COSTA: Contribution optimizing sales territory alignment

The alignment of sales territories has a considerable impact on profit and represents a major problem in salesforce management. Practitioners usually apply the balancing approach. This approach balances territories as well as possible with respect to one or more attributes such as potential or workl oad. Unfortunately, this approach does not necessarily guarantee maximizing profit contribution. Thus, it does not provide an evaluation of the profit implications of an alignment proposal in comparison with the existing one. In consequence, several authors proposed nonlinear integer optimization mo dels in the 1970s. These models attempted to maximize profit directly by co nsidering the problems of allocating selling time (calling plus travel time ) across accounts as well as of assigning accounts to territories simultane ously. However, these models turned out to be too complex to be solvable. T herefore, the authors have either approximated the problem or proposed the application of heuristic solution procedures on the basis of the suboptimal principle of equating marginal profit of selling time across territories. To overcome these limitations, we propose a new approach, COSTA, an acronym for "contribution optimizing sales territory alignment." In contrast to pr eviously suggested profit maximizing approaches, COSTA operates with sales response functions of any given concave form at the level of sales coverage units (SCUs) that cover a group of geographically demarcated individual ac counts. Thus, COSTA works with sales response functions at a more aggregate d level that requires less data than other profit maximization approaches. COSTA models sales as a function of selling time, which includes calling ti me as well as travel time, assuming a constant ratio of travel to calling t ime. In addition, the formulation of the model shows that an optimal soluti on requires only equal marginal profits of selling time across sales covera ge units per territory, but not across SCUs of different territories. Basically, COSTA consists of an allocation model and an assignment model, b oth of which are considered simultaneously. The allocation model optimally allocates the available selling time of a salesperson across the SCUs of hi s or her territory, whereas the assignment model assigns the SCUs to territ ories. Thus, COSTA predicts the corresponding profit contribution of every possible alignment solution, which enables one to perform "what-if"-analyse s. The applicability of the model is supported by the development of a powe rful heuristic solution procedure. A simulation study showed that COSTA pro vided solutions that are on average as close as 0.195% to an upper bound on the optimal solution. The proposed heuristic solution procedure enables on e to solve large territory alignment problems because the computing time in creases only quadratically with the number of SCUs and proportionally to th e square root of the number of salespersons. In principle, we also show how COSTA might be expanded to solve the salesforce sizing as well as the sale spersons' location problem. The usefulness of COSTA is illustrated by an application. The results of th is application indicated substantial profit improvements and also outlined the weaknesses of almost balanced territories. It is quite apparent that ba lancing is only possible at the expense of profit improvements and also doe s not lead to equal income opportunities for the salespersons. This aspect should be dealt with separately from territory considerations by using terr itory-specific quotas and linking variable payment to the achievement of th ese quotas. Furthermore, the superiority of COSTA turned out to be stable i n a simulation study on the effect of misspecified sales response functions . COSTA is of interest to researchers as well as practitioners in the salesfo rce area. It aims to revive the stream of research in the 1970s that alread y proposed sales territory alignment models aimed at maximizing profit. Suc h profit maximizing models are theoretically more appealing than approaches that strive to balance one or several attributes, such as potential or wor kload. COSTA's main advantage over previous profit maximizing approaches is that it is less complex. Consequently, COSTA demands less data so that eve n large problems can be solved close to optimality within reasonable comput ing times.