In 2015, I invested in the world’s first modern bike-sharing startup. Donkey Republic from Copenhagen. We made an investment through the Accelerace fund. Later, I made an angel investment before they went public in 2021.

About 2 years after the launch of Donkey, micro-mobility exploded. Lime, Bird, VOI, and many more attracted immense amounts of funding to flood our streets with scooters.

At the time, the founders of Donkey faced pressure to add scooters to compete. Scooters seemed to become the vehicle of choice. They were fun to ride and needed no pedaling of your own.

But despite the pressure, the founders never did. Why? Because Donkey Republic is not a micro-mobility company. The founders’ motivation for Donkey was not to give people a more convenient way to get around. Instead, the founders wanted to reduce carbon emissions and promote general health. And they believed that bicycling was a key instrument because riders get exercise and burn calories, instead of burning fossil fuels.

Unlike scooters, bikes are good for medium distances, because the rider can sit down and transport luggage. Scooters are mainly used for short distances. In other words, scooters replace walking.

Because bikes replace cars and scooters replace walking, bikes reduce the burning of fossil fuels and promote health. In contrast, scooters increase the burning of fossil fuels and damage health. At least the founders of Donkey claim.

Unfortunately for the investors in Donkey Republic, the company hasn’t become the unicorn many of us hoped for (yet). Still, the company is doing well, and unlike the scooter rivals, Donkey churns a profit in established markets, and have avoided brutal valuation corrections and mass layoffs that have hit their spoiled scooter siblings.

Still, Donkey Republic has not provided the kind of financial return that VCs such as me is looking for. And part of the problem is that the market for their product does not exist (yet).

See, Donkey Republic is not a micro-mobility company. Well, it is. But to the founders, bicycles are means to an end. The founding CEO Erdem is a social entrepreneur by heart. He wanted to create social change. He wanted to promote a lifestyle that included bicycling to reduce carbon emissions and promote health. And they do.

Researchers at the University of Dresden calculated the impact of having Donkey bikes in a city, and they concluded that bicycling saves 1.3 EUR per kilometer in public healthcare costs. On top come the benefits of lowered carbon emissions and reduced time-waste from traffic congestion.

In Q3 2022, 1.7M trips were taken on Donkey bikes. If we extrapolate this number to a full year, we get about 5.2M rides per year. The company reports that each ride has an average distance of 3 km. That equates to more than 20M EUR in healthcare savings alone. But how much did riders pay to use the service in 2021? 5M EUR! That is a quarter of the value Donkey Republic produces in healthcare savings.

And here we arrive at my investment outlook for 2023. Currently, we suffer from a non-existent but unlocked market for societal value propositions. Donkey Republic sells public health more than transportation convenience. In fact, we know exactly how much more. Four times as much.

The problem however is that this “societal-positive-effect-product” cannot be sold. Not because none wants to buy it. Public authorities are screaming for solutions to reduce healtcare costs and carbon emissions. But products that create indirect cost savings and carbon emissions cannot be bought and sold because there is no marketplace for them. And why is there no marketplace? Because we lack a standardized way to value and trade these effects.

In 2021, Stepn was launched and pioneered the move-to-earn Web3 category. The app rewards people for moving. It became the fastest-growing app to date. But its demise was baked into its logic. Why? Because the only way to earn from using the app is to get more people to use the app. This is otherwise known as a Ponzi scheme. However, the inevitable crash of Stepn is a bit unfair because moving creates much societal value. The effect is reduced healthcare costs (and increased revenue for shoe manufacturers and shoe shops). But those effects have not been productized and sold because currently, this market is nonexistent.

If someone came to the public authorities and sold 20M EUR of savings for 10M EUR, they would surely buy it. Why wouldn’t they? However, when the effect is provided as a derivative of a behavior change, it becomes hard.

But hard is not the same as impossible. In fact, hard problems have always created the most fertile ground for entrepreneurs. We have made reusable rockets, solved the Turing test, and made graphic designers obsolete. I think this can be solved as well.

What we need are startups who can unlock this trillion-dollar market opportunity. And that is my outlook for 2023. Startups that can accurately attribute and value societal effects. Startups that take these effects and productize them. Startups that can distribute these products to the public bodies (as the main customer segment). And startups that can help the customers use and harvest the value of this new class of products. I am not sure what they are called, but I guess someone smarter than me will come up with that. My sense is that we need something catchier than “societal-positive-effect-products” or “SPEPs”.

Happy new year everyone.

(We have already made numerous investments that are building blocks in this marketplace described above. Those include Donkey Republic (saves healthcare and environmental costs), Impactly (calculates the monetary impact of social programs), Parazute (saves healthcare costs), SkinBliss (saves healthcare costs), The Upcycl (saves environmental costs), Flexecharge (saves environmental costs), Redo (saves healthcare costs), Chew (saves healthcare and environmental costs) and many others that provide meaningful societal . If you have a startup idea that would fit into this portfolio let us know). 

