以上網行為區隔潛在客戶之研究-以某電信公司為例

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姓名

林冠毅(Kuan-yi Lin) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

以上網行為區隔潛在客戶之研究-以某電信公司為例
(Segment potential customers with internet behavior for a telecommunication company)

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摘要(中)

在無線上網的人口日漸增加以及無線基礎建設的日益普及的情況下，行動上網的人數有了顯著的成長，但是對於過去習慣使用固網寬頻的使用者來說，以往只有一家電信公司的選擇，現在卻是有許多家的電信業者可以比較，而收費的方式也較以往多了很多不同的計算方法，也因為太多不同的計算方式造成許多使用者的疑惑，而使的電信業者在此部分的業績並沒有隨著使用行動上網人口增加而快速增長，因此本研究使用Agglomerative Fuzzy K-Means進行模糊集群分析，利用電信公司本身市調資料，內容包含客戶過去的上網行為，例如：上網動機、上網的頻率…等，在加上客戶的基本屬性，例如：收入、性別…等，利用敘述統計方法找出客戶族群在上網行為的差異，利用這些差異形成指標，再透過專家的比對來驗證此指標的精準度。指標可用於推測客戶行動上網的時間和適合的資費方案，因此業務行銷人員在處理案件時可先詢問構成此指標內問題，藉指標的先行判斷來避免在處理案件時因客戶回答不合理而拉長業務行銷人員在分析問題的時間，電信公司節省處理行動上網新辦續約案件的時間後就可增加每位業務行銷人員每天處理的案件數量，而藉由增加的案件數量便有機會可以使電信公司的業績有所成長。

摘要(英)

With wireless internet access in the growing population and the increasing popularity of wireless infrastructure, the number of mobile internet has been significant growth. But for the past habit of using the fixed-line broadband users, they only have the choice of a telecommunication company. Now there are more telecommunication companies can be compared and the rate of charging are more than the past. Due to too many different calculation methods, it make user become confuse. As result of this situation, telecommunication company’s market of wireless internet does not make high profit with the use of mobile internet population growth. So this study use Agglomerative Fuzzy K-Means clustering analysis with the customer’s past internet behavior, such as: the motives of using internet、the frequency of connect internet ... and so on. Those data are collected by the telecommunication company which is the case in this study .This study also use customers basic properties, such as: income, sex ... and so on .By using descriptive statistical methods to identify customer groups of differences in internet behavior. Use those different attributes become a indicator. It will compare with telecommunication company’ expert, then we can get the recall and precision of this indicator. The indicator can supposition the time of customer may use mobile internet and suitable charging rate. So salesperson can ask the questions about the indicator in the first place. Salesperson can use this indicator analysis to avoid the Analysis of the time delay that due to the customer’s unreasonably. It can increase the quantity of case handle by salesperson by reducing the processing time of cases and the result may increase the company’s performance.

關鍵字(中)

★ 聚合式的模糊 K-平均法
★ 上網行為
★ 集群分析
★ 資料探勘

關鍵字(英)

★ Agglomerative Fuzzy K-Means
★ Data mining
★ Clustering analysis
★ Internet behavior

論文目次

目錄頁數
中文摘要 .......................................i
Abstract ......................................ii
目錄 .....................................iii
圖目錄 ......................................iv
表目錄 .......................................v
第一章緒論....................................1
1.1 研究背景與動機............................1
1.2 研究目的..................................3
1.3 研究架構..................................4
第二章文獻探討................................6
2.1 上網行為(Internet Behavior).................6
2.2 模糊分群(Fuzzy Clustering)..................7
2.3 Agglomeration Fuzzy K-Means與Fuzzy K-Means..9
2.4結論與問題討論..............................10
第三章分群過程...............................13
3.1分群流程....................................14
3.2由市調資料擷取客戶屬性......................14
3.3決定群中心點(center)........................18
3.4 Agglomerative Fuzzy K-Means 演算法計算.....20
3.5合併群......................................24
3.6分群結果....................................27
第四章系統驗證...............................28
4.1分群結果分析................................28
4.2敘述統計分析................................29
4.3針對如何節省時間做分析......................34
4.4計算Precision和Recall.......................35
4.5實驗驗證....................................38
第五章結論與未來研究議題......................40
5.1未來研究議題與結論..........................40
5.2研究限制....................................41
參考文獻.......................................42

參考文獻

參考文獻
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指導教授

許秉瑜(Ping-Yu Hsu)

審核日期

2010-7-9

推文