Optimal customer relationship management in telecalling industry by using data mining and business intelligence

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Decision making system in telecommunication industries plays a more important role where it is required to find customer churn. Customer churn prediction requires finding out and analyzing the information about the business data intelligence techniques which can be done efficiently by adapting the business intelligence techniques. Business intelligence provides tools to predict and analyze the historical, current and predictive views of business operations. However, this would be more complex task with high volume of data which are gathered from million of telephone users for the time being. It can be handled effectively by introducing the data mining techniques which select the most useful information from the gathered data set from which decision making can be done efficiently. In this research method, telecommunication industry is considered in which customer churn prediction application is focused. The main goal of this research method is to introduce the data mining technique which can select the most useful information from the telecommunication industry dataset. This is done by introducing the Hybrid Genetic Algorithm with Particle Swarm Optimization (HGAPSO) method which can select the most useful information. In this research, the hybrid HGAPSO combines the advantages of PSO and GA optimally. From the selected information, decision making about the customer churn prediction can be done accurately. Finally decision making is done by predicting the customer behaviour using Support Vector Machine classification approach. The performance metrics are considered such as precision, recall, f-measure, accuracy, True Positive Rate (TPR), False Positive Rate (FPR), time complexity and ROC. Experimental results demonstrated that HGAPSO provides highly scalable which is used for prediction examination in the business intelligence.


  • Keywords


    Customer relationship management, Business intelligence, Decision making system, useful information, attribute selection, customer churn prediction.

  • References


      [1] Rashidirad M, Salimian H, Soltani E & Fazeli Z, “Competitive strategy, dynamic capability, and value creation: Some empirical evidence from UK telecommunications firms”, Strategic Change, Vol.26, No.4, (2017), pp.333-342.

      [2] Zhao ZY, Tang C, Zhang X & Skitmor M, “Agglomeration and competitive position of contractors in the international construction sector”, Journal of Construction Engineering and Management, Vol.143, No.6, (2017).

      [3] Purdy C, Evelyne, ROCH, Schubert D & Strong P, “U.S. Patent Application No. 15/046,039”, (2016).

      [4] Pucciarelli F & Kaplan A, “Competition and strategy in higher education: Managing complexity and uncertainty”, Business Horizons, Vol.59, No.3, (2016), pp.311-320.

      [5] Brockhoff K, Customer Integration into Continuous Development of IT-based Services, The Palgrave Handbook of Managing Continuous Business Transformation, (2017), pp.315-334.

      [6] Walo MT, “Net chain: An analytical approach to local economic development”, The Marketing Review, Vol.16,No.1,(2016), pp.46-61.

      [7] Wynn M, Turner P, Banik A & Duckworth G, “The impact of customer relationship management systems in small business enterprises”, Strategic Change, Vol.25, No.6, (2016), pp.659-674.

      [8] Pizam A, Pizam A, Shapoval V, Shapoval V, Ellis T & Ellis T, “Customer satisfaction and its measurement in hospitality enterprises: a revisit and update”, International Journal of Contemporary Hospitality Management, Vol.28, No.1, (2016), pp.2-35.

      [9] Yawar SA & Seuring S, “Management of social issues in supply chains: a literature review exploring social issues, actions and performance outcomes”, Journal of Business Ethics, Vol.141, No.3, (2017), pp.621-643.

      [10] Helo P, Gunasekaran A & Rymaszewska A, “Improving Marketing and Operations Strategy through Industrial Services”, Designing and Managing Industrial Product-Service Systems, (2017), pp.37-42.

      [11] Singh H, “Implementation Benefit to Business Intelligence using Data Mining Techniques, Computer Faculty, Education Department Punjab”, International Journal of Computing & Business Research, (2012).

      [12] Vercellis C, Business Intelligence Data Mining and Optimization for Decision Making, Wiley, (2009).

      [13] Joseph V, “Data mining and business intelligence applications in telecommunication industry”, International Journal of Engineering and Advanced Technology, Vol.2, No.3, (2013).

      [14] Daspit JJ, Holt DT, Chrisman JJ & Long RG, “Examining family firm succession from a social exchange perspective: a multiphase, multistakeholder review”, Family Business Review, Vol.29, No.1, (2016), pp.44-64.

      [15] Pagelet. al., “Supply chain management in electronics & telecommunication industry-a literature survey”, (1999).

      [16] Kariuki SW & Rotich G, “Role of Stores Management in Reduction of Redundant Stock, A Case Study Of Keroche Breweries Limited, Kenya”, International Journal of Project Management, Vol.1, No.5, (2017), pp.80-97.

      [17] Jajja MSS, Jajja MSS, Kannan VR, Kannan VR, Brah SA, Brah S A & Hassan SZ, “Supply chain strategy and the role of suppliers: evidence from the Indian sub-continent”, Benchmarking: An International Journal, Vol.23, No.7, (2016), pp.1658-1676.

      [18] Yao Z, Leung SC & Lai KK, “Analysis of the impact of price-sensitivity factors on the returns policy in coordinating supply chain”, European Journal of Operational Research, Vol.187, No.1, (2008), pp.275-282.

      [19] Castellanosm M & Dayal U, “Business Intelligence for the Real-Time Enterprise”, Springer, (2008).


 

View

Download

Article ID: 8907
 
DOI: 10.14419/ijet.v7i1.1.8907




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.