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Using Visual Analytics to Reduce Churn

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Computational Science and Its Applications – ICCSA 2021 (ICCSA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12951))

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Abstract

The technological advance and the use of data for decision-making drive business and increase competitiveness among companies. They must understand the environment in which they operate and quickly respond to the needs of their customers, preventing them from canceling their services, maximizing profit, and benefit their own organizations. The main goal of this work is to present a visual analytics approach to deal with a large amount of unstructured, complex, and dynamic data and improve companies’ ability to detect the probability of losing a client at an early stage. We processed the probability of subscription cancellation using the machine learning algorithm Random Forest, and we allowed similar customers comparison using the k-nearest neighbor’s algorithm. Then, we developed two main visualizations: a general dashboard that displays the probability of subscription cancellation of each client and the variables that can influence this decision; and a radar chart that displays many quantitative variables and allows comparison with other similar customers. To validate our approach, we present a case study with a data set from a hosting services company that uses the Platform as a Service model. Through the application of informal interviews, we concluded that the provided visualizations helped teams in the process of reducing churn rate and therefore maintaining a growing customer base’s company. Our visual analytics solution allowed the analysis and information understanding to create strategies and make assertive decisions for professionals involved in the retention process.

This research was financed in part by PUCRS. Isabel Harb Manssour also would like to thank the financial support of the CNPq Scholarship - Brazil (308456/2020-3).

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Notes

  1. 1.

    https://www.cs.waikato.ac.nz/ml/weka/.

  2. 2.

    https://www.python.org/.

  3. 3.

    https://www.r-project.org/.

  4. 4.

    https://shiny.rstudio.com/.

  5. 5.

    https://www.mysql.com/.

  6. 6.

    https://scikit-learn.org/.

  7. 7.

    https://lineup.js.org/.

  8. 8.

    https://github.com/p-karol/VA-to-Reduce-Churn.

References

  1. Chan, C.C.H.: Intelligent value-based customer segmentation method for campaign management: a case study of automobile retailer. Expert Syst. Appl. 34(4), 2754–2762 (2008)

    Google Scholar 

  2. Chen, S.H.: The gamma cusum chart method for online customer churn prediction. Electron. Commer. Res. Appl. 17, 99–111 (2016)

    Google Scholar 

  3. Dintakurthi, R.M.R., Venkatraman, B., Mahendran, P., Siddappa, S.: Decision support system for identifying customer churn based on buying patterns in a discrete manufacturing industry. In: 2016 IEEE Annual India Conference (INDICON), pp. 1–5 (2016)

    Google Scholar 

  4. Grammel, L.: Supporting end users in analyzing multiple data sources. In: 2009 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), pp. 246–247 (2009)

    Google Scholar 

  5. Humayoun, S.R., Bhambri, K., AlTarawneh, R.: Bid-chord: an extended chord diagram for showing relations between bi-categorical dimensional data. In: Proceedings of the 2018 International Conference on Advanced Visual Interfaces. AVI 2018, Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3206505.3206570

  6. binti Jaafar, H., binti Mukahar, N., Ramli, D.A.B.: A methodology of nearest neighbor: design and comparison of biometric image database. In: 2016 IEEE Student Conference on Research and Development (SCOReD), pp. 1–6 (2016). https://doi.org/10.1109/SCORED.2016.7810073

  7. Jahromi, A.T., Stakhovych, S., Ewing, M.: Managing b2b customer churn, retention and profitability. Ind. Mark. Manage. 43(7), 1258–1268 (2014)

    Google Scholar 

  8. Keim, D., Andrienko, G., Fekete, J.-D., Görg, C., Kohlhammer, J., Melançon, G.: Visual analytics: definition, process, and challenges. In: Kerren, A., Stasko, J.T., Fekete, J.-D., North, C. (eds.) Information Visualization. LNCS, vol. 4950, pp. 154–175. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-70956-5_7

    Chapter  Google Scholar 

  9. Khodabandehlou, S., Rahman, M.: Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behavior. J. Syst. Inf. Technol. 19, 65–93 (2017). https://doi.org/10.1108/JSIT-10-2016-0061

  10. Lai, X.: Segmentation study on enterprise customers based on data mining technology. In: 2009 First International Workshop on Database Technology and Applications, pp. 247–250 (2009)

    Google Scholar 

  11. Ngai, E., Xiu, L., Chau, D.: Application of data mining techniques in customer relationship management: A literature review and classification. Expert Syst. Appl. 36(2, Part 2), 2592–2602 (2009). https://doi.org/10.1016/j.eswa.2008.02.021. http://www.sciencedirect.com/science/article/pii/S0957417408001243

  12. Noyan, F., Şimşek, G.G.: The antecedents of customer loyalty. Procedia - Soc. Behav. Sci. 109, 1220–1224 (2014). https://doi.org/10.1016/j.sbspro.2013.12.615. http://www.sciencedirect.com/science/article/pii/S1877042813052543. 2nd World Conference on Business, Economics and Management

  13. Oelke, D., et al.: Visual opinion analysis of customer feedback data. In: 2009 IEEE Symposium on Visual Analytics Science and Technology, pp. 187–194 (2009)

    Google Scholar 

  14. Park, J.: Integrated visual analytics tool for heterogeneous text data. In: 2014 IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 325–326 (2014)

    Google Scholar 

  15. Qureshi, S.A., Rehman, A.S., Qamar, A.M., Kamal, A., Rehman, A.: Telecommunication subscribers’ churn prediction model using machine learning. In: Eighth International Conference on Digital Information Management (ICDIM 2013), pp. 131–136 (2013)

    Google Scholar 

  16. Rao, R., Card, S.K.: The table lens: merging graphical and symbolic representations in an interactive focus + context visualization for tabular information. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 1994, pp. 318–322. Association for Computing Machinery, New York (1994). https://doi.org/10.1145/191666.191776

  17. Zakaria, I., Abdul-Rahman, B., Othman, A., Azlina, N., Dzulkipli, M.R., Osman, M.A.: The relationship between loyalty program, customer satisfaction and customer loyalty in retail industry: a case study. Procedia - Soc. Behav. Sci. 129, 23–30 (2014). https://doi.org/10.1016/j.sbspro.2014.03.643

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Authors and Affiliations

  1. Pontifical Catholic University of Rio Grande do Sul, PUCRS. School of Technology. Av. Ipiranga, 6681, Porto Alegre, RS, 90619-900, Brazil

    Priscilla A. Karolczak & Isabel H. Manssour

Authors
  1. Priscilla A. Karolczak
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  2. Isabel H. Manssour
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Corresponding author

Correspondence to Isabel H. Manssour .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Monash University, Clayton, VIC, Australia

    David Taniar

  7. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

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Karolczak, P.A., Manssour, I.H. (2021). Using Visual Analytics to Reduce Churn. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12951. Springer, Cham. https://doi.org/10.1007/978-3-030-86970-0_27

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  • DOI: https://doi.org/10.1007/978-3-030-86970-0_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86969-4

  • Online ISBN: 978-3-030-86970-0

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