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博碩士論文 etd-0806101-151839 詳細資訊
Title page for etd-0806101-151839
論文名稱
Title
通信資料庫之資料探勘:客戶流失預測之研究
Telecommunications Data Mining for Churn Prediction
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
62
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2001-07-23
繳交日期
Date of Submission
2001-08-06
關鍵字
Keywords
資料探勘、通信資料探勘、多專家決策分類分析、顧客流失預測、分類分析、顧客流失管理
Data Mining, Churn Prediction, Telecommunications Data Mining, Churn Management, Classification Analysis, Multi-expert Classification Analysis
統計
Statistics
本論文已被瀏覽 5676 次,被下載 5822
The thesis/dissertation has been browsed 5676 times, has been downloaded 5822 times.
中文摘要
中文摘要
國內電信市場自從1997年全面開放行動電話通信業務以來,由於多家業者加入競爭,以及消費者對無線通信服務殷切需求,使得市場呈現蓬勃景象。近年來,隨著行動電話市場日趨飽和,競爭情勢日益激烈,各家業者均面臨客戶嚴重流失(Churn)問題。為了維持市場佔有率,各業者無不卯足全力,提供各種高成本促銷方案來吸引客戶。根據調查,企業吸引一位新客戶所花的成本是維繫一位現有客戶的五至十倍。對企業而言,長期的忠誠顧客比在乎價格的短期顧客更有利可圖。因此,如何維繫既有客戶,早期發現潛在流失客戶,即時進行鞏固,是當前各電信業者亟欲解決的課題。本研究目的是希望能運用資料探勘技術,發展客戶流失預測模式,提供業者解決客戶流失問題參考。
國外對於流失問題的研究,大多以客戶個人基本資料(Profile)、人口統計(Demographics)資料以及客戶消費歷史資料為分析基礎。由於國內業者所握有的客戶基本資料可資分析應用者相當有限,且各業者普遍缺乏人口統計等次級資料。因此,本研究乃摒棄國外的分析方式,針對國內業者資料現況,利用行動電話業者現有的營運資料,從事客戶流失預測模式的研究,經由對客戶大量通聯記錄的分析,探索客戶退租前通話行為變化,從中尋找流失徵兆,進而發展有效的預測模型。本研究採用C4.5決策樹歸納技術,配合多專家決策(Multi-expert Strategy)分類方法從事分析研究。經實驗證實,以本研究模型選取10.03%之全體客戶,平均可預測出50.64%之「會流失客戶」;選取29.00%全體客戶中,平均可預測出68.62%之「會流失客戶」。可見本研究所提出之預測模式確能有效地找出潛在流失客戶,具有實務應用價值。

Abstract
Abstract
As deregulation and new competitors open up the telecommunications industry, the cellular phone market has become more competitive than ever. To survive or maintain an advantage in such a competitive marketplace, many telecommunications companies are turning to data mining techniques to resolve such challenging issues as fraud detection, customer retention, and prospect profiling. In this thesis, we focused on developing and applying data mining technique to support the churn prediction. Constrained by limited customer profiles and general demographics, the proposed approach applied a decision tree induction technique (i.e., C4.5) to discover a classification model for churn predication solely based on the call records. To deal with the training data with a highly skewed distribution on decisions (i.e., around 2% churners and 98% non-churners), a multi-expert strategy was adopted. The empirical results showed that the proposed technique was effective in predicting at-risk cellular phone customers (i.e., potential churners). The proposed technique could identify 50.64% churners by selecting 10.03% of the population, and 68.62% churners by selecting 29.00% of the population.

目次 Table of Contents
圖目錄 ………………………………………………………………Ⅲ
表目錄 ………………………………………………………………Ⅵ
第一章 緒 論 …………………………………………………………1
第一節 研究背景…………………………………………………1
第二節 研究動機與目的 ………………………………………2
第三節 研究方法與流程 ………………………………………4
第四節 論文結構 ………………………………………………6
第二章 文獻探討………………………………………………………7
第一節 資料探勘……………………………………………………7
2.1.1 分類分析 ……………………………………………7
2.1.2 群集分析 ……………………………………………7
2.1.3 聯結法則分析 ………………………………………8
2.1.4 次序相關分析 ………………………………………9
2.1.5 鏈結分析 ……………………………………………9
2.1.6 時間序列相似性分析 ………………………………9
第二節 資料探勘在電信業之應用 ………………………………10
2.2.1 應用案例一..………………………………………12
2.2.2 應用案例二 ………………………………………12
2.2.3 應用案例三 ………………………………………13
2.2.4 應用案例四 ………………………………………14
第三節 資料探勘與客戶流失管理 ………………………………16
第三章 客戶流失預測研究分析技術 ………………………………20
第一節 分類分析技術探討………………………………………20
3.1.1 C4.5決策樹歸納法 ………………………………21
3.1.2決策樹修剪 ………………………………………23
第二節 非對稱分配資料與多專家決策分類 …………………24
第三節 資料搜集分析 …………………………………………26
3.3.1 資料來源 …………………………………………27
3.3.2 分析變數 …………………………………………29
第四節 流失預測系統架構……………………………………32
第四章 實證評估……………………………………………………34
第一節 實驗設計………………………………………………34
4.1.1資料集 ……………………………………………34
4.1.2 評估準則 …………………………………………34
4.1.3 實驗方法 …………………………………………36
第二節 參數調整………………………………………………37
第三節 前置時間評估與分析…………………………………41
第四節 不同用量客戶預測評估與分析………………………42
第五節 模型時效評估與分析…………………………………45
第六節 單一模型與多專家決策預測評估與分析……………47
第七節 預測效果評估…………………………………………48
第五章 結 論..……………………………………………………51
第一節 研究特點..……………………………………………51
第二節 綜合結論與貢獻..……………………………………51
第三節 對電信業者引用建議..………………………………54
第四節 研究限制..……………………………………………56
第五節 未來研究方向..………………………………………56
參考文獻 ……………………………………………………………58

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