So that’s where Pugh’s Charts can help make the ranking and decision making easier and clearer.
Suppose that we have five possible new features to be considered, A, B, C, D. We already have an existing product and want to know if any of these possible five features would improve it.
We first must decide on the criteria used to judge the features against. In this case, we pick just five. The criteria should be based on your company goals, e.g. a specific market penetration, increased OTC sales, gaining a top 10 account, etc. and they should have upper management buy-in before you start.
The next step is for the product manager or team to place a 0, +1, or -1 in each cell for "Considered New Feature" versus "Selection Criteria":
0 = no gain or loss
+1 = gain, improvement for the selected criteria
-1 = loss, detriment to the selected criteria
We then sum the values in each column for considered feature. The completed chart gives us an overall view what the rankings for each considered feature based on the sum. However, not all selection criteria are equal in importance to company goals so this particular chart should be considered unfinished and used only for “go” or “no-go” at most. In our example, "Flashing LED" and "Windows 8 Support" both appear to have the same importance because all selection criteria is treated as equal.
If we now apply our weight factors per selection criteria against the initial assessment per desired feature, we get a much clearer and refined picture of the feature rankings.
This modified Pugh Chart takes away some of the guesswork and emotions in selecting and prioritizing features. It still does not take into account those features that you always get on the wish list but miss the cut-off (they also tend to be the planned features that get chopped out when projects run out of time). In those cases, you have to either accept this as reality or twiddle with the pertinent weight value. Same principle applies to those favorite pet features of upper management. Either face up to the CEO and GM with the Pugh chart results or add another selection criteria named “The big Kahuna wants it ” aka “Keeping my job”.
The weights assigned for selection criteria and effort are not going to be 100% accurate but everything is relative so the scoring should still work out. These charts are by no means perfect so feedback is most welcome!
Much thanks to Martin Wright at Instron ITW (who studied under Stuart Pugh himself) for educating me about Pugh Charts and Bruce McCarthy for introducing me to decision matrix concepts. Bruce is a master at marketing and has a similar version (ref: http://www.slideshare.net/ProductCampBoston/prioritization-301-advanced-roadmapping-class-bruce-mccarthy).