Impact of Direct Marketing Strategies on Consumer Behavior in the Banking Sector

Pilla, Shilpa (2024) Impact of Direct Marketing Strategies on Consumer Behavior in the Banking Sector. Masters thesis, Dublin, National College of Ireland.

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Abstract

Digitalization has radically changed the entire business of banking, increased competition, and increased consumer expectations. This paper discusses the efficiency of some popular direct marketing strategies for the banking industry using machine learning algorithms like logistic regression, decision trees, random forest, and gradient boosting. Some of the major objectives of this paper were to check the performance of these models on customer response prediction, examine demographic factors which may make a difference, and analyze the most important features contributing towards marketing campaign success. Conducted on detailed data from Kaggle, extensive data preprocessing, feature scaling, model training, and model evaluation formed part of the research. The findings revealed that Gradient Boosting and Random Forest models achieved the highest overall performance, with accuracies around 90.2% and ROC AUC scores above 92 %, making them highly effective in distinguishing between positive and negative customer responses. The study also highlighted the role of feature engineering and demographic factors in determining marketing outcomes. Despite the fact that such models have been successful, there are still some areas to improve, such as enhancing recall rates and class imbalance. This study sets a path toward individualized, efficient, and successful campaigns that aim at better customer engagement and retention in the highly competitive banking industry of today.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Simiscuka, Anderson UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HG Finance > Banking H Social Sciences > HF Commerce > Marketing > Consumer Behaviour Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HF Commerce > Marketing
Divisions:	School of Computing > Master of Science in Artificial Intelligence for Business
Depositing User:	Ciara O'Brien
Date Deposited:	02 Jul 2025 17:31
Last Modified:	02 Jul 2025 17:31
URI:	https://norma.ncirl.ie/id/eprint/7997

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