A Hybrid Machine Learning Approach for Customer Loyalty Prediction | SpringerLink
Skip to main content
Springer Nature Link
Log in

A Hybrid Machine Learning Approach for Customer Loyalty Prediction

  • Conference paper
  • First Online:
Neural Computing for Advanced Applications (NCAA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1449))

Included in the following conference series:

Abstract

Customer loyalty prediction is one of the most common applications of machine learning in Customer Relationship Management (CRM). Many research studies have tried to compare the effectiveness of different machine learning techniques applied for the model development. Also due to the simplicity and effectiveness, customer purchase behavioral attributes, such as, Recency, Frequency, and Monetary Value (RFM) are commonly used for predicting the customer lifetime value as a measure of loyalty. However, since RFM focuses on the purchase behaviours of customers only, it often overlooks the effect of other important factors to loyalty such as customer satisfaction and product experience. In this paper, a two-stage hybrid machine learning approach is designed to address this. Firstly, both unsupervised clustering and supervised classification model are used in the predication model building in order to realize the possible incremental value of hybrid model combining two learning techniques. Secondly, the proposed model is trained with behavourial RFM attributes and attitudinal factors such as customer satisfaction and product attributes, in order to better capture the influencing factors to loyalty.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 16015
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 20019
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Pitta, D., Franzak, F., Fowler, D.: A strategic approach to building online customer loyalty: integrating customer profitability tiers. J. Consum. Mark. 23(7), 421–429 (2006)

    Article  Google Scholar 

  2. Liu, C., Wang, T.Y.: A study on the effect of service quality on customer loyalty and corporate performance in financial industry. Probl. Perspect. Manag. 15(2), 355–363 (2017)

    MathSciNet  Google Scholar 

  3. Ramanathan, U., Subramanian, N., Yu, W., et al.: Impact of customer loyalty and service operations on customer behaviour and firm performance: empirical evidence from UK retail sector. Prod. Plan. Control 28(6–8), 478–488 (2017)

    Article  Google Scholar 

  4. Pi, W.P., Huang, H.H.: Effects of promotion on relationship quality and customer loyalty in the airline industry: the relationship marketing approach. Afr. J. Bus. Manag. 5(11), 4403–4414 (2011)

    Google Scholar 

  5. Bose, S., Rao, V.G.: Perceived benefits of customer loyalty programs: validating the scale in the Indian context. Manag. Mark. 6(4) (2011)

    Google Scholar 

  6. Antonios, J.: Understanding the effects of customer education on customer loyalty. Bus. Leadersh. Rev. 8(1), 1–15 (2011)

    Google Scholar 

  7. Cheng, S.-I.: Comparisons of competing models between attitudinal loyalty and behavioral loyalty. Int. J. Bus. Soc. Sci. 2(10), 149–166 (2011)

    Google Scholar 

  8. Trinh, G.T., Anesbury, Z.W., Driesener, C.: Has behavioural loyalty to online supermarkets declined? Australas. Mark. J. (AMJ) 25(4), 326–333 (2017)

    Article  Google Scholar 

  9. Liu, M.T., Liu, Y., Mo, Z., Zhao, Z., Zhu, Z.: How CSR influences customer behavioural loyalty in the Chinese hotel industry. Asia Pacific J. Mark. Logist. 32(1), 1–22 (2019)

    Article  Google Scholar 

  10. Gupta, S., Lehmann, D.R., Stuart, J.A.: Valuing customers. J. Mark. Res. 41(1), 7–18 (2004)

    Article  Google Scholar 

  11. Rust, R.T., Lemon, K.N., Zeithaml, V.A.: Return on marketing: using customer equity to focus marketing strategy. J. Mark. 68(1), 109–127 (2004)

    Article  Google Scholar 

  12. Zhang, J.Q., Dixit, A., Friedmann, R.: Customer loyalty and lifetime value: an empirical investigation of consumer packaged goods. J. Mark. Theory Pract. 18(2), 127–140 (2010)

    Article  Google Scholar 

  13. Chen, D., Sain, S.L., Guo, K.: Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining. J. Database Mark. Customer Strategy Manag. 19(3), 197–208 (2012)

    Article  Google Scholar 

  14. Gupta, S., Hanssens, D., Hardie, B., et al.: Modeling customer lifetime value. J. Serv. Res. 9(2), 139–155 (2006)

    Article  Google Scholar 

  15. Song, M., Zhao, X., Haihong, E., et al.: Statistics-based CRM approach via time series segmenting RFM on large scale data. Knowl.-Based Syst. 132, 21–29 (2017)

