Deciphering Customer Satisfaction: A Machine Learning-Oriented Method Using Agglomerative Clustering for Predictive Modeling and Feature Selection

[1] A.H. Kracklauer, D.Q. Mills, D. Seifert, Customer Management as the Origin of Collaborative Customer Relationship Management, in Collaborative Customer Relationship Management, A. H. Kracklauer, D.Q. Mills, et D. Seifert, Éd., Berlin, Heidelberg: Springer Berlin Heidelberg, 2004, pp. 3–6. doi: 10.1007/978-3-540-24710-4_1. Search in Google Scholar

[2] E.W. Ngai, L. Xiu, D.C. Chau, Application of data mining techniques in customer relationship management: A literature review and classification, Expert systems with applications, vol. 36, no 2, pp. 2592–2602, 2009. Search in Google Scholar

[3] E.Y. Lee, S. Yoo, D.W. Lee, Does the Variance of Customer Satisfaction Matter for Firm Performance? Asia Marketing Journal, vol. 18, no 4, p. 3, 2017. Search in Google Scholar

[4] B.A. Tama, Data mining for predicting customer satisfaction in fast-food restaurant, Journal of Theoretical & Applied Information Technology, vol. 75, no 1, 2015, Consulté le: 26 mai 2024. [En ligne]. Disponible sur: https://www.jatit.org/volumes/Vol75No1/3Vol75No1.pdf Search in Google Scholar

[5] R.D. Polding, M. Eizaguirre Dieguez, An Investigation into the Effectiveness of Big Data in Organizations, the Use of Customer Data, and the Ethical Implications of the Data Economy, in 2021 International Symposium on Electrical, Electronics and Information Engineering, Seoul Republic of Korea: ACM, févr. 2021, pp. 599–607. doi: 10.1145/3459104.3459201. Search in Google Scholar

[6] H. Li, Y. Liu, C.-W. Tan, F. Hu, Comprehending customer satisfaction with hotels: Data analysis of consumer-generated reviews, International Journal of Contemporary Hospitality Management, vol. 32, no 5, pp. 1713–1735, 2020. Search in Google Scholar

[7] Y. Zhao, X. Xu, M. Wang, Predicting overall customer satisfaction: Big data evidence from hotel online textual reviews, International Journal of Hospitality Management, vol. 76, pp. 111–121, 2019. Search in Google Scholar

[8] M.M.H. Goode, F. Davies, L. Moutinho, A. Jamal, Determining Customer Satisfaction From Mobile Phones: A Neural Network Approach, Journal of Marketing Management, vol. 21, no 7-8, pp. 755–778, août 2005, doi: 10.1362/026725705774538381. Search in Google Scholar

[9] G. Ming, Application research of customer big data analysis for online shop based on smart cloud platform tools, in 2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA), IEEE, 2022, pp. 1142–1145. Consulté le: 26 mai 2024. [En ligne]. Disponible sur: https://ieeexplore.ieee.org/abstract/document/9719088/ Search in Google Scholar

[10] S.B. Abkenar, M.H. Kashani, E. Mahdipour, S.M. Jameii, Big data analytics meets social media: A systematic review of techniques, open issues, and future directions, Telematics and informatics, vol. 57, p. 101517, 2021. Search in Google Scholar

[11] S. Angelopoulos, M. Brown, D. McAuley, Y. Merali, R. Mortier, D. Price, Stewardship of personal data on social networking sites, International Journal of Information Management, vol. 56, p. 102208, 2021. Search in Google Scholar

[12] A. Karimzadeh, A. Zakery, M. Mohammadi, A. Yavari, An explainable machine learning-based approach for analyzing customers’ online data to identify the importance of product attributes. arXiv, 3 février 2024. Consulté le: 26 mai 2024. [En ligne]. Disponible sur: http://arxiv.org/abs/2402.05949 Search in Google Scholar

[13] Y. Li, Y. Dong, Y. Wang, N. Zhang, Product design opportunity identification through mining the critical minority of customer online reviews, Electron Commer Res, févr. 2023, doi: 10.1007/s10660-023-09683-8. Search in Google Scholar

[14] J. Wang, J.-Y. Lai, Y.-H. Lin, Social media analytics for mining customer complaints to explore product opportunities, Computers & Industrial Engineering, vol. 178, p. 109104, avr. 2023, doi: 10.1016/j.cie.2023.109104. Search in Google Scholar

