以往針對客戶流失與轉換行為所研究的對象，多偏重以消費品產業為主，較少探討工業品產業客戶流失對於企業經營所造成之影響。本研究針對工業品產業中之營建工具業，探討其客戶流失之原因及行為表現，並期望透過相關研究，使業者可預先發現可能流失之客戶，並做為後續發展客戶慰留專案之參考。 本研究可分為五部份，第一部份首先將回顧與本論文有關之文獻。第二部份則提出本研究之研究架構及研究方法。第三部份則以SPSS軟體進行實證研究，統計方法係利用敘述性統計分析、因素分析、信度分析、單因子變異數分析及區別分析等進行資料分析。第四部分為討論前述之研究發現，並將其與消費品市場之客戶流失行為模式相比較。最後則為結論與建議。 研究結果發現：1.流失原因可萃取出產品及服務因素及價格因素兩大構面。轉購原因可萃取出服務及品牌策略、產品策略及價格策略三大構面。2.流失行為係以降低購買頻率及轉換新的供應商兩種方式表現。3.營建工具業與服務業客戶轉換行為模式有明顯差異。4.購買持續時間較長之客戶，對於送貨時間太久之重要性認知程度與購買持續時間較短之客戶有明顯差異。產品價格太高及採購頻率兩項變數可作為判別流失客戶是否會轉換供應商之模式。但由於區別力不甚良好，故並不適合以此兩項變數作為判斷預測之基準。 / A great deal of effort has been made on the causes of customer churn in the consumer products industry. What seems to be lacking, however, is this subject in the industrial products industry. This study will focus the discussion on the causes of customer churn and customer switching behavior in the construction products supplier, in order to provide guidance for developing retention and loyalty programs. This study can be divided into five parts; the first part reviews the literature on this subject. The second part introduces the methodology to be utilized throughout the study, first with structural diagram of study followed by study methods, and object in study. The third part utilizes using SPSS for Windows as the tool to conduct statistical analysis, including description statistical analysis, reliability test, Discriminant Analysis, Factor Analysis, and One-way ANOVA. The fourth part discusses the experimental result of this study, and compares it with customer switching behavior in the consumer products industry. The last part is a conclusion of the thesis. The results of this study show as follows. 1. The main causes of customer churn are product and service oriented or price oriented. The main causes of customer switch are service and brand strategy, product strategy or price strategy. 2. Customer switching behavior includes decreasing purchased frequency and transferring to a new service provider. 3. Customer switching behavioral model in the service industry is different from the model in the construction products supplier. 4. The customers who have longer purchasing duration have higher recognition of importance for deliver time. Purchasing frequency and product price are not the best variables to predict if the customers would churn or not.

This research study focuses on the application models of Data Mining and Machine Learning covering cellular network traffic, in the objective to arm Mobile Network Operators with full view of performance branches (Services, Device, Subscribers). The purpose is to optimize and minimize the time to detect service and subscriber patterns behaviour. Different data mining techniques and predictive algorithms have been applied on real cellular network datasets to uncover different data usage patterns using specific Key Performance Indicators (KPIs) and Key Quality Indicators (KQI). The following tools will be used to develop the concept: RStudio for Machine Learning and process visualization, Apache Spark, SparkSQL for data and big data processing and clicData for service Visualization. Two use cases have been studied during this research. In the first study, the process of Data and predictive Analytics are fully applied in the field of Telecommunications to efficiently address users’ experience, in the goal of increasing customer loyalty and decreasing churn or customer attrition. Using real cellular network transactions, prediction analytics are used to predict customers who are likely to churn, which can result in revenue loss. Prediction algorithms and models including Classification Tree, Random Forest, Neural Networks and Gradient boosting have been used with an exploratory Data Analysis, determining relationship between predicting variables. The data is segmented in to two, a training set to train the model and a testing set to test the model. The evaluation of the best performing model is based on the prediction accuracy, sensitivity, specificity and the Confusion Matrix on the test set. The second use case analyses Service Quality Management using modern data mining techniques and the advantages of in-memory big data processing with Apache Spark and SparkSQL to save cost on tool investment; thus, a low-cost Service Quality Management model is proposed and analyzed. With increase in Smart phone adoption, access to mobile internet services, applications such as streaming, interactive chats require a certain service level to ensure customer satisfaction. As a result, an SQM framework is developed with Service Quality Index (SQI) and Key Performance Index (KPI). The research concludes with recommendations and future studies around modern technology applications in Telecommunications including Internet of Things (IoT), Cloud and recommender systems. / Cellular networks have evolved and are still evolving, from traditional GSM (Global System for Mobile Communication) Circuit switched which only supported voice services and extremely low data rate, to LTE all Packet networks accommodating high speed data used for various service applications such as video streaming, video conferencing, heavy torrent download; and for say in a near future the roll-out of the Fifth generation (5G) cellular networks, intended to support complex technologies such as IoT (Internet of Things), High Definition video streaming and projected to cater massive amount of data. With high demand on network services and easy access to mobile phones, billions of transactions are performed by subscribers. The transactions appear in the form of SMSs, Handovers, voice calls, web browsing activities, video and audio streaming, heavy downloads and uploads. Nevertheless, the stormy growth in data traffic and the high requirements of new services introduce bigger challenges to Mobile Network Operators (NMOs) in analysing the big data traffic flowing in the network. Therefore, Quality of Service (QoS) and Quality of Experience (QoE) turn in to a challenge. Inefficiency in mining, analysing data and applying predictive intelligence on network traffic can produce high rate of unhappy customers or subscribers, loss on revenue and negative services’ perspective. Researchers and Service Providers are investing in Data mining, Machine Learning and AI (Artificial Intelligence) methods to manage services and experience. This research study focuses on the application models of Data Mining and Machine Learning covering network traffic, in the objective to arm Mobile Network Operators with full view of performance branches (Services, Device, Subscribers). The purpose is to optimize and minimize the time to detect service and subscriber patterns behaviour. Different data mining techniques and predictive algorithms will be applied on cellular network datasets to uncover different data usage patterns using specific Key Performance Indicators (KPIs) and Key Quality Indicators (KQI). The following tools will be used to develop the concept: R-Studio for Machine Learning, Apache Spark, SparkSQL for data processing and clicData for Visualization. / Electrical and Mining Engineering / M. Tech (Electrical Engineering)

