It should come as little surprise that Amazon collects data based on what you buy. If a customer frequently purchases bulk quantities of cat litter, the e-commerce giant will likely infer that they own cats. Similarly, a steady stream of anti-acne skin products might lead the algorithm to guess that the user is prone to breakouts. Even though most modern internet users understand that digital storefronts routinely customize shopping experiences based on purchase histories, seeing those personal assumptions laid out in a convenient list still carries a distinct element of shock—especially when those algorithmic deductions cross the line into uncomfortably intimate territory.
This reality went viral recently when a Threads user stumbled upon a specific section of her Amazon account settings designed to display who the shopping giant believes she is. Alongside relatively benign assumptions such as “Shops from women’s departments” and “Probably owns a Shark robot vacuum,” she discovered a much more personal conclusion: Amazon’s algorithm had deduced that she “has flat buttocks.”
The revelation quickly captured the attention of social media users worldwide. “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, known online as @fangirlinmegan, in a 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, driving a massive wave of curiosity as countless online users rushed to check their own Amazon profiles to see what kind of digital dossier the retailer had compiled on them.
For those navigating the platform on a desktop computer, discovering this hidden data profile involves a few specific clicks. By hovering over the greeting in the upper right corner where Amazon displays the user’s name, customers can access their main account dashboard. Navigating down to the ordering and shopping preferences section leads to a page titled “Your Shopping preferences.” While this area is ostensibly meant for users to manually input information—such as shoe sizes, specific interests, and dietary preferences—scrolling to the bottom of the page reveals a crucial blue hyperlink labeled “Manage your information.” Clicking that option opens the curtain on what the algorithm has quietly deduced behind the scenes.
Mobile app users can find the same information by tapping the hamburger menu icon located on the bottom navigation bar, then proceeding through the account settings to shopping preferences and finally opening the profile details about the user.
When inspecting these profiles, the breadth of data collection becomes immediately apparent. For many users, the platform accurately pieces together complex lifestyle traits. One reviewer noted that Amazon had correctly gleaned that they are someone who practices photography, plays collectible card games, and remains deeply invested in the Apple ecosystem. In some instances, the algorithm even flatters its subjects, such as by noting that a user reads diverse non-fiction—eliciting reactions from users who appreciate the algorithmic validation of their intellectual pursuits.
Other digital footprints reveal aesthetic and lifestyle preferences with remarkable precision. Colleagues and tech commentators examining their own accounts discovered that Amazon correctly noted specific interests in vinyl records, ceramics, a penchant for natural materials in home decor, and clothing that prioritizes comfort. While none of these professional reviewers found anything quite as jarring or explicitly personal as the assumption regarding flat buttocks, the original Threads user offered a logical explanation for her unique algorithmic label: she had previously purchased a batch of "butt scrunch leggings."
The viral moment highlights a broader, ongoing cultural unease regarding the sheer volume of data technology companies quietly amass. Big tech firms understand their consumer base intimately, and it can be jarring for everyday shoppers to realize just how closely automated systems pay attention to granular browsing and buying habits. In an era defined by alarming trends in digital surveillance and data tracking, stumbling upon a blunt, algorithmically generated physical assessment serves as a stark reminder of the invisible profiles being built behind everyday online transactions—even if, in this particular viral instance, it simply proved that Amazon is paying closer attention to clothing fit than most consumers realize.
