Amazon roasts users with shopping habits and controls us
Amazon’s ‘About You’ Feature: The Shocking (and Hilarious) Truth About the Data It Collects
Every time you shop on Amazon, you are participating in a massive data exchange. But what happens when the platform doesn’t just track what you buy, but attempts to read your personality, habits, and even your physical attributes? Amazon’s recently rolled out ‘About You’ feature is an attempt to peek behind its vast data curtain, revealing exactly what it has learned about its customers. And the results, while sometimes flattering, are often spectacularly absurd.
Launched in May, this feature is designed to create a personalized shopping experience, but it quickly became a source of viral amusement once users began noticing the surprisingly specific—and sometimes wildly inaccurate—insights the algorithm generates. It’s a fascinating glimpse into the increasingly intimate relationship between consumer data and corporate profiling.
The feature’s capacity for unexpected humor truly shone when users explored their own profiles. Some results were hilariously bizarre. One user found that Amazon had apparently assessed their physical appearance, noting they had flat buttocks. Others discovered the algorithm had noted they apparently do not trust people easily, highlighting the platform’s attempt to categorize deeper psychological traits based on purchasing behavior.
The data processing extends beyond physical appearance and into lifestyle. One observation noted that Amazon had recorded that a user apparently gets a tired gray cast from lack of sleep, demonstrating the algorithm’s focus on correlating purchases with inferred physical states.
The feature also tackled intellectual pursuits, offering some flattering, if superficial, assessments. One user, a writer, found that Amazon had deduced they favor classic and intellectual literature and strategically purchase books during major sales. While these insights are sweet, they reveal the algorithm’s reliance on easily quantifiable purchasing patterns.
The results further illustrate the sometimes farcical nature of predictive data. For instance, one hardware enthusiast found that Amazon suggested they probably have microwave ovens. The system apparently reasoned this based on past purchases, suggesting the user had bought two units because the first one “blew up while making popcorn.” This is a perfect example of how context, even when applied to large datasets, can lead to absurd conclusions.
Even specific identities were sometimes inadvertently revealed. A comic artist found that Amazon had noted their name was Shingo after they purchased several volumes of a specific manga. This shows how closely correlated transactional data can become with personal information.
Ultimately, the ‘About You’ feature serves as a brilliant, if slightly mischievous, reminder that every click and purchase leaves a digital footprint. While the data collected is incredibly detailed, the interpretation is often hilarious. It pushes us to ponder the future of personalization: how do we balance the convenience of tailored recommendations with the privacy of knowing exactly what the machine knows about us?
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