One user on Meta’s Threads platform recently came across a specific section of her Amazon settings where shoppers can see precisely who the retail behemoth believes they are. Alongside relatively benign assumptions like "Shops from women’s departments" and "Probably owns a Shark robot vacuum," she discovered an algorithmically generated conclusion that was far more specific and surprisingly personal: Amazon assumed that she "has flat buttocks."
"I stumbled upon a page of assumptions that Amazon has made about me based on my purchases and I’m literally speechless," an Amazon customer who posts under the handle @fangirlinmegan wrote 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, sparking widespread amusement and a wave of curiosity as thousands of other users rushed to check their own Amazon profiles to see what the company’s data models had concluded about them.
For users browsing on a desktop computer, this hidden profile page can be found by navigating to the upper right corner where Amazon displays a greeting with the user’s name, clicking on "Account" under the main account section, and then selecting "Your Shopping preferences" under the ordering and shopping preferences heading. From there, shoppers can manually add information of their own, such as shoe sizes, personal interests, and dietary preferences. However, the feature also houses a deep algorithmic profile. By scrolling to the bottom of the page and clicking a blue hyperlinked option that reads "Manage your information," users are granted a window into the digital dossier that Amazon has quietly compiled on them.
For those using the mobile shopping application, the path requires tapping the hamburger menu icon from the bottom navigation bar, and then navigating consecutively through Account, Shopping preferences, and finally the "About you" section.
Exploring these profiles reveals a fascinating, sometimes startlingly accurate picture of how algorithms interpret consumer habits. For some users, Amazon correctly gleans that they are individuals who practice photography, play collectible card games, and are deeply invested in the Apple hardware ecosystem. Algorithms have even flattered certain customers by noting that they read diverse non-fiction, prompting humorous reflections on whether the machine learning models are attempting to flatter their human subjects with assessments of open-minded intellect and an intellectual thirst for knowledge.
Other shoppers and tech reporters exploring the feature found that Amazon correctly noted specific lifestyle interests ranging from vinyl records and ceramics to a penchant for natural materials in home decor and clothing that prioritizes comfort. None of those surveyed found anything quite as startlingly direct or affronting as the conclusion regarding body shape, but the original Threads user noted that her unique algorithmic takeaway was likely the direct result of having previously purchased "butt scrunch leggings," a clothing item designed to accentuate specific physical features.
Incidents like this highlight a broader, ongoing cultural conversation about the sheer volume of data technology companies accumulate about everyday consumers, and how jarring it can be to realize just how closely automated systems are paying attention to individual behavior. In an era marked by increasingly sophisticated digital tracking and data collection, seeing algorithms translate mundane shopping carts into intimate psychological and physical profiles serves as a vivid reminder of the invisible machinery driving modern e-commerce. While contemporary society lives in a world of alarming mass surveillance and data profiling, most consumers scrolling through their retail profiles can at least take a small, bemused comfort in discovering the bizarre and hyper-specific ways artificial intelligence attempts to categorize their daily lives.
