Machine Learning Prediction and Loyalty of Online Food and Grocery Customers in Nairobi City County Kenya

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.