Cloud-Based Customer Relationship Management: Driving Business Success in the E-Business Environment

Abstract

Cloud-based Customer Relationship Management (CRM) systems have altered organizational performance by centralizing and automating customer data management in today's fast-paced e-commerce industry. This research looks into the integration of modern Artificial Intelligence (AI) and Machine Learning (ML) techniques into cloud-based CRM to address the essential issue of customer turnover. The study uses a customer credit card dataset to evaluate and compare the predictive accuracy of several ML algorithms, including Random Forest Classifier, Decision Tree Classifier, Logistic Regression, Support Vector Classifier (SVC), K- Neighbors Classifier, Gaussian Naive Bayes (GaussianNB), and Artificial Neural Networks (ANN). The methodology includes painstaking data preparation, feature selection, and model training with an 80-20 train-test split, followed by a thorough performance evaluation using measures such as accuracy, precision, recall, F1 score, and AUC-ROC. The Random Forest Classifier performed the best, with an accuracy of 92.5%, followed by the Decision Tree Classifier at 89.8%. Other models, such as Logistic Regression, SVC, and K-Neighbors Classifier, achieved competitive accuracies of approximately 85%. The accuracies of GaussianNB and ANN models were lower, indicating that more comprehensive tuning could lead to improvements. The study highlights the effectiveness of ensemble approaches for dealing with complicated churn data, as well as the trade-off between model complexity and interpretability. These findings provide e-businesses with concrete insights to strengthen CRM tactics, increase client retention, and achieve long-term success in the competitive digital marketplace. Future research should focus on improved hybrid models and real-time prediction systems to improve churn prediction skills.