Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
The Journal of Supercomputing
Published in: The Journal of Supercomputing 6/2020

13-10-2017

Just-in-time customer churn prediction in the telecommunication sector

Authors: Adnan Amin, Feras Al-Obeidat, Babar Shah, May Al Tae, Changez Khan, Hamood Ur Rehman Durrani, Sajid Anwar

Published in: The Journal of Supercomputing | Issue 6/2020

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Due to the exponential growth in technologies and a greater number of competitors in the telecom sector, the companies are facing a rigorous problem of customer churns. The customer churn is a phenomenon that highlights the customer’s intention who may switch from a certain service or even the service provider company. Many customer churn prediction (CCP) techniques are developed by academics and practitioners to handle the customer churn in order to resolve the problems pertaining to customer retention. However, CCP is not widely studied in the scenario where the company is not having enough historical data due to either been a newly established company or due to the recent start of a new technology or even because of the loss of the historical data. The just-in-time (JIT) approach can be a more practical alternative to address this issue as compared to state-of-the-art CCP techniques. Unfortunately, similar to traditional churn prediction models, JIT also requires enough historical data. To address this gap in the traditional CCP models, this study uses the cross-company data, i.e., data from another company, in the context of JIT for addressing CCP problems in the telecom sector. We empirically evaluated the performance of the proposed model using publicly available datasets of two telecom companies. It is found from the empirical evaluation that in the JIT-CCP context: (i) it is possible to evaluate the performance of the predictive model using cross-company dataset for training purposes and (ii) it is evident that heterogeneous ensemble-based JIT-CCP model is more suitable approach to use as compared to individual classifier or homogeneous ensemble-based technique.
previous article Editor’s note
next article Swarm-based clustering algorithm for efficient web blog and data classification

