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Callplan: An Interactive Salesman's Call Planning System

Author

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  • Leonard M. Lodish

    (University of Pennsylvania)

Abstract

CALLPLAN is an interactive computer system designed to aid salesmen or sales management in allocating sales call time more efficiently. The system increases their capacity to consider the allocation in a logical and consistent manner. CALLPLAN uses as input the salesman's own best estimates of expected contribution of all possible call policies for each account and prospect. The computer can help the estimating procedure by fitting curves through estimated points on a response function or by obtaining expected values from probability estimates. The system solves a mathematical program which determines the best time allocation to maximize contribution according to these estimates. Factors considered by the system include travel time and costs to get to geographical areas within the territory, amount of time required per call on an account within an area, account profitability, and minimum and maximum account call frequency limitations. An efficient incremental analysis routine is discussed as a solution procedure for the mathematical program. CALLPLAN seems best suited to repetitive selling situations where the amount of time the salesman spends with an account is an important factor in the magnitude of sales generated. Preliminary applications have been made by fourteen salesmen in six sales situations. A transcript of a session at the computer terminal of one application is presented. Anticipated sales increases, based on the salesman's judgmental inputs, for the call policy generated by CALLPLAN were between five and twenty-five per cent in the majority of applications.

Suggested Citation

  • Leonard M. Lodish, 1971. "Callplan: An Interactive Salesman's Call Planning System," Management Science, INFORMS, vol. 18(4-Part-II), pages 25-40, December.
    • Handle: RePEc:inm:ormnsc:v:18:y:1971:i:4-part-ii:p:p25-p40
    DOI: 10.1287/mnsc.18.4.P25
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    Cited by:

    1. Gijsenberg, Maarten & Nijs, Vincent R., 2018. "Advertising Timing," Research Report 2018004-MARK, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    2. Bernd Skiera & Sönke Albers, 1998. "COSTA: Contribution Optimizing Sales Territory Alignment," Marketing Science, INFORMS, vol. 17(3), pages 196-213.
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    5. Murali Mantrala & Sönke Albers & Fabio Caldieraro & Ove Jensen & Kissan Joseph & Manfred Krafft & Chakravarthi Narasimhan & Srinath Gopalakrishna & Andris Zoltners & Rajiv Lal & Leonard Lodish, 2010. "Sales force modeling: State of the field and research agenda," Marketing Letters, Springer, vol. 21(3), pages 255-272, September.
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    7. Drexl, Andreas & Haase, Knut, 1996. "Fast approximation methods for sales force deployment," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 411, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
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    11. Andreas Klein, 2011. "Die Entwicklung eines agentenbasierten Basismodells zur Bestimmung der deckungsbeitragsmaximierenden Anzahl von Außendienstmitarbeitern," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 21(2), pages 189-210, January.
    12. Govind, Rahul & Chatterjee, Rabikar & Mittal, Vikas, 2008. "Timely access to health care: Customer-focused resource allocation in a hospital network," International Journal of Research in Marketing, Elsevier, vol. 25(4), pages 294-300.
    13. Albers, Sönke, 2012. "Optimizable and implementable aggregate response modeling for marketing decision support," International Journal of Research in Marketing, Elsevier, vol. 29(2), pages 111-122.
    14. David Godes, 2003. "In the Eye of the Beholder: An Analysis of the Relative Value of a Top Sales Rep Across Firms and Products," Marketing Science, INFORMS, vol. 22(2), pages 161-187, May.
    15. V. Kumar & Rajkumar Venkatesan & Tim Bohling & Denise Beckmann, 2008. "—The Power of CLV: Managing Customer Lifetime Value at IBM," Marketing Science, INFORMS, vol. 27(4), pages 585-599, 07-08.
    16. John D. C. Little, 2004. "Comments on ÜModels and Managers: The Concept of a Decision CalculusÝ," Management Science, INFORMS, vol. 50(12_supple), pages 1854-1860, December.
    17. van Bruggen, G.H. & Smidts, A. & Wierenga, B., 2000. "The Powerful Triangle of Marketing Data, Managerial Judgment, and Marketing Management Support Systems," ERIM Report Series Research in Management ERS-2000-33-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    18. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    19. AgralI, Semra & Geunes, Joseph, 2009. "Solving knapsack problems with S-curve return functions," European Journal of Operational Research, Elsevier, vol. 193(2), pages 605-615, March.
    20. Mesak, Hani I., 1999. "On the generalizability of advertising pulsation monopoly results to an oligopoly," European Journal of Operational Research, Elsevier, vol. 117(3), pages 429-449, September.
    21. Berend Wierenga & Gerrit H. Van Bruggen & Richard Staelin, 1999. "The Success of Marketing Management Support Systems," Marketing Science, INFORMS, vol. 18(3), pages 196-207.

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