本研究所欲探討的主題，為國內 ISP 業者降低客戶流失率所採用的做法。由於目前ISP業競爭十分激烈，各業者為了爭奪市場，常常以低價，乃至於不計成本的做法爭取客戶。然而這些虧本爭取的客戶若不能有效的留住，之前的投資可為白白浪費。因此，如何能夠有效的降低客戶流失率，是ISP業者非常重要的課題。 降低客戶流失的可行手段之一，是提高服務品質，讓客戶滿意於供應商的服務而樂於長期使用。另一種方式則是產品之鎖定機制 (Lock-in) 的運用，使客戶一旦購買某一項服務，因受到產品機制的鎖定而提高轉換成本，而不願轉換 ISP。一旦ISP能夠以適當有效的做法降低客戶流失率，就能以各種即使沒有利潤乃至於虧損的做法先吸引客戶進門，再由後續的服務回收應得的利潤。也唯有如此，ISP業者才能在嚴酷的競爭中存活。 本研究透過瞭解 ISP 產業之特性與類型；對台灣的十一家具有代表性之ISP業者，調查與整理其服務品質，以及所使用的產品鎖定機制；探討服務品質高低與市場佔有率之關係，以及不同類型之 ISP 所採用之做法的差異，並給予不同的改進建議。主要的研究結論如下： ◆ 高品質的服務有助於大型ISP業者維持市場佔有率，同時對於中小型業者的市場佔有率提昇有所助益。反之，低服務品質對於ISP維持市場佔有率有不利的影響。 ◆ 同時有助於吸引新客戶與維繫舊客戶的服務要素，以及可直接增加營收，或是減少成本的服務要素，為ISP優先重視之項目。 ◆ 服務品質領先之業者與落後者的差異，主要為交易中的服務要素。 ◆ 大型與中小型ISP在服務品質有差異之項目上，大型業者具有絕對領先優勢。兼營IDC業務之ISP與純ISP，在服務品質有差異之項目上，兼營IDC之業者具有絕對領先優勢。 ◆ 簡單之產品鎖定機制最受到ISP業者的歡迎。

User retention is a key factor for Software as a Service (SaaS) companies to ensure long-term growth and profitability. One area which can have a lasting impact on a digital product’s user retention is its user onboarding experience, that is, the methods and elements that guide new users to become familiar with the product and activate them to become fully registered users. Within the area of user onboarding, multiple authors discuss “best practice” design patterns which are stated to positively influence the user retention of new users. However, none of the sources reviewed showcase any statistically significant proof of this claim. Thus, the objective of this study was to: Design and implement a set of commonly applied design patterns within a digital product’s user onboarding experience and evaluate their effects on user retention Through A/B testing on the SaaS product GetAccept, the following two design patterns were evaluated: Reduce friction – reducing the number of barriers and steps for a new user when first using a digital product; and Monitor progress – monitoring and clearly showcasing the progress of a new user’s journey when first using a digital product. The retention metric used to evaluate the two design patterns was first week user retention, defined as the share of customers who after signing up, sign in again at least once within one week. This was tested by randomly assigning new users into different groups: groups that did receive changes related to the design patterns, and one group did not receive any changes. By then comparing the first week user retention data between the groups using Fisher’s exact test, the conclusion could be drawn that with statistical significance, both of the evaluated design patterns positively influenced user retention for GetAccept. Furthermore, due to the generalizable nature of GetAccept’s product and the aspects evaluated, this conclusion should also be applicable to other companies and digital products with similar characteristics, and the method used to evaluate the impact of implementing the design patterns should be applicable for evaluating other design patterns and/or changes in digital products. However, as the method used for data collection in the study could not ensure full validity of it, the study could and should be repeated with the same design patterns on another digital product and set of users in order to strengthen the reliability of the conclusions drawn.