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 on the Kernel Stewards team, where we help ~2,000 fellows understand the what the development of blockchains mean to humanity on anthropological scales. I’m particularly interested in enabling fellows to build things with blockchains that are altruistic and prudent.

I’m also building a communication tool for community groups and unconferences. It focuses on autonomising teams rather than “coordinating”.

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

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

Random Projects

  • Cuppa - decentralised collaboration protocol (WIP)
  • Nonfungo - completely on-chain NFT sale notification bot for Discord. (Look ma! No Opensea API!)
  • Powerplays - real-time token launches