 Evaluation of Salesforce Size and Productivity Through Efficient Frontier BenchmarkingDan Horsky and
Paul Nelson
Marketing Science, 1996, vol. 15, issue 4, 301-320 Abstract: The efficient operation of a salesforce is a critical element in the profitability of many firms. Three factors play key roles: the salesforce's size, its allocation and its productivity. This gives rise to the following questions: can salesforce performance be improved by (1) hiring more salespeople, (2) allocating them more effectively to the various sales districts and/or (3) improving salesperson productivity through better calling patterns in terms of consumers and product line items? The practice of most firms and the methodology used in most of the academic literature to address salesforce design and productivity questions is a “Bottom Up” approach. This approach starts with assessments by each salesperson of the sales and effort corresponding to each customer and prospect in their territory. These assessments are then aggregated to the territory, district and national levels. This paper takes an alternative “Top Down” approach. It is based on an estimated relationship between district level sales and salesforce size, effort and other variables. This more macro level decision tool can be used by management in parallel to, and as an objective check of, the more conventional and more subjective “Bottom Up” approach. We develop an efficient frontier methodology which allows us to estimate how total district sales respond to salesforce size, district potential and competitive activity in the firm's best performing districts. The methodology utilized is based on Data Envelopment Analysis (DEA) and yields a benchmark measure of each district's efficient frontier sales (sales assuming the district's salesforce allocates its effort as done in the best performing districts). Based on the estimated response function we discuss the three potential sources of increased profitability: closing the inefficiency gap of each of the lower performing districts, optimally reallocating the current salesforce to the various districts, and changing the current size of the salesforce to its optimal level. The inefficiency gap issue is addressed through comparison of the parameter estimates for the best districts obtained through our methodology with those of an average district sales response function obtained using regression analysis. This comparison points to an important methodological finding. The use of multiple estimation results may lead to an improved understanding of the phenomenon being studied (in our case, the identification of the likely causes of district productivity inefficiencies). The latter two sources of increased profitability, salesforce reallocation and changes in the current salesforce size, are addressed analytically given the district level efficient frontier sales response function. The proposed “Top Down” procedure using the efficient frontier methodology and the insights it provides are examined by evaluating the operations of two different salesforces, one selling manufacturing equipment and the other business equipment. In both cases, regression-based analysis would have resulted in a declaration that the status-quo was close to optimal, while the frontier-based analysis pointed out that strong gains were possible in certain districts. In particular, for both firms, the greatest increases in profit are obtained through improved salesforce efficiency in the lower performing districts, not through salesforce size or district allocation adjustments. At the more micro-level, a comparison of the frontier and regression parameters made it possible to identify which specific changes in the daily operations of the salesforces would allow the realization of these potential productivity gains. In our two cases this could be obtained through more emphasis on pursuing prospective accounts. Keywords: salesforce; benchmarking; frontier estimation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:15:y:1996:i:4:p:301-320 Access Statistics for this article More articles in Marketing Science from INFORMS Contact information at EDIRC. |
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