In 2017, the equivalent of the arrival of an alien spaceship happened. A fund of a size none thought possible appeared from the edge of the world and descended on Silicon Valley. At the helm of the ship stood Masayoshi Son. An eccentric business daredevil with a risk appetite that would have made Genghis Khan wet his pants. The ship was loaded with 100 billion USD ready to be deployed through cash canons of doomsday-sized calibers. Once Son gave the command to fire, the cash cannons colored the skies green for the following years. When the cash rain ceased in 2019, the Soft Bank Vision Fund had deployed close to 100M USD per day, every day, since its arrival.

The enormity of the Vision Fund can best be illustrated by comparing it to the most legendary VC of all time. Sequoia Capital was founded in 1974 and has delivered above-market returns ever since. Consequently, investors from around the globe vie for receiving an allocation. Naturally, such pressure makes their funds grow. Still, their 2017 fund Sequoia Capital Global Growth Fund II, their biggest to date, was “just” 2 billion USD.

Despite Sequoia being dwarfed by the Vision Fund, Sequoia perhaps best depicts the development in VC fund sizes. The first Sequoia Capital fund I from 1974 was a mere 3 million USD. The second fund grew to 20M USD. The third amassed 44M USD. In 2011 and 12 funds in, Sequoia Capital crossed the 1 billion USD mark for the first time.

When VC funds grow, so does the number of people needed to manage them. The first Sequoia fund had one partner. Don Valentine. Later, more partners joined. Later still, the partners started employing investment professionals. Like Sequoia, most VCs start out as a group of partners investing together. These partners share the workload, and only have a couple of people employed. Perhaps a CFO and a secretary. But as VC funds grow, they employ more people. These investment professionals differ from the partners in one important aspect. They do not have their own money in the fund. Consequently, these people tend to regard the firm as an employer, and themselves as employees. To many, this fact may seem trivial. But in the world of investing, this is anything but. As we shall see.

The House Money Effect

When VCs assess deals, they must weigh risks and opportunities. But risk and opportunities are interpreted values. Interpreted by the people meeting the founders. Until a few years ago, startup founders would meet partners in the funds. And the partners would make the assessment of the risk and opportunities. But recently, founders mostly meet with investment professionals.

Because investment professionals are essentially employees, their frame of risk and opportunity is quite different from the partners. Partners can make or lose money. Investment professionals can be promoted or lose their job.

One might conclude, that losing money is worse than losing a job. And this should make Investment professionals more risk-tolerant. However, this conclusion would be mistaken because of something called the house-money effect.

The truth is that even though the partners risk losing their money. The money they invest typically stems from profits from previous investments. It is the equivalent of going to the slot machine, winning money, and only playing with the money you have just won while keeping the original amount safe. Money recently won feels like “free” money. And the behavior that stems from someone gambling recently gained money, is called the house-money-effect. The effect gives one much more risk appetite than if one was risking money diligently collected. Did Masayoshi Son have house-money? Son made close to 100 billion USD when betting on a young Jack Ma, founder of Alibaba.

Arguably, the importance of the house-money effect in venture capital is under-appreciated. One definition of startups is that they are: a temporary organization designed to search for a repeatable and scalable business model.” (Steve Blank). The definition oozes risk and uncertainty. A temporary organization that is searching does not seem like a sound investment. And it isn’t. Thus, some version of the house-money effect must excerpt influence to make otherwise smart people engage in such risky bets.

In fact, one could argue that the house-money effect underpins venture capital. But then what happens when investment professionals take over the risk and opportunity assessment? Will venture capital change? It already has. And a large group of founders suffers.

The tale of two founders

Many investment professionals come from finance and consulting. That means investment banking, PE funds, hedge funds, pension funds, McKinsey, Bain, etc. These people are used to hard data. However, startup investing seems opaque because there is little hard data available. This conundrum has two consequences. First, investment professionals gravitate towards companies that can present hard data. In practice, SaaS companies with three years’ operating history and 100K MRR. Second, and perhaps even more consequential, they gravitate towards founders with scaleup resumes. That means Founders, executives, and VPs from Zendesk, Klarna, Bolt, etc. Why would they do this? Well, imagine that the investment decision proves bad, and the firm loses money. To an investment professional, it seems much easier to defend having invested in proven people with impressive resumes, than a team of nobodies. The latter requires some explaining to do.

Does this mean that only founders with scaleup resumes get funding? Not entirely, because there are not enough scaleup founders for investment professionals to solely bet on them. But it does produce a tale of two founders. Founders with scaleup resumes who can raise exorbitant sums of money at fantasy valuations. And everyone else who struggles to merely solicit a term sheet. Besides being a frustrating experience for the latter group (which is the vast majority), it might also prove the beginning of the end.

The only way to make money investing

Much research finds that first-time funds perform best. One example comes from another legendary VC. In 2019, we got a rare view into the returns of Andreessen Horowitz (A16Z) when internal data slipped into public hands. The first A16Z fund from 2009 returned 44% net IRR. The second A16Z fund from 2010 returned 16% net IRR. The third fund from 2012 returned 15% net IRR. Although the final outcome could differ from these figures, there is a trend. And the trend is declining performance. But wait, there is another trend! The first fund was 300M UDS. The second was 656M USD. The third is 997M USD. I suspect you know where I am going.

