Blended Churn Predictive System for Quadruple-Patterned Churn Classification Towards Effective Customer Behavioural Management

Ayodeji Ibitoye; Olufade Onifade

doi:doi:10.11648/j.ijdst.20200602.12

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Blended Churn Predictive System for Quadruple-Patterned Churn Classification Towards Effective Customer Behavioural Management

Ayodeji Ibitoye, Olufade Onifade

Published in International Journal on Data Science and Technology (Volume 6, Issue 2)

Received: 17 June 2020 Accepted: 2 July 2020 Published: 20 August 2020

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Abstract

The adoption of product centric approach to customer acquisition by many subscriber based companies has become a factor, which influences customer misclassification in existing churn predictive models. While the transaction volume, velocity, and varieties for basic churn processes continues to increase exponentially, every customer remained a potential churner to a certain degree. Although, existing churn prediction models classifies customers as churner or non-churner, many of its approaches assign equal weight to features while the customer’s power of influence from socio-transactional data mining are neglected in churn behaviour management. Here, the developed Churn Predictive System is a composite of Recency-Frequency-Monetary-Influence model through customer segmentation management and Fuzzy-Weighed Feature Engineering model, which trained and tested transactional records using Random Forest and Adaboost Ensemble Learning in a 5-fold cross validation protocol. This System was coupled (Customer Segmentation + Ensemble Learning) to achieve a quadrupled customer’s churn category as Churner, Potential Churner, Inertia Customer and Premium Customers. The results from the developed system juxtapose the need for a new approach to churn prediction in customer behavioural management.

Published in	International Journal on Data Science and Technology (Volume 6, Issue 2)
DOI	10.11648/j.ijdst.20200602.12
Page(s)	56-59
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Churn Prediction, Fuzzy-weight, Ensemble Machine Learning, Customer Segmentation, Customers’ Behavioural Management

References

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[2]	Ahmad, A. K., Jafar, A. and Aljoumaa, K., 2019.“Customer churn prediction in telecom using machine learning in big data platform”. Journal of Big Data, 6 (1), p. 28. Springer.
[3]	Kiranjot Kaur and Sheveta Vashisht (2015) “ A Novel Approach for Providing the Customer Churn Prediction Model using Enhanced Boosted Trees Technique in Cloud Computing” International Journal of Computer Applications (0975 – 8887) Volume 114 – No. 7.
[4]	Prashanth, R., Deepak, K. and Meher, A. K., 2017, July.“High accuracy predictive modelling for customer churn prediction in telecom industry”. In International Conference on Machine Learning and Data Mining in Pattern Recognition (pp. 391-402). Springer, Cham.
[5]	X. Zhang, J. Zhu, S. Xu, and Y. Wan, (2012) “Predicting customer churn through interpersonal influence,” Knowl.-Based Syst., vol. 28, pp. 97–104, Apr. 2012.
[6]	Pýnar Kisioglu, Y. Ilker Topcu, (2008) “Applying Bayesian Belief Network approach to customer churn analysis: A case study on the telecom industry of Turkey”, Expert Systems with Applications vol. 38 7151–7157.
[7]	Chih-Fong Tsai, Mao-Yuan Chen, (2015) “Variable selection by association rules for customer churn prediction of multimedia on demand”, Expert Systems with Applications Vol 37.
[8]	Ayodeji O. J IBITOYE and Olufade F. W ONIFADE (2016) “Intelligent Predictive Feature Selection Model for Effective Customer Churn Prediction” Proceedings of the 1st International Conference on Transition from Observation to Knowledge to Intelligence (TOKI 2016) held in University of Lagos, Nigeria during August 25-26, 2016. pp 231-239.
[9]	Ayodeji O. J IBITOYE, Onifade F. W Onifade (2019)“Customer Churn Predictive Analytics using Relative Churn Fuzzy Feature-Weight Model in Telecoms– International Journal of Information, Business and Management. Volume 11, Issue 3, pp 163–167.
[10]	Guangli Nie, Wei Rowe, Lingling Zhang, Yingjie Tian, Yong Shi, (2011) “Credit card churn forecasting by logistic regression and decision tree”, Expert Systems with Applications 38 15273–1528.
[11]	Ionut Brandusoiu, Gavril Toderean, (2013) “Churn Prediction in the Telecommunications Sector Using Support Vector Machines”.
[12]	Amjad Hudaib, Reham Dannoun, Osama Harfoushi, Ruba Obiedat, Hossam Faris (2015) “Hybrid Data Mining Models for Predicting Customer Churn”, J. Communications, Network and System Sciences, May 2015, 8, 91-96.
[13]	Hanif, E., 2019. “Applications of data mining techniques for churn prediction and cross-selling in the telecommunications industry” (Doctoral dissertation, Dublin Business School).
[14]	Sohrabi B, Khanlari A (2007). Customer lifetime value (CLV) measurement based on RFM model. Iranian Acc. Aud. Rev., 14 (47): 7-20.
[15]	Cheng CH, Chen YS (2009). Classifying the segmentation of customer value via RFM model and RS theory. Expert. Syst. Appl., 36: 4176-4184.
[16]	Ayodeji O. J IBITOYE, Onifade F. W Onifade Customers’ Behavioural Management in Telecoms using Socio-Transactional Influence on Recency-Frequency-Monetary (RFM) Approach in Churn Prediction –Springer International Journal of Information Technology- Accepted 2020.

