A Novel Deep Learning Framework for Customer Segmentation and Satisfaction Analysis in Tourism using Multimodal User-generated Content

The tourism industry has experienced substantial growth in recent years, propelled by the evolving customer preferences and pervasive influence of social media platforms. To gain a competitive advantage, it is imperative to develop innovative approaches that enable the delivery of personalized services and a deeper understanding of customer satisfaction. By leveraging user-generated content, firms can better understand dynamic customer trends and optimize their marketing strategies accordingly. However, our research aims to develop a novel customer segmentation framework using advanced deep learning techniques to analyze user-generated content within the rapidly growing tourism industry. The methodology employs a novel hybrid deep learning approach that integrates Sentence-BERT for contextualized embeddings and a GRU-based Autoencoder for feature reduction in an adaptive manner. This process is followed by k-means clustering, which segments customers using both multi-criteria ratings and online reviews to provide a holistic understanding of customer experiences. The proposed framework's effectiveness is compared with multiple state-of-the-art models using robust evaluation metrics such as the silhouette coefficient and the Davies–Bouldin index. The study’s findings highlight that the proposed model classifies customers into four distinct segments, and the statistical test performed shows the significance of our result. The research study contributes to the advancement of user-generated content based market segmentation in the tourism industry by unifying textual and numerical feedback into a single analytical framework. The findings offer valuable insights for tourism businesses seeking to enhance customer satisfaction through personalized service strategies and data-driven decision-making.