International Journal on Data Science and Technology

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

Received: 17 June 2020    Accepted: 02 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.

DOI 10.11648/j.ijdst.20200602.12
Published in International Journal on Data Science and Technology (Volume 6, Issue 2, June 2020)
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), 2024. Published by Science Publishing Group

Keywords

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

References
[1] Azeem, M. and Usman, M., 2018. A fuzzy based churn prediction and retention model for prepaid customers in telecom industry. International Journal of Computational Intelligence Systems, 11 (1), pp. 66-78.
[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.
Author Information
  • Computer Science Department, Bowen University, Iwo, Nigeria

  • Computer Science Department, Bowen University, Iwo, Nigeria; Computer Science Department, University of Ibadan, Ibadan, Nigeria

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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://download.sciencepg.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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    T1  - Blended Churn Predictive System for Quadruple-Patterned Churn Classification Towards Effective Customer Behavioural Management
    AU  - Ayodeji Ibitoye
    AU  - Olufade Onifade
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    PY  - 2020
    N1  - https://doi.org/10.11648/j.ijdst.20200602.12
    DO  - 10.11648/j.ijdst.20200602.12
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    JO  - International Journal on Data Science and Technology
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    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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