Author(s): Tushar K Savale
The evolution of the middle class, growth of international tourists, and governmental policies, such as ‘Incredible India’, have catalyzed the integration of Indian tourism and hospitality into the economy, facilitating rapid development. Strengthening customer loyalty and satisfaction concerning relations tourism and hospitality for strategic development has gained attention due to such persistent expansion. Although some past research attempts to address the determinants of customer loyalty and satisfaction, the context of the rapidly expanding Indian tourism and hospitality industry is drastically under-researched considering evidence-based, predictive inquiry. This aim of this research is to construct a comprehensive framework for modelling customer loyalty and satisfaction around emotions and behaviors through predictive analytics. Primary data for the hypotheses will be captured through questionnaires given to tourists at various Indian tourist attractions, whereas secondary data will be sourced from TripAdvisor and MakeMyTrip’s online customer reviews. Concerning the study’s hypothesis, the proposed predictors are service quality, emotional engagement, price, convenience, deferment, and trust. The data will be measured and sophisticated predictive models crafted using Logistic Regression, Decision Trees, Random Forest, and XGBoost. The objective of the study is to identify the most important factors with deeper influence customer satisfaction and retention in India and extend relevant suggestions to the service and the policymakers in tourism sector. This study aims to address the gap for forecasting analytics by incorporating the behavior and emotion segments by proposing how the experience and loyalty initiatives can be improved in India’s tourism industry