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Using ensemble classification algorithms to predict airline customer satisfaction

Author

Listed:
  • Dinh, Dung Hai

    (Lecturer and Academic Coordinator, Vietnam)

  • Lap Le, Son Nguyen

    (Student, Vietnamese—German University, Vietnam)

Abstract

The COVID-19 pandemic significantly impacted the airline sector, which has seen a shift in passenger behaviours and a decline in revenue. To navigate this challenging environment and regain customer trust, airlines must prioritise actions that focus on improving customer satisfaction, as satisfaction is a key driver of post-pandemic revenue growth. This paper proposes a novel predictive model for customer satisfaction using ensemble learning techniques. The analysis provides a comparison between the results of single supervised machine-learning methods, such as K-nearest neighbours and decision trees, and those of ensemble methods. The AdaBoost method with a decision tree as the base learner is found to achieve the highest accuracy, at 90.74 per cent. By enabling airlines to proactively address customer concerns and personalise offerings, this model has the potential to significantly improve customer satisfaction and ultimately drive sustainable revenue growth in the post-pandemic era.

Suggested Citation

  • Dinh, Dung Hai & Lap Le, Son Nguyen, 2025. "Using ensemble classification algorithms to predict airline customer satisfaction," Applied Marketing Analytics: The Peer-Reviewed Journal, Henry Stewart Publications, vol. 11(2), pages 175-190, September.
    • Handle: RePEc:aza:ama000:y:2025:v:11:i:2:p:175-190
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    JEL classification:

    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising

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