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

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<dc:creator>Liao, Xiyue </dc:creator>
<dc:creator>Chen, Guoqiang</dc:creator>
<dc:creator>Ku, Ben</dc:creator>
<dc:creator>Narula, Rahul </dc:creator>
<dc:creator>Duncan, Janet </dc:creator>
<dc:date>2020-03-02</dc:date>
<dc:description xml:lang="es">Sumario: 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.</dc:description>
<dc:identifier>https://documentacion.fundacionmapfre.org/documentacion/publico/es/bib/171218.do</dc:identifier>
<dc:language>eng</dc:language>
<dc:rights xml:lang="es">InC - http://rightsstatements.org/vocab/InC/1.0/</dc:rights>
<dc:subject xml:lang="es">Empresas de seguros</dc:subject>
<dc:subject xml:lang="es">Atención al cliente</dc:subject>
<dc:subject xml:lang="es">Opinión del cliente</dc:subject>
<dc:subject xml:lang="es">Asegurados</dc:subject>
<dc:subject xml:lang="es">Casos prácticos</dc:subject>
<dc:subject xml:lang="es">Estados Unidos</dc:subject>
<dc:type xml:lang="es">Artículos y capítulos</dc:type>
<dc:title xml:lang="es">Text mining methods applied to insurance company customer calls : a case study</dc:title>
<dc:relation xml:lang="es">En: North American actuarial journal. - Schaumburg : Society of Actuaries, 1997- = ISSN 1092-0277. - 02/03/2020 Tomo 24 Número 1 - 2020 , p. 153-163</dc:relation>
<dc:coverage xml:lang="es">Estados Unidos</dc:coverage>
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