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Chukwu Callistus Nkemjika
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Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Energy Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Materials Science & Nanotechnology Medicine & Pharmacology Neuroscience Nursing Physics Veterinary

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PREDICTING CUSTOMER LOYALTY IN AUTOMOTIVE SERVICES: EVIDENCE FROM MACHINE LEARNING ON SATISFACTION AND SERVICE COSTS IN NIGERIA Onyekachukwu Ekwueme, Godspower; Chukwuemeka Godwin, Harold; Chukwu Callistus Nkemjika; Ogochukwu Chinedum, Chukwunedum
SciencePlus Vol. 1 No. 2 (2025): SciencePlus
Publisher : Barkah Publishing

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Abstract

Customer loyalty in Nigeria’s automotive service sector has become volatile due to digital competition, variable pricing, and shifting satisfaction patterns. Traditional regression models fail to capture the nonlinear links between satisfaction, cost, and loyalty. This study used machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), to predict loyalty using customer satisfaction, service cost, and behavioral indicators from Anaval Mechanic Workshop (January–December 2023). Model performance was evaluated using accuracy and Area Under the Curve (AUC). XGBoost performed best (AUC = 0.985; accuracy = 97.1%), followed by RF (AUC = 0.962) and SVM (AUC = 0.485). Findings confirm satisfaction, cost, and uncertainty as key loyalty drivers, highlighting XGBoost’s superiority in modeling complex satisfaction–cost dynamics
Co-Authors Chukwuemeka Godwin, Harold Ogochukwu Chinedum, Chukwunedum Onyekachukwu Ekwueme, Godspower