Integrating Machine Learning and Text Mining to Enhance Customer Value Propositions in Hotel Supply Chain

Document Type : SI: SD of ISC

Authors

1 Department of Public Policy and Management, I-Shou University, Kaohsiung, Taiwan

2 Department of Hospitality, MICE Marketing Management, National Kaohsiung University of Hospitality and Tourism, Kaohsiung, Taiwan

Abstract

The accelerated digital transformation in the contemporary business landscape, propelled by the Fourth Industrial Revolution, has fundamentally reshaped marketing research practices. This study leverages machine learning techniques and big data analytics to extract critical customer value propositions from extensive online reviews, aligning with predictive marketing strategies. Using a hybrid approach that combines qualitative and quantitative analyses, the research examines 8,290 customer reviews sourced from an online platform within the tourism industry. Two advanced analytical techniques were applied: clustering analysis to identify 20 distinct value components prioritized by tourists and associative rule mining to uncover seven essential patterns embedded in customer feedback. The results highlight the potential of big data and machine learning in accelerating marketing research processes, improving precision, and lowering operational costs. The findings emphasize the transformative role of digital tools in modern marketing practices, enabling businesses to enhance customer satisfaction, optimize services, and maintain competitive advantages in a data-driven economy.

Keywords

References

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International Journal of Supply and Operations Management
Volume 12, Issue 1
Special Issue: SD of ISC
February 2025
Pages 48-63

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