Rosa Arboretti, Elena Barzizza, Nicolò Biasetton, Riccardo Ceccato, Luigi Salmaso

Abstract: Understanding and analysing customer satisfaction is crucial for businesses striving to maintain or enhance their competitive edge in today's rapidly evolving markets. Customer satisfaction serves as a direct indicator of how well a company meets consumer expectations, making it a key determinant of long-term success. By focusing on customer satisfaction levels, companies can improve product development to better align with consumer needs. A machine learning-based tool is introduced, designed to analyse customer satisfaction data and identify the most impactful drivers. By clustering drivers before analysis, the tool provides a comprehensive understanding of the relationship among various aspects of customer satisfaction. A case study is conducted to demonstrate the practical application and effectiveness of the proposed tool, offering valuable insights for research and development purposes. Future efforts will focus on integrating generative AI to enhance the monitoring of customer satisfaction by scraping online consumer reviews. These reviews can be analysed to conduct aspect-based sentiment analysis, which will help identify emerging aspects of customer satisfaction that require attention.

Keywords: Customer satisfaction, Machine learning, Cluster.

Date Published: February 25, 2025
DOI: 10.11159/jmids.2025.002

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