智慧型客戶保留管理系統｜國立臺灣科技大學博碩士論文系統

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研究生：	蕭凱中 Kai-chung Hsiao
論文名稱：	智慧型客戶保留管理系統 An Intelligent Customer Retention Management System
指導教授：	何正信 Cheng-seen Ho
口試委員:	許清琦 Ching-chi Hsu 簡志誠 Chih-cheng Chien 陳錫明 Shyi-ming Chen 李漢銘 Hahn-ming Lee
學位類別：	碩士 Master
系所名稱：	電資學院 - 資訊工程系 Department of Computer Science and Information Engineering
論文出版年：	2005
畢業學年度：	93
語文別：	英文
論文頁數：	67
中文關鍵詞：	資料挖掘、客戶流失、分類、分群、客戶關係管理、知識本體
外文關鍵詞：	Data mining, Ontology, CRM, Clustering, Churn, Classification
相關次數：	點閱：232 下載：2
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在通信業激烈的競爭下，為了維護公司營利，通信業者必須防止用戶流失，亦即因用戶轉換電信公司所減少的獲利。本論文提出ㄧ個智慧型客戶保留管理系統來處理客戶保留之工作。首先，本系統分析歷史用戶資料庫來學得用戶流失率的預測模組，並將此模組最佳化。本模組可用來預測任一輸入用戶之流失率。本系統亦建構ㄧ策略模組，將保留策略知識本體中適當的保留策略，對應到據特定的關聯屬性值群組。本模組可在判斷潛在流失客戶的價值後，藉由分析客戶的特性，自動提供適當的保留策略來吸引高附加價值的客戶。建構本模組所需之保留策略知識本體係廣泛收集與分析業界及學術界之相關客戶保留策略所建構而成，而關聯屬性群組則是分析從歷史流失客戶資料庫所挖掘出來的屬性間的關係而來。我們的實驗結果顯示，用戶流失率預測模組可以達到百分之八十五的準確度。此外，跟某ㄧ電信業者網佔所提供之費率方案估算系統比較，由本系統自動建議潛在流失客戶的費率方案與該系統所提供的費率方案也幾乎完全吻合。
本論文的貢獻主要分有三：第一，本論文所提出的系統確可依照潛在流失客戶不同的特性，自動建議適當的保留策略。第二，我們所挖掘出來的流失客戶關聯屬性群顯示了重要的資訊，確可幫助我們分析流失客戶的行為模式。最後，我們所建構的保留策略知識本體不僅提供了本系統所需的多元化保留策略，並且可以支援ㄧ般性保留策略的設計與分析。

Competition in the wireless telecommunications industry is fierce. To maintain profitability, carriers must control churn, meaning the loss of subscribers who defects from one carrier to another. This thesis proposes an intelligent customer retention management system to deal with the customer retention problem. The system first learns a churn prediction model by the decision tree-based technique from a historical subscriber database. The churn model is then optimized to support the prediction of defection probability of a customer. The system also constructs a policy model from mapping the analyzed churn attribute clusters to the retention policy ontology. The policy model supports automatic proposal of suitable retention policies to retain a potential churner provided that she is a valuable subscriber. Our experiment shows the learned churn model has around 85% of correctness in tenfold cross-validation. And a preliminary test on proposing suitable package plans shows the policy model works equally well as a commercial website.
The contributions of the work are as follows. First, the churn attribute clusters we discovered show some significant relationships among the attribute values, which can reveal why a customer defects more effectively. Second, the retention policy ontology we constructed not only can support the construction of the policy model in our system but also can support general retention policy design and analysis. Finally, the fact that the system can automatically propose proper retention policies for potential churners according to their specific characteristics is new and important in customer retention study.

摘要	i
Abstract	ii
誌謝	iii
TABLE OF CONTENTS	iv
LISTS OF FIGURES	vi
LISTS OF TABLES	vii
Chapter 1 Introduction	1
1 Motivation	1
2 Problem Specification	4
3 Contributions	4
4 Thesis Organization	5
Chapter 2 Related Work	6
1 Data Mining	6
1.1 Definition	6
2 Classification Analysis	8
3 Prediction Analysis	11
4 Decision Tree-based Algorithms	13
4.1 Information Gain Criterion	16
4.2 Gain Ratio Criterion and C4.5, C5.0	17
4.3 Cross-Validation	18
5 Association Rule Mining Techniques	19
5.1 Apriori Algorithm	20
6 Ontology	21
Chapter 3 System Architecture	23
1 System Architecture	23
2 Churn Prediction	25
2.1 Historical Subscriber Database	25
2.2 Churn Model Learner	28
2.3 Churn Model	30
2.4 Model Optimization	31
3 Policy Generation	32
3.1 Churn Attribute Cluster Constructor	32
3.2 Retention Policy Ontology	35
3.3 Policy Model Constructor	40
4 Application Mode	43
Chapter 4 System Implementation and Evaluation	45
1 Churn Model Implementation	45
1.1 Model Construction	45
1.2 Model Optimization	48
2 Policy Model Implementation	52
2.1 Churn Attribute Cluster Model Construction	52
2.2 Retention Policy Ontology Construction	53
2.3 Knowledge Mapping	54
3 System Operation	57
4 System Evaluation	58
Chapter 5 Conclusions and Future Work	60
1 Conclusions	60
2 Comparison with [Tsai02]	61
3 Contribution	62
4 Discussion and Future Work	63
References	65
作者簡介	67

                                

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