I’ve seen it too many times: Startup gets funding, startup runs out of funding, the CEO writes a nice memo on why the market wasn’t quite right to sell their product/service or they weren’t able to raise more funding. If the startup doesn’t secure enough funding, the founders love to attribute a lack of funding as the root cause of the failure.
I think it’s all bullshit, or as Mark Suster calls it, entrepreneurshit. This will come as a surprise to some, but it’s not about funding! My (somewhat-biased) personal philosophy is that it’s about the number of iterations you can tolerate before giving up or getting it right; hopefully the latter.
It turns out everyone is fearful of failure, go figure. Each founder I’ve shaken hands with, every CEO, politician or any human being on this little blue rock is scared to admit it, but fear shows itself every now and then. I’ve learned that the people who communicate openly and honestly have come to terms with their fear better than those who go out of their way to hide it.
So what am I driving at? I’d like to start with an example, and there are many examples out there to choose from, but I have to pick one to keep things in context and get to the point, so I’ll go with Slack. Great success in just 12 months, right? If we dive a little deeper we realize that the façade of success is really a curtain across previous failures for the founding Slack team. The real history actually reveals a couple failed attempts over many years before the final pivot to Slack.
But the point isn’t to scrutinize media coverage and public fascination with startup unicorn successes. In fact, Stewart Butterfield is everything an entrepreneur aspires to be, and he’s been honest about his path and feelings openly. The point I’m trying to make is that Butterfield learned to adapt, pivot, and iterate on opportunities until the right one made an impact for customers. That’s the real entrepreneur’s journey.
Mattermark is another great example. It took Danielle Morrill 3 years to start Referly, and less than a month to realize she had created a Zombie Startup. She could have given up, and in fact, a lot of her articles and tweets suggested she was going to get out of the startup racket, but she kept going. Danielle claims Mattermark wasn’t a pivot, but that word means so many different things to so many people. I personally feel Mattermark was just another iteration. It was an iteration influenced by the school-of-hard-knocks education, startup lifestyle and pursuit of Product-Market Fitness.
There’s definitely overlap between core concept and dynamics between her two companies: Both consumed large amounts of public data and made sense of them, both had a website presentation of that data and both needed APIs (consume and publish) to work. Once again, entrepreneur didn’t give up, kept iterating, and finally achieved Product-Market Fit. By the way, great article on Product-Market Fit here. Brad Feld also happens to be an investor in Mattermark :o)
So where the heck am I going with all this? Well, the common denominators within the two stories above are:
- Founders failed at one or more products previously
- Founders took big emotional and financial hits
- Longevity seems to win; how long you can focus on solving a problem with the same team
- A new opportunity presented itself
- Entrepreneurial drive took over again (some call this passion)
People make mistakes every day; but I strongly believe founders make more than the nationwide average because the daily number of decisions increases with a startup. You can listen to advice, read blogs, attend conferences, but most entrepreneurs need to make their own mistakes to learn how to become better (decision-makers, operators and leaders). These mistakes are part of the iteration formula also! So without further delay, here’s my first attempt at that formula:
Startup Success: Accumulated Iteration >= Product-Market Fit Factor
Iterations: New product versions as a result of refining (designing + developing) based on mistakes and lessons learned, per month.
Longevity: Length of time (months) team members can survive (low or no salaries, stress, personal obligations, debt and continued passion).
Accumulated Iteration Factor: Iterations/Longevity for Current Month + Previous Month Iteration Factor
Product-Market Fit Factor: The required Accumulated Iteration Factor to achieve success for your particular market and product combination (skewed by competitive forces, market dynamics and economic laws).
Graphing this out in Excel, we can see some typical models below, with the following assumptions:
- Same company in all scenarios
- Ability to iterate and release product is different in each scenario (variable Iteration Factor)
- Each iteration makes the product better, closing more leads and attracting more net new customers than before
- Product-Market Fit factor is constant in all scenarios (arbitrarily set to 10). This means when a company has achieved an Accumulated Iteration Factor of 10
- The Longevity for all models is set to 18 months, the typical amount of time it takes for startups to succeed, get on a successful trajectory, or fail.
This is the model every entrepreneur attempts to emulate and it shows a startup that can iterate quickly, and release iterations of product at a rate of 4 times per month. With a Product-Market Fit factor of 10, this startup will achieve market success within 7 months and continues to increase Product-Market Fit thereafter.
This model shows a struggling startup, perhaps due to product complexity, market complexity, regulatory requirements, or just the wrong team to execute quick. Whatever the reason, this startup only released a product once (at the 12 month mark) and never reached Product-Market Fit:
Based on other articles out there, and talking with friends and fellow entrepreneurs with startups, this is the norm in terms of Iteration Factor, about once or twice a month:
The issue is that even with the right team and the ability to endure the Longevity, this startup never reaches Product-Market Fit. We could project the Iteration Factor for another 18 months, and make some assumptions that the team gets better (faster) at iterating, but the reality is most startups fail within 18 months.
So how does this relate to Slack and Mattermark? These are founders and teams of startups that never achieved Product-Market fit, similar to the Average model above. But there’s a catch. These are also founders and teams that were willing to pivot or leverage lessons learned and experiences to start something new. In which case, the starting Iteration-Factor, the new company’s ability to iterate and release product quickly, has increased and starts at a new origin than before. Here’s the revised model:
With this revised model, we see the previous founders and founding team have not only started at their previously set Iteration Factor, but they are iterating faster as well (now 3 times a month) which means they are also shipping product more regularly than average startups. Those two things combined give the startup more leverage towards achieving Product-Market Fit and continuing the journey to improving Product-Market fitness.
In fact, I feel a major reason a great consulting agency stays in business is because they have this core team that’s been through many iterations together, focusing on solving problems together, overcoming major challenges together, and starting new projects together with a higher Iteration Factor because of the tacit knowledge and Tuckman’s stages of group development.
So this begs the billion-dollar question, how can we formulize the startup process and identify Iteration Factor and the Product-Market Fit threshold? If not for the sake of the entrepreneur, I would think venture capital and serious investors would want the answer :o)
Short answer: I’m working on it. Anyone who’s started a deep analytics company that has to go through big data knows this process well: Acquire data, refine data, test data, come up with a theory, test theory, refine data more, test data more, test theory more, repeat from the top. Please reach out if you have thoughts and guidance, always open to community ideas!
Longer answer: Even though this is a simple model, likely filled with holes, the part that’s not illustrated is complexity in predicting the correct Product-Fit threshold. My theory is that there’s a dynamic nature to the Product-Market Fit marker, and it’s fed by variables that would need to be tracked across microeconomics, investment landscapes, news and media, and of course customer happiness and traction.
My thought process is that we can look at the past to understand the Product-Market Fitness markers of successful companies from the past, and come up with a model to apply that understanding to emerging startups. Perhaps someone like Mattermark is well suited to run those historical calculations :o)