Optimising Direct Marketing Through Data-Driven Analytics and Predictive Models

Vale de Sousa, Karla Priscila (2024) Optimising Direct Marketing Through Data-Driven Analytics and Predictive Models. Masters thesis, Dublin, National College of Ireland.

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Abstract

Direct marketing campaigns are essential for preserving and growing market leadership in today's demanding environment. This research focuses on enhancing the effectiveness of these campaigns through data-driven analytics, employing advanced methodologies such as exploratory data analysis (EDA), customer segmentation via K-means clustering, and predictive classification models including logistic regression, decision trees, and K-Nearest Neighbours (KNN). The study aims to optimise marketing campaigns by identifying profitable customer segments and accurately predicting customer responses. The research employs the CRISP-DM methodology to methodically address business objectives, prepare data, build models, and evaluate their performance using a dataset from iFood, Brazil’s leading food delivery service. The findings offer practical insights that help direct marketing campaigns, improve client interaction, reduce costs, and reverse profit declines. Beyond resolving business challenges, this study contributes to the theoretical knowledge of data-driven marketing strategies and provides insightful information for both academic research and real-world implementations in the industry.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Del Rosal, Victor UNSPECIFIED
Uncontrolled Keywords:	Direct Marketing; Data-Driven Analytics; Exploratory Data Analysis; Customer Segmentation; Predictive Classification Models
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science 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 18:07
Last Modified:	02 Jul 2025 18:07
URI:	https://norma.ncirl.ie/id/eprint/8002

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