Insights from BV4's startup database - Business Model
In this series of articles, we will look at different data-driven insights that BV4 gathered from startups. In this article, we will have a look at important qualitative aspects of the business model and how they correlate with each other.
Recap of the underlying model:
BV4 built a qualitative score card model which focuses specifically on such qualitative aspects (e.g. team complementarity, previous founding experience, development stage, scalability potential or the go-to-market strategy of a startup). Thus far, BV4 has scouted over 13'000 startups of which more than 3'000 were assessed. Over 200 parameters have been considered to perform the assessments to obtain the scores in four distinct dimensions: product, business model, team, and market. BV4 assesses several qualitative parameters within each dimension. The following graph shows some parameters of the BV4 model:
The result is a score which represents the risk profile of the startup at its current stage. The colored rating scale on the right side shows that a high score indicates that the startup is an attractive case for the average investor. Startups should achieve a higher score over time as their products get closer to the market and they gain more traction, clarity on the business model as well as important target clients.
The analysis of the data on the business model can be found below:
Startups in our database show three important relationships when it comes to the business model:
· Detailed roadmaps of the products help founders in understanding and developing a relevant Go-To-Market strategy. The startups in the database indicate that whenever there was a clear product roadmap, the startup was able to identify important features for its clients which impacted their Go-To-Market strategy.
· B2C businesses were not focusing on partnerships to increase their reach. This is a rather surprising finding as partnerships are relevant for B2B and B2C. It appears that B2Cstartups might focus too much on performance marketing only rather than exploring partnership options which could be a cost-efficient way to reach the market.
Looking at this finding in context of the first bullet point, it is worth mentioning that a startup might have a good Go-To-Market strategy but was not able to implement it properly (e.g. B2C startups that were not able to form relevant partnerships) or requires more time to do so.
It is important to understand that the data sample consists of 490 startups of which the majority operate in the field of Med/HealthTech, FinTech, EnterpriseTech and DeepTech. The startups were mainly operating in the Pre-Seed and Seed stage. While the data sample represents only a fraction of the overall ecosystem, it already gives insights into some points that founders should consider when building their ventures.
We will focus on the last dimensions in the next article and show the most important data patterns of the market dimension.