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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
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    Citations

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    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. 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.
    3. 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.
    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. 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.
    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. 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.
    9. 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).
    10. 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.
    11. 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.

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