Digital Marketing Data Classification by Using Machine Learning Algorithms

Abstract

Early in the 20th century, as a result of technological advancements, the importance of digital marketing significantly increased as the necessity for digital customer experience, promotion, and distribution emerged. Since the year 1988, in the case when the term "Digital Marketing" first appeared, the business sector has undergone drastic growth, moving from small startups to massive corporations on a global scale. The marketer must navigate a chaotic environment caused by the vast volume of generated data. Decision-makers must contend with the fact that user data is dynamic and changes every day. Smart applications must be used within enterprises to better evaluate, classify, enhance, and target audiences. Customers who are tech-savvy are pushing businesses to make bigger financial investments and use cutting-edge technologies. It was only natural that marketing and trade could be one of the areas to move to such development, which helps to move to the speed of spread, advertisements, along with other things to facilitate things for reaching and winning customers. In this study, we utilized machine learning (ML) algorithms (Decision tree (DT), K-Nearest Neighbor (KNN), CatBoost, and Random Forest (RF) (for classifying data in customers to move to development. Improve the ability to forecast customer behavior so one can gain more business from them more quickly and easily. With the use of the aforementioned dataset, the suggested system was put to the test. The results show that the system can accurately predict if a customer will buy something or not; the random forest (RF) had an accuracy of 0.97, DT had an accuracy of 0. 95, KNN had an accuracy of 0. 91, while the CatBoost algorithm had the execution time 15.04 of seconds, and gave the best result of highest f1_score and accuracy (0.91, 0. 98) respectively. Finally, the study's future goals involve being created a web page, thereby helping many banking institutions with speed and forecast accuracy. Using more techniques of feature selection in conjunction with the marketing dataset to improve diagnosis.