[15] H. Bhimani, A.-L. Mention, P.-J. Barlatier, Social media and innovation: A systematic literature review and future research directions, Technological Forecasting and Social Change, vol. 144, pp. 251–269, juill. 2019, doi: 10.1016/j.techfore.2018.10.007. Search in Google Scholar

[16] P. de Camargo Fiorini, B.M. Roman Pais Seles, C.J. Chiappetta Jabbour, E. Barberio Mariano, A.B.L. de Sousa Jabbour, Management theory and big data literature: From a review to a research agenda, International Journal of Information Management, vol. 43, pp. 112–129, déc. 2018, doi: 10.1016/j.ijinfomgt.2018.07.005. Search in Google Scholar

[17] S. Çalı, A. Baykasoğlu, A Bayesian based approach for analyzing customer’s online sales data to identify weights of product attributes, Expert Systems with Applications, vol. 210, p. 118440, déc. 2022, doi: 10.1016/j.eswa.2022.118440. Search in Google Scholar

[18] H. Kalro, M. Joshipura, Product attributes and benefits: integrated framework and research agenda, Marketing Intelligence & Planning, vol. 41, no 4, pp. 409–426, janv. 2023, doi: 10.1108/MIP-09-2022-0402. Search in Google Scholar

[19] T. Briard, C. Jean, A. Aoussat, P. Véron, Challenges for data-driven design in early physical product design: A scientific and industrial perspective, Computers in Industry, vol. 145, p. 103814, févr. 2023, doi: 10.1016/j.compind.2022.103814. Search in Google Scholar

[20] N. Rezki, M. Mansouri, Improving supply chain risk assessment with artificial neural network predictions, AL, vol. 10, no 04, p. 645–658, déc. 2023, doi: 10.22306/al. v10i4.444. Search in Google Scholar

[21] J.-W. Bi, Y. Liu, Z.-P. Fan, E. Cambria, Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model, International Journal of Production Research, vol. 57, no 22, pp. 7068–7088, nov. 2019, doi: 10.1080/00207543.2019.1574989. Search in Google Scholar

[22] T. Yang, Y. Dang, J. Wu, How to prioritize perceived quality attributes from consumers’ perspective ? Analysis through social media data, Electron Commer Res, janv. 2023, doi: 10.1007/s10660-022-09652-7. Search in Google Scholar

[23] A. Haleem, M. Javaid, M. Asim Qadri, R. Pratap Singh, R. Suman, Artificial intelligence (AI) applications for marketing: A literature-based study, International Journal of Intelligent Networks, vol. 3, pp. 119–132, 2022, doi: 10.1016/j.ijin.2022.08.005. Search in Google Scholar

[24] S. Choudhary, N. Kaushik, B. Sivathanu, N.P. Rana, Assessing Factors Influencing Customers’ Adoption of AIBased Voice Assistants, Journal of Computer Information Systems, pp. 1–18, févr. 2024, doi: 10.1080/08874417.2024.2312858. Search in Google Scholar

[25] D.E. Bock, J.S. Wolter, O.C. Ferrell, Artificial intelligence: disrupting what we know about services, Journal of Services Marketing, vol. 34, no 3, pp. 317–334, janv. 2020, doi: 10.1108/JSM-01-2019-0047. Search in Google Scholar

[26] N. Ameen, A. Tarhini, A. Reppel, A. Anand, Customer experiences in the age of artificial intelligence, Computers in Human Behavior, vol. 114, p. 106548, janv. 2021, doi: 10.1016/j.chb.2020.106548. Search in Google Scholar

[27] Y.-C. Wang, M. Uysal, Artificial intelligence-assisted mindfulness in tourism, hospitality, and events, International Journal of Contemporary Hospitality Management, vol. 36, no 4, pp. 1262–1278, 2024. Search in Google Scholar

[28] N. Syam, A. Sharma,Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice, Industrial Marketing Management, vol. 69, pp. 135–146, févr. 2018, doi: 10.1016/j.indmarman.2017.12.019. Search in Google Scholar

[29] J. (Justin) Li, M.A. Bonn, B.H. Ye, Hotel employee’s artificial intelligence and robotics awareness and its impact on turnover intention: The moderating roles of perceived organizational support and competitive psychological climate, Tourism Management, vol. 73, pp. 172–181, août 2019, doi: 10.1016/j.tourman.2019.02.006. Search in Google Scholar

