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中文摘要: 为了解决客服专员业务能力不一、培训工作复杂难度高的问题,通过对超过4000名客服专员、涉及超过300类业务需求的业务行为、个人特征、质检工单等近2年数据的初步处理与分析,从超过亿条工单数据中统计出21个业务能力评价维度,建立决策树与神经网络混合模型,以决策树模型筛选出的业务能力影响因子应用于 LMBP 神经网络预测模型得出较准确的业务能力评价客服专员分群。为了规划客服专员培训体系,通过模型的分群结果分析,对不擅长处理某业务的客服专员针对最细业务划分进行定制化培训,准确高效提升客服专员业务短板能力。同时反馈培训结果到模型分析,完善业务能力评价维度,优化分群结果。与客服中心平均业务培训时间对比,应用此分类结果进行定制化培训可以有效节约工作时间,且培训业务范围更广。
Abstract:In order to solve the problems of different professional abilities and complicated training work of the Customer Service Commissioner, through the preliminary processing and analysis of the data of more than 4000 Customer Service Commissioners, involving more than 300 types of business needs, such as business behavior, personal characteristics, quality inspection work orders, etc. in the past two years, 21 business ability evaluation dimensions are counted out from more than 100 million work orders, and a hybrid model of decision tree and neural network is established. The business capability impact factor selected by the decision tree model is applied to the LMBP neural network prediction model to get a more accurate business capability evaluation of customer service specialists clustering. In order to plan the training system of Customer Service Commissioner, through cluster analysis of the model, Customer Service Commissioner who is not good at dealing with a certain business is trained customized for the smallest business division, so as to improve the ability of Customer Service Commissioner"s business shortboard accurately and efficiently. At the same time, feedback the training results to the model analysis, improve the dimensions of business competence evaluation, and optimize the clustering results. Compared with the average business training time of customer service center, customized training based on the classification results can effectively save working time, and the training business scope is wider.
keywords: customer service specialist business capability business training neural network decision tree hybrid model
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