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APA Style

Ayodeji Ibitoye, Olufade Onifade. (2020). Blended Churn Predictive System for Quadruple-Patterned Churn Classification Towards Effective Customer Behavioural Management. International Journal on Data Science and Technology, 6(2), 56-59. https://doi.org/10.11648/j.ijdst.20200602.12

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ACS Style

Ayodeji Ibitoye; Olufade Onifade. Blended Churn Predictive System for Quadruple-Patterned Churn Classification Towards Effective Customer Behavioural Management. Int. J. Data Sci. Technol. 2020, 6(2), 56-59. doi: 10.11648/j.ijdst.20200602.12

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AMA Style

Ayodeji Ibitoye, Olufade Onifade. Blended Churn Predictive System for Quadruple-Patterned Churn Classification Towards Effective Customer Behavioural Management. Int J Data Sci Technol. 2020;6(2):56-59. doi: 10.11648/j.ijdst.20200602.12

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@article{10.11648/j.ijdst.20200602.12,
  author = {Ayodeji Ibitoye and Olufade Onifade},
  title = {Blended Churn Predictive System for Quadruple-Patterned Churn Classification Towards Effective Customer Behavioural Management},
  journal = {International Journal on Data Science and Technology},
  volume = {6},
  number = {2},
  pages = {56-59},
  doi = {10.11648/j.ijdst.20200602.12},
  url = {https://doi.org/10.11648/j.ijdst.20200602.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20200602.12},
  abstract = {The adoption of product centric approach to customer acquisition by many subscriber based companies has become a factor, which influences customer misclassification in existing churn predictive models. While the transaction volume, velocity, and varieties for basic churn processes continues to increase exponentially, every customer remained a potential churner to a certain degree. Although, existing churn prediction models classifies customers as churner or non-churner, many of its approaches assign equal weight to features while the customer’s power of influence from socio-transactional data mining are neglected in churn behaviour management. Here, the developed Churn Predictive System is a composite of Recency-Frequency-Monetary-Influence model through customer segmentation management and Fuzzy-Weighed Feature Engineering model, which trained and tested transactional records using Random Forest and Adaboost Ensemble Learning in a 5-fold cross validation protocol. This System was coupled (Customer Segmentation + Ensemble Learning) to achieve a quadrupled customer’s churn category as Churner, Potential Churner, Inertia Customer and Premium Customers. The results from the developed system juxtapose the need for a new approach to churn prediction in customer behavioural management.},
 year = {2020}
}

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TY  - JOUR
T1  - Blended Churn Predictive System for Quadruple-Patterned Churn Classification Towards Effective Customer Behavioural Management
AU  - Ayodeji Ibitoye
AU  - Olufade Onifade
Y1  - 2020/08/20
PY  - 2020
N1  - https://doi.org/10.11648/j.ijdst.20200602.12
DO  - 10.11648/j.ijdst.20200602.12
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 56
EP  - 59
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20200602.12
AB  - The adoption of product centric approach to customer acquisition by many subscriber based companies has become a factor, which influences customer misclassification in existing churn predictive models. While the transaction volume, velocity, and varieties for basic churn processes continues to increase exponentially, every customer remained a potential churner to a certain degree. Although, existing churn prediction models classifies customers as churner or non-churner, many of its approaches assign equal weight to features while the customer’s power of influence from socio-transactional data mining are neglected in churn behaviour management. Here, the developed Churn Predictive System is a composite of Recency-Frequency-Monetary-Influence model through customer segmentation management and Fuzzy-Weighed Feature Engineering model, which trained and tested transactional records using Random Forest and Adaboost Ensemble Learning in a 5-fold cross validation protocol. This System was coupled (Customer Segmentation + Ensemble Learning) to achieve a quadrupled customer’s churn category as Churner, Potential Churner, Inertia Customer and Premium Customers. The results from the developed system juxtapose the need for a new approach to churn prediction in customer behavioural management.
VL  - 6
IS  - 2
ER  -