[30] J. van Doorn et al., Domo Arigato Mr. Roboto: Emergence of Automated Social Presence in Organizational Frontlines and Customers’ Service Experiences, Journal of Service Research, vol. 20, no 1, p. 43–58, févr. 2017, doi: 10.1177/1094670516679272. Search in Google Scholar

[31] P. Cunningham, S.J. Delany, k-Nearest Neighbour Classifiers – A Tutorial, ACM Comput. Surv., vol. 54, no 6, pp. 1–25, juill. 2022, doi: 10.1145/3459665. Search in Google Scholar

[32] J.R. Quinlan, Induction of decision trees, Mach Learn, vol. 1, no 1, pp. 81–106, mars 1986, doi: 10.1007/BF00116251. Search in Google Scholar

[33] A.K. Jain, J. Mao, K.M. Mohiuddin, Artificial neural networks: A tutorial, Computer, vol. 29, no 3, pp. 31–44, 1996. Search in Google Scholar

[34] N. Dong, H. Huang, L. Zheng, Support vector machine in crash prediction at the level of traffic analysis zones: assessing the spatial proximity effects, Accident Analysis & Prevention, vol. 82, pp. 192–198, 2015. Search in Google Scholar

[35] H. Li, X. B. Bruce, G. Li, H. Gao,Restaurant survival prediction using customer-generated content: An aspect-based sentiment analysis of online reviews, Tourism Management, vol. 96, p. 104707, 2023. Search in Google Scholar

[36] C.-J. Liu, T.-S. Huang, P.-T. Ho, J.-C. Huang, C.-T. Hsieh, Machine learning-based e-commerce platform repurchase customer prediction model, Plos one, vol. 15, no 12, p. e0243105, 2020. Search in Google Scholar

[37] G. Dash, K. Kiefer, J. Paul, Marketing-to-Millennials: Marketing 4.0, customer satisfaction and purchase intention, Journal of Business Research, vol. 122, pp. 608–620, janv. 2021, doi: 10.1016/j.jbusres.2020.10.016. Search in Google Scholar

[38] Y. Wang, X. Lu, Y. Tan, Impact of product attributes on customer satisfaction: An analysis of online reviews for washing machines, Electronic Commerce Research and Applications, vol. 29, pp. 1–11, mai 2018, doi: 10.1016/j.elerap.2018.03.003. Search in Google Scholar

[39] T. Hou, B. Yannou, Y. Leroy, E. Poirson, Mining customer product reviews for product development: A summarization process, Expert Systems with Applications, vol. 132, pp. 141–150, oct. 2019, doi: 10.1016/j.eswa.2019.04.069. Search in Google Scholar

[40] S. Park, H. Kim, Extracting product design guidance from online reviews: An explainable neural network-based approach, Expert Systems with Applications, vol. 236, p. 121357, févr. 2024, doi: 10.1016/j.eswa.2023.121357. Search in Google Scholar

[41] H.-S. Kim, Y. Noh, Elicitation of design factors through big data analysis of online customer reviews for washing machines, J Mech Sci Technol, vol. 33, no 6, pp. 2785–2795, juin 2019, doi: 10.1007/s12206-019-0525-5. Search in Google Scholar

[42] Y. Du, D. Liu, J.A. Morente-Molinera, E. Herrera-Viedma, A data-driven method for user satisfaction evaluation of smart and connected products, Expert Systems with Applications, vol. 210, p. 118392, déc. 2022, doi: 10.1016/j.eswa.2022.118392. Search in Google Scholar

[43] Md. N. Imtiaz, Md. K. Ben Islam, Identifying Significance of Product Features on Customer Satisfaction Recognizing Public Sentiment Polarity: Analysis of Smart Phone Industry Using Machine-Learning Approaches, Applied Artificial Intelligence, vol. 34, no 11, pp. 832–848, sept. 2020, doi: 10.1080/08839514.2020.1787676. Search in Google Scholar

[44] D. Suryadi, H.M. Kim, A Data-Driven Methodology to Construct Customer Choice Sets Using Online Data and Customer Reviews, Journal of Mechanical Design, vol. 141, no 11, p. 111103, nov. 2019, doi: 10.1115/1.4044198. Search in Google Scholar

[45] J. Joung, H. Kim, Interpretable machine learning-based approach for customer segmentation for new product development from online product reviews, International Journal of Information Management, vol. 70, p. 102641, juin 2023, doi: 10.1016/j.ijinfomgt.2023.102641. Search in Google Scholar