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An optimization method to estimate models with store-level data: A case study

Author

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  • Trindade, Graça
  • Ambrósio, Jorge

Abstract

The quality of the estimation of a latent segment model when only store-level aggregate data is available seems to be dependent on the computational methods selected and in particular on the optimization methodology used to obtain it. Following the stream of work that emphasizes the estimation of a segmentation structure with aggregate data, this work proposes an optimization method, among the deterministic optimization methods, that can provide estimates for segment characteristics as well as size, brand/product preferences and sensitivity to price and price promotion variation estimates that can be accommodated in dynamic models. It is shown that, among the gradient based optimization methods that were tested, the Sequential Quadratic Programming method (SQP) is the only that, for all scenarios tested for this type of problem, guarantees of reliability, precision and efficiency being robust, i.e., always able to deliver a solution. Therefore, the latent segment models can be estimated using the SQP method when only aggregate market data is available.

Suggested Citation

  • Trindade, Graça & Ambrósio, Jorge, 2012. "An optimization method to estimate models with store-level data: A case study," European Journal of Operational Research, Elsevier, vol. 217(3), pages 664-672.
  • Handle: RePEc:eee:ejores:v:217:y:2012:i:3:p:664-672
    DOI: 10.1016/j.ejor.2011.08.032
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    References listed on IDEAS

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    1. González-Benito, Óscar & Martínez-Ruiz, María Pilar & Mollá-Descals, Alejandro, 2009. "Using store level scanner data to improve category management decisions: Developing positioning maps," European Journal of Operational Research, Elsevier, vol. 198(2), pages 666-674, October.
    2. Shen, Chungen & Xue, Wenjuan & Chen, Xiongda, 2010. "Global convergence of a robust filter SQP algorithm," European Journal of Operational Research, Elsevier, vol. 206(1), pages 34-45, October.
    3. repec:eee:ijrema:v:26:y:2009:i:4:p:345-355 is not listed on IDEAS
    4. Ostermark, Ralf, 1999. "Solving Irregular Econometric and Mathematical Optimization Problems with a Genetic Hybrid Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 13(2), pages 103-115, April.
    5. Sivakumar, K., 2004. "Manifestations and measurement of asymmetric brand competition," Journal of Business Research, Elsevier, vol. 57(8), pages 813-820, August.
    6. Still, Claus & Westerlund, Tapio, 2010. "A linear programming-based optimization algorithm for solving nonlinear programming problems," European Journal of Operational Research, Elsevier, vol. 200(3), pages 658-670, February.
    7. Randolph E. Bucklin & Sunil Gupta, 1999. "Commercial Use of UPC Scanner Data: Industry and Academic Perspectives," Marketing Science, INFORMS, vol. 18(3), pages 247-273.
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    Cited by:

    1. repec:eee:apmaco:v:296:y:2017:i:c:p:277-288 is not listed on IDEAS
    2. Fernández, Arturo J., 2012. "Minimizing the area of a Pareto confidence region," European Journal of Operational Research, Elsevier, vol. 221(1), pages 205-212.

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