | 麦当劳预计我每单的消费金额。 |
| Estimated Total Spend During the Next 6 Weeks | $29.15 | My total predicted value to McDonald’s, over those six weeks. |
| 未来 6 周预计总消费额 | 29.15 美元 | 我在这六周内对麦当劳的预计总价值。 |
| Customer Attrition Likelihood | 0 | Potential odds I’ll stop being a customer. Ouch! |
| 客户流失可能性 | 0 | 我不再光顾的潜在概率。扎心了！ |
| Customer Group Based on Historical Transactions | CV2 | McDonald’s doesn’t provide details about what this internal value tier means. |
| 基于历史交易的客户群 | CV2 | 麦当劳未提供该内部价值层级的具体含义。 |
| Most Transacted Occasion Type | Food-Led Afternoon Snack; On the Go Lunch in a Rush | My two behavioral archetypes based on past orders. |
| 最常交易场景类型 | 以食物为主的下午茶；匆忙中的午餐 | 基于过往订单的两种行为原型。 |
| Most Visited Geographic Location | San Francisco, Santa Barbara (61 visits) | The main market where I eat McDonald’s and how often. |
| 最常访问地理位置 | 旧金山、圣巴巴拉（61 次） | 我消费麦当劳的主要市场及频率。 |
| Recency, Frequency, Monetary Derived Top Product | Large Diet Coke | The item McDonald’s recommendation math ranks as my number one. |
| RFM 模型推导出的首选产品 | 大杯健怡可乐 | 麦当劳推荐算法中排名第一的单品。 |
| Recency, Frequency, Monetary Derived Top Product Category | Wraps–Chicken | The menu section McDonald’s expects me to order from. |
| RFM 模型推导出的首选品类 | 鸡肉卷 | 麦当劳预计我会下单的菜单类别。 |
“McDonald’s takes data privacy and security seriously, and we take robust steps to safeguard customer information,” a McDonald’s spokesperson tells WIRED via email. “Like many digital loyalty programs, we use information such as past purchases to provide a more engaging, personal customer experience—like delivering the most relevant deals, offers and messages. Our customers continue to have privacy choices available to them as outlined in our privacy statement.”
“麦当劳非常重视数据隐私和安全，我们采取了强有力的措施来保护客户信息，”麦当劳发言人通过电子邮件告诉《连线》（WIRED）。“像许多数字忠诚度计划一样，我们利用过往购买记录等信息来提供更具吸引力、更个性化的客户体验——例如推送最相关的优惠、促销和信息。正如我们的隐私声明中所述，我们的客户始终拥有隐私选择权。”
Much of my data report contains a detailed account of past McDonald’s transactions as well as offers the fast-food company sent me and how many loyalty points I accumulated. Essentially, every time I opened up the app to grab a bite to eat, it created a detailed record about when, where, and what I purchased. The log was even more thorough than I anticipated, including a record of every time I’ve scanned a code as part of its returning Monopoly sweepstakes along with the prize I received.
我的数据报告中大部分内容详细记录了过往的麦当劳交易，以及该公司发送给我的优惠信息和我积累的忠诚度积分。本质上，每当我打开应用程序去买点吃的，它都会创建一份关于我何时、何地、买了什么的详细记录。这份日志比我预想的还要详尽，甚至包括了我每次参与其“大富翁”抽奖活动时扫描代码的记录，以及我所获得的奖品。
While the sheer amount of data McDonald’s stored over the years caught me off guard at first, the way it was used by the company’s predictive algorithms was what I found most concerning. The company predicted how often I would visit in the next six weeks (2.16 times), what I would spend on average ($13.49), and what I would spend in total ($29.15). One reporter at WIRED joked that McDonald’s had created a Minority Report-style dossier to predict my next snack wrap purchase.
虽然麦当劳多年来存储的数据量之大让我起初感到措手不及，但该公司预测算法对这些数据的使用方式才是我最担心的。该公司预测了我在未来六周内会光顾的次数（2.16 次）、平均消费金额（13.49 美元）以及总消费额（29.15 美元）。《连线》的一位记者开玩笑说，麦当劳已经建立了一份《少数派报告》式的档案，用来预测我下一次购买零食卷的行为。
Beyond the numbers, McDonald’s also tracked a large Diet Coke as my favorite item to repeatedly purchase. It labeled my core purchasing habits as getting a “Food-Led Afternoon Snack” or an “On the Go Lunch in a Rush.” (Jokes about grabbing a “food-led” snack have now become a meme in my household.) Notably, the report also listed my “attrition” score as zero, which could mean that Ronald McDonald sees me as a lifelong customer who’s never going to stop grabbing french fries and a soda on the walk home.
除了数字之外，麦当劳还追踪到大杯健怡可乐是我最常重复购买的单品。它将我的核心购买习惯标记为“以食物为主的下午茶”或“匆忙中的午餐”。（关于“以食物为主”的零食的笑话现在已经成了我家里的梗。）值得注意的是，报告还将我的“流失”评分列为零，这可能意味着麦当劳叔叔视我为终身客户，认为我永远不会停止在回家的路上买薯条和汽水。
“The 500 pages is kind of a wake-up call,” says Lorrie Cranor, a professor at Carnegie Mellon University and director of the CyLab Security and Privacy Institute. “But it’s not in a format that people will readily understand.” She says a more beneficial approach for consumers would be highly detailed disclosures during the sign-up process that lay out what’s going to be stored in your “permanent record” and how that data will be used to make specific inferences about you.
“这 500 页文件算是一个警钟，”卡内基梅隆大学教授兼 CyLab 安全与隐私研究所所长洛里·克兰诺（Lorrie Cranor）说。“但它的格式并不是人们容易理解的。”她表示，对消费者更有益的做法是在注册过程中提供高度详细的披露信息，明确说明哪些内容会被存入你的“永久记录”，以及这些数据将如何被用于对你进行具体的推断。
Rank 10 Products McDonald’s Ranks as Most Relevant to Me
麦当劳认为与我最相关的 10 款产品排名
- Large Diet Coke (大杯健怡可乐)
- Spicy Snack Wrap (辣味零食卷)
- The Grinch McShaker Fry Large (格林奇摇摇薯条大份)
- Double Cheeseburger (双层吉士汉堡)
- Ramyeon McShaker Fries Large (拉面味摇摇薯条大份)
- Buffalo Ranch Snack Wrap (水牛城牧场风味零食卷)
- Hot Honey Snack Wrap (热蜂蜜零食卷)
- Quarter Pounder With Cheese (足尊牛肉堡)
- L French Fries (大份薯条)
- 20-Piece McNuggets (20块麦乐鸡)
When it comes to this kind of data collection, there’s a stark power imbalance between the individual consumer and a well-organized company. “You’re mediated by the technology,” says Ryan Calo, a professor at the University of Washington and faculty cofounder of its Tech Policy Lab. “You’re going through their drive-thru. You’re using their kiosk. You’re using their app. You’re in their ecosystem, and they have a bunch of people with letters after their names trying to design it in…”
在涉及此类数据收集时，个人消费者与组织严密的公司之间存在着巨大的权力失衡。“你被技术所中介，”华盛顿大学教授、该校科技政策实验室（Tech Policy Lab）联合创始人瑞安·卡洛（Ryan Calo）说。“你通过他们的得来速（Drive-thru）点餐，使用他们的自助点餐机，使用他们的应用程序。你身处他们的生态系统中，而他们拥有一群头衔显赫的专家，正试图设计这一切……”