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Finding Product-Market Fit: The “n=1” rule

By Andreas Olmes (Principal / Proxy)

Co-authored by Ulrike Kalapis (Investment Manager), Yann Fiebig (Senior Investment Manager) , Gregor Haidl (Investment Manager) and Fabian Hogrebe (Investment Manager)

In a nutshell

When you start looking for product-market fit, the rule of “n = 1” prescribes that you should initially focus on exactly one single customer. By contrast, introducing personas or generic customer segments is often found misleading.


Quick wins

  • Consistently focus on a single customer: “n=1”
  • Create a persona from five single, uniform and paying customers
  • If you are backed by venture capital, start from “n=1” and check the scalability, i.e. applicability of the PMF to further customers
  • Consider any further topics such as additional features, sales partnerships or scalability topics as secondary and concentrate on a single customer

Status Quo

As found in our 2019 portfolio analysis, around 75% of our Industrial Tech businesses fail due to a lack of product-market fit (PMF). This far exceeds the failure rate of 30% for team-related factors and 10% for purely technological reasons. These findings align with observations with 101 US companies reviewed in the same year by CB Insights.

When we ask entrepreneur teams to describe their own PMF, it is surprising that customers are often described only superficially. What’s more, customers’ problems are not captured in a tangible manner. In 2018, Gregor Haidl suggested that customer segments should be presented based on a concrete individual customer example. This was the basis for setting up and implementing the first PMF workshops in 2018. During these workshops, we work out with the founding team the basic success factors: customer -> problem -> solution -> cash customer + cash start-up. In an upcoming article, we will shed light on how such a workshop is conducted in detail.

Even though we emphasize the focus on single customers prior to the workshops, we notice: Many teams still tend toward an abstract and generalized description of the target customers in the first PMF workshop. Obviously, we did not yet manage to communicate our important lessons learned to the founders.

Here’s a short excursion on how PMF is typically addressed these days, and how start-up teams are characterized:

  • Based on a customer survey, a PMF canvas is made
  • Here all relevant themes are described: customer segment, problem, solution, added value, sales channels, suppliers, etc.
  • Unlike the strategy in the 2000s of asking customers after the product was developed, this method prevents founders building something no one is willing to buy 😉
  • Additionally, by focusing on “n=1” a persona is naturally captured: “This is Alice. Alice is 35 years old, works as a production manager and faces the problem that she is forced to stop production every 8 weeks for maintenance, etc. …”
    We recommend, however, not to focus on personas, as many start-ups end up stuck in long sales cycles, lose deals, and have a poor ratio of revenue to sales effort.

So, what is going wrong?

First, the generalization resulting from customer interviews to a canvas lead to the loss of a single customer focus. Even a persona is too general at the very beginning and not a tangible description.
Further, sophisticated canvas models lead to a massive distraction for founders from the overarching understanding of the actual customer need. The canvas is thoughtfully filled out and the work gets done, but there is not enough deep questioning on whether the customer situation has really been understood.
Our conclusion using existing canvas:

  • Too little focus on customer and problem box
  • Too many other boxes, which end up distracting from the first (decisive) step

How do we solve this?

Physics provides us with a toolkit to solve problems. One is to imagine extreme cases to derive key insights. With the goal to sell to as many customers in a target group as possible, looking at a single customer can help us understand more accurately what is happening than reviewing any other number of customers.

  • Describe exactly one customer, i.e. rule of “n = 1”
  • Focus on essential points: customer -> problem -> solution -> cash customer + cash start-up

What are the resulting advantages?

The main advantage is the founding team can – and must – focus on a single customer. Details are no longer side lined and importantly the work becomes much easier. It is simply “only” about the customer Alice from company XY, who is described in great detail:

  • In the best case, this immediately leads to a first turnover through an order by Alice, because a real problem has been solved. And this is our goal.
  • In the worst case, we understand why a customer like Alice is not a good fit.

As a positive side effect, the “story” of a single customer is a perfect basis to describe the start-up to investors and supporters.

Is there a guideline for this?

We have significantly simplified the standard canvas and focused on these essentials. Find out more in one of our future articles.

What needs to be considered in parallel?

If start-ups seek venture capital (VC), then they must be able to present a scalable product or service. Otherwise, the start-up is a project business. This can be quite attractive from an entrepreneurial point of view but does not offer a viable venture case. Therefore, start-ups with VC ambitions should critically examine the scalability or transferability of the PMF to further customers already with the first customer (“n=1”). Note that the road to scalability can include project business as a first phase to build a highly scalable product or service business.
An additional consideration when analysing the customer Alice is to view her situation and her specific problem exclusively from her perspective (!) (customer’s problem space). Only then can a start-up evaluate if a solution can be found to Alice’s problem (solution space). As the saying goes: The problem is delivered by the customer, the solution by the start-up. This is also what Fitzpatrick writes in the warmly recommended book “The Mom-Test”.

So, what comes after “n=1”?

After “n=1” comes “n=2”, i.e. focus on the next single customer. Customer Bob should be as similar as possible to customer Alice. This allows us to start at a much higher level than with Alice. Though experience shows that surprisingly often further significant differences are revealed in the process. This is to be expected and affirms how important it is to focus on a single customer. As with Alice, the goal is to convert Bob into a paying customer.
After Alice and Bob there is Carol, Dave, and so on. The idea is to step from one single customer to the next. The sum of these customers then forms the targeted market segment of the start-up, i.e. starting from “n=1”, “n=market segment” is developed.
Over time, this enables you to uncover some common ground between customers, which can then be mapped to a persona or target customer segment to make it easier to identify the next customer.
To the mathematicians among you: This is analogous to proof by induction.😉

Literature:

  • [Bla 2006] Blank: The Four Steps to the Epiphany (2006)
  • [Kna 2016] Knapp: Sprint, How to solve big problems and test new ideas in just five days (2016)
  • [Fit 2016] Fitzpatrick: Der MOM Test (2016 translation, original 2013)
  • [Cag 2018] Cagan: Inspired, How to create tech products customers love (2018)
  • [Bra 2018] Bradley, Hirt, Smit: Strategy beyond the hockeystick (2018)
  • [CBI 2019] CB Insights, The Top 20 Reasons Startups Fail, https://www.cbinsights.com/research/startup-failure-reasons-top/ (6. November 2019, laut Recherche vom 12. February 2021)
  • [Hog 2019] Hogrebe, Haidl, Fiebig, Olmes: Product-Market Fit: Der Hauptgrund für das Scheitern von Industrial Tech Startups im HTGF-Portfolio (May 2019)
  • [Feh 2019] Fehr, Haidl, Hogrebe, Fiebig, Olmes: Product-Market Fit im Industrial Tech – Der Weg zum Kundenverständnis (July 2019)
  • [Hai 2019] Haidl, Fiebig, Hogrebe, Olmes: Woran erkenne ich Product-Market Fit als Industrial Tech Gründer? (September 2019)
  • [Hai 2020] Haidl, Fiebig, Hogrebe, Olmes: Wie erkennen Industrial-Tech Gründer den Product-Market Fit? (February 2020)
  • [Hog 2020] Hogrebe, Haidl, Kalapis, Fiebig, Olmes: Wie nähere ich mich dem Product-Market Fit an, wenn ich keinen habe? – Der Customer Development Prozess (July 2020)
  • [Olm 2021] Nachtwei, J. & Nachtwei, K. (2021). HR Consulting Review, Band 13 (Fokus Startups). VQP. p166-172