What the Fortune 1000 Can Learn from the Startup Genome Project

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)

  • janklimo

    Great article, guys. You used the terms “prematurely” and “too early” a few times without explaining what exactly you mean by them. Would be nice if you could elaborate on that.

  • Jeff Savage

    Well-done! The amount of interest you have sparked is truly amazing. As an academic scholar, I am always trying to find ways to involved more people in my research (and findings). How can I get more involved?

  • miguelcecchetti

    Fantastic article! Certainly helps start-up teams keep their perspective on things! THANK YOU!

  • Kushagra Saxena

    The startup genome report has thrown up interesting findings and generated a lot of buzz, which is great. That said, your assertions regarding on what large corporations do/not do re. new business initiatives/internal startups are largely unfounded. For instance, your assertions #1, #3, #4, #5 are largely untrue. On #1 and #3, large companies are often criticized for over-pivoting given their need to get buy-in from the multiple parties involved and not being nimble/not scaling fast enough subsequently losing momentum. On #4, large companies are fairly successful at testing/validation of the value propositions with their core customers though they often miss the opportunity to identify new customers/segments (which is the real issue around value prop). On #5, large companies have relatively limited revenue/profitability pressures given any revenues from these new ventures will be insignificant relative to their core business revenues and they have significant appetite/ability to incubate loss leaders. Only assertion #2, re. large companies applying learnings from core businesses onto internal startups is absolutely true and perhaps the biggest cause for them not to be able to realize full return on their investments.

    I think there’s a lot of value large companies can derive from your findings but you’ll need to go beyond elementary assertions and deeply understand the core issues that bottleneck at scale innovation in large companies to successfully bridge this gap. I am happy discussing this further if helpful.

  • Bjoern Lasse Herrmann

    @Kushagra: thank you for you thoughts. This post is indeed not based on a rigorous research but based on just 30 relevant interviews and conversations with executives from companies like Microsoft, Deutsche Telekom, Sony, Sony Ericson, Mars, Arab Emirates, EADS, Bornier, Coca Cola, Facebook, etc. That said – the insights are obviously not statically significant but I think they are a good indicator. I am not sure how many datapoints you base your assumptions on but I’d be happy to learn that.
    I am guessing one key assumption that may lead to disagreement is that we are talking about disruptive innovation instead of sustaining innovation. Large companies are excellent with sustaining but very poor with disruptive innovation. Based on our research and interviews that is mostly due to the fact that larger companies have a hard time in dealing with uncertainty and are much slower in learning from their customers. Following some comments on your points of doubt.

    #1 The reason why internal innovation project typically don’t pivot (a pivot being a major shift in the business such as going from a palm based door opening product to an email based money transaction product) is because they don’t seem to align with the goals of the company. If the innovation initiative is mostly irrelevant for the core business and merely an experimental R&D initiative you tend to have too many pivots. Both are very common and I have seen it myself many times with a previous startup of mine that provided software/consulting services for organizational restructuring of large companies. #3 Large companies typically act as if they are under complete certainty and project the same behavior on disruptive innovation projects. Therefor they do not mitigate risk in the actual development process but only before & after the initial development. Because of that they get ahead of themselves in most of the 5 dimensions (see the earlier blogposts) too quickly before they have the appropriate customer response. #4 Large companies are excellent in doing market research and sometimes even at testing concepts. But they generally perform poorly in the actual testing and validation cycle as they develop the product. Startups iterate optimally on a daily bases whereas large companies often operate in monthly, 3 month or 6 month cycles. #5 Its not about how much money they commit but how much money they expect in return. Large companies again predict returns under the illusion of certainty and tend to create false expectations that lead to friction and potential failure. Hope that helps to clarify at least some of your thoughts.

  • Kushagra Saxena

    Thanks for your comments, Bjoern. My assertions are based on interactions over 7-10 years with CXOs of 50+ Fortune 500 companies with over 3 months to 1 year per company.

    I agree that our source of disagreement is indeed anchored in the definition of innovation in the context of a large company (innovative vs. disruptive). That said, I think we should be clear that pursuing “disruptive” innovation, many times, doesn’t make sense for a large company and therefore conditions for disruptive innovation do not (and sometimes should not) apply to a large company. This is because the CEOs/Board of a large company will (and should) optimize for composite shareholder value, which for a large company is sometimes anchored on the low risk/continuity of its core business balanced with a portfolio of growth initiatives that are synergistic with its core. That is, infact, the right answer for a large company. We are seeing this play out real time with HP where despite expressing intent to get out of a low margin, low growth core business in lieu of a disruptively innovative new business, its market value declined by 10-20%+.

    And so, my central thesis in my last comment was that the learnings for large companies mentioned in the article likely have limited applicability for large companies because the conditions those learnings seem to address don’t (and often shouldn’t) apply to large companies.

    Like I mentioned, I do think there are other very meaningful learnings from the genome project which would be very helpful for large companies (as we discussed on our call) and I know they would be interested in because they are all struggling to figure out what role they should play in this new tech. revolution but don’t have a good answer, but instinctively I know the above learnings would get limited traction, because the conditions those learnings seem to alleviate don’t fully apply to them.

    As for the individual points – there are examples of Fortune 1000 companies that have pivoted a lot and are inherently moving towards an uncertainty based capital allocation mechanism (please read about McKinsey’s paper on portfolio of initiatives piloted in mid-90s, which is now the defacto capital allocation approach for most Fortune 500 cos). But these are best discussed in-person / over a drink.

  • Bjoern Lasse Herrmann

    @Kushagra: Did you read the last sentence 😉 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.

  • Lars Føleide

    Broken Sandhill.com link: “Oops. The page you’re looking for is lost in the cloud.”