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Author Information

Ayodeji Ibitoye

Computer Science Department, Bowen University, Iwo, Nigeria
Olufade Onifade

Computer Science Department, Bowen University, Iwo, Nigeria

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Plain Text BibTeX RIS

APA Style

Ayodeji Ibitoye, Olufade Onifade. (2020). Blended Churn Predictive System for Quadruple-Patterned Churn Classification Towards Effective Customer Behavioural Management. International Journal on Data Science and Technology, 6(2), 56-59. https://doi.org/10.11648/j.ijdst.20200602.12

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ACS Style

Ayodeji Ibitoye; Olufade Onifade. Blended Churn Predictive System for Quadruple-Patterned Churn Classification Towards Effective Customer Behavioural Management. Int. J. Data Sci. Technol. 2020, 6(2), 56-59. doi: 10.11648/j.ijdst.20200602.12

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AMA Style

Ayodeji Ibitoye, Olufade Onifade. Blended Churn Predictive System for Quadruple-Patterned Churn Classification Towards Effective Customer Behavioural Management. Int J Data Sci Technol. 2020;6(2):56-59. doi: 10.11648/j.ijdst.20200602.12

Copy | Download

@article{10.11648/j.ijdst.20200602.12,
  author = {Ayodeji Ibitoye and Olufade Onifade},
  title = {Blended Churn Predictive System for Quadruple-Patterned Churn Classification Towards Effective Customer Behavioural Management},
  journal = {International Journal on Data Science and Technology},
  volume = {6},
  number = {2},
  pages = {56-59},
  doi = {10.11648/j.ijdst.20200602.12},
  url = {https://doi.org/10.11648/j.ijdst.20200602.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20200602.12},
  abstract = {The adoption of product centric approach to customer acquisition by many subscriber based companies has become a factor, which influences customer misclassification in existing churn predictive models. While the transaction volume, velocity, and varieties for basic churn processes continues to increase exponentially, every customer remained a potential churner to a certain degree. Although, existing churn prediction models classifies customers as churner or non-churner, many of its approaches assign equal weight to features while the customer’s power of influence from socio-transactional data mining are neglected in churn behaviour management. Here, the developed Churn Predictive System is a composite of Recency-Frequency-Monetary-Influence model through customer segmentation management and Fuzzy-Weighed Feature Engineering model, which trained and tested transactional records using Random Forest and Adaboost Ensemble Learning in a 5-fold cross validation protocol. This System was coupled (Customer Segmentation + Ensemble Learning) to achieve a quadrupled customer’s churn category as Churner, Potential Churner, Inertia Customer and Premium Customers. The results from the developed system juxtapose the need for a new approach to churn prediction in customer behavioural management.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Blended Churn Predictive System for Quadruple-Patterned Churn Classification Towards Effective Customer Behavioural Management
AU  - Ayodeji Ibitoye
AU  - Olufade Onifade
Y1  - 2020/08/20
PY  - 2020
N1  - https://doi.org/10.11648/j.ijdst.20200602.12
DO  - 10.11648/j.ijdst.20200602.12
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 56
EP  - 59
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20200602.12
AB  - The adoption of product centric approach to customer acquisition by many subscriber based companies has become a factor, which influences customer misclassification in existing churn predictive models. While the transaction volume, velocity, and varieties for basic churn processes continues to increase exponentially, every customer remained a potential churner to a certain degree. Although, existing churn prediction models classifies customers as churner or non-churner, many of its approaches assign equal weight to features while the customer’s power of influence from socio-transactional data mining are neglected in churn behaviour management. Here, the developed Churn Predictive System is a composite of Recency-Frequency-Monetary-Influence model through customer segmentation management and Fuzzy-Weighed Feature Engineering model, which trained and tested transactional records using Random Forest and Adaboost Ensemble Learning in a 5-fold cross validation protocol. This System was coupled (Customer Segmentation + Ensemble Learning) to achieve a quadrupled customer’s churn category as Churner, Potential Churner, Inertia Customer and Premium Customers. The results from the developed system juxtapose the need for a new approach to churn prediction in customer behavioural management.
VL  - 6
IS  - 2
ER  -

Copy | Download