We are trialling computer models to improve entrepreneurial mentoring

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  • April 03, 2018
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Entrepreneurial mentoring has historically lacked a rigorous approach. Some mentors adopt from traditional coaching techniques; other institutions use industry focussed expertise (such as London Business School’s 100-odd sector specific mentors), and accelerators (such as TechStars) use strictly scripted playbooks investor panels that give the entrepreneurs feedback.

All such approaches are inappropriate (if not dangerous) for academics who are creating entrepreneurially focused universities. Mentors who advise based on their intuition or instinct result in misleading mentees by lacking the rigour required for each aspect of the business viability to be considered. Academics, on the other hand, need to ensure that two different mentors would both provide broadly similar guidance to the venture, thereby building a premise of consistent advice. This business evaluation mentoring model applies to ventures that are at the initial conceptual stage when products and detailed plans are yet to be developed.

For our programmes in the GCC, Kevin Hindle joined us from Melbourne to resolve this problem. He uses a tool that has captured thousands of both successful (and unsuccessful) applications for funding and reverse-engineered a model of how the business should be evaluated based on a series of robust set of parameters for it to have the best chance of succeeding. This cloud-based tool uses the statistical technique of logistical regression analysis and facilitates the prediction of a set of drivers that would improve the chance of entrepreneurial success.

We trialled this successfully in Oman in February 2018 with 40 academics from 22 institutions on five different ventures and managed to get striking consistent results. Using a data-informed model removes the emotion out of dragon’s den, strips the personalities and quirks of the glamour-fame hungry judges and provides advice based on historical models of success. A natural extension would be to make the tool dynamic and feed through the new cases as it is deployed to help it develop further (from regression to adaptive learning)

Using this approach in entrepreneurial mentoring makes it a three-way dialogue between the mentor, the tool’s recommendations and the entrepreneur. It eliminates personal bias to identify clear, data-informed pathways where the entrepreneur needs to focus their energy to de-risk and create value. This technology assisted mentoring is one pillar of entrepreneurial mentoring.

It raises entrepreneurial mentoring to a new level whilst still maintaining the full element of human contact.

It also helps the entrepreneur with the best chance of avoiding the blind spots that all too often cause early venture deaths.

- Viren Lall