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Intelligent data analysis approaches to churn as a business problem: a survey

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Abstract

Globalization processes and market deregulation policies are rapidly changing the competitive environments of many economic sectors. The appearance of new competitors and technologies leads to an increase in competition and, with it, a growing preoccupation among service-providing companies with creating stronger customer bonds. In this context, anticipating the customer’s intention to abandon the provider, a phenomenon known as churn, becomes a competitive advantage. Such anticipation can be the result of the correct application of information-based knowledge extraction in the form of business analytics. In particular, the use of intelligent data analysis, or data mining, for the analysis of market surveyed information can be of great assistance to churn management. In this paper, we provide a detailed survey of recent applications of business analytics to churn, with a focus on computational intelligence methods. This is preceded by an in-depth discussion of churn within the context of customer continuity management. The survey is structured according to the stages identified as basic for the building of the predictive models of churn, as well as according to the different types of predictive methods employed and the business areas of their application.

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Notes

  1. Through the increase in services used (up-selling); the increase in consumption or wallet share (cross-selling); the construction of stronger loyalty bonds; the proactive retention actions on customers who intend to leave the actual provider; the launch of new products and services (innovation) and/or the adjustment of commercial costs, giving each costumer as expected.

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Acknowledgments

We thank anonymous reviewers for their useful comments and suggestions. This research was partially supported by Spanish MINECO TIN2012-31377 research project.

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Authors and Affiliations

  1. Department of Computer Science, Universitat Politècnica de Catalunya (UPC BarcelonaTech), 08034, Barcelona, Spain

    David L. García, Àngela Nebot & Alfredo Vellido

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  1. David L. García
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Correspondence to Alfredo Vellido.

Appendix

Appendix

The detailed tables of publications are reported in this appendix, following the description in Sect. 3.2.

Table 2 Literature on abandonment prediction modelling, listed in chronological order and corresponding to the use of standard methods
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Table 3 References on abandonment prediction modelling, listed in chronological order and for the use of CI methods
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Table 4 References on abandonment prediction modelling, listed in chronological order and concerning the use of alternative methods
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Table 5 References on abandonment prediction modelling, listed in chronological order for the telecommunications application area
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Table 6 References on abandonment prediction modelling, listed in chronological order (2001–2015) for the Banking and Financial Services field
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Table 7 References on abandonment prediction modelling, listed in chronological order for the Retail, Mail and Delivery Services and Other fields of application
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García, D.L., Nebot, À. & Vellido, A. Intelligent data analysis approaches to churn as a business problem: a survey. Knowl Inf Syst 51, 719–774 (2017). https://doi.org/10.1007/s10115-016-0995-z

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