Enhancing Airline Customer Satisfaction: A Machine Learning and Causal Analysis Approach

TL;DR

This paper combines machine learning and causal analysis to identify how digital service improvements, especially online boarding passes, significantly boost airline customer satisfaction and help airlines make data-driven strategic decisions.

Contribution

It introduces a novel approach integrating machine learning with causal inference to evaluate the impact of service enhancements on customer satisfaction in airlines.

Findings

Digital service improvements significantly increase customer satisfaction.

Causal analysis identifies key factors influencing satisfaction.

Data-driven strategies can enhance airline competitiveness.

Abstract

This study explores the enhancement of customer satisfaction in the airline industry, a critical factor for retaining customers and building brand reputation, which are vital for revenue growth. Utilizing a combination of machine learning and causal inference methods, we examine the specific impact of service improvements on customer satisfaction, with a focus on the online boarding pass experience. Through detailed data analysis involving several predictive and causal models, we demonstrate that improvements in the digital aspects of customer service significantly elevate overall customer satisfaction. This paper highlights how airlines can strategically leverage these insights to make data-driven decisions that enhance customer experiences and, consequently, their market competitiveness.