A company that manufactures healthcare products for companion animals (primarily dogs) was interested in realigning their territories so that they were balanced on a data metric that more closely paralleled the amount of work in each territory rather than the total potential for their products. This is commonly referred to as an "effort based" data metric. Their sales reps spend most of their time calling on veterinarians however, previous territory alignments have been balanced on a combination of prior sales revenues and total domestic pet populations. While this initially seemed like a good strategy, over time it became clear that in some territories vets were not getting called on often enough and in other territories there were not enough vets to keep reps busy. Turnover was high - particularly in territories with below average numbers of vets.
Data on target vets was easily acquired. Next the client needed to rank the vets into categories based on how frequently they were to be called on. Vets were rated as "A", "B" or "C" using historical sales information and other qualitative information available from the field sales personnel. Finally, each category was assigned a call frequency. "A" vets were to receive 1 call each month, "B" vets one call every 3 months and "C" vets one call every 6 months. Using estimates on call duration and the average number of calls made per day they backed into a workload data metric that was then loaded into AlignStar to be used for load balancing. The chart below depicts the (unequal) workload of the territories before optimizing them.
The example above displays territories before the Optimization. Note from the chart on the right how unbalanced the territories are with respect to work load (hours). These territories are unbalanced and inefficient resulting in poor rep performance, high travel costs and wasted resources.
After several iterations of AlignStar's Optimizer a much better solution was obtained. The territories displayed below seem very similar at first but after careful study you will notice several significant differences. The solution below requires the relocation of one sales rep. Additional fine-tuning will enable them to maintain all reps in their current locations with much improved territories.
The example above displays territories after Optimization. The changes may appear subtle however, they are far-reaching. Note from the chart on the right how the territories are all now balanced with respect to workload. These territories are efficiently designed with minimized travel time and equalized work. Optimized territories such as these typically result in a 2-7% improvement in sales revenues, better sales force morale and increased sales rep longevity.