The Potential of Chatbots: Analysis of Chatbot Conversations

Mubashara Akhtar, J. Neidhardt, H. Werthner
{"title":"The Potential of Chatbots: Analysis of Chatbot Conversations","authors":"Mubashara Akhtar, J. Neidhardt, H. Werthner","doi":"10.1109/CBI.2019.00052","DOIUrl":null,"url":null,"abstract":"The idea of utilizing computers for question answering tasks has been around from the early beginning of these systems. First algorithms with the aim to accomplish this were already implemented in the early 1960s. In recent years, chatbots have been gaining enormous popularity in various fields. In the context of business applications, they are considered as useful tools for improving customer relationships. In this paper, chat conversations between customers and the chatbot of a telecommunication company are analysed to find out if these interactions can be used to determine a) users' topics of interests and b) user satisfaction. To reach this goal, chat conversations are interpreted as sequences of events and user inputs are analysed with the help of text mining techniques. The study shows that based on users' written conversational contributions, valuable insights on users' interests and satisfaction can be gained. The majority of users leave the chat conversation after a short period of time if the chatbot was not able to give the desired answer right away. Moreover, a huge number of conversations deal with similar topics. Our results imply that companies offering chatbots must thoroughly analyse the collected data to gain more insights into their customers' needs. Based on our findings, they can improve customers' satisfaction by offering personalized service and implementing real-time feedback.","PeriodicalId":193238,"journal":{"name":"2019 IEEE 21st Conference on Business Informatics (CBI)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 21st Conference on Business Informatics (CBI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBI.2019.00052","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 20

Abstract

The idea of utilizing computers for question answering tasks has been around from the early beginning of these systems. First algorithms with the aim to accomplish this were already implemented in the early 1960s. In recent years, chatbots have been gaining enormous popularity in various fields. In the context of business applications, they are considered as useful tools for improving customer relationships. In this paper, chat conversations between customers and the chatbot of a telecommunication company are analysed to find out if these interactions can be used to determine a) users' topics of interests and b) user satisfaction. To reach this goal, chat conversations are interpreted as sequences of events and user inputs are analysed with the help of text mining techniques. The study shows that based on users' written conversational contributions, valuable insights on users' interests and satisfaction can be gained. The majority of users leave the chat conversation after a short period of time if the chatbot was not able to give the desired answer right away. Moreover, a huge number of conversations deal with similar topics. Our results imply that companies offering chatbots must thoroughly analyse the collected data to gain more insights into their customers' needs. Based on our findings, they can improve customers' satisfaction by offering personalized service and implementing real-time feedback.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
聊天机器人的潜力:聊天机器人对话分析
从这些系统的早期开始,利用计算机进行问答任务的想法就已经存在了。第一批旨在实现这一目标的算法在20世纪60年代初就已经实现了。近年来,聊天机器人在各个领域都获得了极大的普及。在业务应用程序的上下文中,它们被认为是改善客户关系的有用工具。在本文中,客户和电信公司的聊天机器人之间的聊天对话进行了分析,以找出这些交互是否可以用来确定a)用户的兴趣话题和b)用户满意度。为了实现这一目标,聊天对话被解释为事件序列,并在文本挖掘技术的帮助下分析用户输入。研究表明,基于用户的书面会话贡献,可以获得关于用户兴趣和满意度的有价值的见解。如果聊天机器人不能马上给出想要的答案,大多数用户会在很短的时间后离开聊天。此外,大量的对话涉及类似的话题。我们的研究结果表明,提供聊天机器人的公司必须彻底分析收集到的数据,以更深入地了解客户的需求。根据我们的研究结果,他们可以通过提供个性化服务和实施实时反馈来提高客户满意度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Preconditions for the Use of a Checklist by Enterprise Architects to Improve the Quality of a Business Case A Framework for Industrial Symbiosis Systems for Agent-Based Simulation Conceptual Modeling Meets Customer Journey Mapping: Structuring a Tool for Service Innovation Are We Ready to Play in the Cloud? Developing new Quality Certifications to Tackle Challenges of Cloud Gaming Services Shadow IT and Business-Managed IT: Where Is the Theory?
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1