AAA Lessons learned from Startup Genome Project

Lessons learned from Startup Genome Project

In February last year we started a very ambitious project to crack the innovation code of Silicon Valley and increase the success rate of start-ups all over the world.

We recently launched the Startup Genome Compass, a benchmarking tool for start-ups and our new research on the primary cause of failure for start-ups. The response More than 8,500 high-tech start-ups started using the application and our research reports more than 25,000 times.

Now it can be found all over the web in blog posts, infographics, and in more than 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 past six months the Startup Genome Project has collected a tremendous amount of data on start-ups, built a theoretical model based on synthesising 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 start-up 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 overengineered 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 start-ups are differentiated by the complexity of their customer interaction and customer acquisition.

Stages are described by the lifecycle through which a start-up 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 start-up performs more quantitative testing with a working software prototype.

Since we have 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 to their work.

While our original focus was on start-ups, we have discovered our methodology extends beyond just measuring the progress of start-ups to measuring 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. 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.

Blank then connected the emerging science of entrepreneurship to the disruptive innovation in large companies by noticing that the ideal organisational structure for disruptive innovation was a start-up.

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 of which only the superhuman are capable, because it is 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 cases where we believe our tools and research can help.

1. Most successful start-ups pivot at least once. Start-ups that pivot once or twice raise 2.5-times more money, have 3.6-times better user growth, and are 52% less likely to scale prematurely than start-ups that pivot more than twice or not at all. A pivot is when a start-up decides to change a major part of its business. Large companies tend to inhibit pivoting among their internal start-ups.

2. Different type of markets and products require different type of founders and resources. B2C versus B2B is not a meaningful segmentation any more because the internet has changed the dynamics of customer interaction. We found four major groups of start-ups that all have very different behaviour regarding customer acquisition, time requirements, market risk and team composition. Large companies tend to project lessons from their main business on to their innovation initiatives, which leads to mistakes.

3. The major reason for the failure of startups is premature scaling. About 70 percent of our dataset showed up as premature scaling or inconsistency. Driving factors for inconsistency are too much capital, teams that are too large, bad team compositions, too little testing and so on – pretty much everything a large company does, anticipating high certainty in their planning.

The results:
 No start-up that scaled prematurely passed the 100,000 user mark.
 Ninety-three percent of start-ups that scaled prematurely never broke the $100,000 monthly revenue threshold.
 Start-ups that scale properly grow about 20 times faster than start-ups that scale prematurely.

4. Large companies tend to pressure their internal start-ups to scale prematurely.

5. Early-stage start-ups spend most of their time discovering. Consistent start-ups spend two to four times asmuch time discovering who their customers are, whereas inconsistent start-ups are focused on validating the premise that customers want their product. Consistent start-ups are searching. Inconsistent start-ups are executing.

It is widely believed among start-up thought leaders that successful start-ups succeed because they are good searchers and failed start-ups fail 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.

6. Start-ups that monetise too early are more likely to fail. Trying too hard to monetise leads to inconsistency.

Ninety-three percent of inconsistent start-ups make less than $100,000 a month when scaling the business. While important validation indicator, stressing it too heavily will lead start-ups 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.

We believe our tools and research can:
 Help large companies assess start-ups and make decisions on the right time to invest.
 Help large companies assess internal startups in order to make more effective buy or build decisions.
 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% to 95% of acquisitions fail. A significant percentage is due to the friction created by trying to integrate the start-up with the large company’s financials, human resources department, product, market and business model.

Most start-ups when they are acquired are uncertain on many of these dimensions, and forcing them to conform on any one of these can stunt their growth and often kill them.

For example, a parent company may want to use a start-up for lead generation that has many users but no business model. As a result, the start-up’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 start-up and begin integrating the start-up only once it has 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 are working on building the tools and infrastructure for healthier living.

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