OPTIMIZING CUSTOMER RELATIONSHIP MANAGEMENT THROUGH IMPLEMENTING AI TECHNOLOGIES IN ERP SYSTEMS

Abstract

CRM, or customer relationship management, is essential to the performance, longevity, competitiveness, and profitability of businesses in the increasingly competitive and customer-focused worldwide market. The purpose of the study is to give a general overview of how developing technologies affect social and conventional CRM. The following paper examines the use of the Enhancing Artificial Neural Networks (ANN) in Enterprise Resource Planning (ERP) systems for Customer Relationship Management (CRM). The experiment was carried on a credit card customer churn dataset. To counteract the problem of class imbalance, various methods including Synthetic Minority Over-sampling Technique (SMOTE) are used. To address this issue of churn rate, predictive analytics which employs machine learning techniques are used in analyzing customers’ behaviors and predicting that certain customers are likely to churn while offering recommendations to other customers based on their behaviours. Subsequently, the accuracy, precision, recall, and F1 score of the suggested ANN model are evaluated and contrasted with those of alternative models, such as K-Nearest Neighbours and Support Vector Machine. Comparing the experimental results, it is observed that our proposed ANN model delivers better functionality than both KNN and SVM, specifically attaining an precision of 88%, recall of 97%, accuracy of 86%, and F1-score of 92%. These outcomes show the applicability of the proposed ANN model in improving the CRM aspects within ERP systems and propose the model’s ability in increasing customer retention, satisfaction and overall CRM effectiveness.