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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/105700

    Title: 應用類神經網路於客服中心客服員之績效評比
    Other Titles: Performance appraisal of call center agents by neural networks
    Authors: 詹尉聰;Chan, Wei-Tsung
    Contributors: 淡江大學資訊工程學系碩士在職專班
    許輝煌;Hsu, Hui-Huang
    Keywords: 客服中心;倒傳遞類神經網路;Call Center;Back Propagation Neural Networks
    Date: 2015
    Issue Date: 2016-01-22 15:03:01 (UTC+8)
    Abstract: 客服中心早已成為企業與客戶之間溝通的橋樑,客服員在兩者間更扮演重要的角色,企業在人才的培養與網羅都需要一套制定的評分標準,該如何避免人為因素影響客服員績效分數,並傳承這寶貴的知識,各家企業無不絞盡腦汁思考。
    Call center has become the communication bridge between enterprises and customers, agents play an important role to between enterprises and customers. Enterprises in agents training and snares set of assessment criteria need to be developed. How to avoid human factors affecting service agent performance score, and pass this valuable knowledge, all enterprises are brainstorming.
    In this study, the sample data are based on historical data of internal systems, and with reference to 23 quantitative indicators about call center operations. The experimental process includes analyzing data correlation coefficient between performance score and each attribute, selecting feature sets by feature selection technology, and predicting the performance score of call center agents by back-propagation neural networks.
    By selecting a specific attribute set, the experiment result not only contains enterprises managers to score factors but also the main standard for performance appraisal of call center agents. We can know that each attribute in the feature set has a high probability to be considered when managers are making performance appraisal.
    The average error and the correlation coefficient between the output value from the prediction model and the real call center agents performances score are 2.78 and 0.9821, respectively. The experiment results show that the output from the network prediction model is very close to the real call center agents performance score. Its deviation is also lower than the expected value 3 of this study. In the end of the study, we correct some erroneous and further lower data the deviation to 1.97 and the correlation coefficient to 0.9907.This is very well.
    Appears in Collections:[資訊工程學系暨研究所] 學位論文

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