Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 408
The alignment of sales territories represents a major problem in salesforce management with considerable impact on profit. The common approach to this alignment is the balancing approach, which establishes territories balanced as well as possible with respect to one or more attributes such as potential or work-load. Unfortunately, this approach does not necessarily guarantee an alignment that maximizes profit. As a consequence, it does not allow evaluation of the profit implications of any alignment proposal in comparison with the existing one. Because of this, several authors have already proposed nonlinear integer optimization models that attempt to directly maximize profit by simultaneously considering the problems of selling time allocation across accounts as well as the assignment of accounts to territories. However, these models proved to be too large to be mathematically solvable, such that the authors have either simplified the problem or proposed application of heuristic solution procedures. The latter is based on the principle of equating the marginal profit of time for each salesperson. We show here that an optimal solution does not possess the property of equal marginal profits of time. We thus propose a new approach, COSTA, for the derivation of contribution optimizing sales territory alignments. COSTA is based on sales response functions at the level of sales coverage units incorporating selling time as independent variable and using a new concept designed to take travel times into account. This makes it possible to simultaneously solve the allocation and assignment problem. Furthermore, COSTA provides the structure for evaluating the effects on profit of different salesforce sizes and different locations of the salespersons. The suitability of COSTA for practical problems is supported by a real-world application in which COSTA improved an existing territory alignment by 5.8% in terms of profit contribution.
salesforce research industrial marketing forecasting