The Role of Machine Learning Models in Optimizing High-Volume Customer Engagement and CRM Transformation

Abstract

This paper explores how machine learning models can be applied to maximize customer engagement strategies and customer Relationship Management transformation in business settings with high volumes of customer interactions with special focus to businesses based in Nigeria that work within competitive markets. The study uses a mixed-methodology by integrating quantitative evaluation of machine learning model performance indicators with qualitative evaluation of CRM transformation findings of fifteen companies in Nigeria with large populations of customers in the banking, telecommunications, e-commerce, and healthcare industries. The most important data were obtained by use of structured surveys that were conducted on 127 CRM managers and IT professionals, with semi-structured interviews of the key stakeholders, and the secondary data were analyzed by use of company databases to determine model effectiveness on various performance dimensions. The results show that machine learning models can make customer segmentation more accurate by 34-42 %, predict customer behavior with higher precision rates of up to 81 % and automate the engagement process, which results in quantifiable increases in the retention rates (average of 23 %) and operational efficiency (reduced costs by 18-31 %). It has been shown that successful CRM transformation is strongly associated with proper selection of the models, data quality and organizational preparedness. To help organizations aiming at CRM transformation, machine learning models can be utilized to process a large amount of customer interactions with the organization at less cost and better customer satisfaction rates. The research is relevant to the existing scanty research on the subject of machine learning-based CRM transformation in African markets, as it provides empirical data on Nigerian enterprises as well as offers a practical model of implementation with consideration of infrastructure limitations and situational reali