Análise preditiva de Churn com ênfase em técnicas de Machine Learning: uma revisão

Data
2016-07-27
Orientador(res)
Souza, Renato Rocha
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Resumo

In the last two decades, the growth of the Internet and its associated technologies, are transforming the way of the relationship between companies and their clients. In general, the acquisition of a new customer is much more expensive for a company than the retention of a current one. Thus, customer retention studies or Churn management has become more important for companies. This study represents the review and classi cation of literature on applications of Machine Learning techniques to build predictive models of customers loss, also called Churn. The objective of this study was collecting the largest possible number of documents on the subject within the proposed methodology and classi es them as per application areas, year of publication, Machine Learning techniques applied, journals and repositories used and in uence level of the documents. And thus, bringing to the light the existing studies in this eld of activity, consolidating what is the state of the art of research in this area, and signi cantly contribute as a reference for future applications and researches in this area. Although, the study has not been the rst in the literature of Machine Learning related to the loss of customer or customer retention in the way of literature review, it was the rst, among the ones we have found, with focus on documents studying, not exclusively, loss or retention of customers by Machine Learning techniques, and without any kind of restriction. Furthermore it was the rst to classify documents by in uence, through the quotations from each document. As a nal database was collected and analyzed 80 documents, from which were found as main application areas: Telecommunications, Financial, Newspapers, Retail, among others. As per Machine Learning techniques applied, the most applied techniques founded related to the problem, were the following: Logistic Regression, Decision Tree and Neural Networks, among others. And based on the results, this kind of study is dated since 2000.


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