Role of Machine Learning Algorithms in Enhancing Customer Relationship Management Systems in Rwanda

Abstract

Purpose: The aim of the study was to assess the role of machine learning algorithms in enhancing customer relationship management systems in Rwanda. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Algorithms analyze vast amounts of data, allowing businesses to personalize interactions and anticipate customer needs more effectively. For instance, predictive analytics can forecast customer churn, prompting proactive retention strategies. Moreover, sentiment analysis tools gauge customer emotions from feedback, enabling tailored responses and improved service delivery. Machine learning also automates routine tasks like email marketing segmentation, optimizing resource allocation and enhancing overall efficiency. Consequently, CRM systems empowered by machine learning algorithms foster stronger customer relationships, driving business growth through enhanced customer satisfaction and loyalty. Implications to Theory, Practice and Policy: Technology acceptance model (TAM), resource-based view and diffusion of innovations theory may be used to anchor future studies on assessing the role of machine learning algorithms in enhancing customer relationship management systems in Rwanda. Businesses should adopt advanced ML techniques, such as Long Short-Term Memory (LSTM) models and ensemble learning, to enhance the accuracy of customer segmentation and churn prediction, leading to more personalized marketing strategies and improved customer retention. Policymakers should develop and enforce stringent data privacy and security regulations to protect customer data used in ML-enhanced CRM systems.