DC Field | Value | Language |
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dc.contributor.advisor | Kim, Soung-Hie | - |
dc.contributor.advisor | 김성희 | - |
dc.contributor.author | Song, Hee-Seok | - |
dc.contributor.author | 송희석 | - |
dc.date.accessioned | 2011-12-27T04:19:44Z | - |
dc.date.available | 2011-12-27T04:19:44Z | - |
dc.date.issued | 2003 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=181200&flag=dissertation | - |
dc.identifier.uri | http://hdl.handle.net/10203/53403 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 경영공학전공, 2003.2, [ x, 118 p. ] | - |
dc.description.abstract | Understanding and adapting to changes of customer behavior is an important aspect to survive in a continuously changing environment. Knowing what is changing and how it has been changed is important because it allows businesses to provide the right products and services to suit the changing market needs (Liu et al. 2000). This thesis handles topics on how to detect changes of customer behavior and where to apply the detected changes in eCRM. Here, the term ‘customer behavior’ means action-oriented activities like calling, visiting websites, and making purchases. Detecting the change of customer behavior can be considered at two different levels, organizational and individual level. In organizational level, it is critical to understand the changes of behavior pattern (i.e. buying pattern or visiting pattern or usage pattern) of their customer groups over time because business manager can promote the desirable trends and control the undesirable trends with this information. More specifically, most business managers have a strong need to know and adapt to the answers to following questions about their customers. Which aspects are changed in behaviors and preferences of customer before and after financial crisis? What are the differences in customer behavior before and after loyalty campaign? What are the differences in purchasing pattern before and after adoption of new service? For this business needs, we propose a change mining methodology, which automatically discovers changes of customer behavior between two datasets, which are collected over time. The change mining methodology forms the first part of this thesis. On the other hands in individual level, if we detect the abnormal (or outlier) behavior, which is different from normal behavior pattern for a certain customer at the right time, we can investigate the reason and prevent undesirable results such as defection or fraud (Ng and Liu 2000; Raghavan et al. 2000). The second part of this thesis proposes a m... | eng |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Change detection | - |
dc.subject | Defection Detection | - |
dc.subject | Data Mining | - |
dc.subject | Customer Relationship Management | - |
dc.subject | Self-Organizing Map | - |
dc.subject | 자기조직화 지도 | - |
dc.subject | 변화 탐지 | - |
dc.subject | 이탈 탐지 | - |
dc.subject | 데이터마이닝 | - |
dc.subject | 고객관계관리 | - |
dc.title | Detecting the change of customer behavior for eCRM | - |
dc.title.alternative | 인터넷 기반 고객관계관리를 위한 조직 및 개인관점에서의 고객 행위변화 탐지 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 181200/325007 | - |
dc.description.department | 한국과학기술원 : 경영공학전공, | - |
dc.identifier.uid | 000995212 | - |
dc.contributor.localauthor | Kim, Soung-Hie | - |
dc.contributor.localauthor | 김성희 | - |
dc.title.subtitle | Organizational and individual perspective | - |
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