When it comes to assessing a startup’s chance of success, equity investors apply a specific set of criteria to minimize risk. In their decision-making process, most venture capitalists (VCs) agree with giving priority to the team composition, hence the popular saying: “Always consider investing in a grade-A team with a grade-B idea. Never invest in a grade-B team with a grade-A idea.” In this paper, we explore the profile of technology-based startup teams that are most likely to secure a Series-A funding round from VCs. From a methodological point of view, we applied a strongly quantitative approach, integrating several data mining techniques according to a multidisciplinary perspective, between data science and entrepreneurship. As for the company information, we used Crunchbase as our primary source, considering a set of U.S.-based startups founded from 2000 to 2017. For each venture we algorithmically integrated team-related information from the founders’ public LinkedIn profiles. Overall, we analysed more than 2,100 teams, involving a total of about 4,600 founders. Each founders’ experience was analysed by considering their professional background. Overall, more than 29,000 work experiences have been taken into consideration. Statistical analysis was carried out on both individual founders and their team organization. Both founders and teams were evaluated in terms of heterogeneity of prior experience and similarity of co-founder profiles using the Gini coefficient and Jaccard index, respectively. Statistics are expressed according to the companies’ sector and their fundraising profile. In fact, the different sectors are mapped on a 4-quadrant chart to identify different combinations between founders’ profiles (specialists VS generalists) and teams characteristics (combining co-founder with similar or diverse background). Results reveal the impact of team similarity and variety in terms of prior working experience. The findings provide valuable insights for scholars dealing with tech-driven startups teams, aspiring entrepreneurs looking for co-founders and for VCs seeking to invest in promising startups.
Patterns of Successful Founding Team Composition and Funding Outcomes
Ferrati F.
;Muffatto M.
2023
Abstract
When it comes to assessing a startup’s chance of success, equity investors apply a specific set of criteria to minimize risk. In their decision-making process, most venture capitalists (VCs) agree with giving priority to the team composition, hence the popular saying: “Always consider investing in a grade-A team with a grade-B idea. Never invest in a grade-B team with a grade-A idea.” In this paper, we explore the profile of technology-based startup teams that are most likely to secure a Series-A funding round from VCs. From a methodological point of view, we applied a strongly quantitative approach, integrating several data mining techniques according to a multidisciplinary perspective, between data science and entrepreneurship. As for the company information, we used Crunchbase as our primary source, considering a set of U.S.-based startups founded from 2000 to 2017. For each venture we algorithmically integrated team-related information from the founders’ public LinkedIn profiles. Overall, we analysed more than 2,100 teams, involving a total of about 4,600 founders. Each founders’ experience was analysed by considering their professional background. Overall, more than 29,000 work experiences have been taken into consideration. Statistical analysis was carried out on both individual founders and their team organization. Both founders and teams were evaluated in terms of heterogeneity of prior experience and similarity of co-founder profiles using the Gini coefficient and Jaccard index, respectively. Statistics are expressed according to the companies’ sector and their fundraising profile. In fact, the different sectors are mapped on a 4-quadrant chart to identify different combinations between founders’ profiles (specialists VS generalists) and teams characteristics (combining co-founder with similar or diverse background). Results reveal the impact of team similarity and variety in terms of prior working experience. The findings provide valuable insights for scholars dealing with tech-driven startups teams, aspiring entrepreneurs looking for co-founders and for VCs seeking to invest in promising startups.
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