Methodology
The methodology of this project is a result of an exhaustive, multi-step process. By combining internal data from EQT Ventures together with a large external dataset from the Oxford Internet Institute at Oxford University, it combines scientific methodologies with practical applications for founder evaluation and development.
1. Comprehensive data integration
External data: Over 21,000 startups were analyzed, focusing on extrinsic success metrics such as IPOs and acquisitions.
Internal data: More than 5,000 psychometric profiles of founders from EQT’s portfolio were evaluated to identify distinct personality patterns.
Benchmark groups: Thousands of non-founders, as well as founders whose ventures did not achieve success, served as control groups. This approach enabled robust comparative analyses and deeper insights into what differentiates successful founders.
Together, these sources were cross-referenced and harmonized to ensure the six identified traits were both statistically and practically significant. This integration ensures that the findings reflect universal characteristics rather than those specific to a single region or sector.
2. Analyzing traits against startup success
Success was evaluated through multiple internal and external sources. These analyses highlighted the critical role of founder traits and their interaction in determining outcomes.
3. Advanced statistical and computational techniques
Initial t-tests identified significant differences between founders and non-founders. Machine learning methods such as Random Forests further validated the predictive power of the identified traits with a confidence level over 99%.
4. Structured expert input
Interviews with EQT partners were conducted to align data-driven insights with real-world investment expertise. These discussions highlighted critical nuances and helped ensure that the identified traits were not only statistically robust but also practically applicable.
