Published Paper Details:

Market deregulation and globalization have caused competitive situations across a range of industries to constantly change, which has resulted in a rise in customer attrition. In order to maintain business development and keep their client base, organizations must effectively anticipate and mitigation of customer churns. This study explores the use of machine learning techniques in the Power Bi tool to anticipate client turnover. Emphasis is placed on the incorporation of these features, while focusing on well-known industries like telecommunication, employee churn for developing a predictive model, and features capturing customer social interaction and communications graphs and customer feedback are highlighted in the review. The findings encourage the use of profit-based evaluation criteria to increase profitability, improve customer retention, and support decision-making. Additionally, they draw attention to the dearth of studies on the profitability aspect of churn prediction models. In order to promote further advancements, the research concludes with recommendations that support the usage of ensembles, machine learning techniques, interaction visualization data utilizing Power BI, and explainable methodology. Churn prediction, which identifies customers who are likely to leave for a rival, has become the most important Business Intelligence (BI) application as a result. The purpose of this study is to provide widely used data mining approaches for identifying potential churning customers. These techniques look for patterns in past data that may point to potential churners.