always a consequence, not the root cause. You work – you get paid. You sell –
customer pays. You roll the dice and get lucky – you get rich. You have a
business (plan) that works – you get funding.
funding is not the end goal, not even for a startup. The end goal is to be able
to pay all that funding back, and some more. To reach that you need to have a
business that works. For getting there, you need the right strategy. The
strategy should be all about your business: who is your customer, what is your
offering, how you plan to win etc.
from where you are today to where you need to be one day is typically so long
that you may need to top up some fuel on the way. Funding is your fuel, helping
you to get where you need to go. But it’s just a means to an end, not the
reason your startup exists and definitely not your Northern Star. It should not
be the driver for your thinking and activities, do not let “what do I need to do to get funded” to
Corporate way of thinking: We carefully prepare a plan. Then we implement it precisely. The results will be as stated in the plan. Failure is due to bad execution.
Startup way of thinking: My plan is not really a plan, it’s just a sum of my hypothesis. For sure it will be wrong but I don’t know where and how. I need to run lots of structured experiments to test my hypothesis. When I have validated my assumptions, I may have a plan that is worth something. Failure is due to not running enough different “tests” with customers.
startups behave like corporates in this regard, assuming their plan will work –
just throw in money and people and voila. No. Be very aware that until you have
validated your assumptions you do not have a plan. Then you can not be
implementing a plan (read: scaling), all you can do is run experiments.
of an experiment is to increase your knowledge – does this work, Yes/No. The
job of a startup is to run experiments. A good startup learns a lot while
spending very little effort and money. A bad startup spends a lot and doesn’t
learn much.Cost efficiency of your learning should be a key
objective while in the experimental phase (=all phases before scaling, by which
time you should have a validated plan you can just implement). Cost efficiency
of running your operation should be your key objective once at the scaling
Most modern literature refers to just OKRs (https://www.perdoo.com/the-ultimate-okr-guide/) but I prefer adding Goals to it as well – as Microsoft did back in the 90´s. You need to have defined a clear Goal first before Objectives makes sense. Anyway, the operational stuff is captured in the OKRs (without the G) so no need to split hair on semantics, both work.
The power of the OKR driven operation is explained well in (https://www.amazon.com/Measure-What-Matters-Google-Foundation/dp/0525536221). If you google “measure what matters” you find a lot of material, Youtube videos etc to give you a crash course. Though the examples in the book are very big companies, the method works well for startups as well. Actually they may be even more critical for a startup, as “what matters” is dependant on the stage of the J Curve you are at. As you make progress, “what matters” should change. The thing to drive your everyday activity is the Key Result. The name is a bit misleading, it should rather be “Key Activity”, but this is the standard term so we stick with it.
no strict rules for that as the whole PMF concept is somewhat abstract and
vague. But there are (semi)objective ways to assess it. What exactly works is
depending on the context so you need to identify the relevant criteria for your
case. However at the end it boils down
to this (by Eric Ries)
“If you have to ask if you have found it, you
to monitor/measure that are indicators of PMF (more precisely, the “customer
love” or “market pull” part of PMF):
widely used measure is provided by Sean Ellis, who coined the term Growth
Hacking. He states you’ve reached Product/Market-Fit when at least 40% of the
respondents answer the question “How disappointed would you be if this
product no longer existed tomorrow?” with “Very Disappointed”.
Word of Mouth happening? Are your customers telling about you to 3rd
parties, with no involvement from you, which results in winning new business?
you get inbound leads that you can not track back to your own activity?
customers trying to buy before you even tried to sell?
your sales cycles getting shorter?
customer requests generate so much workload that you have no time to pursue
your own development ideas?
formulation of a litmus test to determine whether there might be PMF:
evidence of 3 separate measured tests of your customer behaviour that
demonstrate your product/service is significantly (=order of magnitude) better
than the existing solution.
“Product Market Fit is when customers sell for
“Glimmers of false
hope is not the same as customers wanting to rip it out of your hands. Product
Market fit feels like a landmine going off.” Peter Reinhardt
“The number one problem I’ve seen for startups,
is they don’t actually have product/market fit when they think they do.”Alex Schultz
Problem/solution fit needs to indentified & verified prior you can start looking for product/market fit or scaling. Here is a presentation that can help you on identifing the problem/solution fit.