When fund sizes increase, the firms get “institutionalized”.  The risk tolerant house-money-effect is diluted and replaced by hard data seeking I-cannot-be-fired-from-investing-in-a-Klarna-product-manager conviction making. But if the house-money-effect is underpinning venture capital, what happens to venture capital when it is “institutionalized”? The answer might be comforting, or the opposite, depending on who you are.

Investing is a self-correcting game where non-viable strategies will disappear over the long run. And paying exorbitant premiums for founders with scaleup resumes is not viable. Why would I think so? Because there is a law in investing that not even all the money in the world and legions of Bain consultants can buck. The law is this: One can only make money investing if one is both right and non-consensus. Essentially the law says: if everyone agrees that something is a good investment, that something will become so expensive that no money can be made. When scaleup-resume founders obtain stratospheric valuations, it can only have one reason: Everyone agrees that it is a good investment. Consequently, the law ensures the firms engaging in this strategy will suffer in the long run.

However, time is relative and venture capital has painfully slow feedback loops. The self-correcting nature can take years, and perhaps even decades to exert itself. Until then, startup founders without scaleup resumes will suffer. And guess who are among these: yes, most women and minorities! But more importantly, a lot of entrepreneurs with the ideas and talent to create all the house-money needed to keep fueling the wonderful world of startups.

At Accelerace we insist on being indifferent to the resumes of founders. We do not care who you are. We care only about what you do. If you are a founder and our views resonate, then visit our website here.

Folklore /ˈfəʊklɔː/: the traditional beliefs, customs, and stories of a community, passed through the generations by word of mouth.

Each competitive realm has folklores. Stories of fame, success, and paths to notoriety. In golf, we know the story of a young Tiger Woods demonstrating his putting skills on national television at the age of 3.

In acting, we tell stories of the crazy dedication by Matthew McConaughey that willingly embodies his characters to an extent that they fuse.

In science, we tell stories of the lifelong obsession of Jane Goodall, who lived in the jungle studying chimpanzees.

In the modern world, folklore is more influential than ever. Because when we have access to infinite information, stories echoed by communities stand out as authentic and real.

Folklore shapes our beliefs about the realm that it depicts. The story of Tiger Woods primes us to believe that becoming a professional golfer is hard. Unless one has demonstrated remarkable talent from an early age, becoming the next Tiger is impossible, we believe. Consequently, most parents would not support the idea of their kids dropping out of university to start a potential career in golf. Nor would the trainers, or even their friends.

The story of Matthew McConaughey means few people suffer from the delusion that becoming a movie star is easy. And few career advisors would recommend trying.

The story of Jane Goodall tells us that becoming a renowned scientist requires lifelong immersion. And the few who embark on this quest, understand the sacrifices.

Golf, Hollywood, and Science share the characteristic that making a living, let alone becoming a top performer, is hard. We understand the odds, the sacrifices, and the obsession. And most stay away.

Startups share the same characteristic of being hard. Making a living, let alone making it onto the unicorn list, is as difficult as becoming a Jane Goodall. In 2020, 120 startups became unicorns. It is estimated that about half a million startups are founded per year. That is a chance of 0.024%.

Even when we decrease the ambition from unicorn to just raising a series-A, the numbers illuminate the hardship. In Denmark in 2020 (where I live), we had about 12 series-A investments in Danish startups. It is estimated that 500-something startups are founded each year in Denmark. That is a chance of raising a series-A of about 2%

However, the facts do not shape the perception of the realm of startups. Startup folklore does. And unlike Golf, Hollywood, and Science; teachers, parents, peers, and career advisors seemingly support everyone to pursue a startup. For a long time, this puzzled me. But I have come to understand the phenomenon to be the power of folklore.

Startup folklore is heavy on stories of people materializing billion-dollar companies by conceiving of a good idea. These stories make us believe that the idea is what matters. Equal to talent in golf. Dedication in acting. Or obsession in science. If you have it, you can make it.

The forgiving thing about this belief is that everyone has ideas. Not everyone has talent, dedication, or obsession. But everyone has ideas. Thus, startups can be done by everyone, the logic goes.

Unfortunately, the facts tell a different story. But more importantly, those of us who have spent a lifetime working with startups know that ideas have very little to do with success. Instead, the foundation for success is ‘original insight’. And not everyone has it.

The misconception has the effect that many people are attempting startups without having the foundation to succeed. But that is not the problem. Because, through this experience, many people obtain the lessons for later success.

The real problem is that because everyone thinks they have a chance of startups, equally many people think they can mentor and advise startups.

Few people think they can mentor and advise golfers, actors, or scientists. We understand it requires intimate understanding, expertise, and experience.

Ultimately, the victims are startups. Because when true startup expertise is neglected, many programs and organizations created to help startups are useless.

In these places, startups meet mentors who are interested in startups. And sometimes passionately so. But interest does not equal expertise. Founders do not need cheering, idea jamming, and being retold the content of books they could otherwise buy. Well, sometimes founders need those things, but it won’t be enough.

Founders need insight into the unique challenges of their business model, their stage, and their team composition. They need experience from analogous startups and sparring from people with battle scars and costly paid learnings from years of doing what the founders are about to attempt.

Startups are one of the hardest realms of human activity. To truly help startups, we must see past the folklore, and organize real help to startups. And only by recognizing that startup is an area of expertise, it can be done.

