While almost everyone now accepts the value of analytics and metrics-driven decision making, one area where it is often neglected is in implementing innovation. Even data driven companies are hampered in implementing innovation because their data is backward looking. In the absence of sufficient data to inform decisions about proposed innovations, managers often rely on their experience, intuition and conventional wisdom, and none of these is necessarily relevant. Although many of my readers are from the mobile game space, where much is tested, even in the game space pure innovation often is not. An article in the Harvard Business Review, “Increase your chances of success with innovation test-drives” by Stefan Thomke and Jim Manzi, does a great job of showing how to test these hypotheses.
Most companies do not conduct rigorous tests of their risky overhauls because they are reluctant to fund proper business experiments and have considerable difficulty executing them. Although the concept of experimentation is straightforward, there are many organizational, cultural and technical challenges to implementing experiments. While running an A/B test on a website is simple, many business need to deal with complex distribution systems, sales territories, bank branches, etc. Business experimentation in such environments suffers from many analytic complexities, most importantly that sample sizes are often too small to be significant (e.g., only a few stores). Continue reading “Testing innovation opportunities”