How deep-tech startups can significantly improve their pitch

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While we were recruiting for our first batch of startups at Iterative, we spoke with many deep-tech firms in Southeast Asia.

Just a few months ago, the Singapore government injected S$300 million into Startup SG Equity for deep-tech startups. As we listened to some pitches within this vertical, we noticed a few areas that could be improved. By pointing these out, we hope this will help a few of you pitch your next great idea.

Image credit: Dmitry Moiseenko

1. An answer searching for a solution

Great startups typically start pitches with the problem they are trying to solve. They spend considerable time making sure that investors can understand and relate to the pain point they want to focus on before talking about their solution.

Deep-tech pitches, we noticed, will sometimes skip the problem slide altogether or spend one short slide on the problem and spend five to 10 slides on their solution.

This happens because deep-tech founders usually come from technical backgrounds and/or from academia. Working in academia, you have a singular focus of testing state-of-the-art ideas in domains that are relatively well-defined. However, translating this idea into a solution that handles specific customer pain points usually isn’t that simple.

Remember that by definition, a solution must have a problem, but in the case of startups, it also needs an audience.

So when you reach the problem slide in your pitch, make sure you clearly define the problem, the pain points associated with it, who has this problem, and why it’s important.

While you’re pitching, try to gauge the audience’s response and see if your pain points are resonating and that you’re being understood. If not, try to ask questions and see if you can clarify your problem statement.

2. Defining your competitive advantage

Most deep-tech startups do a great job of explaining their solution and how it works, but they usually don’t explain enough about their competitive advantage. Just being a deep-tech startup doesn’t shield you from competitors.

Let’s take an AI company, for example. For most AI companies, just being able to build the model isn’t a sufficient competitive advantage. Open-source tools and the availability of online resources makes model building extremely accessible.

A better competitive advantage would revolve around your data acquisition strategy. Although the tools to start an AI firm are accessible, it’s generally difficult to create a large, clean, and representative dataset. That’s the reason why Tesla has such a huge advantage in self-driving cars. All too often, I see companies forget to explain this.

Here are some basic questions to ask yourself:

3. Building a customer acquisition strategy

4. Why it matters

Conclusion