System Overview

What it is: Klarna built a custom AI-powered recommendation system to deliver personalized product suggestions, optimize user experience, and tailor content. The engine drives higher engagement by adapting shopping feeds to each user’s preferences and behavior.

Core capabilities

AI-driven product recommendation engine
Personalized shopping feeds based on browsing and purchase history
Real-time UX optimization with dynamic content adjustments
Integration with merchant catalogs and user profiles
Continuous learning models that refine recommendations over time
A/B testing framework for validating personalization strategies
Analytics dashboards for CTR, engagement, and conversion tracking

Business problems solved

Low click-through rates (CTR) on generic shopping feeds
Lack of personalization in large, diverse product catalogs
Poor customer engagement due to irrelevant content
Limited visibility for merchants in a crowded marketplace
Difficulty scaling personalization for millions of users

Facing these challenges in your marketplace? AI recommendations can solve them efficiently.

Industries served

E-commerce, fintech, retail marketplaces, consumer goods.

Actor Identification

Primary actor: Klarna shopper browsing the app or shopping feed.
Secondary actors: AI recommendation engine, merchant catalog systems, Klarna UX platform, marketing team.

Want to deliver relevant product suggestions to millions of users effortlessly? Let’s explore your AI setup.

Actor Goals

Shopper: Discover relevant products and deals quickly.
Merchants: Increase visibility and CTR for their products.
AI Engine: Generate accurate recommendations that boost engagement.
Klarna UX Platform: Deliver seamless, personalized shopping experiences at scale.

Context and Preconditions

Merchant product catalogs integrated with Klarna’s recommendation engine
Shopper behavioral data (browsing, purchase, clicks) fed into models
UX optimized to display personalized feeds in real time
A/B testing framework running to validate recommendation performance
Compliance with data privacy and personalization regulations

If your data is ready, personalized AI-driven feeds can transform engagement - start now.

Basic Flow (Successful Scenario)

Shopper opens the Klarna app or shopping feed.
AI engine analyzes user’s behavior and purchase history.
Personalized product recommendations are displayed in real time.
Shopper clicks suggested items, increasing CTR and engagement.
Recommendations are logged, and models retrain with new data.
Merchants gain insights into product visibility and CTR performance.

Outcome: Klarna achieves a 25% increase in click-through rates by delivering personalized shopping feeds powered by its AI recommendation engine.

Alternate Flows

A1: Cold start (new user): If no history exists, engine shows trending/popular products until enough data is collected.
A2: Out-of-stock products: If a recommended item is unavailable, engine substitutes alternatives.
A3: Data sync failure: If catalog updates fail, engine falls back to cached data.
A4: Irrelevant recommendations: If CTR drops, models are recalibrated through A/B testing.

Transform your e-commerce experience with Klarna-style AI recommendations. Deliver personalized shopping feeds, boost engagement, and increase conversions - start building your AI-powered platform today.

AI-Powered Shopping Feeds Drive Higher Conversions at Klarna