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now
Footnotes
Literature
1.
Amin A, Faisal R, Muhammad R, Sajid A (2015) A prudent based approach for customer churn prediction. In: 11th International Conference, BDAS 2015, Ustroń, Poland, pp 320–332CrossRef
2.
Farquad MAH, Ravi V, Raju SB (2014) Churn prediction using comprehensible support vector machine: an analytical CRM application. Appl Soft Comput 19:31–40CrossRef
3.
Chitra K, Subashini B (2011) Customer retention in banking sector using predictive data mining technique. In: ICIT 2011 5th International Conference on Information Technology, pp 1–4
4.
Prasad Devi U, Madhavi S (2012) Prediction of churn behavior of bank customers. J Bus Intell 5(1):96–101
5.
David Nunez-Gonzalez J, Grana M, Apolloni B (2014) Reputation features for trust prediction in social networks. Neurocomputing 166:1–7CrossRef
6.
Verbeke W, Martens D, Baesens B (2014) Social network analysis for customer churn prediction. Appl Soft Comput 14:431–446CrossRef
7.
Lin C-S, Tzeng G-H, Chin Y-C (2011) Combined rough set theory and flow network graph to predict customer churn in credit card accounts. Expert Syst Appl 38(1):8–15CrossRef
8.
Soeini RA, Rodpysh KV (2012) Applying data mining to insurance customer churn management. Int Proc Comput Sci Inf Technol 30:82–92
9.
Saron M, Othman ZA (2012) Academic talent model based on human resource data mart. Int J Res Comput Sci 2(5):29–35CrossRef
10.
Suznjevic M, Stupar I, Matijasevic M (2011) MMORPG player behavior model based on player action categories. In: Proceedings of the 10th Annual Workshop on Network and Systems Support for Games. IEEE Press, pp 1–6
11.
Chen K, Lei C (2006) Network game design: hints and implications of player interaction. In: Proceedings of the 5th ACM SIGCOMM Workshop on Network and Systems Support for Games, pp 1–9
12.
Gök M, Jida J (2015) A case study for the churn prediction in Turksat Internet service subscription. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp 1220–1224
13.
Coussement K, Van den Poel D (2008) Churn prediction in subscription services: an application of support vector machines while comparing two parameter-selection techniques. Expert Syst Appl 34(1):313–327CrossRef
14.
Kim K, Jun C-H, Lee J (2014) Improved churn prediction in telecommunication industry by analyzing a large network. Expert Syst Appl 41(15):6575–6584CrossRef
15.
Kirui C, Hong L, Cheruiyot W, Kirui H (2013) Predicting customer churn in mobile telephony industry using probabilistic classifiers in data mining. IJCSI Int J Comput Sci Issues 10(2):165–172
16.
Shaaban E, Helmy Y, Khedr A, Nasr M (2012) A proposed churn prediction model. Int J Eng Res Appl 2(4):693–697
17.
Jaudet M, Hussain A, Iqbal N (2004) Neural networks for fault-prediction in a telecommunications network. In: Proceedings of the 8th IEEE International Multi-topic Conference (INMIC’2004), pp 24–26
18.
Zakaryazad A, Duman E (2016) A profit-driven artificial neural network (ANN) with applications to fraud detection and direct marketing. Neurocomputing 175:121–131CrossRef
19.
Kang C, Pei-ji S (2008) Customer churn prediction based on SVM-RFE. In: 2008 International Seminar on Business and Information Management, vol 1, pp 306–309
20.
Burez J, Van den Poel D (2009) Handling class imbalance in customer churn prediction. Expert Syst Appl 36(3):4626–4636CrossRef
21.
Mozer MC, Wolniewicz R, Grimes DB, Johnson E, Kaushansky H (2000) Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry. IEEE Trans Neural Netw 11(3):690–696CrossRef
22.
Abbasimehr H (2011) A neuro-fuzzy classifier for customer churn prediction. Int J Comput Appl 19(8):35–41
23.
Huang B, Kechadi MT, Buckley B (2012) Customer churn prediction in telecommunications. Expert Syst Appl 39(1):1414–1425CrossRef
24.
Fukushima T, Kamei Y, Mcintosh S, Yamashita K, Ubayashi N (2014) An empirical study of just-in-time defect prediction using cross-project models. In: MSR-2015, Hyderabad, India
25.
Kamei Y, Fukushima T, McIntosh S, Yamashita K, Ubayashi N, Hassan AE (2016) Studying just-in-time defect prediction using cross-project models. Empirical Softw Eng 21(5):2072–2106CrossRef
26.
García DL, Nebot À, Vellido A (2017) Intelligent data analysis approaches to churn as a business problem: a survey. Knowl Inf Syst 51(3):719–774CrossRef
27.
Radisic M (2009) Just-in-time concept. Reterived May 20, pp 1–9
28.
Fuchs M, Zanker M (2016) E-commerce and Web technologies, vol 123, p 2012
29.