And if you are a startup founder: Evaluate the help being offered. See past the self-proclaimed titles of accelerator, incubator, advisor, mentor, business angel. They mean nothing! Find out who is behind them and evaluate them as if your life depended on it. Because it does.

Check out Accelerace and Overkill Ventures where I serve as a General Partner.

There is a fear that has no name. But most startup founders experience it.

Perhaps, it is the fear that kills most startups. And no, it is not the fear of failure. It is a fear much more visceral. I call it Beachhead Phobia.

The Beachhead 

In our acceleration program, we teach all founders the concept of the Beachhead. It is the most important tool for finding product-market fit. 

We teach our startups to focus all their resources on a single homogeneous segment that has a desperate need for their product. The desperation usually arises from the fact the segment is new and fast-growing. Consequently, the Beachhead has not yet found a solution that adequately solves their problem. This makes the Beachhead willing to test an early product from an unknown startup.

The beachhead is borrowed from military strategy. Here, invading forces must focus all their resources on a single spot on the beach to conquer enemy territory. 

All successful startups find a Beachhead. But before they do, startups typically begin with a very broad customer definition. Then they learn that customers are different and want different things. This eventually leads startups to focus on a Beachhead. Once, startups dominate the Beachhead, they slowly broaden their focus again. 

The puzzling thing is that even though all successful startups go through this process, all founders fight it. And after having accelerated startups for a decade, I see what is going on.

The fog of startup

When launching a startup, founders feel the intoxicating promise of infinite opportunity. The sense arises from the “fog-of-startup”. We want to be the next big startup success. But we are not completely sure how to get there. The space between the current situation and the future aspiration is the fog-of-startup. 

In the fog-of-startup, we expect advantageous things will happen. Perhaps, a famous VC will flood us with cash. Or a big company will start distributing our product. Or a celebrity will endorse us. But our biggest hope is that we will immediately get flooded by customers from around the globe. 

To keep this dream alive, we communicate in the biggest and broadest terms possible. We call our product the one-stop shop. Or the platform. Or the go-to software. We claim to be born global and be blitz scaling.

Accordingly, we launch and prepare champagne bottles. But instead of servers crashing due to insane customer demand. Things get murky. Some people sign up. But not nearly the numbers we hoped. The “fog of startup” has been lifted and it hid no miracles.

At this point, many founders make a fatal mistake. We surmise that we did not communicate to enough people. Not enough people understood the brilliance of our product. So, we respond by painting an even broader picture. We might state that our product is relevant for all industries or all consumers. Surely, this will make us seem bigger and relevant to more people. 

But it does not have the intended effect. The response turns even murkier.

At this point, we get worried. Maybe we did something wrong. So, we seek advice (and funding). At some point, we encounter people who know about startups. That could be investors, other founders, and advisors. These people will tell us to “focus”. But at first, this advice seems strange. 

Because we already focus all of our time on our startup. So, the advice seems patronizing and unnecessary. Sometimes, those providing the advice manage to convey that the focus is related to customers. But since launch, we have done little else than answering requests for features and bug reports from customers.

At some point, lucky founders encounter the concept of the Beachhead. The logic is clear. We must focus on a single homogenous segment to whom we can offer a perfect product. Once, we have conquered this Beachhead, we can focus on the next adjacent segment. 

In other words, we must abandon the one-stop shop for all companies. Instead, we must offer a unique product for a specific person, in a specific type of company, with a specific problem, to be used in a specific use case.

We get it. But then we feel it. The fear that has no name. So, I dubbed it Beachhead Phobia . 

Beachhead Phobia

Successful founders realize they must focus on a Beachhead. Still, most founders hesitate. The reason is the unpleasant sensation when contemplating the change. That sensation is Beachhead Phobia.

The sensation stems from the fact that the advice seemingly conflicts with several common beliefs.

The first belief is that VCs only invest in billion-dollar markets. Consequently, many founders articulate their market in the widest possible terms. Unfortunately, these founders confuse different time perspectives. When VCs talk about billion-dollar markets, they mean markets 10 years from now. But when we advise founders to focus on a Beachhead, we mean for the next six months.

The second belief is that “thinking small” means lowering our ambition and impact. Many founders are avid readers of books with titles like: The magic of thinking big. In addition, our personalities compel us to make a “dent in the universe”. 

Going from declaring that you serve all companies everywhere! to serving a small group of specific people in specific companies, simply feels unambitious. But again, we confuse time perspectives. Anyone who succeeds in anything big, first succeeds in something small. The Beachhead is just the first step.

The third belief is not a belief. It is a feeling. And for this reason, it is the strongest cause for Beachhead Phobia. It is the psychological truth that it feels much worse to be rejected by someone specific than to be ignored by a crowd. 

During our program, we ask founders to name and list the Beachhead. If a startup claims their Beachhead is HR managers in SMEs. Then we ask the founders to make a list with names of the exact HR managers they plan to sell to. And then create a “perfect” value proposition for these people.

Creating a specific value proposition to a specific person infinitely increases the chance of a positive response. Any woman using dating apps can attest to this. And so can you (even if you are not a women using dating apps).

