In February, 2011, we started a very ambitious project to crack the innovation code of Silicon Valley and increase the success rate of startups all over the world.

Two weeks ago we launched the Startup Genome Compass, a benchmarking tool for startups and our new research on the primary cause of failure for startups. The response was overwhelming. More than 8,500 high-tech startups started using the application and our research reports have been downloaded more than 25,000 times. Now it can be found all over the Web in blog posts, infographics, and in over 15 languages. It has been extremely humbling for us to be able to touch the lives of thousands of entrepreneurs living around the globe. 

In the last six months the Startup Genome Project has collected a tremendous amount of data on startups, built a theoretical model based on synthesizing ideas from entrepreneurship's eminent thought leaders and taken big strides towards demystifying the process of entrepreneurship and innovation. 

The basis of our theoretical model is looking at a startup as a product centric organism that interacts with its environment, the market. The core dimensions that define this organism are customer, product, team, business model and financials. The key challenge for a startup is to keep those five dimensions in sync with the actual customer response. An example for getting out of sync would be moving too quickly on the product dimension. The result would be an over-engineered product that is less likely to be adopted. 

In order to group and benchmark startups, we segment them by type and stage. Different types of startups are differentiated by the complexity of their customer interaction and customer acquisition. Stages are described by the life cycle through which a startup evolves on its path to becoming a large company. Each stage has a different set of goals and key activities. For example, in the first stage, Discovery, the startup performs a mostly qualitative search process, where the exit criteria are problem/solution fit. In the next stage, Validation, the startup performs more quantitative testing with a working software prototype. (More details about our methodology can be found here.)

Since we've been working on the Startup Genome Project, numerous Fortune 100 executives have reached out to us, wondering if our tools and research could also be applicable for their work. While our original focus was on startups, we've discovered that our methodology extends beyond just measuring the progress of startups to being able to measure the progress of a diverse array of innovation projects. Much of the theoretical groundwork for this leap of insight was laid by Clayton Christenson and Steve Blank.

Clayton Christenson made the important distinction that disruptive innovation was fundamentally a different activity from sustaining innovation, requiring different rules, different managerial tactics, and different types of people. Steve Blank then connected the emerging science of entrepreneurship to the disruptive innovation occurring in large companies by noticing that the ideal organizational structure for disruptive innovation was a startup. The problem is despite the emerging science of entrepreneurship, innovation is still perceived as somewhat of a dark art. Bill Gates, Steve Jobs and Marc Benioff appear to have performed innovative feats capable only by the super-human, because it's very difficult to describe how they were able to disrupt enormous markets with seemingly unbeatable foes.

But now the Startup Genome can begin to uncover what makes these innovation projects succeed or fail and can offer a new paradigm for the management and accounting of innovation.

Here are a few relevant findings from our research, and three use cases where our tools and research can help.

Selected Research findings

1. Most successful startups pivot at least once. Startups that pivot once or twice raise 2.5x more money, have 3.6x better user growth, and are 52 percent less likely to scale prematurely than startups that pivot more than two times or not at all. A pivot is when a startup decides to change a major part of its business.

   Large companies tend to inhibit pivoting for their "internal startups."

2. Different type of markets and products require different type of founders and resources. B2C vs. B2B is not a meaningful segmentation anymore because the Internet has changed the dynamics of customer interaction. We found four different major groups of startups that all have very different behavior regarding customer acquisition, time requirements, market risk and team composition.

   Large companies tend to project learnings from their main business on their innovation initiatives, which leads to mistakes.

3. The major reason for failure of startups is premature scaling. About 70 percent of our dataset showed up as premature scaling or inconsistency. One driving factor for inconsistency is too much capital, teams that are too large, bad team compositions, too little testing, etc. - pretty much everything a large company does, anticipating high certainty in their planning. 

   The results:
   

   • No startup that scaled prematurely passed the 100,000 user mark.
   • 93 percent of startups that scale prematurely never break the $100k revenue per month threshold.
   • Startups that scale properly grow about 20 times faster than startups that scale prematurely.
   
