Predictive Testing of Opportunities

An innovative growth opportunity presents itself to a company. Should it be pursued? What is the likelihood of success? Despite decades of struggling with these questions, companies still find them very difficult to answer.

Over the past two decades, many advances in innovation theory, processes, methods and tools have been developed. One of these advances is that we can now recognize different types of opportunities. There are several different classification schemes and mechanisms but, in general, three classes of opportunities are relevant when trying to predict success.

  1. Sustaining – The no-brainers. It’s relatively easy to determine whether or not to pursue them but they also will have only an incremental impact on the business. Low risk, low reward.
  2. Stretch – It’s outside your current business but is a clear adjacency – a little riskier than sustaining opportunities.
  3. Transformational – It’s clear that if the opportunity is pursued successfully, it will significantly change your company.

For opportunities in class two and especially in class three, the ‘batting average’ seems to be stuck at no more than .300 (30%), and it’s often much worse. Not only will 70% or more of these opportunities not be successful, it’s virtually impossible to predict which ones will be and which ones won’t be successful.

The reason for this situation is multi-faceted but it all comes down to the lack of data-driven, analytic methods for deciding what is in the best interests of our uncertain future.

Just think about your company’s process for ‘vetting’ opportunities. It usually comes down to a combination of one or more of the following approaches:

  1. Star chamber – the ‘experts’ decide. This includes events like Venture (VC) ‘pitches’
  2. Wisdom-of-crowds – activities like jams/games, voting systems, crowdsourced feedback, etc.
  3. Scorecard – a ranking of weighted factors that is ripe for ‘gaming the system’
  4. Champion – a passionate advocate who drives the opportunity no matter the obstacles

All of these methods ultimately rely on intuition, that elusive human propensity to render judgment based on what ‘feels’ right. While intuition may serve us well in some circumstances, in this case, it has proven over and over to deliver, at best, only 30% accuracy.

Why do we use intuition? We’ve found several reasons given over the years:

  1. The future is inherently uncertain and complex
  2. We have no real objective metrics for determining the value of an opportunity
  3. We have no common language or framework for describing an opportunity
  4. What kills an opportunity is as much what the company does (or does not do) as it is what the market or technology does (or does not do).

It is no wonder that we can’t seem to get more than a 30% success rate. Isn’t there a better way?

  • Imagine that there was a way to describe an opportunity that would allow it to be accurately compared to other opportunities (apples to apples).
  • Imagine having a reference database of thousands and thousands of successful and unsuccessful opportunities that have been analyzed to determine what caused their success or failure.
  • Imagine being able to use this analysis as the basis for a simulation system that can determine the probability of the success or failure of a new opportunity.
  • Imagine that you are now able to predict the success or failure of a new opportunity with approximately 70% accuracy (as opposed to 30% accuracy).

This is the promise of a new approach to analyzing strategic opportunities called ‘Predictive Testing of Opportunities’ (or PTO for short). A startup company called Growth Science has developed an analytic simulation engine to predict whether a specific opportunity will be successful – or whether your portfolio will deliver the growth that is expected. There are thousands of historical examples, from large organizations and startups alike, of what makes an opportunity live or die, and this approach uses all of them to reach its conclusions. It is yet one more example of how even the practice of innovation is changing to become more scientific, more rigorous and more predictable.

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Selected Readings

Overconfidence, Bad Models and Delusion

Growth Science founder and CEO, Thomas Thurston, writes about the future of predictive business simulation.

Original article | Read our short take

The Man Who Knows Whether Any Startup Will Live or Die

Want to predict if your innovation will succeed. Don’t use intuition, use analytics. Growth Science has figured out how.

Original article | Read our short take

The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future

An insightful analysis of what is really driving change. Beyond technology and social forces or ‘mega-trends’ to the underlying ‘inevitable’ causes of our future.

Original article | Read our short take