Data:re: Helping insurance brokers better understand their customers

Data:re: Helping insurance brokers better understand their customers

Worked on
user research
product discovery
TL;DR: I was approached by the team at Data:re to help them build the initial offering they could start selling to customers. It was a fun space and a fun project, mostly because I was able to flex my service design and data visualization muscles. Unfortunately, after struggling to find a product-market fit for a while, the team decided to go their separate ways.
Visual identity, User research, Design, Prototyping
Tools & methods
Kano surveys, user interviews, contextual interviews, Sketch
My role
As the only designer on the team, I worked with the founders on developing the existing visual identity into a design system-ish UI kit, running generative user research, designing, and prototyping selected ideas.


Data:re was a Berlin-based early stage startup founded by people with lots of experience in insurance and insurance technology. Their goal was to build a dashboard in which insurance brokers could quickly find their best possible addressable market, compare offerings from different insurance companies to provide to their customers, and - generally - make better offers and more money in the process.


The challenge was to gather as many customer requirements and insights as possible and turn that into a working prototype that the team could show to the customers and iterate. It was a very fun user research and design project that allowed us to understand what people are looking for and turn that into a product. I was responsible for gathering insights, working with the founders on mapping out the user journey and eventually designing the product the customer needed. The work also involved some brand design and creating a rudimentary design system that the team could later expand.

Gathering insights

We spent a lot of time doing user research - interviews, surveys, and so on. The moment we had some understanding of what would be needed, we worked on defining the components of the product, the user journey, the technical feasibility, as well as minimum viable product we want to go for.
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In our process, we found that people wanted to understand:
  • what insurance products they currently have in their portfolio and how they change over time
  • where their product shows up in different comparison engines relatively to their competition and how and why their rating is changing over time
  • how their product compares to competition (feature-wise) for certain parts of the market that they’re interested in selling to
  • what is the best possible market they can address with the product portfolio they’re selling
  • what are the most common features available in certain products that their products might lack, and where their product stand out, so they can focus their marketing on that

Design work

Style guide

Initially, I worked with the engineers to build a quick UI elements kit to be able to move faster. The goal was simple: the quicker we can go from a sketch on a piece of paper to final, implemented UI, the better. Since this happened pre-Figma, we used Sketch + Abstract to build out the design part, and the engineers worked on building a React equivalent in the front-end.
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This was the fun part that allowed me to flex my data visualization muscles a bit. We spent a bunch of time experimenting with different data visualizations, testing which ones are the most understandable for the users, and where we missed the mark. We also spent some time customizing the prototypes we’re showing to potential customers to make sure they look like something they would encounter in their life - paying extra attention to what data is being shown, the product names etc. being as close to reality as possible.
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After designing the dashboards we tested our prototypes with some early adopters that signed up to check what we’re up to, we received a lot of good feedback and insights that we started to prioritize and line up for building. The biggest takeaways were around data that is interesting to people, as well as that we should consider helping them automate other parts of the sales funnel, e.g. building landing pages or creating ads.

Learnings & takeaways

  • attention to detail in prototypes and making sure to use real data is very important when working with customers that are mostly non-technical
  • spending large chunk of time up front doing user research - even in its simplest form: surveys and interviews - helped us focus a lot on core customer problems
  • even the most complex data set can be simplified and made easy to understand by applying smart data visualizations
  • speaking customer’s language is incredibly important