應用資料探勘技術於電話行銷成功與否之預測

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Title:	應用資料探勘技術於電話行銷成功與否之預測
Other Titles:	Using data mining techniques to predict the success of telemarketing
Authors:	張凱評;Chang, Kai-Ping
Contributors:	淡江大學資訊工程學系碩士在職專班許輝煌;Hsu, Hui-Huang
Keywords:	電話行銷;倒傳遞類神經網路;支援向量機;決策樹;資料不平衡問題;telemarketing;Back Propagation Neural Networks;Support Vector Machine;Decision tree;imbalanced data problem
Date:	2016
Issue Date:	2017-08-24 23:50:39 (UTC+8)
Abstract:	近年來越來越多企業開始注重與客戶之間的關係，從傳統的商品導向銷售模式，逐漸轉型為客製化經營思維，行銷方式也轉變為客戶導向行銷模式，電話行銷也因此蓬勃發展，許多企業紛紛成立自己的電銷中心，並期望透過電話行銷為企業帶來豐厚的利潤。為了能夠了解客戶的需求及喜好，企業需要透過大量的資料分析，來得知客戶的價值與重要性，針對高價值客戶，企業可以對其喜好做適當的調整，為重要的客戶提供更完善的服務。本研究以UC Irvine Machine Learning Repository所提供的銀行客戶電話行銷記錄，做為研究數據的基礎，針對客戶名單進行前置處理，解決類別不平衡的問題，使用支援向量機、決策樹、類神經網路建立分類系統，並觀察分類器的預測結果，進行分類模型比較分析。實驗結果顯示，分類模型搭配取樣技術可有效改善資料類別不平衡問題，降低分類模型誤判的機率，其中以合成少數類別技術(SMOTE)搭配類神經的結果最佳，真陽性率(TPR)可達97.69%，其次為隨機減少多數類別法(Random Under-Sampling)搭配支援向量機，真陽性率為95.46%，由此可知，應用資料探勘技術於電話行銷，可幫助企業降低銷售成本，挖掘潛在客戶，增加企業利潤。 In recent years, more and more enterprises began to focus on the relation between customers and corporation. The traditional commodity-oriented sales model is gradually transformed into customized business thinking. The way of marketing turn to customer-oriented marketing model and, in consequence, telemarketing has booming. Many companies have set up their own telemarketing center, and expect huge profits for the enterprise through telemarketing. In order to realize customer’s needs and taste, companies need to get a large amount of information through the data analysis to know the value and importance of customers. Companies can make the appropriate adjustments for high-value customers, in addition to provide better service for important customer. In this study, the data of the research base on bank customer telemarketing records which provided by UC Irvine Machine Learning Repository (UCI). Pre-processing the customer list in order to solve the problem of imbalanced data. Using Support Vector Machine (SVM), Decision Trees, Artificial Neural Network classification system established, and observation classifier prediction, comparative analysis of classification models. Experimental results show that the classification model with Sampling techniques can effectively improve the imbalanced data problem, reduce the chance of false negative classification model, which Synthetic Minority Over-Sampling Technique (SMOTE) with Artificial Neural Network had best results, True Positive Rate (TPR) up to 97.69%, followed by Under-Sampling with Support Vector Machine (SVM), True Positive Rate (TPR) is 95.46%. Consequently, the application of data mining in telemarketing, can help companies reduce the cost of sales, and thus potential customers, increase corporate profits.
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