It should come as little surprise to modern consumers that e-commerce giants collect vast amounts of data based on their daily online habits and purchasing decisions. If a customer frequently buys cat litter, algorithms will promptly infer that they own cats; similarly, a steady stream of anti-acne skin products might lead an automated system to guess that the buyer is prone to breakouts. Even though most internet users are well aware that platforms like Amazon customize shopping experiences according to past purchase histories, it remains a jarring experience to see all of those intimate personal details laid out in a clean, convenient list—especially when those algorithmic deductions turn out to be uncomfortably specific or brutally blunt.

One user on Meta’s Threads platform recently stumbled upon a hidden section of her Amazon account settings where the shopping conglomerate outlines its precise assumptions about who she is. Alongside relatively benign assumptions such as "Shops from women’s departments" and "Probably owns a Shark robot vacuum," she discovered a far more personal and unexpected data point: Amazon had concluded that she "has flat buttocks."

The viral discovery quickly captured the attention of millions 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," 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, sending a wave of curious shoppers scurrying to their own accounts to see what kind of psychological and demographic profile the retail giant had silently constructed behind the scenes.

The feature in question is buried deep within Amazon’s account management settings, located under a menu dedicated to shopping preferences and personal information. While the platform allows users to voluntarily input details such as shoe sizes, specific hobbies, and dietary preferences to help tailor future product recommendations, it also quietly generates its own automated inferences based on transaction histories. For shoppers who decide to investigate their own profiles, navigating through the desktop or mobile app settings reveals a dedicated section where the platform outlines these behavioral deductions, sometimes laying bare surprising insights derived from seemingly mundane purchases.

For many users, the profiles generated by Amazon range from mundane to remarkably accurate. For instance, some accounts reveal algorithmic insights indicating that an individual "practices photography," "plays collectible card games," or is deeply "invested in the Apple ecosystem." These reflections of daily consumer habits can often feel harmless or even mildly flattering. Other users have reported that Amazon accurately identified their appreciation for diverse non-fiction reading, vinyl records, artisanal ceramics, a preference for natural home decor materials, or a clothing style that prioritizes everyday comfort.

However, the viral Threads post highlighted the sometimes awkward reality of automated data collection, where algorithms process purchases without any filter for tact or diplomacy. The user behind the viral post noted that her particular profile entry was likely the result of purchasing "butt scrunch leggings," a popular athletic wear item that apparently tipped off the algorithm in a very literal way.

Incidents like this serve as a stark reminder of just how much technology companies know about their customers and how closely major platforms pay attention to individual browsing and purchasing behaviors. In an era increasingly defined by pervasive digital tracking and widespread automated surveillance, encountering a hyper-specific algorithmic profile can be a jarring experience. Yet, despite the occasional awkward revelation—ranging from harmless product recommendations to uncomfortably direct physical assessments—these digital profiles ultimately underscore the immense scale of data collection that underpins modern e-commerce.