Enhancing CRM Outcomes: An Ensemble Machine Learning Approach to Predicting Customer Behavior

Advancements in customer relationship management (CRM) systems have significantly leveraged data-driven strategies to understand and predict customer behavior effectively. However, a critical gap exists in the transition from sophisticated data analytics to actionable marketing strategies, particularly concerning issues like data imbalance and the interpretability of predictive models. Addressing these challenges, we proposed a refined ensemble model that synthesizes multiple predictive algorithms to enhance the accuracy and usability of forecasts regarding customer responses to marketing initiatives. The model employs the Synthetic Minority Oversampling Technique (SMOTE) to correct data imbalances and utilizes feature importance analysis to pinpoint the most influential factors driving customer decisions. This proposed method increases the precision of predictions and ensures that the outputs are actionable for marketing professionals. Based on real-world tests, our ensemble model does much better than standard single-predictor methods, with an accuracy of 94.82% and a recall of 85.51%. These results highlight the model’s effectiveness in integrating various machine learning techniques and adjusting the dataset to boost predictions’ robustness and reliability. Our research bridges the gap by delivering a robust predictive tool that enhances strategic marketing decisions, transforming complex customer data into practical marketing assets. The study sets a foundation for further exploration into incorporating sophisticated machine learning models into real-world marketing tactics, potentially revolutionizing the CRM landscape.