Publicação
Machine Learning for Credit Card User Segmentation in the Financial Sector
| Resumo: | The competitive landscape of the financial sector requires innovative approaches in marketing, prompting banking institutions to integrate data science methodologies. This study aims to bridge the gap in delivering personalized customer experiences by transitioning from traditional rule-based segmentation to data-driven segmentation using machine learning. Focusing on credit card users from a confidential European financial institution, we propose the development of multiple machine learning models to segment customers based on their purchasing behavior. Several clustering algorithms were evaluated, including K-means, Hierarchical Clustering, Gaussian Mixture (GMM), and DBSCAN. The goal is to provide actionable insights to the marketing team, enabling them to make informed decisions regarding marketing campaigns and fostering more personalized customer relationships. We define and utilize various data sources, establish exclusion rules, and apply statistical techniques to ensure data quality and relevance. By employing diverse clustering algorithms, we aim to create dynamic customer segments that can inform personalized marketing strategies. Based on existing literature, it is anticipated that data-driven segmentation will enhance customer satisfaction, retention, and marketing efficiency. This research contributes to the financial marketing field by providing a robust framework for customer segmentation and offering insights into customer behaviors and preferences, which is expected to drive business growth. |
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| Autores principais: | Sá, Rebeka Santos |
| Assunto: | Machine learning Customer Segmentation Financial Sector Marketing Strategies SDG 8 - Decent work and economic growth |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso embargado |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | The competitive landscape of the financial sector requires innovative approaches in marketing, prompting banking institutions to integrate data science methodologies. This study aims to bridge the gap in delivering personalized customer experiences by transitioning from traditional rule-based segmentation to data-driven segmentation using machine learning. Focusing on credit card users from a confidential European financial institution, we propose the development of multiple machine learning models to segment customers based on their purchasing behavior. Several clustering algorithms were evaluated, including K-means, Hierarchical Clustering, Gaussian Mixture (GMM), and DBSCAN. The goal is to provide actionable insights to the marketing team, enabling them to make informed decisions regarding marketing campaigns and fostering more personalized customer relationships. We define and utilize various data sources, establish exclusion rules, and apply statistical techniques to ensure data quality and relevance. By employing diverse clustering algorithms, we aim to create dynamic customer segments that can inform personalized marketing strategies. Based on existing literature, it is anticipated that data-driven segmentation will enhance customer satisfaction, retention, and marketing efficiency. This research contributes to the financial marketing field by providing a robust framework for customer segmentation and offering insights into customer behaviors and preferences, which is expected to drive business growth. |
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