Predicting the effectiveness of sales support resources – part 2By: Alexander Group Sales Analytics, Sales Benchmarking, Sales Coverage, Sales Strategy
When it comes to investment decisions, timing is everything. In theory, deciding when to start investing and when to stop should be an informed process. In practice, however, investment decisions rely heavily on intuition and trial and error. The key to making such decisions in a more precise and scientific way …and you may already know what I’m about to say… is through thoughtful, rigorous data analysis.
Let’s return to last week’s topic – deciding whether or not to add Sales Support headcount to an existing sales force. In this case, we find that it’s best to take two distinct measures of performance. The first measure, which I discussed last week, is taken during the initial “test phase” and is used to predict whether a full-scale implementation will be worthwhile. The second measure, and the focus of this week’s post, takes place after full-scale deployment and sheds light on when to stop investing further, as the optimal point has reached.
As I mentioned last week, AGI helped a medical device client decide whether to roll out a clinical specialist program. Once they had rolled out the program to the entire sales force, they settled on a near 2:1 support ratio, i.e., two sales reps for every clinical specialist. At this point, AGI stepped in to conduct its second measure of performance. We found that, while the results of the general deployment were still very positive, the average increase in rep productivity had fallen from our initial measure of $230K (during the test phase) to $180K (sales force -wide implementation). It seems that the law of diminishing returns was at work here. So, should the client stop investing now? Or should they continue ahead full steam, bringing the CS-Rep ratio all the way to 1:1?
The answer would be found in a time series analysis of individual rep performances. We constructed a panel set of data with two records for every rep – one detailing their revenue performance during the experimental period six months prior, and one detailing revenue performance after the full-scale deployment. Using time series regression, we calculated that the effect of a clinical specialist on revenue was between $150K and $500K – a wide range indeed. But exactly where in that spectrum the true number lay would determine whether or not to push on with the program.
By isolating the differences between the two records for the same rep, we created a new set of variables with which to run another regression. The result showed that having CS support should increase a given rep’s revenue by an average of $154K. Therefore we advised the client to cease further investment in clinical specialists, as the theoretical benefit of a clinical specialist was fast approaching the actual cost of one.
This serves only as one example of how to gauge when to stop investing in sales resources. Other methods, e.g., regression with data in logarithmic format and maximum likelihood calculation, would have given us the same answer. But when simpler calculations can get the job done, what’s the point in show-boating advanced statistical knowledge? To achieve the stated goal using the most efficient method – that’s the commitment of AGI consultants.
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Originally published by: Ian Zhao