Research Model of Churn Prediction Based on Customer Segmentation and Misclassification Cost in the Context of Big Data

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"Research Model of Churn Prediction Based on Customer Segmentation and Misclassification Cost in the Context of Big Data"
written by Yong Liu, Yongrui Zhuang,
published by Journal of Computer and Communications, Vol.3 No.6, 2015

has been cited by the following article(s):

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CrossRef

[1]	An Enhanced Bank Customers Churn Prediction Model Using A Hybrid Genetic Algorithm And K-Means Filter And Artificial Neural Network
	2021

[2]	Swarm intelligence goal-oriented approach to data-driven innovation in customer churn management
	2021

[3]	Customer churn prediction for commercial banks using customer-value-weighted machine learning models
	Journal of Credit Risk, 2021

[4]	Customer Churn Prediction Models For PT. XYZ Insurance
	2020

[5]	CHURN PREDICTION TECHNIQUES IN TELECOM INDUSTRY FOR CUSTOMER RETENTION: A SURVEY
	2020

[6]	Penerapan Data Mining Dalam Analytic CRM
	2020

[7]	Research on The Anonymous Customer Segmentation Model of Telecom
	2020

[8]	Improving Algorithm Accuracy K-Nearest Neighbor Using Z-Score Normalization and Particle Swarm Optimization to Predict Customer Churn
	2020

[9]	Analisa Klasifikasi Loyalitas Siswa Lembaga Pendidikan Tari dengan Metode Naïve Bayes
	2020

[10]	Analisa Komparasi Algoritma Decision Tree C4. 5 dan Naïve Bayes untuk Prediksi Churn Berdasarkan Kelas Pelanggan Retail
	2019

[11]	Optimum Profit-Driven Churn Decision Making: Innovative Artificial Neural Networks in Telecom Industry
	2019

[12]	Applications of data mining techniques for churn prediction and cross-selling in the telecommunications industry
	2019

[13]	A Robust Model for Churn Prediction using Supervised Machine Learning
	2019

[14]	VU Research Portal
	2019

[15]	Modelling and Predicting User Engagement in Mobile Applications
	2018

[16]	Big Data Analytics Evaluation
	2018

[17]	Prediksi Customer Churn dengan Algoritma Decision Tree C4. 5 Berdasarkan Segmentasi Pelanggan untuk Mempertahankan Pelanggan pada Perusahaan Retail
	2018

[18]	Behavioral attributes and financial churn prediction
	2018

[19]	Exploring nested ensemble learners using overproduction and choose approach for churn prediction in telecom industry
	Neural Computing and Applications, 2018

[20]	Big data provenance and analytics in telecom contact centers
	2017

[21]	A review and analysis of churn prediction methods for customer retention in telecom industries
	2017

[22]	A Survey of Evolution in Predictive Models and Impacting Factors in Customer Churn
	Advances in Data Science and Adaptive Analysis, 2017

[23]	MCS: Multiple classifier system to predict the churners in the telecom industry
	2017

[24]	Exploration space of human-data interaction
	2017

[25]	Self-organizing and error driven (SOED) artificial neural network for smarter classifications
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[26]	Mining Big Data in Telecommunications Industry: Challenges, Techniques, and Revenue Opportunity
	2016

[27]	HA: Mining big data in telecommunications industry: challenges, techniques, and revenue opportunity
	2016

[1]	Swarm intelligence goal-oriented approach to data-driven innovation in customer churn management International Journal of Information Management, 2021 DOI:10.1016/j.ijinfomgt.2021.102357

[2]	Swarm intelligence goal-oriented approach to data-driven innovation in customer churn management International Journal of Information Management, 2021 DOI:10.1016/j.ijinfomgt.2021.102357

[3]	Optimum profit-driven churn decision making: innovative artificial neural networks in telecom industry Neural Computing and Applications, 2020 DOI:10.1007/s00521-020-04850-6

[4]	Research on The Anonymous Customer Segmentation Model of Telecom 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC), 2020 DOI:10.1109/ITOEC49072.2020.9141572

[5]	Customer Churn Prediction Models For PT. XYZ Insurance 2020 8th International Conference on Orange Technology (ICOT), 2020 DOI:10.1109/ICOT51877.2020.9468788

[6]	Modelling and predicting User Engagement in mobile applications Data Science, 2019 DOI:10.3233/DS-190027

[7]	A Robust Model for Churn Prediction using Supervised Machine Learning 2019 IEEE 9th International Conference on Advanced Computing (IACC), 2019 DOI:10.1109/IACC48062.2019.8971494

[8]	Exploring nested ensemble learners using overproduction and choose approach for churn prediction in telecom industry Neural Computing and Applications, 2018 DOI:10.1007/s00521-018-3678-8

[9]	Behavioral attributes and financial churn prediction EPJ Data Science, 2018 DOI:10.1140/epjds/s13688-018-0165-5

[10]	A Survey of Evolution in Predictive Models and Impacting Factors in Customer Churn Advances in Data Science and Adaptive Analysis, 2017 DOI:10.1142/S2424922X17500073

[11]	A review and analysis of churn prediction methods for customer retention in telecom industries 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), 2017 DOI:10.1109/ICACCS.2017.8014605

[12]	Big data provenance and analytics in telecom contact centers TENCON 2017 - 2017 IEEE Region 10 Conference, 2017 DOI:10.1109/TENCON.2017.8228107

[13]	MCS: Multiple classifier system to predict the churners in the telecom industry 2017 Intelligent Systems Conference (IntelliSys), 2017 DOI:10.1109/IntelliSys.2017.8324367

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