Data mining: concepts and techniques
Data mining: concepts and techniques
Machine Learning
The Growing Hierarchical Self-Organizing Map
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
An intelligent customer retention system
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Self organization of a massive document collection
IEEE Transactions on Neural Networks
Predicting credit card customer churn in banks using data mining
International Journal of Data Analysis Techniques and Strategies
Expert Systems with Applications: An International Journal
A proximate dynamics model for data mining
Expert Systems with Applications: An International Journal
Variable selection by association rules for customer churn prediction of multimedia on demand
Expert Systems with Applications: An International Journal
Modeling derived from Bayesian filtering: analysis of estimation process
INES'09 Proceedings of the IEEE 13th international conference on Intelligent Engineering Systems
Data Mining Using Rules Extracted from SVM: An Application to Churn Prediction in Bank Credit Cards
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Short communication: New results in modelling derived from Bayesian filtering
Knowledge-Based Systems
CRBT customer churn prediction: can data mining techniques work?
International Journal of Networking and Virtual Organisations
Churn prediction in telecom using a hybrid two-phase feature selection method
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Understanding consumer heterogeneity: A business intelligence application of neural networks
Knowledge-Based Systems
Combined rough set theory and flow network graph to predict customer churn in credit card accounts
Expert Systems with Applications: An International Journal
Data mining via rules extracted from GMDH: an application to predict churn in bank credit cards
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
Customer grouping for better resources allocation using GA based clustering technique
Expert Systems with Applications: An International Journal
Predicting customer churn through interpersonal influence
Knowledge-Based Systems
Distributed customer behavior prediction using multiplex data: A collaborative MK-SVM approach
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Mobile phone customer retention strategies and Chinese e-commerce
Electronic Commerce Research and Applications
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The prevention of subscriber churn through customer retention is a core issue of Customer Relationship Management (CRM). By minimizing customer churn a company maximizes its profit. This paper proposes a hybridized architecture to deal with customer retention problems. It does so not only through predicting churn probability but also by proposing retention policies. The architecture works in two modes: learning and usage. In the learning mode, the churn model learner seeks potential associations from the subscriber database. This historical information is used to form a churn model. This mode also calls for a policy model constructor to use the attributes identified in the churn model to divide all 'churners' into distinct groups. The policy model constructor is also responsible for developing a policy model for each churner group. In the usage mode, a churn predictor uses the churn model to predict the churn probability of a given subscriber. When the churn model finds that the subscriber has a high churn probability the policy model is used to suggest specific retention policies. This study's experiments show that the churn model has an evaluation accuracy of approximately eighty-five percent. This suggests that policy model construction represents an interesting and important technique in investigating the characteristics of churner groups. Furthermore, this study indicates that understanding the relationships between churns is essential in creating effective retention policy models for dealing with 'churners'.