[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121047-en":3,"doc-seo-121047-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121047,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","AI-driven Customer Analytics - Implementation of Machine Learning Solutions into bank’s CRM - Internship Report","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.","MDDM  \nMaster Degree Program in  \nData-Driven Marketing  \nAI-driven Customer Analytics  \nImplementation of Machine Learning Solutions into bank’s CRM  \nJulia Marianna Trzos  \nInternship Report  \npresented as partial requirement for obtaining the Master Degree Program in Data-Driven Marketing  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nAI – DRIVEN CUSTOMER ANALYTICS – IMPLEMENTATION OF MACHINE LEARNING SOLUTIONS INTO BANK’S CRM  \nBy  \nJulia Marianna Trzos  \nInternship Report presented as partial requirement for obtaining the Master’s degree in Data-Driven Marketing, with a specialization in Digital Marketing and Analytics  \nSupervisor/Orientador(a): Miguel de Castro Simões Ferreira Neto  \nOr Co-Supervisors/Co-Orientadores: João Bruno Morais de Sousa Jardim  \nNovember 2023  \nSTATEMENT OF INTEGRITY  \nI 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.  \nLisbon, 23.11.2023  \nACKNOWLEDGEMENTS  \nI would like to express my gratitude for Asseco PST and its team for accepting my internship and allowing me to develop this project.  \nI 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.  \nABSTRACT  \nIn 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.  \nKEYWORDS  \nBusiness Intelligence, Machine Learning, Banking, Customer Relationship Management, Artificial Intelligence, Customer Segmentation  \nINDEX  \n1. Introduction .................................................................................................................. 1  \n1.1. Company overview ................................................................................................4  \n1.2. Objectives and Methodology ................................................................................5  \n2. Literature review ..........................................................................................................7  \n2.1. CRM and AI in banking ..........................................................................................7  \n2.2. Customer Segmentation in Banking ","cbCaictoD2DICCgP","https://ap.wps.com/l/cbCaictoD2DICCgP","pdf",2664718,1,80,"English","en",105,"# Introduction\n## Company overview\n## Objectives and Methodology\n# Literature review\n## CRM and AI in banking\n## Customer Segmentation in Banking\n## Recommendation Systems\n## Literature review conclusions and limitations\n## Next Steps\n# Methodology\n## Research Design\n## CRISP-DM Methodology\n## Business Understanding\n## Data Understanding\n## Data Preparation\n## Machine Learning Methods\n## K-Means Clustering\n## Multi-Class Classification","[{\"question\":\"What is the main objective of the project in Asseco PST’s CRM context?\",\"answer\":\"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.\"},{\"question\":\"How does the project generate personalized product recommendations?\",\"answer\":\"It develops a clustering-based recommendation system that delivers customized product recommendations based on the segmented clients.\"},{\"question\":\"What does the deployment demonstration include?\",\"answer\":\"It includes creating applications designed to support scalable clustering and predictive modelling, facilitating the implementation process for new clients.\"}]","AI-driven Customer Analytics - 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