Publicação
AI-driven Customer Analytics: Implementation of Machine Learning Solutions into bank’s CRM
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | pt_PT |
| dc.contributor.advisor | Neto, Miguel de Castro Simões Ferreira | |
| dc.contributor.advisor | Jardim, João Bruno Morais de Sousa | |
| dc.contributor.author | Trzos, Julia Marianna | |
| dc.date.accessioned | 2024-04-04T17:46:22Z | |
| dc.date.available | 2024-04-04T17:46:22Z | |
| dc.date.issued | 2024-01-31 | |
| dc.description | Internship Report presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Digital Marketing and Analytics | pt_PT |
| dc.description.abstract | In the ever-evolving landscape of modern banking, the incorporation of emerging technologies, specifically Artificial Intelligence and machine learning, holds great significance in order to remain relevant and efficiently address customer needs, having a potential for significant enhancements in customer relationship management practises within the banking industry. The objective of this project, conducted in collaboration with the Asseco PST Data & Analytics team, is to improve their CRM solution by incorporating a comprehensive machine learning framework. This involves utilising machine learning techniques to segment clients, with the goal of optimising customer relationship management (CRM) and providing data-driven campaigns for their bank clients. The project aims to develop a clustering-based Recommendation System that delivers customised product recommendations. Furthermore, the project presents a deployment demonstration involving the creation of apps aimed at achieving a scalable solution for clustering and predictive modelling, hence facilitating the implementation process for new clients. Additionally, this project intends to establish itself as an important component within Asseco PST's comprehensive offering. The integration of this work within their pre-existing CRM development offer serves to underscore its importance and possible influence within the constantly developing world of present-day banking. | pt_PT |
| dc.identifier.tid | 203568443 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/165797 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Business Intelligence | pt_PT |
| dc.subject | Machine Learning | pt_PT |
| dc.subject | Banking | pt_PT |
| dc.subject | Customer Relationship Management | pt_PT |
| dc.subject | Artificial Intelligence | pt_PT |
| dc.subject | Customer Segmentation | pt_PT |
| dc.title | AI-driven Customer Analytics: Implementation of Machine Learning Solutions into bank’s CRM | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Marketing Analítico, especialização em Marketing Digital e Análise de Dados | pt_PT |