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

Daniel Baier () and Sarah Frost ()
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Daniel Baier: University of Bayreuth
Sarah Frost: Brandenburg University of Technology Cottbus-Senftenberg

Advances in Data Analysis and Classification, 2018, vol. 12, issue 1, No 8, 155-171

Abstract: 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 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11634-017-0282-1

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