• DocumentCode
    2241540
  • Title

    The application of factor cluster composite analysis in market segmentation research

  • Author

    Chun-li, Li ; Bo, Lian ; Hu-sheng, Lu

  • Author_Institution
    Sch. of Econ. & Manage., Inner Mongolia Univ. of Sci. & Technol., Baotou, China
  • fYear
    2011
  • fDate
    13-15 Sept. 2011
  • Firstpage
    563
  • Lastpage
    568
  • Abstract
    Starting from the diversity that different elements influence the satisfaction, this paper applies factor analysis and cluster analysis to study market segmentation. First, the basic theories and study methods of market segmentation are summarized. Second, this paper takes newspaper retail industry as empirical research object and carries on sample survey to readers through the design of Likert five-category attitude scale so as to understand the readers´ preferences and satisfaction to the newspaper. This paper uses factor analysis to descend dimension of multiple observation variables, extracts and explains the public factors, and make cluster and market segmentation of the sample according to factor scores using K-Means cluster analysis, which puts forward advice for selecting target market of newspaper issuing and drawing marketing strategies.
  • Keywords
    consumer behaviour; customer satisfaction; market research; publishing; retailing; statistical analysis; Likert five category attitude scale; factor analysis; k-means cluster analysis; market segmentation research; marketing strategies; newspaper retail industry; readers preferences; readers satisfaction; Educational institutions; Layout; Loading; Q factor; Reliability; Subscriptions; cluster analysis; factor analysis; market segmentation; satisfaction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Management Science and Engineering (ICMSE), 2011 International Conference on
  • Conference_Location
    Rome
  • ISSN
    2155-1847
  • Print_ISBN
    978-1-4577-1885-4
  • Type

    conf

  • DOI
    10.1109/ICMSE.2011.6070018
  • Filename
    6070018