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Evaluation of the Effectiveness of a Model Based Salesman's Planning System by Field Experimentation

Author

Listed:
  • William K. Fudge

    (United Air Lines)

  • Leonard M. Lodish

    (University of Pennsylvania and Management Decision Systems, Inc.)

Abstract

During 1975 twenty United Airlines' salesmen in New York and San Francisco participated in an experiment to evaluate the sales effects of supporting call frequency decisions with an interactive management science model. The CALLPLAN model [Lodish, L. M. 1971. CALLPLAN: An interactive salesman's call planning system. Management Sci. 18 (4, December, Part II) 25--40.] integrates judgemental sales response estimates to various call frequencies made by the salesman and his manager for each account into a mathematical program. Travel time, time per call, and account profitability are also considered. The model output is an account call frequency schedule which maximizes anticipated sales or profit subject to limited time available.Four passenger and one cargo sales representative utilized the model in each city. The users were initially skeptical of the system. However, after they used the interactive model, they realized that they were in control of it and viewed the experience as productive.The ten CALLPLAN participants were chosen randomly from ten pairs of salesmen who were Individually matched by local management using personal characteristics, compatability of territory size, revenue, and account mix. The remaining ten salesmen in the control group were told that they were participating in an experiment and each member manually estimated anticipated sales to compare with the CALLPLAN group. After six months the average CALLPLAN salesperson had 8.1% higher sales than his matched counterpart ( t = 2.42, 9 degrees of freedom). The probability that such a large increase could occur by chance is less than 2.5%. Combining both judgemental and objective data with a mathematical program resulted in behavioral changes which significantly improved sales performance.

Suggested Citation

  • William K. Fudge & Leonard M. Lodish, 1977. "Evaluation of the Effectiveness of a Model Based Salesman's Planning System by Field Experimentation," Interfaces, INFORMS, vol. 8(1-part-2), pages 97-106, November.
    • Handle: RePEc:inm:orinte:v:8:y:1977:i:1-part-2:p:97-106
    DOI: 10.1287/inte.8.1pt2.97
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    Cited by:

    1. R C Hanna & P D Berger & L J Abendroth, 2005. "Optimizing time limits in retail promotions: an email application," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(1), pages 15-24, January.
    2. Charles Abramson & Imran S. Currim & Rakesh Sarin, 2005. "An Experimental Investigation of the Impact of Information on Competitive Decision Making," Management Science, INFORMS, vol. 51(2), pages 195-207, February.
    3. J. S. Armstrong & R. Brodie & S. McIntyre, 2005. "Forecasting Methods for Marketing:* Review of Empirical Research," General Economics and Teaching 0502023, University Library of Munich, Germany.
    4. Gary L. Lilien & Arvind Rangaswamy & Gerrit H. Van Bruggen & Katrin Starke, 2004. "DSS Effectiveness in Marketing Resource Allocation Decisions: Reality vs. Perception," Information Systems Research, INFORMS, vol. 15(3), pages 216-235, September.
    5. Darmon, Rene Y., 2002. "Salespeople's management of customer information: Impact on optimal territory and sales force sizes," European Journal of Operational Research, Elsevier, vol. 137(1), pages 162-176, February.
    6. 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.
    7. 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.
    8. 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.
    9. Lilien, G.L. & Rangaswamy, A. & Starke, K. & van Bruggen, G.H., 2001. "How and Why Decision Models Influence Marketing Resource Allocations," ERIM Report Series Research in Management ERS-2001-33-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.
    10. Murali K. Mantrala & Surya Rao, 2001. "A Decision-Support System that Helps Retailers Decide Order Quantities and Markdowns for Fashion Goods," Interfaces, INFORMS, vol. 31(3_supplem), pages 146-165, June.
    11. 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.
    12. Bachler, Sebastian & Haeussler, Stefan & Momsen, Katharina & Stefan, Matthias, 2024. "Do people willfully ignore decision support? Evidence from an online experiment," VfS Annual Conference 2024 (Berlin): Upcoming Labor Market Challenges 302404, Verein für Socialpolitik / German Economic Association.

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