Understanding Customers' Evaluations Through Mining Airline Reviews

TL;DR

This paper analyzes airline customer reviews to identify customer profiles and understand their evaluation of in-flight services, aiming to help airlines improve satisfaction through data-driven insights.

Contribution

It introduces feature-based and clustering-based modeling approaches to analyze customer reviews and model customer groups for better service understanding.

Findings

Customer reviews reveal key service evaluation factors.

Clustering identifies distinct customer segments.

Regression models predict customer satisfaction levels.

Abstract

Data mining can be evaluated as a strategic tool to determine the customer profiles in order to learn customer expectations and requirements. Airline customers have different characteristics and if passenger reviews about their trip experiences are correctly analyzed, companies can increase customer satisfaction by improving provided services. In this study, we investigate customer review data for in-flight services of airline companies and draw customer models with respect to such data. In this sense, we apply two approaches as feature-based and clustering-based modelling. In feature-based modelling, customers are grouped into categories based on features such as cabin flown types, experienced airline companies. In clustering-based modelling, customers are first clustered via k-means clustering and then modeled. We apply multivariate regression analysis to model customer groups in both cases. During this, we try to understand how customers evaluate the given services and what dominant characteristics of in-flight services can be from the customer viewpoint.