The problem is that contacting a specific person with a tailored message feels wildly uncomfortable. Why? Because suddenly our actions are measurable, and rejection becomes impossible to ignore. 

In a nightclub, it feels much worse to approach a specific person and be rejected, than to be ignored on the dancefloor. 

On the dancefloor, we can convince ourselves that someone attractive will soon appear. But approaching a specific person with a personalized compliment and be rejected, ruins the night. 

But the best founders overcome Beachhead Phobia. They target the Beachhead, get rejected, learn from it, adjust their value proposition, and do it again. They feel visceral pain with every invalidation of their assumptions, but they never succumb to the fear. And neither will you.

You want to learn more about Beachhead, visit Accelerace and Overkill Ventures. We accelerate and invest in startups.

This is not a blog entry. Instead, it is a white paper I have produced in my line of work as General Partner at Accelerace Invest. But I post it here to log advances in my thinking.

Introduction 

Startups are defined by growth.  

Growth is critical because startups are founded, build, and invested in on the assumption of rapid growth. Few founders, founding employees, or investors would bet on a startup with poor prospects for growth. Nor would the same people bet on a startup with prospects for slow growth. 

To pre-seed investors, the potential for rapid growth is challenging to assess. Later stage investors enjoy the benefit of historical performance on actual growth. If a startup has grown rapidly over the past three years, it is reasonable to assume that the startup will continue its rapid growth. 

But if the startup is less than 12 months old, meaningful historical data is nonexistent. Growth has not yet set in. What can pre-seed investors do? 

Despite the lack of historical data, a startup should still be growing. However, instead of looking at the historical growth, pre-seed investors must look at the Momentum. Instead of asking: how fast has the startup grown? Pre-seed investors must ask: how fast is the startup growing? Or phrased differently: how strong is the Momentum of the startup?  

The answer would allow pre-seed investors to use Momentum as an indicator of future growth. Just like later-stage investor use past performance.  

Defining Momentum 

But to answer the question: how strong is the Momentum of the startup? we must first define Momentum.  

To pre-seed investors, Momentum is complex because in most cases financial metrics such as MRR, GMV, sales are absent. Instead, pre-seed investors must evaluate the accumulation of the resources that are foundational to financial growth. To use a race car analogy. Pre-seed investors must evaluate the making of the race car. Later stage investors can evaluate the lap times of the finished race car.  

The pre-seed investors must look at the bits and pieces of the car and evaluate their combined quality to assess the prospects of the car becoming a great race car. The better the different pieces, the better the faster the race car.  

Steve Blank argues that a startup is a temporary organization searching for a scalable business model. The search process is focused on obtaining insights and attract resources. Insights and resources are the bits and tools of the race car.  

It can be assumed that startups with great insight and strong resources have a higher likelihood of success in the future. Again, the better the bits and pieces, the better the car will perform. 

Or put simply, startups that have accumulated the most insight and resources are in a better position to generate growth in the future. Consequently, the accumulation of resources could be a good indicator of future performance.  

But what are the resources that define a pre-seed startup? 

A startup accumulates resources on four key dimensions. Those are Team, Technology, Customer insight, and Customer commitments. We will do brief reasoning to these four dimensions below: 

The team is the driving force behind the startup. Naturally, high quality teams outperform low-quality teams. Consequently, a key dimension of Momentum is improvements to the team. The critical part of the team consists of founders and founding employees. Founders participated in the founding of the startup, while founding employees joined later. Both are critical to the startup and own shares in the entity. Often founding employees are more senior than the founders and are defined by “paying” big opportunity costs when joining the startup. (Danish examples are Jesper Lindhardt in Trustpilot, Mette Lykke in Toogoodtogo, and Thor Angelo in Mymonii). Because of the critical nature of these founders and founding employees, the evaluation of Momentum should concentrate on the expansion of this group. Consequently, a startup that manages to attract the best people should increase the chance of success. 

The Technology is the basis of the value proposition. Most startups build their product on technology, and any advancement in the technology should improve the value proposition. Consequently, a key dimension of Momentum is technology. A startup that rapidly advances its technology should increase the chance of success. 

The Customer insight is another basis of the value proposition. Customer insight is the information founders use to turn their technology into a product. Obtaining customer insight is a key activity for startups, and deeper understanding improves the value proposition. Consequently, a key dimension of Momentum is customer insight. A startup that deepens their level of insight should increase the chance of success. 

The Customer commitments are de-risking the venture. If customers commit to pilot projects, payments, and contracts, the startup obtains proof of business points that can be leveraged when raising funding and attracting team members. Consequently, a key dimension of Momentum is customer commitments. A startup that amasses customer commitments should increase the chance of success. 

Now that we understand what defines Momentum for pre-seed startups, we can almost answer the question: how strong is the Momentum of the startup? 

However, we still need to define strong. Strength describes the efficiency of the progress. A startup might accumulate resources on the Team, Technology, Customer insight, and Customer commitments dimensions, but the price of this accumulation matters. The price is the constraint and consists of time and money. 

Momentum only makes sense if it is related to the time and money that has been available to the startup.  

