How you can calculate whether your startup will reach PMF or not
TL;DR
- You find a lot of great reading on Product Market Fit (PMF) and its relevance to a startup. The WHAT and WHY parts, are covered well, highlighting the essential nature of PMF.
- Less well covered is the HOW aspect, as more than just iterative development and user interviews go into reaching PMF.
- So far, I was missing an integrated explanation of how the relevant drivers of getting to PMF fit together. This article aims to cover that gap — and to provide a visualization of the interplaing factors.
- Reaching PMF is fundamentally determined by: (1) problem & market understanding, (2) cash available and, (3) iteration/development speed.
Read on to find out more.
In aviation, there has been talk about circular, never ending runways. The startup community reacted with shock, as this would destroy the famous analogy to the remaining months of cash (equals life) of a startup. However, a startup does have a finite runway (last I checked, also most airports), which places a great importance on tacking off, before the runway ends.
The moment of tacking off, is reached when a startup achieves Product Market Fit (PMF). I won’t go into the details of the PMF definition here, as this has been covered substantially, e.g. here (Marc Andreessen), here (Masa Hamada), or here (John Danner).
The argument of this article is that whether or not you reach PMF, is determined by the relations of (1) problem & market understanding, (2) available cash and, (3) iteration/development speed.
The three factors interact, and the likelihood of achieving PMF is greatest, if you can show strong performance along all three.
I am a fan of visualizations, as this helps my mind understand things, whether they are simple or complex. So for the sake of understanding and fun of illustrating — and the up to this point lack of anything similar — I have created an illustration of reaching PMF:
Remember, the goal of any startup is to reach PMF. The likelihood of achieving PMF is greatest in the gray shaded “PMF trapezoid” (yes a nerdy term, for which I welcome better sounding alternative suggestions) — and the three factors listed above, determine whether you get there. By saying that “the likelihood is greatest” I want to emphasize that achieving PMF is also possible in other parts of the illustrated cube, however much rarer.
Let’s look at these three factors a little more closely:
Problem and market understanding
These are indeed two things, merged on one axis: Problem (what is the problem exactly, who has it) and market (how big, what players, how could our go-to-market work). There are lots of tools and methods out there to understand the problem better (look e.g. at any of the material put out by Alexander Osterwalder or David Bland). Regarding market understanding, your path won’t lead around basic market research. You will have to google, you will have to fill sheets with number and notes (you can of course also use an advanced research tool). If you are building a solution for a problem that you encounter yourself, in your life or in the industry you are working in since years, your problem and market understanding is by nature greater from the get go. Investors know this, hence they are keen on seeing an industry-native on any founding team.
Understanding and also validating your problem hypothesis is a key process in the entrepreneurial journey. The more you advance on that axis, the more likely it is that you will achieve PMF — as you will produce a solution that addresses a real need, and you will gain traction as you know how to distribute to your target audience.
Note that in the illustration above, the green dashed line, that illustrates the learning curve, starts with a dip. Where you start on the green Y axis is case dependent, but the fact that you will likely have a dip is based on the Dunning Kruger effect.
Iteration speed
The pace with which you can iterate on your idea, prototype, mvp, product has substantial influence on reaching PMF. The iteration speed factor is closely tied with the problem understanding; whenever you confront a potential user with your idea, prototype, mvp, or product, you will learn something. Note that user research does not stop at showing your mvp to strangers on the street, as you have learned it in that design thinking seminar. User research is a permanent task at all stages pre- and post-PMF.
To embed your learnings in your product, you have to be able to iterate fast. While at first, iteration is based on sketches and mockups, later iteration speed will be determined by your development processes in place. The golden chain link of course, being fully agile in the front of the process with continuous user research and learning — and fully agile in the back with an automated deployment pipeline that transfers your user research into new/adjusted features.
Iteration speed does not have a “learning curve” similar to the one outlined in the section above. Usually iteration speed is harder to change — but can still be optimized for your needs. Here you can see nicely how the factors of the illustration interact:
If you start with profound problem and market understanding, you can live with lower iteration speed to still reach the PMF trapezoid. If you are an industry outsider, you will need more time, more iterations — and hence likely more cash to reach the same level of understanding, the same level of PMF likelihood.
Available cash
For most startups the situation here is similar: the cash you have available diminishes over time — and funding is only sparsely available, before you reach any PMF. Looking at the illustration, you see that your burn rate has to be flatter (giving you more time), the more your problem & market understanding are low, and your iteration speed limited.
The clear advice here is also to spend all your money only on the two other axis, before you have reached PMF. Do not hire complete HR, Sales, or even CSR teams, before you have reached PMF.
Spend your money on understanding the problem and market — and on getting as many iterations as needed. Here too you can see how these factors interplay; you could scale your development team (spend more) to get more iterations in faster. For that to work out you need great development systems and a strong CTO in place, otherwise diminishing marginal returns will hit you hard.
Sorry for the software focused writing here and in the article overall, but the same applies in general if you are building any other sort of product or service. Furthermore, iterations go beyond software development, even in a software startup. You can an likely will have to iterate over several aspects of your business model, not just few features in your shiny new app.
On the trapezoid shape: The illustration is based on the interplays described above: the learning curve is steeper with a higher iteration speed — and a higher iteration speed will likely be more expensive (hard to visualize, but hence the trapezoid shape).
Here is a nice quote by Sam Altman, that both speaks to, and summarizes much of the content in this article:
In general, hiring before you get product/market fit slows you down, and hiring after you get product market fit speeds you up. Until you get product/market fit, you want to a) live as long as possible and b) iterate as quickly as possible.
All this of course assumes that there is at all a market for what you are building. Your advancing on the green Y axis should teach you about that.
If you are more of a formula person, than a visualization person, try this:
The number of iterations you get out of your runway, times the problem and market understanding = a number signifying the PMF likelihood. The greater, the better.
If you truly want to calculate your likelihood of achieving PMF (as the title of this article suggests), you can put in research and real numbers of your startup and benchmarks who did or did not make it…
Related to this: see this story on the marketing challenge of launching category defining products.
This story was researched and written using Varia Research.
Thank you for reading, comments & feedback!
