Here is a nice post on the Stripe Atlas site about pitching your startup. It is written from the perspective of a very early-stage startup pitching for a seat in an incubator such as Y Combinator. (If the entrepreneur is not yet that experienced, an achievement like having climbed Mount Everest adds a datapoint about persistence).
Here are some quotes that struck me from the post:
- "Explain assumptions in your pitch like you would to a smart friend in a different field."
- "The investor is not your user, so pitching users and pitching investors are completely different."
- "This pitch says nothing, in 18 words: COMPANY will help e-commerce stores sell more products using cutting-edge AI-enabled algorithms and machine learning."
- "Clarity is particularly important when you’re tackling recently popular ideas, like blockchains or machine learning"
- 'Your reviewer will read literally thousands of data points today–the average pitch includes more than 10. No one can remember that many arbitrary numbers, so reviewers compress them to “zero”, “non-zero”, and “impressive.”'
- 'Nobody has ever written in their comments on a company “Wow, their sign-in screen blew me away. I want to invest in that sign-in screen.” '
- "Risk-taking is encouraged in startups; stupid risks are not. Walking into the office of a person in a position of authority without a meeting scheduled is a risk, but it suggests ambition and sales ability. Describing crimes you’ve committed generally suggests poor judgment."
The people evaluation bit of this Y Combinator screening process seems very similar to the way we were on the lookout for new talent in McKinsey. Especially for young hires with little work experience, you had to comb for indicators of possible future success, fit. In every interview you really wanted the candidate to succeed. And the dialogue with the candidate, the way she responded and thought was very important.