本論文共一冊，內含五章廿一節，約四萬餘字，主要以一般企業的銷售人員為對象， 利用便利抽樣方法，以問卷調查方式，蒐集約三百六十個樣本，探討下列問題： 一、一般企業銷售人員對顧客影響方式，大致可分為那幾類。 二、各類方式的使用效果、使用頻率與使用對象之間有何關係。 三、各類方式的使用效果、使用頻率與銷售人員的人口特質、生活態度、組織特性間 有何關係。

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. In addition, a stochastic mathematical model is also attempted to enhance the recommendation prediction.