Company leaders pride themselves on their ability to make critical decisions to drive their business. Yet, sadly, most companies don’t use hard data to make innovation decisions. The result is an 85- to 95-percent failure rate in innovation.
Why don’t leaders use data to make more decisions? It’s because they don’t know that they can!
Leaders aren’t stupid. They would use data to aid their decision making if they had it. The reason why they don’t is that they believe they can’t gather data to help them make decisions. Ten to twenty years ago, they were right. It was not cost-effective to use data to make smarter decisions.
Fortunately, discoveries and technologies now make it possible to gather data to support 100 percent of critical decisions cost-effectively. For example, a long-term marketplace tracking study by the Eureka! Ranch’s MERWYN Decision Support Group found that the Truth Teller research methodology was seven times smarter at making go/no go decisions than company leaders.
The new generation of data-driven decision support is based on three principles that have been proven to work for industrial, business to business, consumer, services, nonprofit, and even government innovations. These principles include:
1. Testing at the speed of decision making. Old world research methods were practical, prudent, and pondered. Leaders often relied on judgment because it wasn’t possible to get data in the time available to make a decision. In the new world, research timing and cost is diminished by up to 90 percent. One multinational found that the new methods helped cut costs by some 93 percent. Speed and cost savings are a result of utilizing 100 percent digital systems for design, fielding, and analysis of research. The result is a statistically reliable single product and paired comparison tests in 60 to 90 minutes.
2. Measure twice or three times — decide once. When facing challenging decisions, there’s often no “magic test” that can be run. In these situations, the new speed mentioned above makes it possible to use two or three distinct methods to triangulate on Decision Support data. A few of the ways that can be utilized include: 1) Rapid Research with key stakeholders, customers, etc., 2) Digital Delphi calibrated quantification of those with domain expertise, 3) Idea Coach artificial intelligence idea evaluation, and 4) Truth Teller benchmarking of new ideas versus 50 validated success factors.
3. Confront reality with risk-adjusted forecasting. Multiple measures and the inherent uncertainty of forecasting assumptions introduce risk that’s often minimized or ignored. The new mindset is to embrace the uncertainty/variance in a problem and to model them. Tools like Monte Carlo simulations and five-year trial, Repeat, and Diffusion Models quantify the uncertainty enabling leaders to know the high-risk issues. Importantly, risk-adjusted forecasting can be applied to 100 percent of crucial decisions.
Over the years, the application of these principles, alone and in combination, has enabled Procter & Gamble, American Express, Reckitt Benckiser, J&J, Kraft, Nestle, and many others to make smarter decisions on genuine New to The World Innovations.
