Bibliographic citations
Cortez, L., Estrada, A. (2023). Modelo de análisis predictivo para abandono de clientes en una empresa administradora de fondos colectivos [Tesis, Universidad Peruana de Ciencias Aplicadas (UPC)]. http://hdl.handle.net/10757/667966
Cortez, L., Estrada, A. Modelo de análisis predictivo para abandono de clientes en una empresa administradora de fondos colectivos [Tesis]. PE: Universidad Peruana de Ciencias Aplicadas (UPC); 2023. http://hdl.handle.net/10757/667966
@misc{renati/1294281,
title = "Modelo de análisis predictivo para abandono de clientes en una empresa administradora de fondos colectivos",
author = "Estrada Valderrama, Alvaro Antonio",
publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
year = "2023"
}
Currently, the category of collective funds is gaining more and more position in South America as an alternative to vehicle credit from banks, a modality in which people, through monthly contributions and raffle and monthly auctions modalities, can achieve the dream of owning a car or home. However, this sector is being affected by the large percentage of clients who stop paying their installments for 3 months and the organization considers them as clients who have abandoned the collective fund. This situation is going to increase more and more and the entities in this sector have not risked opting for solutions supported by machine learning technologies and techniques, which is why they currently make decisions based on the data provided by an external entity called Equifax, which provides information about the segment of each client, however this process is not bringing good results since the percentage of clients that enter the company and then leave is increasing more and more, thus harming the groups in which they belong and the organization monthly income. For this reason, this project seeks to develop a predictive analysis model based on machine learning, which allows identifying the behavior of deserting customers and thus being able to predict the percentage probability that each customer has to abandon the organization, in order to that the authors involved in the customer retention business process can make decisions and guide their commercial campaigns expanded on data.
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