Interpretable machine learning-based approach for customer segmentation for new product development from online product reviews
DC Field | Value | Language |
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dc.contributor.author | Joung, Junegak | - |
dc.contributor.author | Kim, Harrison | - |
dc.date.accessioned | 2023-05-03T09:59:15Z | - |
dc.date.available | 2023-05-03T09:59:15Z | - |
dc.date.created | 2023-03-08 | - |
dc.date.issued | 2023-06 | - |
dc.identifier.issn | 0268-4012 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/184990 | - |
dc.description.abstract | For new product development, previous segmentation methods based on demographic, psychographic, and purchase behavior information cannot identify a group of customers with unsatisfied needs. Moreover, segmentation is limited to sales promotions in marketing. Although needs-based segmentation considering customer sentiments on product features can be conducted to develop a new product concept, it cannot identify commonalities among customers owing to their diverse preferences. Therefore, this paper proposes an interpretable machine learning-based approach for customer segmentation for new product development based on the importance of product features from online product reviews. The technical challenges of determining the importance of product features in each review are identifying and interpreting the nonlinear relations between satisfaction with product features and overall customer satisfaction. In this study, interpretable machine learning is used to identify these nonlinear relations with high performance and transparency. A case study on a wearable device is conducted to validate the proposed approach. Customer segmentation using the proposed approach based on importance is compared with that employing a previous approach based on sentiments. The results show that the proposed approach presents a higher clustering performance than the previous approach and offers opportunities to identify new product concepts. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCI LTD | - |
dc.title | Interpretable machine learning-based approach for customer segmentation for new product development from online product reviews | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Joung, Junegak | - |
dc.identifier.doi | 10.1016/j.ijinfomgt.2023.102641 | - |
dc.identifier.scopusid | 2-s2.0-85149059468 | - |
dc.identifier.wosid | 000946547800001 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT, v.70, pp.1 - 12 | - |
dc.relation.isPartOf | INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT | - |
dc.citation.title | INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT | - |
dc.citation.volume | 70 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 12 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | ssci | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Information Science & Library Science | - |
dc.relation.journalWebOfScienceCategory | Information Science & Library Science | - |
dc.subject.keywordPlus | IMPORTANCE-PERFORMANCE ANALYSIS | - |
dc.subject.keywordPlus | MARKET-SEGMENTATION | - |
dc.subject.keywordPlus | SATISFACTION | - |
dc.subject.keywordPlus | INTELLIGENCE | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordPlus | RFM | - |
dc.subject.keywordAuthor | Customer segmentation | - |
dc.subject.keywordAuthor | Explainable artificial intelligence | - |
dc.subject.keywordAuthor | Online review mining | - |
dc.subject.keywordAuthor | Product planning | - |
dc.subject.keywordAuthor | Text mining | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S0268401223000221?via%3Dihub | - |
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