Data Driven Amenity Strategies: Evidence from Online Reviews and Hotel Ratings in Bali - Adyatma, Prastha; Kartini, Luh Putu; Priliani, Ni Luh Dita (PT FORMOSA CENDEKIA GLOBAL, 2025)
This study translates large-scale online reviews into actionable guidance for hotel amenity investment in Bali. We quantify how specific amenities relate to guest ratings using three complementary lenses: (i) bivariate rating uplift (Welch’s t, effect sizes, confidence intervals) to estimate standalone associations, (ii) a coverage–impact matrix that maps market prevalence against rating uplift to categorize amenities into INVEST, MAINTAIN, HYGIENE, or NICETOHAVE, and (iii) interpretable machine learning (Random Forest with modelagnostic importance and optional SHAP) to validate which amenity bundles best separate highrated properties (≥4.7). Additionally compare resort vs. city segments to reflect contextdependent preferences. Results indicate a consistent “core utilities + family readiness + convenience” bundle free Wi Fi, kid friendly facilities, laundry service, air conditioning, parking, and onsite restaurant as the strongest predictors of high ratings, with outdoor pool, free breakfast, spa, beach access, and airport shuttle providing further differentiation. The coverage impact matrix highlights where to allocate capital: for example, high impact/low coverage features fall into INVEST, while widely available, reliabilitycritical utilities align with MAINTAIN. discuss managerial implications for amenity roadmapping and budgeting, note the study’s associational nature and outline robustness steps to enhance generalizability.