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The implementation challenge of pricing decision support systems for retail managers

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  • Alan L. Montgomery

Abstract

There has been an explosion in the availability of data and computing ability in retail management that has led to a new desire on the part of managers to implement demand based management. Demand based management uses statistical models to predict consumer price response using historical information. These models can be used to construct pricing decision support systems for retail managers. Currently, many firms have begun offering software to perform price optimization. This article considers how recent advances in academic research can contribute to the implementation of these systems, and in turn consider the new questions likely to be posed by the developers and users of these new systems. Copyright © 2005 John Wiley & Sons, Ltd.

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  • Alan L. Montgomery, 2005. "The implementation challenge of pricing decision support systems for retail managers," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 21(4‐5), pages 367-378, July.
    • Handle: RePEc:wly:apsmbi:v:21:y:2005:i:4-5:p:367-378
    DOI: 10.1002/asmb.572
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    Cited by:

    1. Germann, Frank & Lilien, Gary L. & Fiedler, Lars & Kraus, Matthias, 2014. "Do Retailers Benefit from Deploying Customer Analytics?," Journal of Retailing, Elsevier, vol. 90(4), pages 587-593.
    2. Kayande, U. & de Bruyn, A. & Lilien, G.L. & Rangaswamy, A. & van Bruggen, G.H., 2006. "How Feedback Can Improve Managerial Evaluations of Model-based Marketing Decision Support Systems," ERIM Report Series Research in Management ERS-2006-039-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.
    3. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    4. Edward J. Fox & Steven Postrel & John H. Semple, 2009. "Optimal Category Pricing with Endogenous Store Traffic," Marketing Science, INFORMS, vol. 28(4), pages 709-720, 07-08.
    5. Miriam Kie{ss}ling & Sascha Kurz & Jorg Rambau, 2014. "The Integrated Size and Price Optimization Problem," Papers 1401.8142, arXiv.org.
    6. Huang, Tao & Fildes, Robert & Soopramanien, Didier, 2014. "The value of competitive information in forecasting FMCG retail product sales and the variable selection problem," European Journal of Operational Research, Elsevier, vol. 237(2), pages 738-748.
    7. Ujwal Kayande & Arnaud De Bruyn & Gary L. Lilien & Arvind Rangaswamy & Gerrit H. van Bruggen, 2009. "How Incorporating Feedback Mechanisms in a DSS Affects DSS Evaluations," Information Systems Research, INFORMS, vol. 20(4), pages 527-546, December.

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