Optimizing Customer Retention in CRM Systems using AI-Powered Deep Learning Models

Abstract

Customer retention is a key factor in ensuring long-term business success, as retaining existing customers is often more cost-effective than acquiring new ones. Existing Customer Relationship Management (CRM) systems, while effective in managing customer interactions, face challenges in capturing complex customer behavior patterns, dealing with high computational costs, and requiring extensive feature engineering. To address these problems, this paper presents a novel framework by integrating deep learning models into CRM systems for more accurate churn predictions to enhance CRM systems for improved customer retention. The proposed CRM system for customer retention begins with data collection from various sources, including CRM databases, social media, transaction histories, and customer feedback, creating a comprehensive customer profile. The collected data is then processed through data preprocessing, where outliers are removed using the Interquartile Range method, and Z-score normalization is applied to standardize the features. Feature extraction is performed using wavelet transform to capture intricate patterns in customer behavior. The extracted features are fed into a Deep Neural Network, which predicts customer behaviors, such as churn risk and future purchases, by learning complex relationships in the data. The AI model is integrated into the CRM system, providing insights and enabling personalized retention strategies. The results show a significant improvement in accuracy of 99.32%, precision of 98.23%, sensitivity of 98.34%, specificity of 98.87%, and F-Measure of 98.65%, demonstrating the framework's ability to effectively predict churn and optimize customer retention strategies. The main contribution of this work lies in its ability to integrate deep learning with CRM systems, overcoming the limitations of traditional models and providing a more accurate and efficient approach to customer retention.