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

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  • Daniel Baier

    (University of Bayreuth)

  • Sarah Frost

    (Brandenburg University of Technology Cottbus-Senftenberg)

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.

Suggested Citation

  • Daniel Baier & Sarah Frost, 2018. "Relating brand confusion to ad similarities and brand strengths through image data analysis and classification," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(1), pages 155-171, March.
    • Handle: RePEc:spr:advdac:v:12:y:2018:i:1:d:10.1007_s11634-017-0282-1
    DOI: 10.1007/s11634-017-0282-1
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    References listed on IDEAS

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    1. Rocci, Roberto & Vichi, Maurizio, 2008. "Two-mode multi-partitioning," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1984-2003, January.
    2. Daniel Baier & Ines Daniel & Sarah Frost & Robert Naundorf, 2012. "Image data analysis and classification in marketing," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(4), pages 253-276, December.
    3. Akinori Okada & Tadashi Imaizumi, 1997. "Asymmetric multidimensional scaling of two-mode three-way proximities," Journal of Classification, Springer;The Classification Society, vol. 14(2), pages 195-224, September.
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    Cited by:

    1. Atsuho Nakayama & Daniel Baier, 2020. "Predicting brand confusion in imagery markets based on deep learning of visual advertisement content," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(4), pages 927-945, December.

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