Typically entrepreneurs feel pressure - both “internal” and external - to rush to scale. Many time even without understanding their problem/solution fit and how it differs between segments.
This results in not finding product/market fit or in inefficient and pre-mature scaling (read waste of money).
The aim of problem/solution fit process is to be able to identify the best opportunity to pursue further.
Startups have different stages. Howard Love has well articulated the different stages and we have included the problem/solution fit, product/market fit and scaling “fit” for you to understand how these stages overlap each other.
Where is your startup ? What are the KPI’s relevant for the stage you are in ? Have you “overleaped” one of the stages ? What kind of verification and facts do you have ?
Absolutely we want everyone to reach as high as their abilities allow them to! A unicorn would be fantastic! A lot of what we say should be preceded with “until proven otherwise”. As we believe in data and statistics, we assume the median type outcome as the placeholder – UNTILPROVENOTHERWISE. I.e. once you have evidence that you can do better and reach higher than the typical/median case, then raise the bar! But you need to prove you can walk until you try to run. And until you have proven your ability to run, you should stay on a route where running is not mandatory.
One reason is our personality – we are all “glass is half empty” people, so we look at everything from that angle. But we don’t intend to be negative or judgemental, we are simply analytical and fact driven and we believe in statistical math, not fairy tales. We are ultra-curious and we always want to understand. When we ask questions people have no good answers for, some people take it as a negative. Sorry, then we clearly do not have an alignment in the basic philosophies and we are not meant for each other.
The overarching higher cause for us is about making the whole startup community aware of an alternative to the stereotypical “how to raise as much money as possible” thinking (which results in having to tell a really bold story to pump valuations up, and everything that forces you to do). We want to help build more successful startups, which reward the founders and investors for the risk-taking. One cornerstone of that is the acceptance of basic facts such as statistical probabilities of success in different scenarios. Hence we favour a rational approach to risk and funding, as on average the survival rates are much better when your plan does not depend on winning-the-lottery type odds.
(People who have already done several exits at tens of millions – you can skip this part)
If you want to make an informed decision you should understand the odds – some basic statistical math. What matters are not paper valuations on which money has been raised, but realised exits where founders and investors received money back. So lets look at some exit facts:
Median exit value in technology companies in Nordics hovers around 12-15m€ (disclosed exits - public companies have to disclose material transactions, so larger deals tend to be disclosed). There is a large number of non-disclosed exits that are typically less than this.
In the whole of Europe there are only a few >250m€ technology exists every year (half a dozen or so).
Trying to build a unicorn takes a lot of time (>10 years) and multiple investment rounds, resulting in big changes on cap table. Markets change, people change, preferences change, technologies change…
There are hundreds of companies who have raised money at Unicorn valuations, but only a few which have been bought (or IPO’d) at Unicorn level
Your odds of getting a Unicorn exit are much MUCH lower than your odds of hitting a hole in one in golf (regardless of your HCP) By all means dream big and set the target high, but learn to walk before trying to run. How about being worth 10M first, and then deciding whether you want to raise the bar or not.
Trade sale = someone bigger than you buys your company outright. For the buyer, your company/it’s business are a nice complementary add-on to what they already have (they have a brand, customers, channels, salespeople etc – but they have a critical hole your company could fill) Early Stage = this refers to the stage your business is at, not to the calendar age of the company. The sweet spot is that you have found (at least v1.0) of your product/market fit and demonstrated sufficient proof of it working in real life, but you have not yet built “real” business of it. In essence, in an early stage trade sale the seller sells a recipe for growth for a buyer who believes they can do the baking of that growth based on that recipe.
Gorilla partners have founded companies, scaled them (up to 100m€+ turnover) and exited them. We have worked hands-on with startups for 10+ years, of our 2 funds we have to date invested in 50+ startups (+ our own personal angel investments). We have screened thousands of startups, analysed closely hundreds and worked hands-on with 100+. We have an analytical mind so we have seen what works and what doesn’t. Everything we believe in is based on either our own first hand experience, or objective data available to anyone.