TY - GEN
T1 - Predictive Model to Determine Customer Desertion in Peruvian Banking Entities
AU - Barrueta-Meza, Renzo
AU - Castillo-Villarreal, Jean Paul
AU - Armas-Aguirre, Jimmy
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/21
Y1 - 2018/12/21
N2 - In this paper, a predictive model to determine customer desertion in Peruvian banking entities is proposed. The purpose of the model is the early identification of customers that reflect a behavior tending towards desertion based on financial movements, transactions, product acquisition, etc. The model is based on the analysis of a customer dataset to identify common traits through the use of SAP Predictive Analytics, and then comparing these traits to a different customer dataset, identifying those that are more likely to leave the entity. The commercial use of this model is the immediate application of loyalty initiatives that would enable the entity to retain the customer. The model was tested in order to identify the most efficient and precise one, being the R-K Means algorithm the best performing one, with a 93.20% accuracy and a better false positive/negative relation (8 and 3 respectively).
AB - In this paper, a predictive model to determine customer desertion in Peruvian banking entities is proposed. The purpose of the model is the early identification of customers that reflect a behavior tending towards desertion based on financial movements, transactions, product acquisition, etc. The model is based on the analysis of a customer dataset to identify common traits through the use of SAP Predictive Analytics, and then comparing these traits to a different customer dataset, identifying those that are more likely to leave the entity. The commercial use of this model is the immediate application of loyalty initiatives that would enable the entity to retain the customer. The model was tested in order to identify the most efficient and precise one, being the R-K Means algorithm the best performing one, with a 93.20% accuracy and a better false positive/negative relation (8 and 3 respectively).
KW - Auto Classification Algorithm
KW - Customer Desertion
KW - Machine Learning
KW - Predictive Analytics
KW - Predictive Model
UR - https://www.scopus.com/pages/publications/85061027988
U2 - 10.1109/CONIITI.2018.8587084
DO - 10.1109/CONIITI.2018.8587084
M3 - Contribución a la conferencia
AN - SCOPUS:85061027988
T3 - 2018 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2018 - Proceedings
BT - 2018 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2018 - Proceedings
A2 - Lozano-Garzon, Carlos Andres
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th Innovation and Trends in Engineering Congress, CONIITI 2018
Y2 - 3 October 2018 through 5 October 2018
ER -