Illuminating Hotel Customer Satisfaction Through Machine Learning

  • Traditional methods in hospitality fail to capture nuanced customer satisfaction dynamics.
  • A recent study published in Data Science and Management introduces a novel machine learning approach.
  • The study reveals complex relationships between hotel service attributes and guest satisfaction.
  • Utilizing 29,724 TripAdvisor reviews, researchers develop an interpretable machine learning model (IML-DAA).
  • IML-DAA integrates XGBoost with SHAP, providing accurate predictions and insights into attribute impact.
  • The model adapts dynamically, empowering hotel managers to refine service strategies and navigate market fluctuations.

Main AI News:

In the realm of service industries, especially within hospitality, the pursuit of customer satisfaction has always been paramount. While traditional methodologies like the Kano model and importance-performance analysis (IPA) have provided frameworks, they often falter in comprehending the nuanced and nonlinear dynamics of attribute performance-customer satisfaction (AP-CS) relationships.

An article (DOI: 10.1016/j.dsm.2024.01.003) featured in Data Science and Management on January 11, 2024, introduces a groundbreaking machine learning approach to unravel the intricate connection between hotel service attributes and customer satisfaction, offering actionable insights to enhance guest experience.

This research goes beyond conventional methods by presenting a machine learning-based framework that untangles the complex interplay between hotel service attributes and customer contentment. Utilizing 29,724 TripAdvisor reviews of New York City hotels, the research team has developed an interpretable machine learning-based dynamic asymmetric analysis (IML-DAA) model. This pioneering methodology merges extreme gradient boosting (XGBoost) with SHapley Additive exPlanations (SHAP), achieving unparalleled accuracy in forecasting customer satisfaction and elucidating the influence of specific service attributes on overall guest happiness. Unlike previous models, IML-DAA adeptly captures nonlinear relationships and the evolving impact of these attributes over time, providing comprehensive insights into customer preferences. The model’s ability to adapt dynamically to changing customer expectations provides actionable intelligence, enabling hotel managers to strategically refine service attributes, prioritize enhancements, and navigate market dynamics.

As stated by the study’s lead researcher, Prof. Shaolong Sun, “Our approach harnesses interpretable machine learning to not only predict customer satisfaction more accurately but also offer actionable insights into the contribution of various service attributes to overall contentment.”

This methodology empowers stakeholders to make informed decisions regarding service enhancement, resource allocation, and strategic planning, enabling proactive adaptation to evolving consumer demands. This study signifies a significant leap forward in leveraging machine learning to optimize customer satisfaction strategies within the hospitality sector.


This groundbreaking study marks a pivotal moment for the hospitality market, showcasing the potential of machine learning to revolutionize customer satisfaction strategies. By providing accurate insights into the nuanced dynamics of service attributes and guest contentment, businesses can proactively adapt to shifting consumer expectations, enhancing competitiveness and fostering long-term success in the industry.


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