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Advances in Data Analysis and Classification

, Volume 12, Issue 1, pp 155–171 | Cite as

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

  • Daniel Baier
  • Sarah Frost
Regular Article

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.

Keywords

Brand confusion Confusion experiment Image data analysis and classification Multinomial logit model Two-mode hierarchical cluster analysis 

Notes

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Chair of Innovation and Dialogue MarketingUniversity of BayreuthBayreuthGermany
  2. 2.Chair of Marketing and Innovation ManagementBrandenburg University of Technology Cottbus-SenftenbergCottbusGermany

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