If a startup has spent three years and 5 million to develop an app that has 10 pilot customers, one would evaluate the startup negatively, because the Momentum is unsatisfactory in relation to the time and money spent.  

Contrast the above scenarios to a startup that has developed the same app, but only have 2 pilot customers. If this has been achieved in two weeks and 10K, the Momentum would be relatively stronger. 

The examples above illustrate the power of evaluating progress relative to the time and money. Only by relating the progress to the constraints, we get a picture of the Momentum.  

To stay in our race car analogy, Momentum in relation to the constraint gives us a performance indicator equivalent to km/h¹ for cars. Km/h enables us to compare the efficiency of various cars. 

Progress per Time or Progress per Money are the two most important Momentum metrics and they enable us to compare the efficiency of various startups. A metric that could be highly indicative of future growth.  

Standardizing progress to understand Momentum

To measure Momentum, we must standardize the progress a startup has made. To this end, we propose to use standardized levels for each of the dimensions of progress (Team, Technology, Customer insight, and Customer commitment). 

The proposed levels can be seen below: 

The team can be classified depending on the completeness and experience of the team and its team members. We propose the following six levels: 

Level 0 team	Single, first-time founder, no industry insight.	The startup is the typical “Startup Weekend” project. One person who has recently conceived a vague business idea in an industry the person does not know from the inside.
Level 1 team	Incomplete, first-time team, no industry insight.	The startup has a team. Often the lead founder has convinced a friend to join the project, but they lack real startup experience, and many critical skills are not possessed within the founder team. Also, they do not know the industry from the inside.
Level 2 team	Complete, first-time team, no industry insight.	The startup has a complete team meaning that all critical skills are held in the founder team, but they lack real startup experience and industry insight.
Level 3 team	Complete founder team, one person with some startup experience, and related industry insight.	The startup has a complete team meaning that all critical skills are held in the founder team. One of the persons has founded or been a founding employee in a startup before. Also, one of the persons has worked in a related but not the same industry.
Level 4 team	Complete founder team, one person with some startup experience, and same industry insight.	The startup has a complete team meaning that all critical skills are held in the founder team. One of the persons has founded or been a founding employee in a startup before. Also, one of the persons has worked in the same industry.
Level 5 team	Complete founder team, all persons with significant startup experience, and same industry insight.	The startup has a complete team meaning that all critical skills are held in the founder team. All team members have been founders or founding employees in successful startups before. Also, one of the persons has worked in the same industry.

The team will advance as the startup develops. Often a single founder will bring in co-founders. Also, founding employees with significant startup experience tends to join in the early stages. Any advancement from one stage to another is progress on this dimension. Efficient startups will advance through the stages using less time and money than non-efficient startups. 

The technology can be classified according to commonly understood industry taxonomy. We propose the following six defined levels of technology. 

Level 0 technology	Idea	The technology is articulated in writing and verbally. Perhaps the founders have made a slide or document describing the idea. The idea is still rather general and lacks details and specifics.
Level 1 technology	Concept	The technology has been sketched out and it can be described in specifics. There are drawings, models, and roadmaps that detail the idea. Typically, the founders have a full slide deck at this point. Often, they have a video using animations and renderings. It is also the stage that is typical for crowdfunding campaigns.
Level 2 technology	Prototype	The technology has been created to a level where it can be tested for proof of technology. The key components of the product exist and can be interacted with.  This is often the stage for crowdfunding campaigns. Apps are often in TestFlight mode.
Level 3 technology	MVP	The technology has been packaged into a minimal product that can be used by users. It includes the key feature(s) and is complete enough for the beachhead to start gaining value. This is often the stage that select pilot users and pilot customers are testing the product.
Level 4 technology	Version 1	The technology has been shipped as the first full-fledged product that the startup expects the customers to pay full price for.  It is complete enough for the beachhead to put into production and use daily.
Level 5 technology	Version 2	The technology has had its first major upgrade. The technology has stood the test of time and use, and the second generation of the product rebuilt to meet the requests of the customers of the first version and to add new features to start venturing outside the beachhead.

The technology will advance as the startup develops. The startup overcomes technical hurdles and weeds out bugs. In the process, the technology matures and becomes a full product. Any advancement from one stage to another is progress on this dimension. Efficient startups will advance through the stages using less time and money than non-efficient startups.  

The Customer insight can be classified using the proprietary Original Insight tool developed by Accelerace². It is a self-assessment tool provided to founders to help them clarify how well they understand their customers. The tool quantifies the level of customer insight. We propose the following six defined levels of customer insight. 

Level 0 Insight	10 – 30 points.	The founders have no insight and only a vague and over-simplistic idea about their customers.
Level 1 Insight	30 – 50 points.	The founders have little insight and only a vague and over-simplistic idea about their customers.
Level 2 Insight	50 – 70 points.	The founders have some insight, and but still only general ideas about their customers.
Level 3 Insight	70 – 90 points.	The founders some insight and can describe their customers in detail.
Level 4 Insight	90 – 110 points.	The founders have the same level of insight as their customers. Perhaps the founders use to hold that job position themselves.
Level 5 Insight	110 – 130 points.	The founders have deep insight and know the customers better than they know themselves. The founders can be considered expert to a level that a scientist would be an expert in their respective field.

