摘要
将AI与大数据技术应用于电力ICT客户服务,针对传统客服的三大问题:前端坐席知识库缺乏自学习能力、中端积累的ICT客服数据不能有效分析利用、后端质检不能全覆盖且缺乏情感识别。通过构建智能知识库、自学习分析模型、信息系统故障原因关联分析模型等,实现人机结合的智能客服模式、全量话务智能质检,提升客户服务满意度。前端,系统根据用户口述问题,实现语音交互,将用户导航至指定业务条线客服人员,为用户进行精准服务。中端,通过前期积累数据,得出用户实用化大数据分析。主要体现在通过关联词分析用户潜在需求,通过话务量与检修关联分析得出信息系统故障情况,缩短故障时间等,通过话务引导信息系统改善方向,实现辅助分析决策等。后端,为根据情感分析及机器学习的情感分析实现全话务智能质检,提高用户满意度。文章提供了一种提供用户实用化热点检测、故障辅助研判、用户行为分析等智能分析服务的创新研究及实现,为各业务部门提供主动式精准服务,全面提升信息化价值创造力。
In order to solve three big questions of the traditional customer service: lacking self-learning ability in the front,the ICT call center’s data accumulated in the middle end cannot be effectively analyzed and utilized,quality control can ’t cover all and lack of emotion recognition in back-end,AI and big data technology was applied to electric power ICT customer service. By building intelligent knowledge base,self-learning analysis model,the analysis model of the reason of information system’s fault and so on,to Implement human-computer intelligent customer service mode,intelligent quality inspection of all calls,and improve customer service satisfaction;In the front-end,the system realizes voice interaction according to the user’s oral requirements,and navigates the user to the specific customer service line to provide precise service. In the middle end,through the accumulation of data in the early stage,users’ practical data analysis can be shown. Mainly embodied in the analysis of the potential needs of users through the association word,through the traffic and maintenance association analysis of information system failure,shorten the failure time,through the traffic guidance information system improvement direction,to achieve auxiliary analysis and decision-making. Back-end,achieve intelligent quality inspection of all calls based on emotion analysis and machine learning emotion analysis,and improve user satisfaction. This study develops user practical hot spot detection,intelligent analysis services,user behavior analysis and so on. This research provides accurate and active service for each business unit,and also can improve the value of ICT call center.
作者
郑蓉蓉
闫珺路
李莉敏
赵伟
袁兆君
韩笑
李枫
李祥纳
ZHENG Rongrong;YAN Junlu;LI Limin;ZHAO Wei;YUAN Zhaojun;HAN Xiao;LI Feng;LI Xiangna(State Grid Information &Telecommunication Branch,Beijing 100761China;Beijing Guodiantong Network Technology Corporation,Beijing 100700China)
出处
《电力大数据》
2019年第1期71-76,共6页
Power Systems and Big Data
关键词
智能客服
大数据分析
智能分析工具
Intelligent call center
big data
intelligent analyze instrument
