Building an AI-Powered Customer Retention Model

In this mini project, we will develop an AI-powered customer retention model aimed at enhancing business strategies to reduce churn rates and improve customer loyalty. The project begins with data collection, utilizing historical customer data that includes demographics, purchase history, and engagement metrics. We will preprocess this data to clean and normalize it, ensuring it’s suitable for analysis. Using machine learning algorithms, such as logistic regression, decision trees, and ensemble methods, we will train models to predict the likelihood of customer churn. Feature importance will be evaluated to identify key drivers of retention. Additionally, we’ll implement a validation strategy, such as k-fold cross-validation, to ensure model robustness. Finally, we will create a dashboard to visualize retention metrics and insights, allowing stakeholders to implement targeted retention strategies based on predictive analytics. This project not only demonstrates the application of AI and machine learning in a practical context but also emphasizes the importance of data-driven decision-making in enhancing customer relationships .