For many sales leaders, adding support headcount is like walking a tightrope: too little support may not budge the top line, but too much support could hurt the bottom line. Striking a balance is so tricky because support resources have only an indirect impact on revenue. If one could tell for sure that, say, adding X support headcount would increase revenue by Y dollars, then adding support headcount (or not) would be a snap. As it turns out, it’s possible to do just that by leveraging the right sales analytics techniques in three steps…
First, construct two sample groups. Divide your top-performing reps into two groups: an experimental group and a control group. The groups should be large (30+ reps) and similar with regard to demographics and average rep performance (use random assignments to accomplish this). The experimental group will be given the level of sales support that you’re considering making available to the entire sales force. The control group will continue with the current support level without change.
Second, collect data. After a short while has passed, gather information on every rep across both groups, including but not limited to:
Quota, revenue, and pipeline information should be easy to get, as sales departments track them regularly. Sales activities and time allocations can be captured by the use of a survey.
Third, analyze the data. Compare the groups’ results using two statistical methods: hypothesis testing and regression analysis. Hypothesis testing will tell you whether or not the experimental group’s performance is significantly different than the control group’s; regression analysis goes further to map out the causal relationships between the factors at play (obviously, “whether or not a rep has full sales support” is the most interesting variable here). For a more thorough test, include other variables such as education level, tenure, etc. into your regression analysis.
Of course these are just general steps. But eventually you’ll wind up with an equation which predicts the effect of each variable on overall rep performance. Then you can judge for your organization whether or not adding sales support headcount makes sense.
AGI recently ran these analyses to evaluate the effectiveness of a clinical specialist program for a large Medical Device company. The client had a traditional “lone wolf” sales model. Sales reps spent much of their time in operating rooms, limiting their ability to field other sales opportunities. In considering whether to offload OR activities to less expensive clinical specialists, the client brought in AGI to assist the transition. We advised the client on a two-phase implementation: first, involve only a small sub-set of sales reps; then if (and only if) the reps’ performance improved dramatically, implement a large scale deployment.
Initial results were encouraging. Reps with clinical specialist support brought in 28% more revenue than those without, more than enough to cover the added costs. But revenue increase alone didn’t necessarily indicate that the difference was a direct result of the clinical specialists – it may have been due to other factors that set the experimental group apart from the control group.
To investigate, we performed a regression analysis which treated time allocation, tenure, and having clinical specialist support or not, as independent variables. The result showed that, after controlling for other differences, a rep with a clinical specialist should bring in $230K more revenue annually than a rep without one. The positive impact of clinical specialists on revenue was loud and clear. The client went ahead with the full-scale implementation, and AGI was retained as an advisor to measure the final result and disembarked on a journey to find the optimal point for sales support.
Learn more about Alexander Group’s sales analytics insights.
Originally published by: Ian Zhao