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Image data analysis and classification in marketing

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  • Daniel Baier

    ()

  • Ines Daniel

    ()

  • Sarah Frost

    ()

  • Robert Naundorf

    ()

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    Abstract

    Nowadays, the diffusion of smartphones, tablet computers, and other multipurpose equipment with high-speed Internet access makes new data types available for data analysis and classification in marketing. So, e.g., it is now possible to collect images/snaps, music, or videos instead of ratings. With appropriate algorithms and software at hand, a marketing researcher could simply group or classify respondents according to the content of uploaded images/snaps, music, or videos. However, appropriate algorithms and software are sparsely known in marketing research up to now. The paper tries to close this gap. Algorithms and software from computer science are presented, adapted and applied to data analysis and classification in marketing. The new SPSS-like software package IMADAC is introduced. Copyright Springer-Verlag Berlin Heidelberg 2012

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    Bibliographic Info

    Article provided by Springer in its journal Advances in Data Analysis and Classification.

    Volume (Year): 6 (2012)
    Issue (Month): 4 (December)
    Pages: 253-276

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    Handle: RePEc:spr:advdac:v:6:y:2012:i:4:p:253-276

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    Web page: http://www.springer.com/statistics/statistical+theory+and+methods/journal/11634

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    Related research

    Keywords: Image data analysis; Image classification; Market segmentation; 62H30 Classification and discrimination; cluster analysis; 62H35 Image analysis; 90B60 Marketing; advertising; 91D30 Social networks; 68T10 Pattern recognition; speech recognition;

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    1. W. Buckinx & D. Van Den Poel, 2003. "Customer Base Analysis: Partial Defection of Behaviorally-Loyal Clients in a Non-Contractual FMCG Retail Setting," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium, Ghent University, Faculty of Economics and Business Administration 03/178, Ghent University, Faculty of Economics and Business Administration.
    2. Ahmed Albatineh & Magdalena Niewiadomska-Bugaj, 2011. "Correcting Jaccard and other similarity indices for chance agreement in cluster analysis," Advances in Data Analysis and Classification, Springer, Springer, vol. 5(3), pages 179-200, October.
    3. Bearden, William O & Netemeyer, Richard G & Teel, Jesse E, 1989. " Measurement of Consumer Susceptibility to Interpersonal Influence," Journal of Consumer Research, University of Chicago Press, University of Chicago Press, vol. 15(4), pages 473-81, March.
    4. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer, vol. 2(1), pages 193-218, December.
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