Understanding customer satisfaction via deep learning and natural language processing

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dc.coverageDOI: 10.1016/j.eswa.2022.118309
dc.creatorAldunate, Ángeles
dc.creatorMaldonado, Sebastián
dc.creatorVairetti, Carla
dc.creatorArmelini, Guillermo
dc.date2022
dc.date.accessioned05-01-2026 18:04
dc.date.available05-01-2026 18:04
dc.description<p>It is of utmost importance for marketing academics and service industry practitioners to understand the factors that influence customer satisfaction. This study proposes a novel framework to analyze open-ended survey data and extract drivers of customer satisfaction. This is done automatically via deep learning models for natural language processing. According to 11 drivers acknowledged by the marketing literature to determine customer experience, the data is cast into a multi-label classification problem. This expert system not only supports the automatic analysis of new data but also ranks the drivers according to their importance to various service industries and provides important insights into their applications. Experiments carried out using 25,943 customer survey responses related to 39 service companies in 13 different economic sectors show that the drivers can be identified accurately.</p>eng
dc.descriptiont is of utmost importance for marketing academics and service industry practitioners to understand the factors that influence customer satisfaction. This study proposes a novel framework to analyze open-ended survey data and extract drivers of customer satisfaction. This is done automatically via deep learning models for natural language processing. According to 11 drivers acknowledged by the marketing literature to determine customer experience, the data is cast into a multi-label classification problem. This expert system not only supports the automatic analysis of new data but also ranks the drivers according to their importance to various service industries and provides important insights into their applications. Experiments carried out using 25,943 customer survey responses related to 39 service companies in 13 different economic sectors show that the drivers can be identified accurately.spa
dc.identifierhttps://investigadores.uandes.cl/en/publications/16ad24e3-00d2-4084-99d3-9dd360ac406d
dc.languageeng
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.sourcevol.209 (2022) date: 2022-12-15
dc.subjectAnalytics
dc.subjectBERT
dc.subjectCustomer feedback
dc.subjectCustomer satisfaction
dc.subjectDeep learning
dc.subjectNatural language processing
dc.subjectAnalytics
dc.subjectCustomer satisfaction
dc.subjectCustomer feedback
dc.subjectNatural language processing
dc.subjectDeep learning
dc.subjectBERT
dc.titleUnderstanding customer satisfaction via deep learning and natural language processingeng
dc.typeArticleeng
dc.typeArtículospa
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