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Master's Dissertation
DOI
https://doi.org/10.11606/D.11.2012.tde-23102012-163809
Document
Author
Full name
Adriana Maria Marques da Silva
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
Piracicaba, 2012
Supervisor
Committee
Dias, Carlos Tadeu dos Santos (President)
Piedade, Sonia Maria de Stefano
Miazaki, Edina Shisue
Title in Portuguese
Técnicas de Data Mining na aquisição de clientes para financiamento de Crédito Direto ao Consumidor - CDC
Keywords in Portuguese
Árvore de decisão
Crédito direto ao consumidor
Financiamento
Mineração de dados
Redes neurais
Regressão logística
Abstract in Portuguese
O trabalho busca dissertar sobre as técnicas de data mining mais difundidas: regressão logística, árvore de decisão e rede neural, além de avaliar se tais técnicas oferecem ganhos financeiros para instituições privadas que contam com processos ativos de conquista de clientes. Uma empresa do setor financeiro será utilizada como objeto de estudo, especificamente nos seus processos de aquisição de novos clientes para adesão do Crédito Direto ao Consumidor (CDC). Serão mostrados os resultados da aplicação nas três técnicas mencionadas, para que seja possível verificar se o emprego de modelos estatísticos discriminam os clientes potenciais mais propensos dos menos propensos à adesão do CDC e, então, verificar se tal ação impulsiona na obtenção de ganhos financeiros. Esses ganhos poderão vir mediante redução dos custos de marketing abordando-se somente os clientes com maiores probabilidades de responderem positivamente à campanha. O trabalho apresentará o funcionamento de cada técnica teoricamente, e conforme os resultados indicam, data mining é uma grande oportunidade para ganhos financeiros em uma empresa.
Title in English
Data Mining Techniques to acquire new customers for financing of Consumer Credit
Keywords in English
CDC
Data Mining
Decision Tree
Logistic Regression
Neural Network
Abstract in English
The paper intends to discourse about most widespread data mining techniques: logistic regression, decision tree and neural network, and assess whether these techniques provide financial gains for private institutions that have active processes for business development. A company of the financial sector is used as object of study, specifically in the processes of acquiring new customers for adhesion to consumer credit (in Brazil CDC). This research will show the results of the three above mentioned techniques, to check whether the statistical models point out relevant differences between prospects´ intentions to adhere to consumer credit. In the meantime, the techniques are checked whether they leverage financial gain. These gains are expected to came from better focused and directed marketing efforts. The paper presents the operation of each technique theoretically, and as the results indicate, data mining is a great opportunity for a company boost profits.
 
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Publishing Date
2012-11-09
 
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