The level of insight will advance as the startup develops. Typically, pre-seed startups are operating in the range between level 1 to level 3, to begin with. As the startup performs more customer interviews and get feedback from pilots, they advance their level of customer insight. Any advancement from one stage to another is progress on this dimension. Efficient startups will advance through the stages using less time and money than non-efficient startups. 

The Customer commitments can be classified according to commonly understood industry taxonomy. We propose the following six defined levels of commitment levels. 

Level 0 commitment	Interest	The startup has talked to customers and can anecdotally talk about customers who have expressed interest.
Level 1 commitment	LoI	The startup has a signed letter of intent from a relevant customer. For consumer startups, people have signed up on a waitlist.
Level 2 commitment	PoC	The startup has a signed agreement of doing a proof of concept with customers. For consumer startups, people have signed up on a waitlist.
Level 3 commitment	Pilot	The startup has a signed agreement of doing a pilot to prove an articulated business outcome for the customer. For consumer startups, people are using the beta version.
Level 4 commitment	Customers	The startup has paying customer that is using the product in “production”.
Level 5 commitment	Returning customer	The startup has several customers that have renewed or in other ways shown that they are planning to remain customers for a significant time.

The level of customer commitments will advance as the startup develops. As the startup begins to prove the value of their product, the commitments increase. Any advancement from one stage to another is progress on this dimension. Efficient startups will advance through the stages using less time and money than non-efficient startups. 

Now that we have defined standardized progress, we can measure progress along these four dimensions. In other words, turning progress into two Momentum metrics. Once progress has been converted to a number, we can divide this number with the constraints. Either time or money. This gives us the ultimate metrics for pre-seed investors: Progress per Time and Progress per Money. 

Calculating Progress per Time (PpT) 

How much progress does a startup produce per unit of time?  

Below we will lay out the mathematical model for calculating PpT. 

Conceptual equation detail level 1 

Conceptual equation detail level 2 

The PpT model in use 

Example: Imagine a startup that during a period of 10 months has progressed one level on the team dimension. This gives the startup 1 point in our equation. On the technology dimension they have progressed three levels giving them 3 points. On the customer insight dimension, they have progressed two levels giving them 2 points. Finally, they have progressed customer commitments with four levels giving them 4 points. Mathematically the equation will be populated as follows: 

Example level 1 

Example level 2 

Example level 3 

Example level 4 

Calculating Progress per Money (PpM)  

How much progress does a startup produce per unit of money?  

Below we will lay out the mathematical model for calculating PpM. 

Conceptual equation detail level 1 

Conceptual equation detail level 2 

Conceptual equation detail level 3 

The PpM model in use 

Example: Imagine a startup that has spent 1 million DKK and progressed one level on the team dimension. This gives the startup 1 point in our equation. On the technology dimension, they have progressed three levels giving them 3 points. On the customer insight dimension, they have progressed two levels giving them 2 points. Finally, they have progressed customer commitments with four levels giving them 4 points. Mathematically the equation will be populated as follows: 

Example level 1 

Example level 2 

Example level 3 

Example level 4 

Limitations of the model 

Naturally, there are limitations to the PpT and PpM model. The most important are: 

1. Team progress does not take the quality of the individuals into account beyond requiring the team expansion to be of “critical” people. This means that two startups can score equally many points even though one startup has attracted a Nobel prize laureate and the other a merely skilled industry professional. 

2. The technology dimension does not take the difficulty of the science into account. This means that two startups can score equally many points even though one startup has made a scientific breakthrough and the other had mere launched their app. 

3. The customer commitments do not take the difficulty of customers into account. This means that two startups can score equally many points even though one startup sold to SMBs and one has sold multi-year recurring enterprise contracts. 

Some of the limitations can be dealt with by comparing startups within the same category. Thus comparing, enterprise software startups to other enterprise software startups. And consumer apps to other consumer apps. Few investors have big enough portfolios to enable a sub-segmentation. But if possible, it would be advisable.  

On a more generalized notion, the model does not account for all the factors that affect the likelihood of success. From experience, we know that team dynamics, the growth of the market, timing, competition, and other factors play a significant role in the life of a startup. The model only quantifies Momentum. To most, Momentum is just one of many elements investors assess when making investment decisions.  

Implications 

First, Momentum allows us to compare companies that have had different amounts of time and money available to them. In other words, the efficiency of which they create progress. This matters greatly because as pre-seed investors we are investing small tickets and the efficiency of the companies is critical. Also, in the absence of historical financial metrics, Momentum is perhaps the most objective metric for progress at the pre-seed stage and can arguably be a reliable indicator for the future. Having Momentum available, a pre-seed investor can use these metrics to aid them in the decision making when evaluating various investment opportunities.  

Second, the model provides input to the classic problem of making follow on investment decisions. Investors are often victims of the sunk cost fallacy. Often, the urge to support portfolio companies that are in urgent need of money to survive is strong. While this can be the right decision, often it is not. Momentum will provide data about how efficiently the portfolio company is spending the money and time provided with the investment. Startups with high Momentum scores suggest that the money are well spent, and that further investments are advisable.    

