The Co-Founder Dilemma: Calculating the Co-Founder Equity Split : A model for “fair” equity distribution for startup founders
Saharan, Pankaj (2015)
Saharan, Pankaj
Metropolia Ammattikorkeakoulu
2015
All rights reserved
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2015060812759
https://urn.fi/URN:NBN:fi:amk-2015060812759
Tiivistelmä
Startup founders who avoid tough equity negotiations during the early stages of the startup and quickly jump to equal splitting may fail to understand strengths of their founding team members and their expected contributions to the startup. Equal splitting may also be a sign for investors that the founding team lacks entrepreneurial negotiation skills. Multiple research and reports have shown that equal splitting can lead to such unpleasant consequences such as lower pre-money company valuation and less stable startup performance.
The overall high-level objective through this master thesis is to help future startup founders understand the impact and avoid common pitfalls associated with equal division of equity. This master thesis analysed a very simple but effective equity split solution (which is co-developed by the author of this master thesis) based on the three conceptual pillars:
1. Fairness: To understand the best equity split for the founding team based the current situation of the startup.
2. Powerful: Discover the strengths of the founding team.
3. Strategic: Turn uncomfortable equity negotiations into a strategic teamwork.
Founder Equity Solution ("FES") model discussed in this master thesis is based on an algorithm that calculates shares of equity for each founder based on information (selected answers) provided by the users. The quality and precision of the calculated equity split depends on the quality and precision of the answers given. The algorithm used in the model is based on research findings and statistical data.
The master thesis is able to highlight the importance of equity in the startup, why equity distribution could be crucial factor in the long term success of the company, an equal-equity split or 50-50 split is almost never a good solution to split the equity, trust and fairness are the key factors to consider in the equity split, a simple model to share the equity split, key considerations based on your startup prior to deciding on the equity distribution etc. The master thesis is driven mostly by relevant case studies, solutions available and user feed-back. Extensive end-user validation is done to make the solution applicable to most startups especially in the early stages of the startup.
The overall high-level objective through this master thesis is to help future startup founders understand the impact and avoid common pitfalls associated with equal division of equity. This master thesis analysed a very simple but effective equity split solution (which is co-developed by the author of this master thesis) based on the three conceptual pillars:
1. Fairness: To understand the best equity split for the founding team based the current situation of the startup.
2. Powerful: Discover the strengths of the founding team.
3. Strategic: Turn uncomfortable equity negotiations into a strategic teamwork.
Founder Equity Solution ("FES") model discussed in this master thesis is based on an algorithm that calculates shares of equity for each founder based on information (selected answers) provided by the users. The quality and precision of the calculated equity split depends on the quality and precision of the answers given. The algorithm used in the model is based on research findings and statistical data.
The master thesis is able to highlight the importance of equity in the startup, why equity distribution could be crucial factor in the long term success of the company, an equal-equity split or 50-50 split is almost never a good solution to split the equity, trust and fairness are the key factors to consider in the equity split, a simple model to share the equity split, key considerations based on your startup prior to deciding on the equity distribution etc. The master thesis is driven mostly by relevant case studies, solutions available and user feed-back. Extensive end-user validation is done to make the solution applicable to most startups especially in the early stages of the startup.