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New metrics for evaluating preference maps

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  • Faure, Corinne
  • Natter, Martin

Abstract

Preference maps provide a visual representation of market structure, usually depicting brand or product alternatives, product attributes, and customers in a single graphic. Using measures of consideration and attribute sets to establish criterion validity, we develop a set of metrics that can be interpreted by managers and that allow managers to evaluate maps based on their ability to accurately represent market structures for products, attributes, and consumers. Using a Monte Carlo simulation, we test the stability of the metrics for a variety of scenarios and compare them to statistical stress. Our results show that the metrics can help identify specific sources of noise and can therefore be used to interpret map fit at a more disaggregated level than stress. We apply the metrics on an empirical example and use them to develop a reweighted map for a focal product.

Suggested Citation

  • Faure, Corinne & Natter, Martin, 2010. "New metrics for evaluating preference maps," International Journal of Research in Marketing, Elsevier, vol. 27(3), pages 261-270.
    • Handle: RePEc:eee:ijrema:v:27:y:2010:i:3:p:261-270
    DOI: 10.1016/j.ijresmar.2010.03.005
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    References listed on IDEAS

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    Cited by:

    1. van de Velden, M. & de Beuckelaer, A. & Groenen, P.J.F. & Busing, F.M.T.A., 2011. "Nonmetric Unfolding of Marketing Data: Degeneracy and Stability," ERIM Report Series Research in Management ERS-2011-006-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.
    2. Moon, Sangkil & Kamakura, Wagner A., 2017. "A picture is worth a thousand words: Translating product reviews into a product positioning map," International Journal of Research in Marketing, Elsevier, vol. 34(1), pages 265-285.
    3. Tobias Reckmann, 2017. "Verwendung von Word of Mouth-Daten zur Identifikation von Asymmetrie im Wettbewerb: Eine textbasierte Analyse am Beispiel deutscher Automobilmarken [Identification of asymmetric competition by usin," Schmalenbach Journal of Business Research, Springer, vol. 69(2), pages 173-201, June.
    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.

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