It should come as little surprise that Amazon collects an immense amount of data about its customers based on their online shopping habits. If a household frequently orders bulk packages of cat litter, the e-commerce giant’s recommendation algorithms will likely infer that the household includes feline pets. Similarly, if a customer routinely purchases specialized anti-acne skin products, Amazon’s profiling systems might guess that the individual is prone to sudden breakouts. Even though most modern internet users understand that platforms customize shopping experiences based on historical purchase data, it still manages to come as a distinct shock when all of those personal inferences are laid out neatly in a convenient, digestible list—especially when those algorithmic deductions get uncomfortably intimate.
This digital reality recently hit home for a user on Meta’s Threads platform, who stumbled across a hidden section within her Amazon settings that outlines exactly who the shopping conglomerate believes she is. Alongside relatively benign assumptions generated by the platform—such as "Shops from women’s departments" and "Probably owns a Shark robot vacuum"—she discovered that Amazon’s profiling algorithm had boldly concluded that she "has flat buttocks."
The discovery quickly struck a nerve across social media. "I stumbled upon a page of assumptions that Amazon has made about me based on my purchases and I’m literally speechless," wrote the Amazon customer, who posts under the handle @fangirlinmegan, in a viral post on Threads. "I mean it ain’t wrong, but damn did you have to call me out like that?"
In less than a day, the post garnered over a million views, drawing reactions from thousands of curious online users who rushed to check their own Amazon profiles to see what kind of digital portrait the retail giant had painted of them. The sudden surge in visibility shed light on a deeply embedded feature that many consumers never knew existed, sparking broader conversations about the scope of corporate surveillance, data collection, and the uncanny accuracy of modern machine learning algorithms.
For those attempting to locate the feature on a desktop computer, the journey requires navigating several layers of account settings. Users can find the page by hovering over the greeting in the upper right corner where Amazon displays "Hello, [your name]," and then clicking "Account" under the main account management section. From there, navigating down to the "Ordering and shopping preferences" block leads to a subsection titled "Your Shopping preferences." This area typically allows customers to proactively input personal data, such as their shoe sizes, specific interests, and dietary preferences. However, the true intrigue lies deeper within the system. By scrolling to the bottom of the page, users encounter a blue hyperlinked option labeled "Manage your information." Clicking that link reveals the full scope of the automated dossier that Amazon has compiled over the years.
For mobile app users, the path is slightly different but similarly buried. Shoppers can access the same data by tapping the hamburger menu icon located on the bottom navigation bar, and then sequentially navigating through Account, Shopping preferences, and finally the "About you" section.
Upon investigating these hidden profiles, many users find a mix of accurate trivia, mundane preferences, and occasionally startling insights. For instance, looking into one’s own profile might reveal algorithmic deductions confirming that an individual "practices photography," "plays collectible card games," or is deeply "invested in the Apple ecosystem." These data points are often entirely accurate, derived from years of buying camera lenses, tabletop gaming accessories, and Apple-certified cables. Algorithms can even flatter users by noting traits like reading "diverse non-fiction," prompting an amused internal reflection on whether the system has successfully identified an open-minded intellectual with a genuine thirst for knowledge.
Other investigators within tech media circles have found that Amazon correctly cataloged their interests in vintage vinyl records and bespoke ceramics, as well as a distinct penchant for "natural materials" in home decor and clothing that prioritizes comfort over fast-fashion trends. While none of these professional reviewers uncovered anything quite as startling or socially direct as the conclusion regarding flat buttocks, the original Threads user deduced that her specific algorithmic label was likely the direct result of purchasing specialized workout apparel, such as "butt scrunch leggings," which heavily prompt algorithmic classification regarding body shape and clothing fit.
The viral incident serves as a stark reminder of just how much technology companies know about the daily habits, preferences, and physical realities of their consumer base. It can be jarring to realize the sheer depth of attention that platforms pay to how people interact with digital marketplaces. In an era increasingly defined by pervasive monitoring systems, automated profiling, and debates surrounding mass surveillance, discovering that an online retail giant has formed specific physical hypotheses about a customer’s body highlights the invisible ways data permeates modern life. Yet, as millions of users continue to log in and inspect their own digital profiles out of a mixture of dread and morbid curiosity, the phenomenon underscores a strange, collective fascination with how machines perceive humanity—even when those machines are just trying to sell us another pair of leggings.