    Article  Google Scholar 

  16. Cheng, C.H., Chen, Y.S.: Classifying the segmentation of customer value via RFM model and RS theory. Expert Syst. Appl. 36(3), 4176–4184 (2009)

    Article  Google Scholar 

  17. Coussement, K., De Bock, K.W.: Customer churn prediction in the online gambling industry: the beneficial effect of ensemble learning. J. Bus. Res. 66(9), 1629–1636 (2013)

    Article  Google Scholar 

  18. Yeh, I.C., Yang, K.J., Ting, T.M.: Knowledge discovery on RFM model using Bernoulli sequence. Expert Syst. Appl. 36(3), 5866–5871 (2009)

    Article  Google Scholar 

  19. Hallowell, R.: The relationships of customer satisfaction, customer loyalty, and profitability: an empirical study. Int. J. Service Ind. Manag. (1996)

    Google Scholar 

  20. Oliver, R.L.: Customer satisfaction research. In: The Handbook of Marketing Research: Uses, Misuses, and Future Advances, vol. 1 (2006)

    Google Scholar 

  21. Chandrashekaran, M., Rotte, K., Tax, S.S., et al.: Satisfaction strength and customer loyalty. J. Mark. Res. 44(1), 153–163 (2007)

    Article  Google Scholar 

  22. Brito, P.Q., Soares, C., Almeida, S., et al.: Customer segmentation in a large database of an online customized fashion business. Robot. Comput.-Integr. Manuf. 36, 93–100 (2015)

    Article  Google Scholar 

  23. Heilman, C.M., Bowman, D.: Segmenting consumers using multiple-category purchase data. Int. J. Res. Mark. 19(3), 225–252 (2002)

    Article  Google Scholar 

  24. Stone, B.: Successful Direct Marketing Methods, pp. 29–35. NTC Business Books, Lincolnwood (1995)

    Google Scholar 

  25. Shen, C.C., Chuang, H.M.: A study on the applications of data mining techniques to enhance customer lifetime value. WSEAS Trans. Inf. Sci. Appl. 6(2), 319–328 (2009)

    Google Scholar 

  26. Sarvari, P.A., Ustundag, A., Takci, H.: Performance evaluation of different customer segmentation approaches based on RFM and demographics analysis. Kybernetes 45(7), 1129–1157 (2016)

    Article  Google Scholar 

  27. Machado, M.R., Karray, S., de Sousa, I.T.: LightGBM: an effective decision tree gradient boosting method to predict customer loyalty in the finance industry. In: 2019 14th International Conference on Computer Science & Education, pp. 1111–1116. IEEE (2019)

    Google Scholar 

  28. Aleksandrova, Y.: Application of machine learning for churn prediction based on transactional data (RFM analysis). In: 2018 International Multidisciplinary Scientific Geoconference SGEM 2018: Conference Proceedings, vol. 18, no. 2, pp. 125–132 (2018)

    Google Scholar 

  29. Doğan, O., Ayçin, E., Bulut, Z.A.: Customer segmentation by using RFM model and clustering methods: a case study in retail industry. Int. J. Contemp. Econ. Adm. Sci. 8(1), 1–19 (2018)

    Google Scholar 

  30. Sheshasaayee, A., Logeshwari, L.: Implementation of clustering technique based RFM analysis for customer behaviour in online transactions. In: 2018 2nd International Conference on Trends in Electronics and Informatics, pp. 1166–1170. IEEE (2018)

    Google Scholar 

  31. Le, T., Lee, M.Y., Park, J.R., et al.: Oversampling techniques for bankruptcy prediction: novel features from a transaction dataset. Symmetry 10(4), 79 (2018)

    Article  Google Scholar 

  32. Hu, W.H., Tang, S.H., Chen, Y.C., et al.: Promotion recommendation method and system based on random forest. In: Proceedings of the 5th Multidisciplinary International Social Networks Conference, pp. 1–5 (2018)

    Google Scholar 

  33. Maryani, I., Riana, D.: Clustering and profiling of customers using RFM for customer relationship management recommendations. In: 2017 5th International Conference on Cyber and IT Service Management (CITSM), pp. 1–6. IEEE (2017)

    Google Scholar 

  34. Zabkowski, T.S.: RFM approach for telecom insolvency modeling. Kybernetes 45(5), 815–827 (2016)

    Article  MathSciNet  Google Scholar 

  35. Daoud, R.A., Amine, A., Bouikhalene, B., et al.: Combining RFM model and clustering techniques for customer value analysis of a company selling online. In: 2015 IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA), pp.1–6. IEEE (2015)

