Framework for Targeting High Value Customers and Potential Churn Customers in Telecom using Big Data Analytics

Full Text (PDF, 471KB), PP.36-45

Views: 0 Downloads: 0


Inderpreet Singh 1,* Sukhpal Singh 2

1. Mahindra Comviva, Gurgaon- 122001, Haryana, India

2. Thapar University-147001, Patiala, Punjab, India

* Corresponding author.


Received: 22 Sep. 2016 / Revised: 1 Nov. 2016 / Accepted: 5 Dec. 2016 / Published: 8 Jan. 2017

Index Terms

Customer Segmentation, Telecom, Big Data Analytics, RFM Analysis, Data Mining, Customer Value


Since the more importance is played on customer's behavior in today's business market, telecom companies are not only focusing on customer's profitability to increase their market share but also on their potential churn customers who could terminate the relation with the company in near future. Big data promises to promote growth and increase efficiency and profitability across the entire telecom value chain. This paper presents a framework for targeting high value customers and potential churn customers. Firstly, customers are segmented on basis of RFM (Recency-Frequency-Monetary) analysis and finally customers in each segment are targeted by various offers on basis of their similar characteristics.

Cite This Paper

Inderpreet Singh, Sukhpal Singh,"Framework for Targeting High Value Customers and Potential Churn Customers in Telecom using Big Data Analytics", International Journal of Education and Management Engineering(IJEME), Vol.7, No.1, pp.36-45, 2017. DOI: 10.5815/ijeme.2017.01.04


[1] Goggin, G. Cell phone culture: Mobile technology in everyday life. Routledge, (2012).

[2] "Gartner Forecasts 59 Percent Mobile Data Growth Worldwide In 2015". 2016. Gartner.Com.

[3] Acker, O., Blockus, A. and Pötscher, F. "Benefiting from big data: A new approach for the telecom industry." Strategy&, Analysis Report (2013).

[4] Baars, H. and Kemper, H.G. "Management support with structured and unstructured data—an integrated business intelligence framework." Information Systems Management 25, no. 2 (2008): pp 132-148.

[5] The Executive's Guide to Big Data & Apache Hadoop written by Robert D. Schneider; Page9.

[6] Laney, D. "3D data management: Controlling data volume, velocity and variety." META Group Research Note 6 (2001): pp 70.

[7] Data, Big. "for better or worse: 90% of world's data generated over last two years." SCIENCE DAILY, May 22 (2013).

[8] "What Is Customer Segmentation? - Definition from" SearchSalesforce. Accessed August 20, 2016.

[9] "Customer Churn Software: Prediction, Prevention & Analysis | Optimove". 2016. Optimove.

[10] Li, B., Mumford, P., Dempster, A.G. and Rizos, C. "Secure User Plan Location (SUPL): concept and performance," GPS Solutions, vol. 14, pp. 153, (2010).

[11] Chuang, H.M. and Shen, C.C. "A study on the applications of data mining techniques to enhance customer lifetime value—based on the department store industry." In 2008 International Conference on Machine Learning and Cybernetics, vol. 1, pp. 168-173. IEEE, (2008).

[12] Jayawardhana, P., Perera, D., Kumara, A. and Paranawithana, A "Kanthaka: Big Data Caller Detail Record (CDR) Analyzer for Near Real Time Telecom Promotions." In 2013 4th International Conference on Intelligent Systems, Modelling and Simulation, pp. 534-538. IEEE, (2013).

[13] Şenbalcı, C., Altuntaş, S., Bozkus, Z. and Arsan, T. "Big data platform development with a domain specific language for telecom industries." In 2013 High Capacity Optical Networks and Emerging/Enabling Technologies, pp. 116-120. IEEE, (2013).

[14] Shi, J.Y. and Li, L.L. "The Research of Data Mining in Telecom Data Warehouse." In System Science, Engineering Design and Manufacturing Informatization (ICSEM), 2010 International Conference on, vol. 2, pp. 239-242. IEEE, (2010).

[15] Shobha, G. "Social network classifier for churn prediction in telecom data." In Advanced Computing and Communication Systems (ICACCS), 2013 International Conference on, pp. 1-7. IEEE, (2013).

[16] Hung, S.Y., Yen, D.C. and Wang. "Applying data mining to telecom churn management." Expert Systems with Applications 31, no. 3 (2006): pp. 515-524.

[17] Singh, S. and Chana, I. "EARTH: Energy-aware autonomic resource scheduling in cloud computing." Journal of Intelligent & Fuzzy Systems Vol. 30, no. 3 (2016): pp. 1581-1600

[18] Singh, S. and Chana, I. "Resource provisioning and scheduling in clouds: QoS perspective." The Journal of Supercomputing Vol. 72, no. 3 (2016): pp. 926-960.

[19] Singh, S. and Chana, I. "QoS-aware autonomic resource management in cloud computing: a systematic review." ACM Computing Surveys (CSUR) Vol. 48, no. 3 (2016): pp. 42.

[20] Singh, S. and Chana, I. "QRSF: QoS-aware resource scheduling framework in cloud computing", "The Journal of Supercomputing", [Springer], Vol. 71, no. 1, pp: 241-292, 2015.

[21] Ahmed T. "Cloud Computing a Solution for Globalization", International Journal of Education and Management Engineering (IJEME), Vol.6, No.4, pp.30-38, 2016.DOI: 10.5815/ijeme.2016.04.04.

[22] Herrera J. M., González J. V. and Fillad L.L. "Web System Proposal for Control and Monitoring Fleets", International Journal of Education and Management Engineering (IJEME), Vol.6, No.1, pp.1-10, 2016.DOI: 10.5815/ijeme.2016.01.01.

[23] Kolo K.D., Adepoju S.A. and Alhassan J.K.,"A Decision Tree Approach for Predicting Students Academic Performance", International Journal of Education and Management Engineering (IJEME), Vol.5, No.5, pp.12-19, 2015.DOI: 10.5815/ijeme.2015.05.02.