Data needs to be communicated in a way that is easily digestible for evaluation.  The simplest way to implement a dashboard or report may be to simply use the absolute data from the system, but that might leave you with data that is removed from its context.  Instead, the most effective way to show the data might require normalization of that data that makes it so territories are compared on an “apples to apples” basis.

 

When you go about evaluating absolute metrics, you likely mentally attempt to normalize them in your mind. You might think to yourself “Sales calls were low in Territory X again, but they are always low and it is to be expected based on the region.”  Of course, the question then becomes, exactly how much different should the expectations be?  If the results changed appreciably in that region would you notice?  Or would you continue to write it off as normal.

 

Consider the basic (and exaggerated) chart shown.  If evaluating the chart on the left, how would you know if the gap between red and blue is significant?  It would be based on history, quick calculations, and gut feel.  If we move the metrics to where they should be at the same level, a gap that is meaningful is easily identifiable.

 

That is why it is important to find a way if possible to compare apples to apples.  For some metrics, it is possible to use an easy means of comparison like percentages.  For comparing pipelines we can look at conversion rates that show us percentages of the whole.  These naturally compare across territories because they are all on the same percentage scale.  There are other metrics, however, that need to be compared against a less clear baseline: the potential of the territory. 

 

Potential is the natural basis for establishing that baseline for data normalization where there isn’t another clear option.  The question essentially becomes “what is being done as compared to what could be done?”  Isn’t this exactly what you attempt to do mentally?  If there is an underlying issue in the region that cuts down on the ability to sell you give it a bit of a pass, and if things should be going better you have higher expectations.  By normalizing the data for some measure of potential (or a proxy, like total pipeline) you are able to apply that filter first, and in a consistent manner that allows you to see actual variation and make real comparisons.

 

If a layer of complexity can be removed by normalizing the data it will make the comparison simpler for everyone.  Showing results as an absolute is surely important, but providing a clear way to interpret it along side of it is also critical.  The essential point of this all is that SPM may be implemented in software, but it is being interpreted by humans.  For your Sales Performance Management efforts to be successful the data must be brought to a level on which comprehension and insight is intuitive and achievable.

 

Are you looking for ways to improve the effectiveness of your salesforce?  Contact SBI for a demo.

 

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