Machine Learning-Based Dynamic Pricing and Customer Retention Among E-Commerce Businesses in Akwa Ibom State, Nigeria
Abstract
The rapid digitalisation of commerce has made customer retention a critical success factor for e-commerce businesses. This study examines the relationship between machine learning-based dynamic pricing and customer retention among e-commerce firms in Akwa Ibom State, Nigeria. A survey research design was adopted to collect quantitative data from a representative sample of e-commerce businesses. Regression analysis was employed to test the hypothesis that machine learning-driven dynamic pricing positively influences customer retention. The findings revealed a strong positive correlation (R = 0.728) and indicated that machine learning-based dynamic pricing accounts for 53.0% of the variance in customer retention (R² = 0.530). The regression model was statistically significant (F = 436.864, p < 0.05), with a positive coefficient (β = 0.700, p < 0.001), confirming that real-time, personalised pricing significantly enhances customer loyalty. The study concludes that adopting machine learning for dynamic pricing is a strategic tool for improving customer retention by offering competitive, fair, and market-responsive prices. It is recommended that e-commerce firms invest in AI-driven pricing technologies and that policymakers support digital adoption to strengthen business competitiveness.
Keywords
References
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How to cite
Uduak-Obong Ekpo-Ufot Umoh Oku Sunday Udo Agnes Umana (2026). Machine Learning-Based Dynamic Pricing and Customer Retention Among E-Commerce Businesses in Akwa Ibom State, Nigeria. African Multidisciplinary Scholarship Journal, 1(1), 109-123. https://doi.org/10.59413/amsj/v1.i1.9