Kootanae AJ, Babu KN, Talari HF (2012) Just-in-time manufacturing system: from introduction to implement. Int J Econ Bus Financ 1(2):7–25
30.
Witten IH, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques. Morgan Kaufmann Publisher Inc., San Francisco, CA, p 664CrossRef
31.
Catolino G (2017) Just-in-time bug prediction in mobile applications? The domain matters! In: ACM MobileSoft 17. Argentina, Buenos Aires, pp 1–2
32.
Kootanace A (2013) Just-in-time manufacturing system: from introduction to implement. Int J Econ Bus Financ 1(2):1–19
33.
Burak T, Emilia M (2014) A comparison of cross- versus single-company effort prediction models for web projects. In: 40th Euromicro Conference on Software Engineering and Advanced Applications, pp 285–292
34.
Briand K, El-Emam LC, Maxwell K, Surmann D, Wieczorek I (1999) An assessment and comparison of common cost estimation models. In: Proceedings of the 21st International Conference on Software Engineering, ICSE, pp 313–322
35.
Briand IW, Langley LCT (2000) A replicated assessment of common software cost estimation techniques. In: International Proceedings of the 22nd Conference on Software Engineering. ICSE, pp 377–386
36.
Jeffery R, Ruhe M, Wieczorek I (2000) A comparative study of two software development cost modeling techniques using multi-organizational and company-specific data. Inf Softw Technol 42:1009–1016CrossRef
37.
Lefley M, Shepperd M (2003) Using genetic programming to improve software effort estimation based on general data sets. In: Proceedings of the GECCO 2003, LNCS 272a4. Springer, Berlin, pp 2477–2487MATHCrossRef
38.
van Rijn JN, Holmes G, Pfahringer B, Vanschoren J (2015) Having a blast: meta-learning and heterogeneous ensembles for data streams. In: 2015 IEEE International Conference on Data Mining , pp 1–6
39.
Wieczorek I, Ruhe M (2002) How valuable is company-specific data compared to multi-company data for software cost estimation? In: Proceedings of the 8th International Software Metrics Symposium. IEEE Computer Society Press, Ottawa, pp 237–246
40.
Jeffery R, Ruhe M, Wieczorek I (2001) Using public domain metrics to estimate software development effort. In: Proceedings of the 7th International Software Metrics Symposium. IEEE Computer Society Press, London, pp 16–27
41.
Maxwell K, Forselius P (2000) Benchmarking software development productivity. In: IEEE Software, pp 80–88CrossRef
42.
Mendes E, Mosley N, Counsell S (2003) Investigating early Web size measures for web cost estimation. In: Proceedings of the EASE’2003 Conference, Keele, pp 1–22
43.
Idris A, Asifullah K (2014) Ensemble based efficient churn prediction model for telecom. In: 12th International Conference on Frontiers of Information Technology (FIT), pp 1–7
44.
Guyon I, Gunn S, Nikravesh M, Zadeh LA (2006) Feature extraction, foundations and applications. Pattern Recognition, Springer, Berlin
45.
Oyeniyi AO, Adeyemo AB (2015) Customer churn analysis in banking sector using data mining techniques. Afr J Comput ICT 8(3):165–174
46.
Antreas D (2000) Customer satisfaction cues to support market segmentation and explain switching behavior. J Bus Res 47(3):191–207CrossRef
47.
Ballings M, Poel DVD (2012) Customer event history for churn prediction. How long is long enough? Expert Syst Appl 39(8):13517–13522CrossRef
48.
Buckinx W, Poel DVD (2005) Predicting online-purchasing behavior. Eur J Oper Res 166(2):557–575MATHCrossRef
49.
Burez J, Van den Poel D (2007) CRM at a pay-TV company: using analytical models to reduce customer attrition by targeted marketing for subscription services. Expert Syst Appl 32(2):277–288CrossRef
50.
Poku K, Zakari M, Sonali A (2013) Impact of service quality on customer loyalty in the hotel industry, an empirical study from Ghana. Int Rev Manag Bus Res 22:600–609
51.
Kopcke H, Morik K (2004) Analyzing customer churn in insurance data, knowledge discovery in databases. In: PKDD 2004 Lecturer Notes of Computer Science, vol 202, pp 325–336
52.
Mues C, Baesens B, Files CM, Vanthienen J (2004) Decision diagrams in machine learning: an empirical study on real-life credit-risk data. Expert Syst Appl 27(2):257–264MATHCrossRef
53.
Thiyagarajan ABC, Anandha Kumar K (2016) A survey on diabetes mellitus prediction using machine learning techniques. Int J Appl Eng Res 11(3):973–4562
54.
Hadden J, Tiwari A, Roy R, Ruta D (2007) Computer assisted customer churn management: state-of-the-art and future trends. Comput Oper Res 34(10):2902–2917MATHCrossRef
55.
