Machine Learning Prediction and Loyalty of Online Food and Grocery Customers in Nairobi City County Kenya
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Date
2025-10
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International Journal of Business Management, Entrepreneurship and Innovation
Abstract
Customer loyalty in the online food and grocery industry in Nairobi City County faces increasing
challenges due to the rapidly changing preferences and expectations of digitally savvy consumers.
The main objective of this study was to examine the effect of Machine Learning Prediction Systems
on the loyalty of online food and grocery customers in Nairobi City County, Kenya. Specifically,
the study sought to determine how predictive analytics and algorithm-driven personalization
influence repeat purchase behaviour, customer engagement, and trust. The study was anchored
on the Relationship Marketing Theory and the European Customer Satisfaction Index (ECSI)
model, which jointly explain the mechanisms through which data-driven insights foster long-term
customer relationships. A correlational and explanatory research design was employed, targeting
active users of online food and grocery delivery platforms within Nairobi City County. Data were
collected through structured questionnaires and analysed using both descriptive and inferential
statistics. The findings revealed a positive and statistically significant relationship between the use
of machine learning prediction systems and customer loyalty (β = 0.357, p < 0.05). This implies
that increased adoption of predictive algorithms enhances customer retention through
personalized experiences and anticipatory engagement. The study concludes that integrating
machine learning prediction in customer relationship management significantly strengthens
customer loyalty. It recommends that online food and grocery firms adopt ethical and transparent
machine learning practices to ensure fairness, data privacy, and sustained consumer trust.
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Citation
Nyakado, J. Maina, S, Murigi, E. (2025). Machine Learning Prediction and Loyalty of Online Food and Grocery Customers in Nairobi City County Kenya. International Journal of Business Management, Entrepreneurship and Innovation, 7(2), 124-135. https://doi.org/10.35942/ch57sm32