    Google Scholar 

  36. You, Z., Si, Y.W., Zhang, D., et al.: A decision-making framework for precision marketing. Expert Syst. Appl. 42(7), 3357–3367 (2015)

    Article  Google Scholar 

  37. Kaur, K., Vashisht, S.: A novel approach for providing the customer churn prediction model using enhanced boosted trees technique in cloud computing. Int. J. Comput. Appl. 114(7), 1–7 (2015)

    Google Scholar 

  38. Chiang, W.Y.: Applying data mining with a new model on customer relationship management systems: a case of airline industry in Taiwan. Transp. Lett. 6(2), 89–97 (2014)

    Article  Google Scholar 

  39. Coussement, K., Van den Bossche, F.A., De Bock, K.W.: Data accuracy’s impact on segmentation performance: benchmarking RFM analysis, logistic regression, and decision trees. J. Bus. Res. 67(1), 2751–2758 (2014)

    Article  Google Scholar 

  40. Han, S.H., Shui Xiu, L., Leung, S.C.H.: Segmentation of telecom customers based on customer value by decision tree model. Expert Syst. Appl. 39(4), 3964–3973 (2012)

    Article  Google Scholar 

  41. 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 Syst. Appl. 26(2), 181–188 (2004)

    Article  Google Scholar 

  42. Zalaghi, Z., Varzi, Y.: Measuring customer loyalty using an extended RFM and clustering technique. Manag. Sci. Lett. 4(5), 905–912 (2014)

    Article  Google Scholar 

  43. Alpaydin, E.: Introduction to Machine Learning. MIT Press, Cambridge (2020)

    MATH  Google Scholar 

  44. Wu, X., Kumar, V., Quinlan, J.R., et al.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1), 1–37 (2008)

    Article  Google Scholar 

  45. Scornet, E.: Tuning parameters in random forests. In: ESAIM: Proceedings and Surveys, vol. 60, pp.144–162 (2017)

    Google Scholar 

  46. Livingston, F.: Implementation of Breiman’s random forest machine learning algorithm. ECE591Q Machine Learning Journal Paper, pp. 1–13 (2005)

    Google Scholar 

  47. Schapire, R.E.: The boosting approach to machine learning: an overview. In: Denison, D.D., Hansen, M.H., Holmes, C.C., Mallick, B., Yu, B. (eds.) Nonlinear estimation and classification, pp. 149–171. Springer, New York (2003). https://doi.org/10.1007/978-0-387-21579-2_9

    Chapter  Google Scholar 

  48. Dobbin, K.K., Simon, R.M.: Optimally splitting cases for training and testing high dimensional classifiers. BMC Med. Genomics 4(1), 1–8 (2011)

    Article  Google Scholar 

  49. Jaffery, T., Liu, S.X.: Measuring campaign performance by using cumulative gain and lift chart. In: SAS Global Forum, p. 196 (2009)

    Google Scholar 

  50. Vuk, M., Curk, T.: ROC curve, lift chart and calibration plot. Metodoloski zvezki 3(1), 89 (2006)

    Google Scholar 

  51. Ferri, C., Flach, P., Hernández-Orallo, J.: Learning decision trees using the area under the ROC curve. In: ICML, vol. 2, pp. 139–146 (2002)

    Google Scholar 

  52. Tkachenko, Y.: Autonomous CRM control via CLV approximation with deep reinforcement learning in discrete and continuous action space. arXiv:1504.01840 (2015)

  53. Alborzi, M., Khanbabaei, M.: Using data mining and neural networks techniques to propose a new hybrid customer behaviour analysis and credit scoring model in banking services based on a developed RFM analysis method. Int. J. Bus. Inf. Syst. 23(1), 1–22 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, Faculty of Technology, University of Sunderland, London, UK

    Hiu Fai Lee & Ming Jiang

Authors
  1. Hiu Fai Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ming Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hiu Fai Lee .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Haijun Zhang

  2. Nanfang College of Sun Yat-sen University, Guangzhou, China

    Zhi Yang

  3. Hefei University of Technology, Hefei, China

    Zhao Zhang

  4. Chongqing University, Chongqing, China

    Zhou Wu

  5. South China Normal University, Guangzhou, China

    Tianyong Hao

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lee, H.F., Jiang, M. (2021). A Hybrid Machine Learning Approach for Customer Loyalty Prediction. In: Zhang, H., Yang, Z., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2021. Communications in Computer and Information Science, vol 1449. Springer, Singapore. https://doi.org/10.1007/978-981-16-5188-5_16

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-5188-5_16

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-5187-8

  • Online ISBN: 978-981-16-5188-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation