February 6, 2019
I’ve seen a few thousand startups since 2014 and noticed founders tend to give unreasonable value to first-mover advantage. Too many founders are still undervaluing the importance of product-market fit whilst compensating for lack of uniqueness with “we’re the first ones to do this” -catchphrase.
There are probably as many definitions to product-market fit as there are entrepreneurs, and the purpose of this post is not to define it but rather give some thoughs as to how we approach it when talking to founders here at Superhero Capital.
Having the first-mover advantage in a new, emerging industry or market is an undeniably lucrative scenario for startup entrepreneurs. In the short term, there’s the hype of being fresh-out-the-oven, maybe you’ve been featured on TechCrunch, and your hockey stick looks pretty good. You’re riding the fresh wave of innovation cruising on the #1 spot similar to what Snap did when inventing the stories format.
In the long term, however, people catch on, start imitating you, surpass your DAU, and eventually furthermore develop your creation (naughty you, Instagram!). You’re slipping from first-mover to comfortable second and you‘re losing your grip. You were there first but someone’s surfing faster than you are.
Chrome wasn’t the first browser nor iPhone the first phone.
My point here is that first-mover advantage rarely matters in the long run. The first to product-market fit is the true, long-term winner.
For founders, reaching product-market fit should be an obsession from day one and there should be nothing more important than nudging your offering towards as perfect a fit as possible.
Internally at Superhero Capital, we talk a lot about insight and how this helps get closer to product-market fit. There are lots of companies out there who proud themselves in gathering data and think it’s going to somehow save them. On the contrary, you can have all the data in the world but without insight from that data, you’ll never be able to make the right decisions.
Data tells you what happened. Insight tells you why and what to do.
A great example of gaining insight is how Superhuman built an engine to find product-market fit. The way they approached early product development via a simple customer segmentation survey and the amount of insight they got from asking only four questions proves that gathering and crunching the right data is a lot more valuable than just having an abundance of it.
When we screen our dealflow, we don’t mention insight per se, but rather try to understand what type of data the startup gathers, and more importantly, how are they able to process it into actionable information.
“What guides you to product-market fit?”
This question is a tough one, and it truly separates the startups that have long-term plans, motivation, and vision from those who don’t. There is no one right answer to this question and we’re not looking for one either. Moreover, we’re trying to understand how the founders approach product-market fit and what type of data analysis process are they basing their decisions on when deciding what to do next.
The insight created from analyzing data provides a sort of information asymmetry for the beholder and the slope at which you’re able to produce this asymmetry is the velocity at which you’re running towards product-market fit. The steeper the slope, the faster you are compared to everyone else.
It’s important to note that sometimes you may think you have insight while the truth is you don’t. A very, very simplified example of this is that ice cream creates forest fires. The more ice cream is sold, the more forest fires there are. The challenge with this piece of insight is that it doesn’t separate or understand the difference between correlation and causation. In this case, ice cream and forest fires have correlation but the causal relationship, for both, is with weather.
If you have a large enough chunk of data, you’re bound to find relationships that are correlated but not causal. Finding correlations from big data is easy but proving causation is tough. Finding correlation in your data will help make decisions but finding causation will enable you to make decisions that have a more profound effect on your business.
If you want to know more about correlation and causation and get an in-depth look on how ice cream is the evil of this world, check out this animation.
Unlike the ice cream example, insight done right can give true value when you’ve identified a causal relationship. An easy-to-understand example of this is from retail marketing platform I’ve gotten to know. They provide insight by crunching various data sources such as customer loyalty programs or publicly available weather data, or both. What they do is, and this is just one easy example,
Snowing has a causal relationship with shoveling snow, and finding and utilizing these relationships is what gets you results. Running similar, very targeted campaigns they’ve been able to reach sales conversions of up to 40% in the very best cases.
What I’m saying is that finding the elusive product-market fit is not only about gathering data and trying to find correlations. It’s also not a race that has a finish line. The closer you are to product-market fit, the faster you’ll have to produce insight to keep you on top. After all, the market itself is a changing beast, and making decisions by staying on top of what factors cause certain outcomes is a much better way to beat the competition than looking at which two variables seem to change at the same time.
— The Flash