What customer data do retailers feed into personalization algorithms?

Layers of Data Behind a Personalized Experience

Retail personalization algorithms rarely rely on just one category of data. Instead, they typically combine multiple layers, each contributing a different kind of insight into a shopper’s likely preferences. This generally starts with behavioral data collected as a shopper interacts with a site or app — which pages they view, how long they linger on a product, what they search for, and what they add to or remove from a cart. This kind of data is considered valuable because it reflects real, current actions rather than static assumptions about a shopper’s interests.

On top of behavioral signals, retailers typically incorporate transactional history, since actual purchases represent a stronger, more confirmed indicator of genuine preference than browsing alone.

Account, Loyalty, and Stated Preference Data

For shoppers who create accounts or join loyalty programs, retailers can draw on a richer, more persistent data set, including past order history across multiple visits, any preferences explicitly provided by the shopper, such as favorite categories or sizes, and sometimes basic demographic information voluntarily shared during signup. This account-linked data allows for more consistent personalization across devices and over longer periods, compared to relying solely on anonymous session-based browsing data that resets between visits.

Loyalty program participation in particular often provides retailers with a clearer, longer-term view of a shopper’s habits, since repeat engagement over time offers more data points than a handful of isolated browsing sessions.

Third-Party Data and Its Growing Regulation

Some retailers supplement their own first-party data with information obtained from third parties, such as advertising and data partners, to build a fuller profile of shopper interests beyond what’s directly observable on their own site. This practice, however, has become subject to increasing regulatory scrutiny in many jurisdictions, with data protection laws requiring clearer disclosure, consent mechanisms, or opt-out options for certain categories of shared or purchased data. As a result, the extent to which retailers rely on third-party data varies not just by company preference but by what’s legally permissible in a given market.

Because data practices differ so significantly across retailers and are shaped by the specific privacy laws applicable in a shopper’s location, understanding exactly what data a particular retailer uses generally requires reviewing that retailer’s own privacy policy rather than assuming a single universal practice across the industry.

Bottom Line

Retailers typically feed personalization algorithms a mix of behavioral data, transactional history, account and loyalty program information, and in some cases third-party data, combining these layers to build a fuller picture of shopper preferences. The specific data used and how it’s disclosed varies by retailer and is increasingly shaped by privacy regulations in different jurisdictions.

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