Klarna makes user-informed decisions at 10x scale with Maze
Learn how Klarna makes confident product decisions by using Maze to get fast user feedback and iterate quickly during the design process.
About Klarna
Klarna is a ecommerce fintech payment solutions platform for merchants and shoppers.
Industry
Financial Services
Opportunity
Enable designers and product teams to do research autonomously, make user-informed decisions, and iterate quickly and confidently.
Key Maze features used
Prototype Testing
Preference Testing
Surveys
Concept Validation
Automated Reporting
Card Sorting
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As a fintech company that serves 150 million users worldwide and powers 2 million transactions per day, Klarna aims to make online shopping and payments as smooth and intuitive as possible. On the B2B side, Klarna is trusted by half a million global retailers, including H&M, Saks, Sephora, Macy’s, Ikea, Expedia Group, Nike, and Airbnb. Driven by a culture of innovation, Klarna’s teams move at lightning speed as they work on revolutionizing how people shop and pay online.
The challenge Klarna recognized was having a small UX research team relative to the number of designers and product teams working across a wide range of product domains—shopping, banking, payments, and partnerships with merchants and creators.
Klarna saw the opportunity to scale UX research across the organization by democratizing research and empowering teams to run research autonomously, make better decisions, and iterate quickly—through the adoption of Maze.
The need for quick user insights
Being competitive in the fintech space means moving fast while prioritizing seamlessly intuitive user experiences. This requires continuous discovery and fast iteration, which was challenging with a team of 10+ researchers serving an organization of 5,000+ employees.
“The ratio of product teams to UX researchers was so great that it was really difficult to plan and prioritize for research,” says Deanna Wong, who leads UX Research at Klarna. Even with individual researchers embedded into product teams and product marketing teams, the demand for research far exceeded what they could accommodate.
Klarna’s designers and product teams needed a way to get quick user feedback so they could incorporate those insights into their decision-making.
The product teams and researchers being decentralized also meant that different teams were using different tools, methods, and processes, so there wasn’t consistency across the organization. Deanna recognized the need to establish company-wide standards and processes.
Culturally, teams at Klarna value the ability to work autonomously, so Deanna knew they wouldn’t want to be slowed down by relying on a central resource or having to wait for a researcher. The designers and product teams needed a solution that would empower them to do research independently and get the insight they needed to continue designing with confidence.
Product teams, and especially product designers, need quick user feedback while designing. Maze is perfect for product designers to get insight while designing. It enables them to move forward quickly, while UX Researchers tackle more complex research needs.
Deanna Wong, Head of UX Research at Klarna
Getting insights at the pace of product
As part of centralizing and standardizing UX research at Klarna, Deanna’s team evaluated the UX research tools they were using. For Deanna, what made Maze stand out was its speed and ease of use. Its modern and user-intuitive interface made the Maze platform easy for product teams to appreciate and adopt, equipping them with the ability to run research autonomously and make use of research insights without being specialized researchers.
Where Maze really stands out very strongly for our product designers is in its speed and adoptability, from the UI design to functionality like the insights reporting.
Deanna Wong, Head of UX Research at Klarna
Thanks to Maze, product teams can do research and gather insights quickly and independently, which also brings them closer to the learning process and the users they’re building the product for. They’re able to forge ahead with the design process and make confident, user-informed decisions by using Maze to test prototypes, validate concepts, understand preferences, and run surveys. Likewise, product marketers and content designers can test terminology and messaging to validate what resonates and is more easily comprehensible.
Product managers and teams also need to synthesize and compile research in order to share the insights and make them actionable. If done manually, this would require hours of analyzing user data in order to distill and present the results. The Maze platform generates summaries and reports automatically, which saves them the time and manual effort so they can focus on communication and collaboration based on the research insights.
Being able to articulate and share the data that comes from UX research takes a lot of time, so having auto-generated reports is a game-changer.
Deanna Wong, Head of UX Research at Klarna
Testing user preferences with card sorting
Deanna shares an example of how card sorting with Maze helped design and marketing test communication preferences, with the goal of revamping the Communication Preferences Center in the Klarna app so that users can more easily subscribe or unsubscribe from specific types of communications. This is crucial to enhancing the user experience and avoiding complaints, so the Klarna team used card sorting as the first phase before multiple user testing and design iterations.
The results led to several key outcomes:
- Structural enhancement: “The card sorting exercise played a critical role in shaping the new structure for the Communication Preferences Center,” Deanna explains. By analyzing how users naturally grouped and labeled the cards, the Klarna team gained insights into their users’ intuitive mental models, which allowed for a tailored approach to the new structure that aligns closely with user expectations and understandings.
- Clarity and precision in naming: Despite many users finding the card titles and descriptions to be clear, there were certain titles that posed challenges. “The research directly pinpointed these pain points,” says Deanna, “enabling us to focus on refining these specific titles for greater clarity or adding tooltips so users can click and know more.”
- Implementing user-preferred groupings: The similarity matrix, which showcased the relationship between card pairings, was instrumental in determining which communication types were frequently grouped together by users. This information was used to structure the categories in the new preference center, ensuring it aligns closely with user behavior and preferences.
“Based on the above insights and considerations, we worked on two or three design ideas that we also experimented with using Maze,” Deanna concludes.
Shifting from tactical to strategic research
By implementing Maze, Klarna has been able to successfully scale research across the organization’s product teams. Deanna sees Maze as having augmented and extended the reach of Klarna’s UX research team by 10x.
Maze is a key tool that allows us to scale research at Klarna—like an extension of a researcher’s arm, where we can 10X the ability to get user insights, to help product teams make user-informed decisions. Maze puts that insight-gathering and decision-making control into the hands of product managers, product marketers, designers, and content designers.
Deanna Wong, Head of UX Research at Klarna
Klarna promotes independence and autonomy in its work culture, and Maze fits right into that by empowering anyone to conduct research. Whether it’s testing layout design, preferences, or content, everyone now has access to insights using Maze. Deanna notes that “the democratization of research and opening up others to get involved in research” is very much appreciated at Klarna.
What’s more, enabling research throughout the organization has freed up the UX research team to do more generative and strategic research instead of just fulfilling requests for evaluative research. “It’s a win-win,” says Deanna.
Making better and faster decisions—at scale
“Product teams recognize the value of research, but they still need to move quickly,” explains Deanna. “They want to make user-informed decisions and use insight to reduce risk and minimize rework downstream.” With Maze infusing research into the product development process, Klarna’s designers and product teams can iterate quickly while feeling more confident in their decision-making.
For Deanna, Maze “absolutely trims down the amount of time required” to do unmoderated research, which in turn, speeds up the design process.
Maze is a great research platform for Klarna because we move incredibly fast. For a company of our size, it still feels like decisions are made at the speed of a startup. That’s something I can’t stress enough—Maze really fits well with the pace at which our product teams need to move and work.
Deanna Wong, Head of UX Research at Klarna
According to Deanna, even minor changes made using insights from Maze can have an outsize impact due to the scale and magnitude of Klarna’s product domains and active user base. So every decision made with Maze—whether it’s evaluating screen designs or using heat maps to see where users expect to click—ultimately impacts ROI.
If a designer tests two design variants, and one converts even 1% better than the other, across our 150 million global consumers, that’s an enormous impact.
Deanna Wong, Head of UX Research at Klarna
Implementing Maze has enabled Klarna’s product teams to do research autonomously, make better decisions, and iterate faster while freeing up researchers to do more generative, foundational and strategic research. Maze is excited to support Klarna’s ethos of speed, innovation, and scale, and to continue fostering a Klarna’s culture of learning and user-centric design.
