IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v217y2012i3p664-672.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221711008034
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2011.08.032?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Sriram, S. & Kadiyali, Vrinda, 2009. "Empirical investigation of channel reactions to brand introductions," International Journal of Research in Marketing, Elsevier, vol. 26(4), pages 345-355.
    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.
    8. Andrés Musalem & Eric T. Bradlow & Jagmohan S. Raju, 2009. "Bayesian estimation of random‐coefficients choice models using aggregate data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(3), pages 490-516, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Trindade, Graça & Dias, José G. & Ambrósio, Jorge, 2017. "Extracting clusters from aggregate panel data: A market segmentation study," Applied Mathematics and Computation, Elsevier, vol. 296(C), pages 277-288.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Csilla Horváth & Dennis Fok, 2013. "Moderating Factors of Immediate, Gross, and Net Cross-Brand Effects of Price Promotions," Marketing Science, INFORMS, vol. 32(1), pages 127-152, July.
    2. Shaobo Li & Matthew J. Schneider & Yan Yu & Sachin Gupta, 2023. "Reidentification Risk in Panel Data: Protecting for k -Anonymity," Information Systems Research, INFORMS, vol. 34(3), pages 1066-1088, September.
    3. Trindade, Graça & Dias, José G. & Ambrósio, Jorge, 2017. "Extracting clusters from aggregate panel data: A market segmentation study," Applied Mathematics and Computation, Elsevier, vol. 296(C), pages 277-288.
    4. Hugo Storm & Thomas Heckelei & Ron C. Mittelhammer, 2016. "Bayesian estimation of non-stationary Markov models combining micro and macro data," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 43(2), pages 303-329.
    5. K. Sudhir, 2001. "Structural Analysis of Manufacturer Pricing in the Presence of a Strategic Retailer," Marketing Science, INFORMS, vol. 20(3), pages 244-264, October.
    6. Stockton, Matthew C. & Capps, Oral Jr., 2005. "Nerlovian Hedonic Models For Three Different Container Sizes Of Fluid Milk," A.E. Research Series 305038, University of Idaho, Department of Agricultural Economics and Rural Sociology.
    7. Guyt, Jonne & Gijsbrechts, Els, 2018. "On consumer choice patterns and the net impact of feature promotions," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 490-508.
    8. Fok, D. & Horváth, C. & Paap, R. & Franses, Ph.H.B.F., 2004. "A hierarchical Bayes error correction model to explain dynamic effects," Econometric Institute Research Papers EI 2004-27, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    9. Robert P. Rooderkerk & Harald J. van Heerde & Tammo H. A. Bijmolt, 2013. "Optimizing Retail Assortments," Marketing Science, INFORMS, vol. 32(5), pages 699-715, September.
    10. Steven M. Shugan, 2002. "In Search of Data: An Editorial," Marketing Science, INFORMS, vol. 21(4), pages 369-377.
    11. Koen Pauwels & Shuba Srinivasan & Philip Hans Franses, 2007. "When Do Price Thresholds Matter in Retail Categories?," Marketing Science, INFORMS, vol. 26(1), pages 83-100, 01-02.
    12. Masakazu Ishihara & Andrew T. Ching, 2019. "Dynamic Demand for New and Used Durable Goods Without Physical Depreciation: The Case of Japanese Video Games," Marketing Science, INFORMS, vol. 38(3), pages 392-416, May.
    13. Sungho Park & Sachin Gupta, 2012. "Comparison of SML and GMM estimators for the random coefficient logit model using aggregate data," Empirical Economics, Springer, vol. 43(3), pages 1353-1372, December.
    14. Holtrop, Niels & Wieringa, Jaap E. & Gijsenberg, Maarten J. & Verhoef, Peter C., 2017. "No future without the past? Predicting churn in the face of customer privacy," International Journal of Research in Marketing, Elsevier, vol. 34(1), pages 154-172.
    15. German Zenetti & Thomas Otter, 2014. "Bayesian estimation of the random coefficients logit from aggregate count data," Quantitative Marketing and Economics (QME), Springer, vol. 12(1), pages 43-84, March.
    16. Smith, Aaron & Naik, Prasad A. & Tsai, Chih-Ling, 2006. "Markov-switching model selection using Kullback-Leibler divergence," Journal of Econometrics, Elsevier, vol. 134(2), pages 553-577, October.
    17. Uncles, Mark D., 2010. "Is marketing academia losing its way? It depends where you look," Australasian marketing journal, Elsevier, vol. 18(3), pages 165-167.
    18. Kapetanios, George, 2006. "Cluster analysis of panel data sets using non-standard optimisation of information criteria," Journal of Economic Dynamics and Control, Elsevier, vol. 30(8), pages 1389-1408, August.
    19. Sandra Mottner & Steven H. Smith & T. J. Olney, 2011. "Trade Promotions and Suppliers' Market Power," American Journal of Economics and Business Administration, Science Publications, vol. 3(3), pages 460-472, September.
    20. Daniel Minh McCarthy & Elliot Shin Oblander, 2021. "Scalable Data Fusion with Selection Correction: An Application to Customer Base Analysis," Marketing Science, INFORMS, vol. 40(3), pages 459-480, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:217:y:2012:i:3:p:664-672. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.