By Stephen Millard and Joshua Olusanya.
Finding product market fit is the foundation for growth and critical milestone for venture backed businesses when raising money, as well as for the venture capitalists themselves when investing.
Founders rightly focus on achieving revenue targets or growth outcomes as a key deliverable or a measure of their startup success. Whilst this is important, alongside this is the journey of discovery, to turn an idea into a product, to find and build business that endures. To achieve this, they need to find product-market fit, and would need to demonstrate the process by which that was achieved.
There is no map for this journey, every company is different as well as the path they will follow. Nevertheless, what we have found useful is to think of the progression of revenue growth and product market fit as a simple mental model, with revenue on the vertical axis and product-market fit on the horizontal, both improving as the company grows.
We apply this framework to our portfolio as a whole when undertaking our internal benchmarking and considering our capital allocation decisions and many of our portfolio companies have found it useful.
Rocket science it is not.
We simply encourage our companies to consider their progress on these two axes, considering progress on each equally important.
This is a wickedly complex problem to solve, with a poorly defined process, masses of signals and no rigid rules. Despite what follows - a set of different models in logical order - it is far from linear or straightforward. VC Investor Brad Feld defines this well:
Every time you work on something new… recognize that you are searching for incremental product/market fit. The search is a continuous and never ending quest.
Everyone’s journey is different and great founders understand everything is also an experiment. Chris Tottman, Founder, Investor and Notion Partner puts this far better than me:
People talk a lot about conviction-led founders, who believe the world should work differently and are crazy enough to enter the fray and take that idea to market, building a proposition, and never giving up.
But the greatest founders are also self-aware enough to realise their original idea is limited - it’s an alpha, an hypothesis. It’s under-informed, it needs to be market tested.
Starting from the beginning, which seems like a good place, there are five steps to follow:
1. Are you solving a problem worth solving?
This is a story that starts with pain.
In B2B tech, start ups are born of pain. They centre themselves around an industry problem that simply won’t go away and they pivot around that pain. If that pain is sufficiently acute, it will inevitably drive adoption at scale as the industry moves from an old way of doing business to a new way.
For Jos White, Notion Partner, this is a fundamental part of his investment philosophy.
I look for founders who combine an insider's knowledge of an industry with an outsider's perspective - they see something that is broken, inefficient, or just plain wrong and have a burning desire for the world to conform to their way of thinking.” Jos White.
There is a simple narrative that drives the very earliest of stages in a business; three questions constantly nagging at the back of a founders mind:
And they return to these questions over and over:
“Is this the most important problem? Are these the best customers? Is this most value?” Narrowing in on the answer.
Founders must seek to satisfy themselves they have indeed identified a problem worth solving, they know the very best customers and are confident they can deliver enormous value. But more importantly they can look themselves in the mirror and say “Yes, this is worth putting my life on the line for - over the next ten years - to build a massive company”.
“The best startups generally begin by trying to address a really important problem worth solving. If they can nail the solution to this important problem, they have a great chance of building a successful startup”. Growth Hacking Specialist, Sean Ellis
Research only corroborates the need for stat-ups to address a problem worth solving. CB Insights analysed 101 start-up post mortems and explored the top reasons startups fail - not addressing a big enough problem ranked 1st for 42% of startups.
2. Creating the minimal viable product (MVP)
With the burning problem in mind, our attention turns to establishing the MVP (minimum viable product). The process of Customer Discovery, as described in Steve Blank’s excellent Four Steps to the Epiphany, has guided many through this stage using structured customer interviews.
Undertake customer discovery
To paraphrase this approach, we recommend founders to:
The next step is to substantiate these assumptions/impressions through customer discovery interviews. You may ask interviewees to rank the problem and impact statements 1-5, or preferably ask them to pick the most important and least important (the max-diff approach) and then use their answers/feedback to adjust your MVP and your initial customer targets.
At this stage you should also be exploring willingness to pay. Four simple questions will be very revealing:
This may be an on-off exercise, but more likely will be a process you will return to frequently. You can learn more here, or of course just read the book. This is a simple exercise in putting problems and customers at the heart and start of your startup journey. It will help you to narrow your focus, and if done well serves as an excellent resource for initial pipeline development.
