May 30, 2014

The Data Always Says Yes

Our ability to spot opportunities is limited by our intentions, even when we’re trying to be scientific.

Not my business model

More than a few years ago, a friend of mine posted his startup idea on Reddit, looked to see who engaged, and found that the idea resonated with Indians for some reason. He concluded that it wasn’t a good fit, since his financial model required a higher price point than the Indian market could afford. He was looking for signals from Western markets.

You might have noticed that things in India changed pretty quickly! His assumptions about his target market, and his misunderstanding of the Indian economy prevented him from seeing the opportunity.

Not my customer

I once ran an experiment with Leancamp. Instead of selling event tickets, I offered a yearly membership for a lower price than the single tickets, but the conversion rate was low. When I spoke to people that had shown interest, I learned they were all consultants. That explained it - consultants were happy to buy memberships, but not startup founders.

So I moved on; I was mainly trying to get founders to understand Lean Startup.

I only realised later that engaging consultants in this way could have been extremely powerful for founders too. Now, universities are even talking to me about sarting an institute. I missed the early signs.

Where does the data point?

There’s evidence that successful entrepreneurs find positive outlooks in negative results; if we get lemons, we make lemonade.

Being hypothesis-driven gives us a better model of the world, to explain what we see. Interpreting data comes from the opposite direction. If challenges our models. If we get data that we don’t like, chances are it’s useful to spot our assumptions.

The data points us towards reality.

The question is, are we listening?

Originally posted at

What am I up to these days?

I’m a new parent, and prioritising my attention on our new rhythms as a family. I’m also having fun with slow creative pursuits: making a few apps, writing, etc.

Work-wise, I’m trekking along at a cozy pace, with a few non-exec, advisory roles for cryptography and microchip manufacturing programs.

In the past, I've designed peer-learning programs for Oxford, UCL, Techstars, Microsoft Ventures, The Royal Academy Of Engineering, and Kernel, careering from startups to humanitech and engineering. I also played a role in starting the Lean Startup methodology, and the European startup ecosystem. You can read about this here.

Contact me

Books & collected practices

  • Peer Learning Is - a broad look at peer learning around the world, and how to design peer learning to outperform traditional education
  • Mentor Impact - researched the practices used by the startup mentors that really make a difference
  • DAOistry - practices and mindsets that work in blockchain communities
  • Decision Hacks - early-stage startup decisions distilled
  • Source Institute - skunkworks I founded with open peer learning formats and ops guides, and our internal guide on decentralised teams