Non-linear Analysis of Airline Customer Experience: Logistic Regression vs Artificial Neural Network


  • Mohammad Sirajul Islam Assistant Professor


Non-linear analysis; Logistic Regression; Artificial Neural Network (ANN); Classification algorithms; XLMINER; Customer Experience.



Purpose of the study: This study aims to explore the best machine learning (ML) classification algorithm for curve analysis of customer experience survey data.

Methodology:  The study employed a multi-method study to extract the best alternative algorithms. This study used logistic regression and artificial neural networks (ANN) to analyze data. This study used 6000 airline passenger survey datasets. To analyze the quantitative data using XLMINER software.         

Findings: The findings suggest an artificial neural network (ANN) is the best alternative classification algorithm for customer experience analysis. This study also recommends using logistic regression alternatively for simple and comprehensive modeling to analyze customer experience.          

Implications: Practically, this study highlights the benefit of using artificial neural networks to classify customer satisfaction.  

Limitations and Future Direction: The first limitation of this study is that uses cross-sectional data, future studies should use longitudinal data. Secondly, matric reports are very close in both studies. This author refers to future studies that may include another dataset to find the clear superiority of the model.


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How to Cite

Islam, M. S. (2023). Non-linear Analysis of Airline Customer Experience: Logistic Regression vs Artificial Neural Network. AIUB Journal of Business and Economics, 20(1), 76–89. Retrieved from