Mahajan V, Richa M, Renuka M (2015) Review of data mining techniques for churn prediction in telecom. J Inf Organ Sci 39(2):183–197
56.
Kasiran Z, Ibrahim Z, Syahir M, Ribuan M (2014) Customer churn prediction using recurrent neural network with reinforcement learning algorithm in mobile phone users. Int J Intell Inf Process 5:1–11
57.
Verbeke W, Sarraute C, Baesens B, Vanthienen J (2016) A comparative study of social network classifiers for predicting churn in the telecommunication industry. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp 1151–1158
58.
Amin Adnan et al (2016) Customer churn prediction in telecommunication sector using rough set approach. Neurocomputing 4:1–18
59.
Vafeiadis T, Diamantaras KI, Sarigiannidis G, Chatzisavvas KC (2015) A comparison of machine learning techniques for customer churn prediction. Simul Model Pract Theory 55:1–9CrossRef
60.
Rodan A, Fayyoumi A, Faris H, Alsakran J, Al-Kadi O (2015) Negative correlation learning for customer churn prediction: a comparison study. Sci World J 2015:1–7CrossRef
61.
Khan I, Rehman GU, Usman T (2013) Intelligent churn prediction for telecommunication industry. Int J Innov Appl Stud 4(1):165–170
62.
Garc’ıa DL, Nebot A, Alfredo V (2013) Visualizing pay-per-view television customers churn using cartograms and flow maps. In: ESANN 2013 Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp 24–26
63.
Verbeke W, Dejaeger K, Martens D, Hur J, Baesens B (2012) New insights into churn prediction in the telecommunication sector: a profit driven data mining approach. Eur J Oper Res 218(1):211–229CrossRef
64.
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297MATH
65.
Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cam-bridge University Press, Cambridge, UKMATHCrossRef
66.
Hornik K, Maxwell W, Halbert Stinchcombe (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2(5):359–366MATHCrossRef
67.
Wen S, Vahan B, Zeyar A, Wei LW (2013) Ensemble model for day-ahead electricity demand time series forecasting. In: Proceedings of the 4th International Conference Future Energy Systems, Berkeley, CA, USA, 2013, pp 51–62
68.
Burez J, Poel D V d (2012) Data mining concepts and techniques. Pearson Education, London
69.
Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27MATHCrossRef
70.
Taneja S, Gupta C, Goyal K, Gureja D (2014) An enhanced k-nearest neighbor algorithm using information gain and clustering. In: 2014 Fourth International Conference on Advanced Computing and Communication Technologies, pp 325–329
71.
Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques, Elsevier
72.
Peng H (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238CrossRef
73.
74.
75.
76.
Amin A, Khan C, Ali I, Anwar S (2014) Customer churn prediction in telecommunication industry: with and without counter-example. In: 13th Mexican International Conference on Artificial Intelligence, MICAI 2014. Springer, Berlin, pp 206–218CrossRef
77.
Amin A, Anwar S, Adnan A, Khan MA, Iqbal Z (2015) Classification of cyber attacks based on rough set theory. In: 2015 1st International Conference on Anti-cybercrime, ICACC 2015
78.
Basili V, Rombach H (1988) The TAME project: towards improvement-oriented software environment. IEEE Trans Softw Eng 14(6):758–773CrossRef
79.
80.
81.
Smola A (1998) Learning with kernels, dissertation, Department of Computer Science, Technical University Berlin, Germany
82.
Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, pp 1137–1143
83.
84.
Metadata
Title
Just-in-time customer churn prediction in the telecommunication sector
Authors
Adnan Amin
Feras Al-Obeidat
Babar Shah
May Al Tae
Changez Khan
Hamood Ur Rehman Durrani
Sajid Anwar
Publication date
13-10-2017
Publisher
Springer US
Published in
The Journal of Supercomputing / Issue 6/2020
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-017-2149-9

Other articles of this Issue 6/2020

The Journal of Supercomputing 6/2020 Go to the issue

OriginalPaper

Online multi-person tracking assist by high-performance detection

OriginalPaper

Automatic assessment of descriptive answers in online examination system using semantic relational features

OriginalPaper

Parallel multiprocessing and scheduling on the heterogeneous Xeon+FPGA platform

OriginalPaper

Hybrid scheduling to enhance reliability of real-time tasks running on reconfigurable devices

OriginalPaper

Re-engineering the ant colony optimization for CMP architectures

OriginalPaper

Simplifying and implementing service level objectives for stream parallelism

Premium Partner

    Image Credits