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  • 标题:An innovation model of alumni relationship management: Alumni segmentation analysis
  • 本地全文:下载
  • 作者:Natthawat Rattanamethawong ; Sukree Sinthupinyo ; Achara Chandrachai
  • 期刊名称:Kasetsart Journal of Social Sciences
  • 印刷版ISSN:2452-3151
  • 出版年度:2018
  • 卷号:39
  • 期号:1
  • 页码:1-11
  • DOI:10.1016/j.kjss.2017.02.002
  • 出版社:Elsevier B.V.
  • 摘要:The purpose of this study was to cluster alumni into segments to better understand the alumni's characteristics, lifestyles, types of behavior, and interests. A sample of 300 university alumni records was used to obtain their respective attribute values consisting of demographics, preferred communication channels, lifestyle, activities/interests, and expectation from university, needed information, donation willingness, and frequency of contact. The researcher used logistic regression and the k-mean clustering technique to analyze the data from the survey. Five segments could be derived from the analysis. Segment 3, the so-called “Mid Age Religious” contained the highest portion while segment 5, the so-called “Elaborate Cohort” had the least portion. Most of the population under these two segments was female. Differences were identified in age, marital status, education, occupation, position, income, experience, and field of work. The Elaborate Cohort segment represented young females having a bachelor degree, with low experience and low income, working for their first employer, and still enjoying being single. Another segment with similar values of attributes as the Elaborate Cohort was segment 1, the so-called “Activist Mainstreamer” whose field of work was computer technology. The segment called “Senior League” consisted of members older than 41 years like the Mid Age Religious segment, however all members were male. The last segment, the so-called “Passionate Learner” had members aged between 31 and 40 years. In conclusion, the results of this study can assist in formulating strategic marketing by alumni associations to satisfy and engage their alumni.
  • 关键词:cluster ; data mining ; segmentation analysis ; university alumni
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