Amazon shopping profiles assign users quirky personal traits based on activity
Amazon’s shopping data profiles have drawn attention after users shared unexpected descriptions the company had assigned to them. The posts show how personalisation can produce oddly intimate or humorous assumptions, raising questions about what retailers infer from customers’ activity.
One user found Amazon had labelled her as having “flat buttocks”; others reported descriptions including “has no friends” and “treats cats as biological children”. The article says many entries are more generic, such as regularly drinking coffee or playing video games. Amazon users can review, edit or delete these details under “Your Shopping preferences” and then “About You” in their account.
- Amazon users found surprisingly specific profile descriptions.
- The labels are drawn from shopping data.
- Users can review, edit or delete them.
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Amazon collects and analyses shopping data from its users to build personalised profiles of their characteristics and preferences. These profiles help the company tailor product recommendations and marketing to individual customers. The profiles are stored in users' accounts where they can be viewed, edited and deleted if desired.
The system generates descriptions based on shopping patterns, which can range from the generic—such as noting regular coffee purchases or video game play—to surprisingly specific inferences about lifestyle or personal characteristics. Some descriptions are mundane, whilst others are oddly intimate or unusual, reflecting what algorithms determine from analysing shopping behaviour. This raises questions about what retailers can reliably infer about customers based solely on their purchasing patterns.
Users can access and manage these profiles through their account settings. In the "Your Shopping preferences" section under "About You", customers have the option to review, edit or remove the descriptions and personal details Amazon has assigned to them based on their shopping activity.
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The strongest fair case each way — we don't pick a winner.
The case for
Data-driven personalisation helps retailers provide recommendations users genuinely want, and this profiling system is fundamentally transparent—users can inspect, edit, or delete any details Amazon has assigned. The humorous or oddly specific descriptions are likely algorithmic errors or edge cases; most profiling data is generic and useful for matching people with products. Retailers have always inferred things about customers from purchases; digital systems simply make this more explicit and controllable than ever.
The case against
This profiling represents an uncomfortable level of intimate corporate surveillance, regardless of technical controls most users won't know exist. The fact that Amazon extracts such specific personal inferences about bodies, social connections, and private relationships illustrates how deeply retailers penetrate our lives, creating risks around discrimination and manipulation. Beyond individual editing options, there is a troubling principle: that corporations should be cataloguing increasingly intimate details about who we are based on consumption data.
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Originally published by The Verge as “Amazon uses its tracking data to guess whether shoppers have a flat butt and no friends”.