Strategic Customer Segmentation: Harnessing Machine Learning For Retaining Satisfied Customers

Abstract

This research paper explores the burgeoning field of machine learning and its application in strategic customer segmentation within the aviation industry. Leveraging the Airline Passenger dataset, this study assesses the potential of various machine learning classifiers to enhance customer retention by effectively segmenting satisfied customers. Our methodology involves a comparative analysis of five machine learning classifiers: Random Forest, K-Nearest Neighbors (KNN), Decision Tree, Naive Bayes, and Artificial Neural Network (ANN). Each classifier is rigorously tested and evaluated based on key performance metrics including accuracy, precision, recall, and F1-score.

The results indicate a diverse range of classifier effectiveness. Notably, the Random Forest classifier outperforms others with outstanding metrics: accuracy, precision, recall, and F1-score
of 0.96. Decision Tree follows closely, also achieving high performance with a score of 0.95 across all metrics. Naive Bayes and ANN demonstrate respectable performance, with accuracy
scores of 0.86 and 0.90 respectively. In contrast, KNN presents lower but consistent performance, with all metrics at 0.75. These quantitative findings highlight the nuanced performance
differences among classifiers, emphasizing the critical role of algorithm selection in achieving precise customer segmentation.

This study provides significant insights into the application of machine learning for strategic customer retention in the aviation sector, presenting practical implications for airlines aiming to optimize their segmentation strategies and retain satisfied customers. By showcasing the varying performances of different classifiers, this research contributes to the broader discourse on integrating machine learning into customer-centric strategies, ultimately aiding airlines to engage and retain their customer base more effectively.