   Large companies tend to pressure their "internal startups" to scale prematurely. 

4. Early-stage startups spend most of their time discovering. Consistent startups spend two to four times as much time discovering who their customers are, whereas inconsistent startups are focused on validating that customers want their product. Consistent startups are searching. Inconsistent startups are executing. 

   It's widely believed among startup thought leaders that successful startups succeed because they are good searchers and failed startups achieve failure by efficiently executing the irrelevant. 

   Large companies tend to jump to execution after their initial market research and miss out on two import stages: Discovery and Validation.

5. Startups that monetize too early are more likely to fail. Trying too hard to monetize leads to inconsistency. Ninety-three percent of inconsistent startups make less than 100k a month when scaling the business. While money can be an important validation indicator, stressing it too heavily will lead startups to ignore opportunities and drift towards non-scalable opportunities that are likely to turn into small business or custom consultant shops.

   Large companies tend to focus on revenue instead of the key value proposition they want to provide with a new product or service. The result is typically mediocre value propositions.

Use cases 

• We can help large companies assess startups and make decisions on when the right time is to invest.
• We can help large companies assess internal startups in order to make more effective buy or build decisions.
• We can facilitate the integration process after an acquisition by using our framework as an alternative measure of progress and control system.

It is estimated that 70-95 percent of acquisitions fail. A significant percentage is due to the friction that is created by trying to integrate the startup with the large company's financials, HR department, product, market and business model. Most startups when they are acquired are uncertain on many of these dimensions, and forcing them to conform on any one of these dimensions to the large company can stunt their growth and often kill them. 

For example, a parent company may want to use a startup for lead generation that has a lot of users but no business model. As a result, the startup's product deviates from the original value proposition, and this can cause the user base to erode and cause significant vision conflict within the team. 

Our framework can solve some of these ailments by enabling the parent company to measure the stage of the development of the startup and only begin integrating the startup once they've reached a requisite level of maturity and stability.

As competitive pressures continue to increase, innovation will increasingly become the lifeblood of every large company. When innovation stops, a company's days become numbered. The Startup Genome does not provide a serum for infinite living, but we're working on building the tools and infrastructure for healthier living.

(this was also posted on Sandhill.com)

What makes startups succeed or fail? This is a question we are intent on answering. We believe increasing the success rate of startups has the potential to dramatically increase economic growth all around the world. On May 28th, we released our first report at blog.startupcompass.co. On August 29th we released our first benchmark application, the Startup Genome Compass to help startups reduce premature scaling. 

The role of technology startups in our global economy has never been more important. Startups may seem insignificant compared to large multinational companies that have trillions of dollars of wealth sloshing around in public markets, but a recent Kauffman Foundation study found that the majority of job growth in the United States is driven by technology startups.

The power of information technology has been steadily increasing for the last three decades and has recently reached a level of maturity that has started to trigger a reorganization of the global economy. It has never been easier or cheaper to create a startup thanks to infrastructure like open source software, software as a service, cloud hosting, globally ubiquitous payment processing, viral distribution channels, real-time collaboration, on demand logistic services and hyper-targeted advertising.

As a result, the pace of change is speeding up and the implications of this are immense. Billion dollar startups are emerging faster and faster. The quick ascent of startups like Google, LinkedIn, Facebook, Twitter, Zynga and Groupon are harbingers of a major structural economic change on the horizon. The service sector has dominated the global economy for the last few decades but its sun will set. Just as machinery replaced most manual labor, software will replace repetitive intellectual tasks. Turbo Tax eliminated many accountants, Amazon eliminated many retail jobs and E-Trade eliminated the majority of stockbrokers. In the near future jobs that are more complex yet still methodical will also be replaced by software. Creative Commons is reducing the need for lawyers, Khan Academy shows how one good teacher can replace many bad teachers and the profession of doctors will be disrupted by startups like Halcyon Molecular that turn healthcare from emergency care into a preventative self-care. Balancing out that massive decrease in jobs will be what Richard Florida calls the rise of the creative class.

