Complementary Operational and Guest-Experience Evidence for Resort Decision Support: A Bangladesh Case Study

Abstract This study examines how operational performance and guest-experience evidence can complement each other in supporting data-driven management decisions in the resort industry. Using The Slow Deck, a resort in Bangladesh, as a case study, the research analyzes operational records from January to June 2026 alongside 201 archival guest-feedback records. The operational analysis explores revenue patterns, package-level performance, and differences between weekday and weekend business activity. Guest-experience analysis investigates the relationships between service attributes and overall satisfaction through statistical modelling. The study applies descriptive analysis, Poisson Pseudo-Maximum Likelihood (PPML) regression, and logistic regression to examine patterns within the available data. The findings indicate a substantial concentration of revenue in Daylong and Night Stay packages, a positive association between weekends and revenue, and positive relationships between selected guest-experience attributes, particularly ambience and cleanliness, and satisfaction outcomes. These associations are interpreted as observational rather than causal. The study contributes an applied decision-support perspective for resort management by demonstrating how operational and guest-experience data can inform marketing priorities, resource allocation, service improvement, and future analytical practices. Because the datasets are not linked at the individual-customer level, the analysis does not establish direct relationships between guest satisfaction and revenue. Publication status: Independent research working paper developed from an undergraduate final-year project at United International University. This version has not been peer-reviewed or published in an academic journal.