About the authors 

Peter Torstensen and David Ventzel are partners at Accelerace. Accelerace is a startup accelerator and pre-seed investor placed in Copenhagen Denmark. Accelerace was founded in 2009 and have accelerated more than 700 startups to date. 

The authors have been aided by their colleagues Claus Kristensen and Mads Løntoft in the conceptual development of the framework.  

Contact 

If you are interested in the model and collaborating further development of the framework, then contact the authors on David Ventzel: dav@accelerace.io or Peter Torstensen: pto@accelerace.io 

In 2007, a young student from Aarhus Denmark embarked on a doomed mission. But a couple of years later, fate would change the odds against him. Today, he is among the most celebrated Danish founders of our generation.

Peter Holten Mühlmann had noticed a problem. The internet lowered the barrier for commerce. New webshops were constantly popping up. But fraud and bad service followed.

Peter envisioned a software that could help shoppers navigate the mushrooming e-commerce landscape. Like an antivirus program warning you against shops that could not be trusted. He called it Trustpilot.

However, the software needed data. And the most reliable data would be experiences from shoppers. In other words, reviews.

To collect data about webshops, Peter set up a website where people could report bad shopping experiences. Surprisingly, people did. The site found a beachhead among market mavens. The sort of people who find meaning in passing warnings and endorsements to other people. 

Peter started realizing that the reviews might be the product. Consequently, he abandoned the “antivirus” approach and turned Trustpilot into a destination for consumer reviews.

However, a problem arose. How would he make money on this website? Advertising seemed the logical approach, but Trustpilot was not a destination people hung out. Advertising would not be a good way to capitalize, he surmised.

And then it dawned on him. The businesses being reviewed were the customers. Good reviews were gold, and bad reviews were poison. Using good reviews in marketing was worth money. So was the ability to respond to bad reviews.

Trustpilot became a tool to manage reviews. However, this created a new problem for Peter. One that seemingly doomed his startup.

Peter had no experience in selling software to small businesses. Nor did his CTO. The task was daunting, and thousands of startups had shipwrecked on this challenge. 

At the time, software was seldomly sold to small businesses. Only enterprises could afford the high prices needed to cover expensive salespeople in suits doing presentations. 

The pivot meant that the founder team was utterly unqualified to execute the business model. In truth, this is quite common. Founders rarely limit their ideas to their current abilities.

Nonetheless, competence matters. Investors call it founder-market-fit. Investors evaluate how well the founder teams’ current competencies fit the challenges dictated by the market the startup is going after. Founder-market-fit defines whether the founders master the critical disciplines required to win. 

Critical Disciplines. How to win Tour de France

Cycling is a sport. And cyclists are athletes. Consequently, one would imagine that the best athlete would win the most prestigious races. Intriguingly, things are not that simple. 

The world championships in cycling is held every year. And the rainbow-striped winner jersey is a childhood dream for all cyclists. Still, the title of world champion is rivaled by another triumph. Winning Tour De France.

One would think that the same athlete would be a likely winner of both Tour de France and the World Championship. But as many know, that is seldom the case. In fact, this has not happened in more than 30 years.

The thing is that cycling includes different types of races. The World Championship is a one-day event. Tour de France lasts 23 days. Also, the route of the World Championship avoids the highest mountains. Tour de France includes snow-filled alpine peaks. Furthermore, Tour de France has a time trial.

The differences between the two races require riders to master different disciplines. In other words, the critical disciplines of winning the World Championships and Tour de France are different.

Throughout the history of Tour de France, the winners have been marked by mastering both time trials and mountain climbing. In contrast, world champions have been marked by break-away and sprinting abilities. 

The point is that cycling is a sport, but the different races require different critical disciplines for winning them. And trying to win Tour de France without mastering time trials and mountain climbing is a doomed mission. Just like trying to win the market for review management software without mastering small business sales. 

The importance of the Founding Employee

When Peter Holten Mühlmann pivoted to selling to small businesses, he needed someone with a rare competence. Then fate intervened. In 2010, Peter was introduced to someone who could give Trustpilot a winning chance.

Jesper Lindhardt was talented and successful. He had risen through the ranks of Navision, Realtime, SAP, Omniture, and Adobe. Most importantly, his focus was on small business sales. In other words, Jesper had spent 15 years honing the exact critical discipline needed by Trustpilot.

At this point, fate had played its part. Instead, true founder skill took center stage. Somehow, Peter convinced Jesper to abandon his meteoric career, leave Adobe, and join a completely unknown startup to become a founding employee.

Founding employees are probably the most underrated ingredient for startup success. A look at the most successful Danish startups founded by inexperienced founder teams illuminates the importance of this rare breed. 

The founders of Coinify, OrderYoYo, Templafy, TooGoodtoGo, and Planday all attracted one or more founding employees who brought their experience and competence to the startup during infancy. These people mastered the critical disciplines of the business model and gave the startup a winning chance.

After having spent almost ten years as a startup investor, I am continuously puzzled by startup teams that do not master the critical disciplines of their business model. Luckily, certain doom can be avoided. Because true founder skill is the ability to attract resources. And no resource is more important than the founding employee. If you are lucky to meet Peter Holten Mühlmann, he tells you the same.