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John R. Hauser. Timoshenko, Artem Sloan School of Management. Sloan School of Management 2017-06-06T19:23:23Z 2017-06-06T19:23:23Z 2017 2017 http://hdl.handle.net/1721.1/109648 987002329 Thesis: S.M. in Management Research, Massachusetts Institute of Technology, Sloan School of Management, 2017. Cataloged from PDF version of thesis. Includes bibliographical references (pages 23-24). Understanding customer needs is an important part of marketing strategy, product development, and marketing research. The explosive growth of user-generated content (UGC) creates an opportunity to enhance industry-standard interview-based approaches for identifying customer needs. However, the traditional manual review approach is neither efficient nor effective when applied to a large UGC corpus because non-informative and repetitive content crowd out information about customer needs. We identify customer needs from UGC by combining machine learning methods to select content for review with human judgement to formulate customer needs. In particular, we use a convolutional neural network to filter out non-informative content and dense sentence representations to identify sufficiently different sentences for manual review. An empirical proof-of-concept compares customer needs for oral care products identified from online reviews (UGC) with customer needs identified by a third-party professional consulting firm using industry-standard methods. In this application, UGC identifies additional customer needs, unreachable by the interview-based approach. Our approach improves efficiency of manual review in terms of a number of unique customer needs per unit effort. by Artem Timoshenko. S.M. in Management Research 24 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Sloan School of Management. Identifying customer needs from user-generated content Identifying customer needs from UGC Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="8986fb82-93d6-4d1c-b176-8ba6f6414724"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Identifying customer needs from user-generated content</Title> <Subtitle>Identifying customer needs from UGC</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2017</PublicationDate> <Authors> <Author> <DisplayName>Timoshenko, Artem</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Sloan School of Management.</Keyword> <Abstract>Understanding customer needs is an important part of marketing strategy, product development, and marketing research. The explosive growth of user-generated content (UGC) creates an opportunity to enhance industry-standard interview-based approaches for identifying customer needs. However, the traditional manual review approach is neither efficient nor effective when applied to a large UGC corpus because non-informative and repetitive content crowd out information about customer needs. We identify customer needs from UGC by combining machine learning methods to select content for review with human judgement to formulate customer needs. In particular, we use a convolutional neural network to filter out non-informative content and dense sentence representations to identify sufficiently different sentences for manual review. An empirical proof-of-concept compares customer needs for oral care products identified from online reviews (UGC) with customer needs identified by a third-party professional consulting firm using industry-standard methods. In this application, UGC identifies additional customer needs, unreachable by the interview-based approach. Our approach improves efficiency of manual review in terms of a number of unique customer needs per unit effort.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>