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題名 雲端服務中銷售員支援之研究
A study on sales force support in cloud service作者 翁玉麟 貢獻者 曾淑峰
翁玉麟關鍵詞 線上分析處理
資料採礦
客戶關係管理
銷售員自動化
客戶推薦
Online analytical processing
Data mining
Customer relationship management
Sales force automation
Customer recommendation日期 2015 上傳時間 1-六月-2018 17:38:04 (UTC+8) 摘要 客戶關係管理（Customer Relationship Management, CRM）藉由各種資訊技術來留住客戶，以產生更多的商業價值。然而，許多文獻指出，CRM系統的失敗率很高，尤其是CRM主要的核心能力--銷售員自動化（Sales Force Automation, SFA）。研究指出改善的方式包含更好的管理支援、培訓、系統易用性和強烈的使用動機等等。接續此建議，本文提出了一個銷售員支援（Sales Force Support, SFS）系統，藉由線上分析處理（Online Analytical Processing, OLAP）、資料採礦（Data Mining, DM）和雲端服務（Cloud Service）等技術，協助彙整及提供支援銷售員的客戶推薦 (Customer Recommendation)和自我績效評估(Self Evaluation)功能，以刺激更好的銷售能力、滿足客戶與管理。可望提高系統的易用性和業務人員的使用動機，藉以橋接銷售員和管理人員之間的差異。為了評估推薦功能之適用性，本論文也發展一套驗證指標，並採用一套隨機數學模型（Stochastic Mathematical Model），作為強化推薦預測之嘗試。
Customer Relationship Management (CRM) adopts various information technologies to retain and attain customers in order to generate more business values. However, the earlier studies indicate the failure rate for CRM systems is high and it’s even higher for Sales Force Automation (SFA), a major core in CRM. They usually suggest the enhancement in better management support, more training, user friendliness, and usage motivation, and so on. Following the suggestions, this research proposes a Sales Force Support (SFS) system to integrate technologies like OLAP (Online Analytical Processing), Data Mining (DM), and cloud service, etc. to provide supporting information in customer recommendation and self-evaluation, in order to better stimulate sales and satisfy customer and management. The objectives can be achieved by enhancing the user friendliness and usage motivation, and bridging the differences between sales force and management. To evaluate the fitness of recommendation function, a set of validation measures is also developed. 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國立政治大學
資訊管理學系
94356505資料來源 http://thesis.lib.nccu.edu.tw/record/#G0094356505 資料類型 thesis dc.contributor.advisor 曾淑峰 zh_TW dc.contributor.author (作者) 翁玉麟 zh_TW dc.creator (作者) 翁玉麟 zh_TW dc.date (日期) 2015 en_US dc.date.accessioned 1-六月-2018 17:38:04 (UTC+8) - dc.date.available 1-六月-2018 17:38:04 (UTC+8) - dc.date.issued (上傳時間) 1-六月-2018 17:38:04 (UTC+8) - dc.identifier (其他 識別碼) G0094356505 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/117445 - dc.description (描述) 博士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理學系 zh_TW dc.description (描述) 94356505 zh_TW dc.description.abstract (摘要) 客戶關係管理（Customer Relationship Management, CRM）藉由各種資訊技術來留住客戶，以產生更多的商業價值。然而，許多文獻指出，CRM系統的失敗率很高，尤其是CRM主要的核心能力--銷售員自動化（Sales Force Automation, SFA）。研究指出改善的方式包含更好的管理支援、培訓、系統易用性和強烈的使用動機等等。接續此建議，本文提出了一個銷售員支援（Sales Force Support, SFS）系統，藉由線上分析處理（Online Analytical Processing, OLAP）、資料採礦（Data Mining, DM）和雲端服務（Cloud Service）等技術，協助彙整及提供支援銷售員的客戶推薦 (Customer Recommendation)和自我績效評估(Self Evaluation)功能，以刺激更好的銷售能力、滿足客戶與管理。可望提高系統的易用性和業務人員的使用動機，藉以橋接銷售員和管理人員之間的差異。為了評估推薦功能之適用性，本論文也發展一套驗證指標，並採用一套隨機數學模型（Stochastic Mathematical Model），作為強化推薦預測之嘗試。 