A Case Study
Hazy, a Notion-backed company, went through a similar process following investment in mid 2018.
“Nailing product market fit is critical and it's something I had to learn from scratch. The Notion team were instrumental in guiding us through the process and helped measurably accelerate our development.” Co-founder & CEO of Hazy, Harry Keen.
Customer Validation Case Study featuring Hazy
Hazy, a London based AI startup, conducted a customer validation project to understand three simple things:
Through some surface-level conversations and personal intuition, they hypothesized that there were three main problems in the data science space they were ideally placed to address:
To test the severity of these problems, Hazy interviewed 100+ potential buyers and users, ranging from c-level, down to individual contributors, from a range of pre-defined organisations and sectors.
They reached senior executives through warm intros from investors, cold calling, LinkedIn messaging and networking
The entire customer validation process was 3 months
The responses in these interviews can be summed up simply: Unlocking the value of customer data is a burning problem, especially for mature and technologically advanced companies. The other problems? Not so much.
During the questioning process, Hazy also discussed their idea of three possible solutions:
The results indicated that the possible capabilities of a synthetic data tool were by far the most sought after solution to solve the problem of unlocking data safely for advanced data science applications.
Prospective customers are more mature, technically advanced companies with sensitive, illiquid data who want to innovate with their data as a competitive advantage.
3.Finding product-market fit: user driver, buyer driven, company driven.
As your startup acquires more customers, the next step is to move beyond the MVP and start the never ending quest for product-market fit (PMF).
Serial Entrepreneur, Founder, Partner & Investor at Adreessen Horowitz,.Marc Andreesen first coined this phrase:
“Product/market fit means being in a good market with a product that can satisfy that market.”
Finding product market fit is a creative and iterative process that requires entrepreneurs to be nimble with their value propositions, agile with their products and receptive to feedback that validates or refutes their assumptions. A quote often attributed to Mark Twain puts it well:
It ain’t what you don’t know that gets you into trouble, it’s what you know for sure that just ain’t so.
Our firmly held beliefs about what is and isn’t so, can get us into trouble and stop us from seeing what is right in front of us. At times our hypothesis simply doesn’t stand up, our explanation of the value is incomplete, the value we create is insufficient and our customers’ choices are just plain wrong. We need to have open minds and treat success and failure with equal importance.
There are many ways to find product-market fit, but below we have outlined three different yet complementary ways to think about this challenge, combining data driven, objective and subjective approaches. One, two or all three may be relevant, however, there are many other signals and sources of data you might consider that we won’t cover here such as product engagement data, market data, competitive analysis and so on.
However, one thing is clear. We learn by doing, which means customers must be acquired in order to gain feedback, placing small bets on target customers. Jim Collins calls this "firing bullets", low cost small experiments and see what happens.
A good starting point to establish user generated PMF is gathering empirical evidence from users, as described in the Sean Ellis Test
In order for this test to work, the company must be executing a customer acquisition strategy of sufficient scale to generate the insights required.
In B2B and enterprise tech, finding product market fit requires getting “uncomfortably narrow” - it may feel counterintuitive to some founders, but at an early stage the paradox of customer choice is that more is less. Chris Tottman puts this well:
At Notion, we are a big believer in an uncomfortably narrow definition of pain, use case and form factor which enables our companies focus at an early stage. Its extraordinary how fast this mental model can accelerate success.
But before we can get narrow, we have to go reasonably wide and “shoot some bullets”.
The customer number may vary from 10-20 customers with large average contract values (for example $50,000 plus) to 100 or even 1,000 customers for smaller average contract values, but the user numbers need to ideally be 100+. Most statisticians agree that the minimum sample size to get any kind of meaningful result is 100. If your population is less than 100 then you need to survey all of them.
To conduct this user generated product-market fit test, Ellis recommends asking users the following question:
How would you feel if you could no longer use the product?
Then measure the percentage of participants who answered very disappointed, somewhat disappointed and, quite happy thank you!
