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Machine learning na previsão da conversão de clientes alvo
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Orientador(es)
Resumo(s)
Numa empresa, os clientes alvo representam uma parcela de clientes que são tidos
como foco em ações de marketing para venda de determinado produto ou serviço. A
conversão de clientes alvo a determinado produto ou serviço gera lucros para a empresa,
sendo por isso importante direcionar as ações de marketing a clientes que são mais
propensos à conversão. Neste sentido, este estudo tem como principal objetivo obter a
probabilidade da conversão de clientes alvo à solução de pagamentos Digital Payment
Gateway DPG da SIBS, através de técnicas e algoritmos de Machine Learning. O
desenvolvimento deste estudo seguiu a metodologia Cross-Industry Standard Process for
Data Mining (CRISP-DM). No balanceamento da classe target, foram utilizadas as
técnicas SMOTE e SMOTETomek e os algoritmos de classificação implementados foram:
XGBoost, Random Forest e a Regressão Logística.
O modelo estimado que apresentou melhor desempenho foi obtido através do
algoritmo Random Forest com recurso a dados balanceados através da técnica SMOTE.
Este modelo reflete um acerto de 60% das observações pertencentes à classe minoritária.
In a company, target customers represent a portion of customers who are the focus of marketing actions for the sale of a certain product or service. The conversion of target customers to a certain product or service generates profits for the company, so it is important to direct marketing actions to customers who are more likely to convert. In this sense, this study's main objective is to obtain the probability of conversion of target customers to the SIBS' Digital Payment Gateway DPG payment solution, through Machine Learning techniques and algorithms. The development of this study followed the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. In the balancing of the target class, the SMOTE and SMOTETomek techniques were used and the classification algorithms implemented were: XGBoost, Random Forest and Logistic Regression. The estimated model that presented the best performance was obtained through the Random Forest algorithm using balanced data through the SMOTE technique. This model reflects a hit of 60% of the observations belonging to the minority class.
In a company, target customers represent a portion of customers who are the focus of marketing actions for the sale of a certain product or service. The conversion of target customers to a certain product or service generates profits for the company, so it is important to direct marketing actions to customers who are more likely to convert. In this sense, this study's main objective is to obtain the probability of conversion of target customers to the SIBS' Digital Payment Gateway DPG payment solution, through Machine Learning techniques and algorithms. The development of this study followed the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. In the balancing of the target class, the SMOTE and SMOTETomek techniques were used and the classification algorithms implemented were: XGBoost, Random Forest and Logistic Regression. The estimated model that presented the best performance was obtained through the Random Forest algorithm using balanced data through the SMOTE technique. This model reflects a hit of 60% of the observations belonging to the minority class.
Descrição
Mestrado Bolonha em Métodos Quantitativos para a Decisão Económica e Empresarial
Palavras-chave
Aprendizagem Supervisionada Classe Desbalanceada XGBoost Random Forest Regressão Logística Machine Learning Supervised Learning Unbalanced Class Logistic Regression
Contexto Educativo
Citação
Mota, Iolanda Margarida Lopes da (2022). “Machine learning na previsão da conversão de clientes alvo”. Dissertação de Mestrado. Universidade de Lisboa. Instituto Superior de Economia e Gestão
Editora
Instituto Superior de Economia e Gestão