Discovering the Factors Affecting E-CRM Using Machine Learning Techniques
Keywords:
electronic customer relationship management, customer satisfaction, service quality, trust, customer loyalty, machine learning.Abstract
The majority of business organizations, especially those in developing nations, have adopted E-CRM as a recent strategy, and as a result, managers have strategically invested in online technologies while putting an emphasis on the creation and maintenance of valuable connections with valuable clients. The purpose of this study is to determine the association between E-CRM and service excellence, client satisfaction, loyalty, and trust in Egyptian commercial banks. This study was conducted with the goal of improving E-CRM. For this, 205 valid surveys from bank customers who used E-CRM services were gathered. Data was collected through a survey and utilized for machine learning-based research model assessment (ML). Artificial neural networks (ANN), linear regression models (LRM), random forests (RF), decision trees (DT), K-nearest neighbours (K-NN), and support vector machines (SVM) are examples of machine learning approaches that applied to develop predictive relationship between E-CRM and the other factors. Model performances were evaluated using various statistical indices including the coefficient of determination (R2), Mean square error (MSE), Root mean square error (RMSE), Mean absolute error (MAE) and Mean absolute percentage error (MAPE). The results revealed that E-CRM had a strong effect on service quality, trust, and customer satisfaction while Very Week effect on customer loyalty where The R2 value equal 0.2%.
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