As the waves of disruption come ever faster, the only way for a company to be competitive will be to behave like a startup. In the landmark book the Innovator’s Dilemma, Clayton Christensen found large companies are excellent at sustaining innovation but by and large fail at disruptive innovation. Startups thrive on creating disruptive innovations. Recently, thought leaders in entrepreneurship have come to the conclusion that in order for large companies to be effective at disruptive innovation they need to make structural changes that make them behave nearly identically to startups. 

The increasing economic importance of startups, along with decreased barriers to entry has caused interest in entrepreneurship to explode around the globe. New startup ecosystems are being built up all over the world with the hopes of replicating the success of Silicon Valley. Spearheading this movement are startup accelerators like Seedcamp, Techstars, Opinno, Founders Institute, 500 Startups, and Sandbox, but they are accompanied by hundreds of others. On an individual level, the brightest people worldwide, are increasingly seeing entrepreneurship as the career path of choice. The release of The Social Network has captured the imagination of today’s young people, and catapulted Mark Zuckerberg to the same status as Gordon Gekko in Wall Street almost 25 years ago.

But despite the increasing economic importance of scalable startups, we still don't understand the patterns of successful creation. More than 90% of startups fail, due primarily to self-destruction rather than competition. For the less than 10% of startups that do succeed, most encounter several near death experiences along the way. Simply put, we just are not very good at creating startups yet.

Eight months ago we launched the Startup Genome Project, with the goal of increasing the success rate of startups and accelerating the pace of innovation around the world by turning entrepreneurship into a science. If successful, it's hard to imagine the type of impact this could have.

Some of the world's biggest transformations occurred when arts were turned into sciences. The scientific revolution in the 16th century triggered the age of enlightenment. The development of scientific management, which peaked in the early 1910’s, made large companies dramatically more efficient and arguably was one of the biggest causes of the explosion of wealth the world saw in the last century.

We believe the effects of cracking the code of innovation by turning entrepreneurship into a science will trigger a new era, that we are calling the Entrepreneurial Enlightenment. In the midst of the largest global depression in almost a century, a revolution in entrepreneurship could propel the world to a level of wealth never seen before by enabling scientific discoveries and technological breakthroughs to be integrated into the fabric of society faster than ever before. Offering hope that we may finally be able to master some of the most pressing challenges, including water, energy, food, health, security, poverty and education.

No revolution is triggered alone. In the quest to make entrepreneurship a science, we are standing on the shoulders of giants. In just the last 2-3 years the number of people extracting and codifying the informal learning of entrepreneurs has hit a point of critical mass. Steve Blank kicked off the move towards a science of entrepreneurship with his seminal book The Four Steps to the Epiphany. In the book, he introduced the concept of Customer Development. A few years later Eric Ries combined Customer Development with Agile Development and Lean Manufacturing principles to create the Lean Startup methodology. Interest in the Lean Startup has morphed into a global movement. Other major contributors to the science of entrepreneurship include Dave Mcclure on Metrics, Sean Ellis on Marketing, Alex Osterwalder on Business Models and Paul Graham with his essays.

Yet despite this huge knowledge base emerging about how startups work, startups have been able to absorb little more than the basic patterns of how to build a startup. Most founders don't know what they should be focusing on and consequently dilute their focus or run in the wrong direction. They are regularly bombarded with advice that seems contradictory, which is often paralyzing. And while startups are now gathering way more qualitative and quantitative feedback than they were just a few years ago, their ability to interpret this data and use it to make better product and business decisions is sorely lacking. The primary cause of these problems is that we lack the necessary structure to synthesize our accumulated knowledge on the nature of startups. We are missing a common language and framework to describe and measure entrepreneurship and innovation.

Three days ago we launched the Startup Genome Compass, a benchmarking tool for startups and our new research on the primary cause of failure for startups: premature scaling.

There's been some confusion about exactly what we mean by premature scaling and we wanted to respond to the feedback we've received and elaborate on the findings from our research. To make it clearer, we need to go a little bit deeper into the theory and methodology.