Banca de DEFESA: GUSTAVO HENRIQUE FARIAS BEZERRA

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
STUDENT : GUSTAVO HENRIQUE FARIAS BEZERRA
DATE: 28/05/2024
TIME: 08:00
LOCAL: remoto
TITLE:

Predicting and Interpreting Churn: Integrating Causal Analysis and Machine Learning for Effective Retention Strategies

KEY WORDS:

Churn, CRM (Customer Relationship Management), Predictive Analysis, Machine Learning, Causal Inference.

PAGES: 115
BIG AREA: Engenharias
AREA: Engenharia de Produção
SUBÁREA: Pesquisa Operacional
SUMMARY:

Globalization and the widespread use of the internet have transformed the relationship between consumers and companies, establishing direct and active interaction between them. In this scenario, understanding the customer lifecycle is vital to maintaining the operational and financial stability of organizations, with a sharp focus on factors that promote customer satisfaction and loyalty. Faced with the issue of churn – which reflects the loss of customers – several industries face challenges that directly impact their profitability and sustainability. Therefore, this research aims to develop a tool that improves predictive churn modeling, enriching it with causal analysis to not only predict more accurately, but offer clear interpretations of the reasons for customer loss. Using the IBM Telco Customer churn dataset, version 11.1.3, as empirical support, the study seeks to identify variables that influence churn and evaluate effective retention strategies. The methodological approach includes the use of machine learning techniques such as LGBM combined with advanced causal analysis methods such as Double Robust machine learning and Conditional Average Treatment Effects, CATE, modeling. Developing a tool that helps identify customer retention factors, from demographic aspects to the nature of the services provided, analyzing variables such as type of contract, gender, age, among others. The results are expected to validate the theories of Wu et al. (2021) on churn prediction and reveal profiles of customers with a greater propensity to abandon, as exposed by the authors of Rudd et al. (2021), contributing significantly to customer relationship management and offering strategic data for the development of more assertive retention tactics.

COMMITTEE MEMBERS:
Presidente - 1777131 - MARIANA RODRIGUES DE ALMEIDA
Interno - 1142787 - JOSE ALFREDO FERREIRA COSTA
Externo à Instituição - MARCUS VINICIUS DANTAS DE ASSUNCAO - IFRN