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Relating brand confusion to ad similarities and brand strengths through image data analysis and classification

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Abstract

Brand confusion occurs when a consumer is exposed to an advertisement (ad) for brand A but believes that it is for brand B. If more consumers are confused in this direction than in the other one (assuming that an ad for B is for A), this asymmetry is a disadvantage for A. Consequently, the confusion potential and structure of ads has to be checked: A sample of consumers is exposed to a sample of ads. For each ad the consumers have to specify their guess about the advertised brand. Then, the collected data are aggregated and analyzed using, e.g., MDS or two-mode clustering. In this paper we compare this approach to a new one where image data analysis and classification is applied: The confusion potential and structure of ads is related to featurewise distances between ads and—to model asymmetric effects—to the strengths of the advertised brands. A sample application for the German beer market is presented, the results are encouraging.

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Acknowledgements

The authors would like to thank the editor, the associate editor, and two reviewers for their valuable hints for improving this article.

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Authors and Affiliations

  1. Chair of Innovation and Dialogue Marketing, University of Bayreuth, Universitätsstraße 30, 95447, Bayreuth, Germany

    Daniel Baier

  2. Chair of Marketing and Innovation Management, Brandenburg University of Technology Cottbus-Senftenberg, Erich-Weinert-Straße 1, 03046, Cottbus, Germany

    Sarah Frost

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  1. Daniel Baier
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  2. Sarah Frost
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Correspondence to Daniel Baier.

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Baier, D., Frost, S. Relating brand confusion to ad similarities and brand strengths through image data analysis and classification. Adv Data Anal Classif 12, 155–171 (2018). https://doi.org/10.1007/s11634-017-0282-1

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  • DOI: https://doi.org/10.1007/s11634-017-0282-1

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