基于XGBoost算法的坐席话务量预测  

Call Center Seat Telephone-Traffic Volume Prediction Based on XGBoost Algorithm

作　　者：赵龙 周源 李飞 范文斌 ZHAO Long;ZHOU Yuan;LI Fei;FAN Wenbin(Kedaduochuang Cloud Network Technology Co.,Ltd,Hefei 230000,China)

机构地区：[1]科大国创云网科技有限公司,安徽合肥230000

出　　处：《现代信息科技》2021年第22期86-88,91,共4页Modern Information Technology

摘　　要："呼叫中心"一词来源于英文Call Center,自其诞生以来就作为企业和客户之间的沟通桥梁,是客户和企业沟通最直接的渠道,因此优化呼叫中心的接听应答效率是管理者一直以来的追求。该文为了向管理者提供前瞻的话务量预测信息供管理者决策,提出了使用XGBoost、LightGBM、Catboost算法结合信息价值分析法选择的特征通过累加型滑动窗口法建立话务量预测模型,并在真实数据上比较了三个算法的预测表现。结果表明XGBoost算法对于运营商呼叫中心话务量的预测较为准确,为坐席排班提供数据支撑。The word“call center”comes from English Call Center, since its birth, it has been used as a communication bridge between enterprises and customers, it is the most direct channel for customers and enterprises to communicate. Therefore, optimizing the answering efficiency of call center has always been the pursuit of managers. To provide managers with forward-looking forecast information of telephonetraffic volume for their decision-making, this paper proposes to use XGBoost, LightGBM and Catboost algorithms and combined with the characteristics selected by the information value analysis method to establish the telephone-traffic volume prediction model through the cumulative sliding window method, and compares the prediction performance of the three algorithms on the real data. The results show that the XGBoost algorithm is more accurate in predicting the call center’s telephone-traffic volume, and provide data support for seat scheduling.

关 键 词：呼叫中心 XGBoost 话务量 坐席排班 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]