Experience: towards automated customer issue resolution in cellular networks
Experience: towards automated customer issue resolution in cellular networks
复制标题
经验：在蜂窝网络中实现自动化客户问题解决
DOI：
10.1145/3372224.3419203
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Wang, Jia
中科院分区：
文献类型：
--
作者：
Sheoran, Amit;Fahmy, Sonia;Osinski, Matthew;Peng, Chunyi;Ribeiro, Bruno;Wang, Jia
蜂窝服务运营商通常采用响应策略来帮助遇到非中断相关的单个服务降级问题的客户（例如，不会大规模影响客户的服务性能降级，并且可能是由单个设备的网络供应问题引起的）。在这些问题得到解决之前，客户需要联系客户服务中心请求帮助。本文介绍了我们在PACE（主动客户关怀）方面的经验，这是一种新颖的、主动的系统，可以监控、排除故障并解决个人服务问题，而不必依赖客户首先联系客户服务中心寻求帮助。PACE旨在通过自动检测单个（非停机相关）服务问题、通过预测可能联系维修中心报告问题的客户来确定维修行动的优先级、以及主动触发解决这些问题的行动来改善客户体验和维修操作效率。我们开发了三种基于机器学习的预测模型，并实现了一个完全自动化的系统，该系统集成了这些预测模型并为个人客户采取解决措施。我们使用从美国主要蜂窝运营商收集的真实数据进行了大规模的跟踪驱动评估，并证明PACE能够高精度地预测由于非停机相关的个人服务问题而可能联系护理的客户。我们进一步将PACE部署到这个蜂窝运营商网络中。我们的现场试验结果表明，PACE在主动解决与非中断相关的个人客户服务问题、改善客户体验和减少客户报告其服务问题的需要方面是有效的。
Cellular service carriers often employreactivestrategies to assist customers who experience non-outage related individual service degradation issues (e.g., service performance degradations that do not impact customers at scale and are likely caused by network provisioning issues for individual devices). Customers need to contact customer care to request assistance before these issues are resolved. This paper presents our experience with PACE (ProActive customerCarE), a novel,proactivesystem that monitors, troubleshoots and resolves individual service issues, without having to rely on customers to first contact customer care for assistance. PACE seeks to improve customer experience and care operation efficiency byautomaticallydetecting individual (non-outage related) service issues, prioritizing repair actions by predicting customers who are likely to contact care to report their issues, and proactively triggering actions to resolve these issues. We develop three machine learning-based prediction models, and implement a fully automated system that integrates these prediction models and takes resolution actionsfor individual customers.We conduct a large-scale trace-driven evaluation using real-world data collected from a major cellular carrier in the US, and demonstrate that PACE is able to predict customers who are likely to contact care due to non-outage related individual service issues with high accuracy. We further deploy PACE into this cellular carrier network. Our field trial results show that PACE is effective in proactively resolving non-outage related individual customer service issues, improving customer experience, and reducing the need for customers to report their service issues.
登录
查看更多内容
影响因子：
7.5
作者：
A. Veith;M. Assunção
通讯作者：
A. Veith;M. Assunção
态度的 3D 模型
DOI：
--
发表时间：
2014
期刊：
影响因子：
--
作者：
Vishal Jain
通讯作者：
Vishal Jain
识别蜂窝服务中受影响的用户
DOI：
--
发表时间：
2019
期刊：
Knowledge Discovery and Data Mining
影响因子：
--
作者：
Shobha Venkataraman;Jia Wang
通讯作者：
Jia Wang
服务中心通话记录文本数据挖掘
DOI：
--
发表时间：
2000
期刊：
Knowledge Discovery and Data Mining
影响因子：
--
作者：
P. Tan;H. Blau;S. Harp;R. Goldman
通讯作者：
R. Goldman