We all need to stay ahead of the curve and continuously learn and adapt.

One way to do that is by studying case studies of successful, but moreover of failed companies. Today, I want to share with you a case study of a startup that aimed to disrupt the insurance market but ultimately failed due to miscalculations in their business model. These miscalculations were caused by making the wrong assumptions. It sounds trivial, but they really did their homework before starting. How could it have happened anyway?

The startup aimed to disrupt the insurance market by developing a mobile application and platform that would make the process of submitting a claim much faster and easier.

The market in which they operated was valued at about €800 million, and at that moment in time there was no one else in the market attempting to digitize the process.

The startup did their research, created a great pitch deck, and raised enough money to get started. The application and platform were developed, and it all seemed like a great concept.

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However, there were two fundamentals that had been miscalculated.

The first miscalculation was the value of the repair jobs. Based on available data, the startup assumed the average repair job would be €2,600.00. However, in reality, the average repair job was only €850.00. This meant that the fee they would collect would not be €260, but only €85.

The second miscalculation was the handling costs. The startup had heavily underestimated the manual work that was needed to process the claims. People were calling all the time! This added additional, unexpected costs to the business model.

So, the overall business model would (simply put) look like this:

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As a result of these miscalculations, the startup found themselves in a negative margin situation. Although the orders did come in via the application, the business model was not sustainable.

In that sense, there was a product-market fit, but it did not mean a business validation.

The startup's ambition to disrupt the insurance market was admirable. However, it's essential to understand that sometimes you can only really know the validity of your assumptions by trying. And yes, even after rigorous research you still might fail. It’s part of the risks that we take.