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Effective Customer Churn Prediction on Large Scale Data using Metaheuristic Approach

Affiliations

  • Department of Computer Applications, National Institute of Technology, Trichy - 620015, Tamil Nadu, India

Abstract


Objectives: Customer retention is one of the major requirements of any organization to gain competitive advantage. Accurately predicting the customer’s status can help organizations reduce and prevent churns. Methods/Analysis: This paper presents an analysis of churn data and issues related to churn data in terms of data size, attribute density, data sparsity and abstraction contained in the data. It discusses the advantages of utilizing metaheuristic techniques for churn prediction and in specific analyses ACO for churn prediction and performs a comparison with other metaheuristic algorithms and emphasizes the importance of using ACO. Findings: Experiments were conducted by implementing ACO and applying it on Orange Dataset. It was observed from the ROC curve that the points plotted falls to the top left of the graph, hence indicating good efficiency and a fluctuation from low to moderate false positive rates were observed. It could be observed from the PR curve that the ACO algorithm exhibits high recall rates and moderate precision rates. ROC and the PR plots indicate that there is still scope for enhancement in terms of reduction in false positive rates and increase in precision levels. It was identified that though ACO exhibits effective performance, the size of the dataset acted as a huge downside increasing the time taken. Due to the huge size of the data, memory requirements are very high, but due to the skewed nature of the data most of them contain null values. Applications/Improvement: Findings exhibited scope for improvement, hence research directions namely data structure identification to reduce memory requirements, graph based churn prediction and fuzziness incorporation in the prediction process were proposed.

Keywords

ACO, Churn Prediction, Churn Prevention, Classification, Graph Models

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References


  • Delafrooz N, Farzanfar E. Determining the customer lifetimenvalue based on the benefit clustering in the insurance industry. Indian Journal of Science and Technology. 2016 Jan; 9(1):1–8.
  • Dash P, Pattnaik S, Rath B. Knowledge Discovery in Databases (KDD) as tools for developing customer relationship management as external uncertain environment: A case study with reference to State Bank of India. Indian Journal of Science and Technology. 2016 Jan; 9(4):1–11.
  • Bhattacharya CB. When customers are members: Customer retention in paid membership contexts. Journal of the Academy of Marketing Science. 1998 Jan; 26(1):31–44.
  • Neslin SA, Gupta S, Kamakura W, Lu J, Mason CH. Defection detection: Measuring and understanding the predictive accuracy of customer churn models. Journal of Marketing Research. 2006 May; 43(2):204–11.
  • Jana A, Chandra B. Mediating role of customer satisfaction in the mid-market hotels: An empirical analysis. Indian Journal of Science and Technology. 2016 Feb; 9(1):1–16.
  • Boroumandzadeh M, Mirsarraf MR, Movaghar A. Investigating the customer care based on enhanced telecom operation map standard in third generation of mobile networks. Indian Journal of Science and Technology. 2015 Sep; 8(22):1–6.
  • Au WH, Chan KC, Yao X. A novel evolutionary data mining algorithm with applications to churn prediction. IEEE Transactions on Evolutionary Computation. 2003 Dec; 7(6):532–45.
  • Kisioglu P, Topcu YI. Applying bayesian belief network approach to customer churn analysis: A case study on the telecom industry of Turkey. Expert Systems with Applications. 2011 Jun; 38(6):7151–7.
  • Pendharkar PC. A threshold-varying artificial neural network approach for classification and its application to bankruptcy prediction problem. Computers and Operations Research. 2005 Oct; 32(10):2561–82.
  • Wei CP, Chiu IT. Turning telecommunications call details to churn prediction: A data mining approach. Expert systems with applications. 2002 Aug; 23(2):103–12.
  • Jin X, Yi X, Anqiang H, Dunhu L, Shouyang W. Featureselectionbased dynamic transfer ensemble model for customer churn prediction. Knowledge and Information Systems (Impact Factor: 1.78). 2014 Jan; 43(1):29–51.
  • Qi J, Zhang L, Liu Y, Li L, Zhou Y, Shen Y, Liang L, Li H. ADTreesLogit model for customer churn prediction. Annals of Operations Research. 2009 Apr; 168(1):247–65.
  • Chen K, Hu YH, Hsieh YC. Predicting customer churn from valuable B2B customers in the logistics industry: A case study. Information Systems and e-Business Management. 2015 Aug; 13(3):475–94.
  • Idris A, Khan A, Lee YS. Intelligent churn prediction in telecom: Employing mRMR feature selection and RotBoost based ensemble classification. Applied Intelligence. 2013 Oct; 39(3):659–72.
  • Migueis VL, Van den Poel D, Camanho AS, e Cunha JF. Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences. Advances in Data Analysis and Classification. 2012 Dec; 6(4):337–53.
  • Kumar DA, Ravi V. Predicting credit card customer churn in banks using data mining. International Journal of Data Analysis Techniques and Strategies. 2008 Jan; 1(1):4–28.
  • Lariviere B, Van den Poel D. Investigating the role of product features in preventing customer churn, by using survival analysis and choice modeling: The case of financial services. Expert Systems with Applications. 2004 Aug; 27(2):277–85.
  • Zeithaml VA, Berry LL, Parasuraman A. The behavioral consequences of service quality. The Journal of Marketing. 1996 Apr; 60(2):31–46.
  • Morik K, Kopcke H. Analysing customer churn in insurance data– A case study. Knowledge Discovery in Databases: PKDD 2004. Springer Berlin Heidelberg; 2004 Sep. p. 325–36.
  • Hung SY, Yen DC, Wang HY. Applying data mining to telecom churn management. Expert Systems with Applications. 2006 Oct; 31(3):515–24.
  • Hwang H, Jung T, Suh E. An LTV model and customer segmentation based on customer value: A case study on the wireless telecommunication industry. Expert Systems with Applications. 2004 Feb; 26(2):181–8.
  • KDD Dataset. Available from: http://www.kdd.org/kddcup/ view/kdd-cup-2009/Data.
  • Dorigo M, Maniezzo V, Colorni A. Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics. 1996 Feb; 26(1):29–41.
  • Gutjahr WJ. A graph-based ant system and its convergence. Future Generation Computer Systems. 2000 Jun; 16(8):873–88.
  • Prakasam A, Savarimuthu N. Metaheuristic algorithms and probabilistic behaviour: A comprehensive analysis of Ant Colony Optimization and its variants. Artificial Intelligence Review. 2016 Jan; 45(1):97–130.
  • Prakasam A, Savarimuthu N. Metaheuristic algorithms and polynomial turing reductions: a case study based on ant colony optimization. Procedia Computer Science. 2015 Dec; 46:388–95.
  • Bottou L. Large-scale machine learning with stochastic gradient descent. Proceedings of COMPSTAT’2010 Physica-Verlag HD; 2010 Sep. p. 177–86.
  • Murata N. A statistical study of on-line learning. Online Learning and Neural Networks. Cambridge, UK: Cambridge University Press; 1999. p. 63–92.
  • Dorigo M, Gambardella LM. Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation. 1997 Apr; 1(1):53–66.
  • Dorigo M, Maniezzo V, Colorni A. The ant system: An autocatalytic optimizing process. Technical Report; 1991. p. 1–21.

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