Klarna building shopping habit graph

Klarna is trying to turn checkout data into a consumer demand graph. Knowing whether someone buys skincare every six weeks, compares prices before purchasing, shops luxury versus discount, or tends to return items gives Klarna something more valuable than a one time loan decision. It gives Klarna a way to steer future purchases in its app, sell better leads to merchants, and improve underwriting with real transaction and repayment history.

• Klarna was already moving from a button at checkout to a shopping destination. In the app, users can browse featured stores, track deliveries, save wish lists, get recommendations, and even generate one time cards for merchants without a native integration. That broader flow lets Klarna observe what people browse, buy, and come back for.
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• The business reason is concrete. About 74% of Klarna revenue in 2020 came from merchant commissions, and merchants pay because Klarna can lift conversion and send demand. Richer shopping behavior data helps Klarna show retailers which users are likely to buy, what categories convert, and when to place an offer or financing prompt.
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• This is the same strategic prize that checkout companies like Bolt talk about. Whoever owns the identity layer and transaction flow can recognize shoppers across merchants and personalize the experience. Klarna’s difference is that it combines that data layer with financing, so every purchase can feed both merchant marketing and credit decisions.
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The next step is a more closed loop retail network, where Klarna captures discovery, payment, loyalty, and more offline spend in one system. If that works, shopping habit data stops being a side benefit of BNPL and becomes the core asset that helps Klarna defend margins, deepen merchant dependence, and compete with larger payment incumbents.