First, it is ridiculed. Second, it is violently opposed. Third, it is accepted as being self-evident. —ARTHUR SCHOPENHAU
The need to build and maintain a sustainable Innovation Culture is top-of-mind of many senior executives. The development of leadership teams, creativity enhancement and Ideation efforts all have been tried to unpredictable success. Things are changing. As more companies struggle with an acceptable ROI on their innovation programs they are seeking new actionable insight into the process, into the expected outcomes and into the guarantee that revenue growth will accelerate in acceptable timeframes. This White Paper provides the reader with guidelines on how to approach a quantitative measurement of innovation progress and how to turn the resulting information loop into actionable insight.
The Need for Predictable Innovation Results
Think about how inefficient the innovation process is in both public and private endeavors. Universities try to commercialize their intellectual property. After placing enormous resources and efforts on technology transfer they are still, on average, and just for the top ten performers, delivering .06% of all patent disclosure inventions to revenue producing status. Then private capital takes over delivering less than 10% profitable status to their portfolio companies. The funnel from idea to positive revenue is, to put it mildly, a mine field of failure. Bringing this analysis to internally developed innovation within companies does little to change the picture.
Commonly reported facts point to inefficiency and costs: 1 in 3000 ideas make it to market; Only 1 in 5000 make it to profitability; Only 2% of all patents issued make significant revenue for their inventors (and this according to the Patent Office); 1000 ideas inside a company, with substantial applied resources focused on successful market introduction, may produce one or two products that get introduced to market. These numbers scream cost and inefficiency. Some say that is the price of admission to building sustainable market revenue and companies must pay the price. However, there are mitigating strategies that can make the process more predictable and efficient, even though the ratios will always remain challenging.
Enter Advanced Analytics
Measuring output is the easy part. Current innovation consultants will tell you to measure the number of ideas generated, the number of teams created, and the number of patents filed etc. But the issue goes deeper than that: the modern executive must look to operating analytics to get to the predictability of eventual success and must enlist the use of AI and machine learning tools to gain real insight into the process. More importantly, executives must consider advanced data as a function of time. Both the breadth of data collection and the depth of analysis must be leveraged to get a correct picture of operational and organizational issues. How are groups interacting today versus last week? What content is being discussed today versus yesterday?
Are the teams advancing thought or rehashing old ideas generated by an overindulging group participant? Are groups learning faster when an idea is not good and moving on to more promising ideas? All these questions must be answered continuously to become a truly data driven innovation culture. Below is a ten point plan to help you get started.