• DocumentCode
    650491
  • Title

    Visual Data-Driven Profiling of Green Consumers

  • Author

    Holmbom, Annika H. ; Sarlin, Peter ; Zhiyuan Yao ; Eklund, Tomas ; Back, Barbro

  • Author_Institution
    Dept. of Inf. Technol., Abo Akademi Univ., Turku, Finland
  • fYear
    2013
  • fDate
    16-18 July 2013
  • Firstpage
    291
  • Lastpage
    298
  • Abstract
    There is an increasing interest in green consumer behavior. These consumers are ecologically conscious and interested in buying environmentally friendly products. Earlier efforts at identifying these consumers have relied upon questionnaires based on demographic and psychographic data. Most of the studies have concluded that it is not possible to identify a unanimous profile for a green consumer, because: (1) there might be several profiles for green consumers, and (2) in questionnaires, consumers tend to answer according to their intentions, not according to actual behavior. We apply a new method, the Weighted Self-Organizing Map (WSOM) for visual customer segmentation in order to profile green consumers. The consumers are identified through a data-driven analysis based on actual transaction data, including both demographic and behavioral information. The WSOM accounts for the ´degree´ of how green a consumer is by giving a larger weight to consumers who buy more green products. The identified profiles are verified by comparison to earlier research.
  • Keywords
    consumer behaviour; data analysis; data visualisation; green computing; self-organising feature maps; WSOM; behavioral information; data-driven analysis; demographic data; demographic information; environmentally friendly products; green consumer behavior; psychographic data; transaction data; visual customer segmentation; visual data-driven profiling; weighted self-organizing map; Data-driven profiling; Green consumer behavior; Visual customer segmentation; Weighted Self-Organizing Map (WSOM);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Visualisation (IV), 2013 17th International Conference
  • Conference_Location
    London
  • ISSN
    1550-6037
  • Type

    conf

  • DOI
    10.1109/IV.2013.37
  • Filename
    6676576