Modernized Digital Marketing Strategies to Improve Customer Experience and Engagement

Abstract

This research focuses on DMM promoting innovation through analysing four superior algorithms including Decision Tree, Random Forest, SVM, KNN to optimize customers’ retention and satisfaction. By comparing the engagement scores with the demographic information of the users, activity type and the channels of communication we were able to evaluate the efficacy of these algorithms in anticipations of the customer’s behaviour. Random Forest, therefore, gave the highest accuracy with the figures at 0.88, precision at 0.83, recall at 0.80, and an F1 score of 0.81 this show that Random Forest was the most appropriate algorithm for the complex data set. For performance measurement the values of accuracy of 85% was obtained thus giving precision of 80%, recall of 78% and F1 score of 79%. These are; SVM accuracy = 82%, precision = 78%, recall = 74% and F1 score = 76%. Setting KNN to the lowest index, it achieved 79% for accuracy level, 74% on precision and 70% on recall and 72% for F1 measure. This paper emphasizes on the efficiency of Random Forest and other ensemble systems for the optimization of marketing techniques and improve customer relationship. The results provide evidence of the importance of AI incorporation to use those technologies to fine-tune the relevant marketing strategies.