For those who don’t know the G Forces presentation: it is all about shortening release cycles from months to weeks to days to hours to minutes.
What became clear to me: The release cadence is not the real point. It is all about feedback. You have to be able to incorporate feedback according to your release cadence. Releasing every day doesn’t help too much if you need a month to collect feedback and react on it.
Perhaps we should reflect that with our metrics. What about Feedback Cycle Time and Feedback Coefficient as new metrics for teams?
Feedback Cycle Time would be the time you need from the release of feature A until you are able to release feature B that incorporates the learnings from feature A? For example: You release feature A to production on 1st of february 2012. Then you collect data from the production usage of B for 1 week. You discuss the findings for one week and take another week to redefine some of the features in the backlog. And then you need 3 weeks for implementation, test and release of feature B that incorporates the learning. In this scenario your Feedback Cycle Time would be 6 weeks.
The Feedback Coefficient would be Number of Features for which feedback was collected / Number of Released Features. For the Feedback Coefficient it doesn’t matter if the feedback was positive or not. The only important thing is the learning. When we are honest most teams today achieve a Feedback Coefficient of zero or very near to zero. The optimum would be 1.
I think both metrics would focus on a weak spot in many teams: The teams try to maximize throughput and minimize lead time but don’t really care about feedback from the market.