Characterizing and Predicting Churn in Internet Providers

• Grant number: 23/11831-2
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: October 01, 2023
• End date: September 30, 2024
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Agreement: MCTI/MC
• Principal Investigator: Magnos Martinello
• Grantee: Matheus Saick de Martin
• Host Institution: Centro Tecnológico. Universidade Federal do Espírito Santo (UFES). Ministério da Educação (Brasil). Vitória , SP, Brazil
• Associated research grant: 20/05182-3 - PORVIR-5G: programability, orchestration and virtualization in 5G networks, AP.TEM

Abstract

Currently, the Internet market is becoming increasingly competitive, and service provider companies are showing a growing interest in using new technologies to gain a competitive advantage over others. One way to gain an edge in the market is through customer churn prediction, enabling the company to proactively execute user retention strategies. Hence, preventing the customer from switching to a competing company. In this context, the subject of this work arises, which plans to use classical machine learning and Deep Learning techniques to build prediction models that determine customer churn. To this end, datasets provided by a major Internet service provider will be used for the creation and validation of prediction models. These datasets contain textual information about customer complaints filed with Anatel. Throughout this work, the student will undertake activities of study, creation, validation, and comparison of these models, aiming to achieve a functional and efficient customer churn prediction model for Internet service provider companies. (AU)