IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v35y2016i1p182-197.html
   My bibliography  Save this article

An Analysis and Visualization Methodology for Identifying and Testing Market Structure

Author

Listed:
  • Stephen L. France

    (School of Business, Mississippi State University, Mississippi State, Mississippi 39762)

  • Sanjoy Ghose

    (Sheldon B. Lubar School of Business, University of Wisconsin–Milwaukee, Milwaukee, Wisconsin 53201)

Abstract

We introduce a method for identifying, analyzing, and visualizing submarkets in product categories. We give an overview of the market structure and competitive submarket literature and then describe a classic model for testing competitive submarkets along with associated extensions. In the era of big data and with the increasing availability of large-scale consumer purchase data, there is a need for techniques that can interpret these data and use them to help make managerial decisions. We introduce a statistical likelihood based technique for both identifying and testing market structure. We run a series of experiments on generated data and show that our method is better at identifying market structure from brand substitution data than a range of methods described in the marketing literature. We introduce tools for holdout validation, complexity control, and testing managerial hypotheses. We describe a method for visualization of submarket solutions, and we give several traditional consumer product examples and in addition give an example to show how market structure can be analyzed from online review data.Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2015.0958 .

Suggested Citation

  • Stephen L. France & Sanjoy Ghose, 2016. "An Analysis and Visualization Methodology for Identifying and Testing Market Structure," Marketing Science, INFORMS, vol. 35(1), pages 182-197, January.
  • Handle: RePEc:inm:ormksc:v:35:y:2016:i:1:p:182-197
    DOI: 10.1287/mksc.2015.0958
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.2015.0958
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mksc.2015.0958?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
    ---><---

    Citations

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


    Cited by:

    1. Adam N. Smith & Peter E. Rossi & Greg M. Allenby, 2019. "Inference for Product Competition and Separable Demand," Marketing Science, INFORMS, vol. 38(4), pages 690-710, July.
    2. Purva Grover & Arpan Kumar Kar, 2017. "Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 203-229, September.
    3. Liu, Yezheng & Qian, Yang & Jiang, Yuanchun & Shang, Jennifer, 2020. "Using favorite data to analyze asymmetric competition: Machine learning models," European Journal of Operational Research, Elsevier, vol. 287(2), pages 600-615.
    4. Daniel M. Ringel & Bernd Skiera, 2016. "Visualizing Asymmetric Competition Among More Than 1,000 Products Using Big Search Data," Marketing Science, INFORMS, vol. 35(3), pages 511-534, May.
    5. Ando, Tomohiro & Bai, Jushan, 2021. "Large-scale generalized linear longitudinal data models with grouped patterns of unobserved heterogeneity," MPRA Paper 111431, University Library of Munich, Germany.
    6. Yang Qian & Yuanchun Jiang & Yanan Du & Jianshan Sun & Yezheng Liu, 2020. "Segmenting market structure from multi-channel clickstream data: a novel generative model," Electronic Commerce Research, Springer, vol. 20(3), pages 509-533, September.
    7. Maximilian Matthe & Daniel M. Ringel & Bernd Skiera, 2023. "Mapping Market Structure Evolution," Marketing Science, INFORMS, vol. 42(3), pages 589-613, May.
    8. Kusumah, Echo Perdana, 2018. "Trading Channel Pattern of Cassava Commodity: Double Roles for the Farmers – Is It a Benefit?," MPRA Paper 88245, University Library of Munich, Germany.
    9. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
    10. Alzate, Miriam & Arce-Urriza, Marta & Cebollada, Javier, 2022. "Mining the text of online consumer reviews to analyze brand image and brand positioning," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).
    11. Adam N. Smith & Jim E. Griffin, 2023. "Shrinkage priors for high-dimensional demand estimation," Quantitative Marketing and Economics (QME), Springer, vol. 21(1), pages 95-146, March.

    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:inm:ormksc:v:35:y:2016:i:1:p:182-197. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.