After benchmarking nearly 100 startups, Ellis posited that the threshold for product market fit purposes was 40% i.e. 40+% of users respond with “very disappointed”.
One of the best examples of the application of this process is described by Rahul Vohra, Founder & CEO of Superhuman in his excellent article, “How Superhuman Built an Engine to Find Product/Market Fit”. Below we have summarised - and plagiarised - few pointers from Rahul’s. If you want the real deal, please do read the article it is - again - excellent.
Rahul built on top of the Sean Ellis test with four simple questions to help Superhuman achieve product-market fit:-
Once you have completed the survey and have an aggregate score of PMF.
The first step Rahul describes is to then segment your customers to understand your supporters and detractors and to enable you to narrow your focus to your “High Expectation Customers”.
Use the data to understand who your product resonates with and why, by first developing personas to describe the respondents and then grouping the responses into the three categories of very, somewhat and not disappointed. This allows you to rapidly narrow your segmentation.
By identifying his “High Expectation Customers”, Rahul was able to identify his most discerning customers.
We have often discussed the importance of becoming “uncomfortably narrow” in your go to market strategy. Some companies may worry about limiting the size of their opportunity but 1) we all have finite resources - product or sales - and want those pointed at our best customers and 2) the problem of a ‘local maximum’ rarely occurs. Rather we recommend founders, go narrow and deep to build a big market.
It’s a commonly held view that tailoring the product too narrowly to a smaller target market means that growth will hit a ceiling — but I don’t think that’s the case. Rahul Vohra
Some customers love the product, so how can you help them love it more and find more just like them. Some you need to ignore, but many will be on the fence and you need to understand if you can turn the fence sitters into fanatics.
Using feedback to create a virtuous customer acquisition cycle… is really about figuring out how to identify folks that can become your customer champions and create the right relationships with them such that they not only submit feedback… but they become your champions.
Understanding which users to double down on and which to ignore will be key to hitting that magic 40% number.
Superhuman used a very simple cost-impact analysis, labelling each potential project as low/medium/high cost, and similarly low/medium/high impact.
For the first half of the roadmap - doubling down on what people loved - they intuited the impact. For the second half of the roadmap - addressing what held people back - the impact was clear from the number of requests any given improvement had.
To increase your product/market fit score, spend half your time doubling down on what users already love and the other half on addressing what’s holding others back.
And I would add, aligning a customer acquisition strategy to target new “High Expectation Customers”.
Repeat the process and make the product/market fit score the most important metric.
Within just three quarters, Superhuman took their PMF score from 22% to 58%.
You can learn more about this approach from Rahul’s excellent article and more in Sean’s book, Growth Hacking.
B) Buyer-Generated PMF - objective data gathered from primary research or via the sales process
In B2B tech, the buyer is rarely the user and vice versa, so you ignore buyer behaviours at your peril. Buyer-generated PMF is shaped around learning the buyer’s needs and journey with the aim of understanding how to trigger those needs with your marketing efforts. As buyers become more self sufficient by gathering their own data and doing their own research, your sales and marketing efforts need to focus increasingly on ‘helping them to buy’.
This process involves a lot of inquisitive questioning with buyers, followed by some extensive internal assessment as to whether or not your product/service is meeting those needs.
Before questioning your buyers, do some research on who they are:
The first step is to get feedback from them around their buying process and overall experience with your product/service:
At a later stage you can come back to explore some follow up questions such as: How did the solution deliver on the promise? What benefits did you receive? Etc.
Your intention here is to generate a behaviour model of prospective customers as well as useful insights on your buyers’ experiences with your product/service. This enables you to reshape your product/service accordingly whilst learning what their triggers are - ie. what brought them to you in the first place.
Firstly, David defines a trigger as the following:
A trigger is an event that causes a buyer to have a clear need, which usually converts into a sense of purpose and urgency in their buying process.
It goes without saying that buyer’s triggers will vary depending on the nature of your startup, your target market(s), as well as who you are targeting within these markets. However, having a general understanding of what your buyer’s triggers are is very useful, as it enables you to do the following:
As stated, buyer triggers will vary from customer to customer, therefore the key to determining these triggers is to: