Over the past four months, we’ve explored the world of innovation management.  We learned the importance of understanding the criticality of defining scope, building a robust measurement plan and delivering flawless execution.  These three activities all lead to the desired end – or what in process terms is known as the “output.”  For innovation management, it is where the “tire meets the road”, or the point when the innovation provides return on investment.  Without it, innovation efforts are merely expenses, and we all know what happens to expense laden activities that don’t provide return or value.
 
Let’s examine 3 critical questions regarding output:

• Who should be involved in analyzing the results?
• Should any results ever be a surprise?
• How should the output be structured?

Who Should Participate in Result Reviews?
As we discussed in the development of the measurement plan earlier, objectivity is a key requirement for reviewing test results.  Test designers and change managers that have been actively involved in the development and execution of the test may be challenged to maintain objectivity in output review.  The innovation leader must focus on being an advocate for the overall business. 
 
To minimize risk compromising objectivity, the project team should include participants from other functional areas.  Finance and audit associates can serve as invaluable resources to assist project teams in the assessment of the test results.  If they participated, as recommended earlier, in the measurement strategy development, then the road should have been paved for participation in the metric post-test review.  Using open discussions as opportunities for joint review is another great tactic to deliver high quality analysis of results.  Different functional areas look at information differently, and having open discussions enables real-time input from others to build consensus, a necessary step when conclusions are leveraged for future recommendations.
 
Should There Be Any Surprises?
My short answer is an empathetic “absolutely!”  IF you knew what the answers would be, I would question the need to have completed the test.  What should not be a surprise however, are the metrics and information that will be reviewed to derive conclusions regarding the learning from the test. 
 
Conclusions themselves should fall into a known range, but the actual result data points should come from results.  There should also be healthy discussions to explore a complete understanding of what the results really tell you.  Remember to compare control information so that test results can be directly correlated to test activities and not unrelated influences and market changes.  Be sure to carefully segment qualitative and quantitative results.  Qualitative information is much more subject to interpretation and can present risks for questioning when shared in wider audiences.  Quantitative results usually fall into a more black-and-white arena where you’re comparing numbers versus opinions or softer issues covered in qualitative research.
 
How Should Output Be Structured?
In general, I recommend organizing test output into three distinct categories.  First, document what you have learned.  The leading statement should be a summary identifying the results of the hypothesis testing.  Did the hypothesis fail or pass?  While this is a yes or no answer, the output, especially for written communications, should start with it.  If your audience is unfamiliar with the initial measurement strategy, (which is hopefully not the case), include these with the output results so that the reader can review it.  Beyond proving or disproving the hypothesis (-es), results should also clearly document conclusions and learnings from the team analysis.  Include a clear statement about the absolute truths you learned from your output.
 
Secondly, document all gaps or unknowns.  If there are issues upon which the team cannot come to a consensus, then they should be documented along with the differing opinions.  It is rare for a team to anticipate all information in the planning.  This portion of the output of the process should be a caveat that lets the reader know in advance the issues experienced in the testing and information gathered.
 
Finally, the output should conclude with a recommendation.  It is the “so what” or “what should we do” that concludes all testing efforts.  Innovation leaders often struggle with the recommendation because it relates back to general intolerance of a perception of “failure.”   Ultimately, there are three basic options for recommendation.

1. Move forward with a rollout – meaning the test was successful and there is a business case that justifies the integration of your innovation into business as usual environment;
2. Pend the idea – the innovation has merit, but based on the learning, it is not recommended to move forward until certain conditions are met (i.e., eliminate cost constraints);
3. Eliminate the idea from consideration – this innovation did not prove to be an effective idea.