Enhancing Marketing Strategies Using Collaborative Filtering And Machine Learning Models

Abstract

In this study, the use of CF and ML models is explored as an innovative approach to enhance marketing practices. In a competitive environment, conventional marketing techniques may not be adequate to meet consumer requirements. Collaborative filtering may be employed to analyze previous transactions made by consumers and recommend particular items. On the other hand, machine learning models such as DT, SVM, and NN offer additional information regarding the behavior of users, leading to better targeted marketing activities and customer segmentation. The methodology consists of both CF and ML approaches, where CF is applied to make predictions about the preference of users based on their prior records. Meanwhile, ML algorithms are utilized to categorize users and uncover their complex behavior. As seen in the performance results of the key neural networks, the models perform better than other models, with an accuracy rate of 90%, precision of 88%, and a recall rate of 85%, which implies that the neural network models provide better personalized recommendations for marketing. As it is proven, SVM and decision trees can also be used effectively for user classification and segmentation purposes. It is clear that the results obtained have shown the significant role of the models in terms of real-time marketing campaigns. These models allow increasing engagement and satisfaction among customers. By utilizing this information, companies will be able to adjust their marketing activities accordingly and achieve higher ROI through improved customer experience.