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A Simulation Comparison of Methods for New Product Location

Author

Listed:
  • D. Sudharshan

    (University of Illinois)

  • Jerrold H. May

    (University of Pittsburgh)

  • Allan D. Shocker

    (University of Washington)

Abstract

Four algorithms for locating an “optimal” new product in a multiattribute product space—Albers and Brockhoff's PROPOPP; Gavish, Horsky, and Srikanth's Method IV; May and Sudharshan's PRODSRCH; and GRID SEARCH—are compared in terms of the relative share of preferences the new product will capture under different simulated market environments. These environments were both ones for which the algorithms were designed as well as other “more realistic” environments. Results indicate that algorithm performance is sensitive to the number of customers or segments, and the presence of probabilistic choice, and less sensitive to the numbers of existing products. Gavish, Horsky, and Srikanth IV (GHS IV) and PROPOPP performed best under the market conditions for which they were designed and GHS IV proved quite robust under variation from these conditions. PROPOPP's performance deteriorated, however, in large sample size problems ( ≥ 200). PRODSRCH (a general purpose optimizer) was inferior under these special market conditions, but superior under other more general ones.

Suggested Citation

  • D. Sudharshan & Jerrold H. May & Allan D. Shocker, 1987. "A Simulation Comparison of Methods for New Product Location," Marketing Science, INFORMS, vol. 6(2), pages 182-201.
    • Handle: RePEc:inm:ormksc:v:6:y:1987:i:2:p:182-201
    DOI: 10.1287/mksc.6.2.182
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    Citations

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    Cited by:

    1. Steven M. Shugan, 2002. "In Search of Data: An Editorial," Marketing Science, INFORMS, vol. 21(4), pages 369-377.
    2. Baier, Daniel & Gaul, Wolfgang, 1998. "Optimal product positioning based on paired comparison data," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 365-392, November.
    3. Raphael Thomadsen, 2007. "Product Positioning and Competition: The Role of Location in the Fast Food Industry," Marketing Science, INFORMS, vol. 26(6), pages 792-804, 11-12.
    4. Gruca, Thomas S. & Klemz, Bruce R., 2003. "Optimal new product positioning: A genetic algorithm approach," European Journal of Operational Research, Elsevier, vol. 146(3), pages 621-633, May.
    5. Hansen, Pierre & Jaumard, Brigitte & Meyer, Christophe & Thisse, Jacques-Francois, 1998. "New algorithms for product positioning," European Journal of Operational Research, Elsevier, vol. 104(1), pages 154-174, January.
    6. Sudharshan, Devanathan & Furrer, Olivier & Arakoni, Ramesh A., 2013. "Robust Imitation Strategies," FSES Working Papers 446, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    7. Kilsun Kim & Dilip Chhajed, 2002. "Product Design with Multiple Quality-Type Attributes," Management Science, INFORMS, vol. 48(11), pages 1502-1511, November.
    8. Furrer, Olivier & Sudharshan, Devanathan & Tsiotsou, Rodoula H. & Liu, Ben S., 2016. "A framework for innovative service design," FSES Working Papers 476, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    9. Kwong, C.K. & Luo, X.G. & Tang, J.F., 2011. "A methodology for optimal product positioning with engineering constraints consideration," International Journal of Production Economics, Elsevier, vol. 132(1), pages 93-100, July.
    10. Sudharshan, D. & Ravi Kumar, K. & Gruca, Thomas S., 1995. "NICHER: An approach to identifying defensible product positions," European Journal of Operational Research, Elsevier, vol. 84(2), pages 292-309, July.

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