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Text mining methods applied to insurance company customer calls : a case study

 
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<title>Text mining methods applied to insurance company customer calls</title>
<subTitle>: a case study</subTitle>
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<namePart>Liao, Xiyue </namePart>
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<namePart>Chen, Guoqiang</namePart>
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<namePart>Ku, Ben</namePart>
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<namePart>Narula, Rahul </namePart>
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<abstract displayLabel="Summary">The purpose of this case study is to develop a process for a U.S. personal lines insurance company to improve its customer service, make call center operations more efficient, and reduce costs by analyzing customer calls. Text mining methods such as topic modeling and sentiment analysis are used to study approximately 10,000 nonclaim customer calls from 2016. Results show the most frequent topics of calls and how customer sentiment differs between topics, which will allow the company to adjust its customer service accordingly.</abstract>
<note type="statement of responsibility">Xiyue Liao...[El al.]</note>
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<topic>Empresas de seguros</topic>
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<topic>Atención al cliente</topic>
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<geographic>Estados Unidos</geographic>
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<title>North American actuarial journal</title>
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<publisher>Schaumburg : Society of Actuaries, 1997-</publisher>
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<identifier type="issn">1092-0277</identifier>
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<text>02/03/2020 Tomo 24 Número 1 - 2020 , p. 153-163</text>
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