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Salesperson Efficiency Benchmarking Using Sales Response Data: Who is Working Hard and Working Smart?

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  • Bielecki, Andre
  • Albers, Sönke
  • Mantrala, Murali

Abstract

A key to enhancing sales force productivity is finding ways to help existing reps sell more. In this paper, we focus on the process of internal efficiency benchmarking of a firm’s sales representatives aimed at identifying strong and weak performers and providing meaningful and actionable directions for improving productivity of relatively inefficient performers. We propose to do this by utilizing measures of two fundamental attributes of a salesperson’s controllable work activity as inputs in a DEA (data envelopment analysis) – based procedure: how hard and how smart s/he works. The suggested metrics are derived in an empirical application using archival sales response data from a pharmaceutical company sales force. The application shows that, on average, working smart has larger effects on sales than working hard. In comparison to a conventional DEA benchmarking that simply uses raw sales calls as input measures, the proposed model that uses more ‘processing’ of the sales response data to derive working smart and hard input measures shows much larger potential for efficiency improvement and offers more meaningful and actionable guidance for improving sales force productivity. --

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Bibliographic Info

Paper provided by ZBW - German National Library of Economics in its series EconStor Preprints with number 57427.

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Date of creation: 23 Apr 2012
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Handle: RePEc:zbw:esprep:57427

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Keywords: Working Smart; Working Hard; Salesperson Benchmarking; Data Envelopment Analysis; Efficiency Analysis;

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  1. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
  2. SIMAR, Léopold & WILSON, Paul, 1995. "Sensitivity Analysis to Efficiency Scores : How to Bootstrap in Nonparametric Frontier Models," CORE Discussion Papers 1995043, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  3. Hauser, John & Katz, Gerald, 1998. "Metrics: you are what you measure!," European Management Journal, Elsevier, vol. 16(5), pages 517-528, October.
  4. Simar, L. & Wilson, P.W., 1998. "A General Methodology for Bootstrapping in Nonparametric Frontier Models," Papers 9811, Catholique de Louvain - Institut de statistique.
  5. Simar, L., . "Aspects of statistical analysis in DEA-type frontier models," CORE Discussion Papers RP -1226, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  6. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
  7. Peter Doyle & John Saunders, 1990. "Multiproduct Advertising Budgeting," Marketing Science, INFORMS, vol. 9(2), pages 97-113.
  8. Mahajan, Jayashree, 1991. "A data envelopment analytic model for assessing the relative efficiency of the selling function," European Journal of Operational Research, Elsevier, vol. 53(2), pages 189-205, July.
  9. Hauser, John R. & Katz, Gerald M. & International Center for Research on the Management of Technology., 1998. "Metrics : you are what you measure!," Working papers 172-98, Massachusetts Institute of Technology (MIT), Sloan School of Management.
  10. Bernd Skiera & Sönke Albers, 1998. "COSTA: Contribution Optimizing Sales Territory Alignment," Marketing Science, INFORMS, vol. 17(3), pages 196-213.
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