AI-driven Customer Analytics - Implementation of Machine Learning Solutions into bank’s CRM - Internship Report

Aug 3, 2026

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In modern banking, integrating emerging technologies—especially artificial intelligence and machine learning—supports relevance while better addressing customer needs through improved customer relationship management. This internship report, developed with the Asseco PST Data & Analytics team, upgrades their CRM solution using a machine learning framework to enable client segmentation and data-driven campaign delivery for bank customers. The work includes a clustering-based recommendation system for customized product suggestions, plus a deployment demonstration via apps supporting scalable clustering and predictive modelling for new clients. It also positions the solution as a meaningful addition to Asseco PST’s CRM offering.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages80

Reading Time3 h 22 min

AccessFree to download

Introduction

Company overview

Objectives and Methodology

Literature review

CRM and AI in banking

Customer Segmentation in Banking

Recommendation Systems

Literature review conclusions and limitations

Next Steps

Methodology

Research Design

CRISP-DM Methodology

Business Understanding

Data Understanding

Data Preparation

Machine Learning Methods

K-Means Clustering

Multi-Class Classification

MDDM Master Degree Program in Data-Driven Marketing AI-driven Customer Analytics Implementation of Machine Learning Solutions into bank’s CRM Julia Marianna Trzos Internship Report presented as partial requirement for obtaining the Master Degree Program in Data-Driven Marketing NOVA Information Management School Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa NOVA Information Management School Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa AI – DRIVEN CUSTOMER ANALYTICS – IMPLEMENTATION OF MACHINE LEARNING SOLUTIONS INTO BANK’S CRM By Julia Marianna Trzos Internship Report presented as partial requirement for obtaining the Master’s degree in Data-Driven Marketing, with a specialization in Digital Marketing and Analytics Supervisor/Orientador(a): Miguel de Castro Simões Ferreira Neto Or Co-Supervisors/Co-Orientadores: João Bruno Morais de Sousa Jardim November 2023 STATEMENT OF INTEGRITY I hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledge the Rules of Conduct and Code of Honor from the NOVA Information Management School. Lisbon, 23.11.2023 ACKNOWLEDGEMENTS I would like to express my gratitude for Asseco PST and its team for accepting my internship and allowing me to develop this project. I would like to thank Bruno Jardim for his excellent work and support as my co-supervisor. Finally, I would like to thank professor Miguel Neto for accepting to be my supervisor. 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. KEYWORDS Business Intelligence, Machine Learning, Banking, Customer Relationship Management, Artificial Intelligence, Customer Segmentation INDEX 1. Introduction .................................................................................................................. 1 1.1. Company overview ................................................................................................4 1.2. Objectives and Methodology ................................................................................5 2. Literature review ..........................................................................................................7 2.1. CRM and AI in banking ..........................................................................................7 2.2. Customer Segmentation in Banking

FAQs

What is the main objective of the project in Asseco PST’s CRM context?

To improve Asseco PST’s CRM solution by incorporating a comprehensive machine learning framework that enables client segmentation and data-driven campaigns for bank customers.

How does the project generate personalized product recommendations?

It develops a clustering-based recommendation system that delivers customized product recommendations based on the segmented clients.

What does the deployment demonstration include?

It includes creating applications designed to support scalable clustering and predictive modelling, facilitating the implementation process for new clients.

Aug 3, 2026

Upload by Aria