DC Field | Value | Language |
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dc.contributor.advisor | Han, In-Goo | - |
dc.contributor.advisor | 한인구 | - |
dc.contributor.author | Roh, Tae-Hyup | - |
dc.contributor.author | 노태협 | - |
dc.date.accessioned | 2011-12-27T02:03:07Z | - |
dc.date.available | 2011-12-27T02:03:07Z | - |
dc.date.issued | 2000 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=158296&flag=dissertation | - |
dc.identifier.uri | http://hdl.handle.net/10203/53016 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 경영공학전공, 2000.2, [ viii, 84 p. ] | - |
dc.description.abstract | The strategic importance of managing customer relationships both drives and is driven by technology. (Puckey, 1999) Various methodologies of knowledge discovery in database (KDD), known as data mining, have been progressing as a DB (data base) marketing tool along with advancement of computing storage technology. These technologies include data warehouse, data mart that are becoming one of the core CRM system for the efficiency and effectiveness of front-end activities. This research focuses on managing customer relationship that could promote future cash flow. This is different from the concept of simple customer management. In particular, as the market competition becomes keen, constructing a customer relationship management (CRM) system is coming to the front for winning over the new customer, developing service and products for customer satisfaction and segmenting existing customer for one-to-one target marketing. This research applies KDD methodologies to the telecom market, one of the fiercest competition markets, and suggests prototype and logic for CRM system. In addition to this, marketing strategy is also presented with reference to real telecom market data. Each chapter 4,5,6, builds models, analyes the result and suggests proper market strategy. All data through the whole KDD process i.e. data collection & analysis, sampling, data transformation & modification, modeling, and assessment. Statistical KDD method (Logistic Regression), Artificial Intelligence method, such as artisficial neural network(ANN), and decision trees are used as modeling methods. In Ch. 4 churn management for customer segmentation and target marketing is studied, based on two factors (churn rate and profitability), market basket analysis for additional services and target customer prediction for direct mailing(DM) are suggested in Ch. 5, 6. The first success factor of CRM counts on setting up a concrete purpose fitting both corporate and customer needs. Diverse database of cust... | eng |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Churn management | - |
dc.subject | Data mining | - |
dc.subject | KDD | - |
dc.subject | CRM | - |
dc.subject | DM | - |
dc.subject | 다이렉트 메일링 | - |
dc.subject | 고객 이탈 관리 | - |
dc.subject | 데이터 마이닝 | - |
dc.subject | 지식 추출 기법 | - |
dc.subject | 고객 관계 관리 | - |
dc.title | (The) customer relationship management for telecom market using knowledge discovery in database | - |
dc.title.alternative | 지식 추출 기법을 이용한 통신 시장의 고객 관계 관리 기법 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 158296/325007 | - |
dc.description.department | 한국과학기술원 : 경영공학전공, | - |
dc.identifier.uid | 000973223 | - |
dc.contributor.localauthor | Han, In-Goo | - |
dc.contributor.localauthor | 한인구 | - |
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