zh_TW dc.description.abstract (摘要) Customer Relationship Management (CRM) adopts various information technologies to retain and attain customers in order to generate more business values. However, the earlier studies indicate the failure rate for CRM systems is high and it’s even higher for Sales Force Automation (SFA), a major core in CRM. They usually suggest the enhancement in better management support, more training, user friendliness, and usage motivation, and so on. Following the suggestions, this research proposes a Sales Force Support (SFS) system to integrate technologies like OLAP (Online Analytical Processing), Data Mining (DM), and cloud service, etc. to provide supporting information in customer recommendation and self-evaluation, in order to better stimulate sales and satisfy customer and management. The objectives can be achieved by enhancing the user friendliness and usage motivation, and bridging the differences between sales force and management. To evaluate the fitness of recommendation function, a set of validation measures is also developed. In addition, a stochastic mathematical model is also attempted to enhance the recommendation prediction. en_US dc.description.tableofcontents 目 錄 III表目錄 V圖目錄 VIII一、緒 論 11-1 研究背景與動機 11-2 研究目的 11-3 研究範圍與限制 31-4 研究方法 4二、文獻探討 52-1 客戶關係管理(CRM) 52-2 銷售員自動化(SALES FORCE AUTOMATION, SFA) 62-3 商業智慧(BUSINESS INTELLIGENCE, BI) 82-4 推薦系統(RECOMMENDATION SYSTEMS) 92-5 雲端服務(CLOUD SERVICE) 13三、研究架構與資料收集 153-1 研究架構 213-2 資料收集與清理 243-3 基本資料分析 26四、研究結果與討論 284-1 以OLAP進行銷售員支援 294-2 關聯規則(ASSOCIATION RULES)資料分析 414-3 關聯規則之分析與驗證 534-4 蒙地卡羅馬可夫鏈(MONTE CARLO MARKOV CHAIN)分析與驗證 92五、結論與後續研究方向 1015-1 結論 1015-2 後續研究方向 103參考文獻 105 zh_TW dc.format.extent 4539166 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0094356505 en_US dc.subject (關鍵詞) 線上分析處理 zh_TW dc.subject (關鍵詞) 資料採礦 zh_TW dc.subject (關鍵詞) 客戶關係管理 zh_TW dc.subject (關鍵詞) 銷售員自動化 zh_TW dc.subject (關鍵詞) 客戶推薦 zh_TW dc.subject (關鍵詞) Online analytical processing en_US dc.subject (關鍵詞) Data mining en_US dc.subject (關鍵詞) Customer relationship management en_US dc.subject (關鍵詞) Sales force automation en_US dc.subject (關鍵詞) Customer recommendation en_US dc.title (題名) 雲端服務中銷售員支援之研究 zh_TW dc.title (題名) A study on sales force support in cloud service en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 中文文獻：可樂旅遊（2017年3月）。得獎榮耀。可樂旅遊。取自http://www.colatour.com.tw/webDM/Portal/intro/award.html。江逸之（2014年10月）。建立參謀制度 營收五年翻倍。天下雜誌，559。取自https://www.cw.com.tw/article/article.action?id=5062107交通部觀光局（2010）。中華民國98年國人旅遊狀況調查，臺北市：中華民國政府出版品。交通部觀光局（2012）。中華民國100年國人旅遊狀況調查，臺北市：中華民國政府出版品。交通部觀光局（2014）。中華民國102年國人旅遊狀況調查，臺北市：中華民國政府出版品。唐偉展（2012年12月9日）。四川綿陽市設立台北旅遊服務中心，康福雀屏中選 副市長親授牌。TTN旅報，755，53。取自https://issuu.com/valaissuu/docs/755梁任瑋（2012年5月）。王文傑打造雄獅百億旅遊王國的傳奇。今週刊，0806，85-88。取自https://www.businesstoday.com.tw/article-content-80408-94544-%E7%8E%8B%E6%96%87%E5%82%91%E6%89%93%E9%80%A0%20%E9%9B%84%E7%8D%85%E7%99%BE%E5%84%84%E6%97%85%E9%81%8A%E7%8E%8B%E5%9C%8B%E7%9A%84%E5%82%B3%E5%A5%87蔡瑞、齊佳音、屈啟興（2013）。刻畫客戶行為特徵的概率分布選擇研究，北京郵電大學學報（社會科學版）， 15(2)，79-85。蘇占東、游福成、楊炳儒（2004）。關聯規則的綜合評價方法研究與實例驗證，計算機應用，24(10)，17-20。英文文獻：Accenture. 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