ENSEMBLE LEARNING FOR PREDICTION OF MARKETING CAMPAIGN ACCEPTANCE

Abstract

Artificial intelligence, commonly known as AI, has greatly influenced marketing strategies, including business models, sales processes, customer service options, and customer behaviour in receiving marketing campaigns. In a marketing campaign, all customers are targeted by advertising, including those who will not respond positively to the marketing campaign and reject the offer. This means that the company is inefficient; the marketing campaign is ineffective because customers are not segmented and targeted. As a result, costs increase and the company's profit decreases. Thence, this leads to the failure of the company's marketing campaigns. The purpose of this study is to experiment with using Ensemble Learning and tuning on the Marketing Campaign dataset by providing the classification methods. That classification method is called the Light Gradient Boosting Machine (LightGBM), Gradient Boosting Classifier (GBC), and AdaBoost Classifier (ADA), which have never been used in the classification of the Marketing Campaign dataset. The study results in the highest model with an accuracy value of 98.64%, AUC 0.9994, recall 95.77%, precision 95.77%, F1-score 95.77%, and kappa 94.98% when using the LightGBM for marketing campaign predictions.