Integration of AI in Customer Relationship Management (CRM) for Improved Sales Outcomes
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V6I4P117Keywords:
Customer Relationship Management (CRM), Artificial Intelligence, Predictive Analytics, Machine Learning, sales dataset, hybrid model (GBM-SVM)Abstract
Customer Relationship Management (CRM) systems especially are highly significant to businesses in the sales forecasting procedure and utilizing them as a key instrument in their data-driven decision-making method. In this regard, the authors introduce a GBM-SVM hybrid model that unifies the Gradient Boosting Machine and the Support Vector Machine in order to predict the sales of the Indian supermarket dataset available on Kaggle. The research methodology consists of a number of preprocessing steps, including the following: missing value imputation, error correction, outlier removal, one-hot encoding, and normalization. Feature engineering, too, contributes the creation of temporal attributes such as Month, Year, and Season to sales pattern recognition being more efficient. The dataset is divided for training and testing purposes using stratified sampling to guarantee equal class representation. The proposed GBM-SVM hybrid links the iterative boosting of GBM and the robust optimization of SVM to reduce prediction errors and increase forecasting accuracy. The results from the experiments show an excellent performance with R² of 99.9%, MAE of 5.12214, and MSE of 63.51036 which surpass that of classical models such as Random Forest (RF), Logistic Regression (LR), and ensemble techniques like XGBoost. The model's dependability for CRM is shown by the high connection between the anticipated and actual sales. Thus, smart inventory control, revenue forecasting, and strategic retail decision-making may all benefit from such a model
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