PREDICTING CUSTOMER LOYALTY: A COMPARATIVE ANALYSIS USING AUTOMATED MACHINE LEARNING AND TRADITIONAL STATISTICS METHODS

Authors: Israt Jahan Shithii* & Md. Abdul Hannan Mia PhD

ABSTRACT

This study predicts the factors that impact customer loyalty and a comparative analysis is given with the help of AutoML and traditional statistics model outcomes. Predicting customer loyalty is a strategic tool that helps businesses retain valuable customers, increase revenue, and foster long-term relationships. By anticipating customer behavior, companies can make data-driven decisions to enhance their marketing, customer service, and product offerings. Ultimately, the ability to predict and nurture customer loyalty leads to sustainable growth and a competitive advantage in the marketplace. Machine learning can greatly enhance a business’s ability to predict customer loyalty by analyzing various customer behaviors, identifying at-risk customers, and optimizing retention strategies. Automating tasks like data preprocessing, model selection, and hyperparameter tuning, AutoML enables businesses to quickly and effectively predict customer behavior, optimize retention efforts, and build stronger, more loyal customer bases. This analysis employed automated machine learning for feature generation and model validation which predict the behavior of customers’ loyalty. Binary regression analysis has been also done to compare the result with the automated machine learning outcome. The comparison methods and models found that varying age groups complaints about the service, tariff plan, and usage status directly impact customer loyalty. This will help businesses predict future behavior, retain customers, and build brand loyalty.

Keywords: Customer Loyalty, Prediction, Logistic Regression, Machine Learning

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