An inclusive survey on machine learning for CRM: a paradigm shift


Customer relationship management (CRM) is the tool to enhance customer relationship in any business. Due to the exponential growth of data volume, in any field, it is significant to develop new techniques to discover the customer knowledge, automation of the system and moreover customer satisfaction to win customer lifetime value. CRM with machine learning could bring a catalytic change in business. Several supervised and unsupervised machine learning techniques are utilized to improve the customer experience and profitability of business. This paper reviews the available literature on the CRM with machine learning techniques for customer identification, customer attraction, and customer retention and customer development. This study reveals that supervised learning techniques are 48.48% utilized, unsupervised learning techniques are utilized 15.15%, and 9.09% utilized other techniques in CRM. Paradigm is also shifted toward the deep learning from machine learning as 28.28% text has been reported to deep learning. Decision tree-based algorithm and support vector machine algorithms are most utilized algorithm of supervised learning. E-commerce and telecommunication sectors are the most important areas identified with the exponential growth of the users and hence need a suitable machine learning techniques for customer satisfaction and business profitability.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2


  1. Adebiyi SO, Oyatoye EO, Amole BB (2016) improved customer churn and retention decision management using operations research approach. EMAJ Emerg Mark J 6(2):12–21

    Article  Google Scholar 

  2. Ahmad AK, Jafar A, Aljoumaa K (2019) Customer churn prediction in telecom using machine learning in big data platform. J Big Data 6(1):28

    Article  Google Scholar 

  3. Alexiei D, Vincent M, Nicole S (2017) Fournier, enhancing customer retention through data mining techniques. Mach Learn Appl Int J (MLAIJ) 4(1-3)

  4. Ali, M., & Lee, Y. (2018, April).CRM Sales Prediction Using Continuous Time-Evolving Classification.In Thirty-Second AAAI Conference on Artificial Intelligence.

  5. Alkhayrat M, Aljnidi M, Aljoumaa K (2020) A comparative dimensionality reduction study in telecom customer segmentation using deep learning and PCA. J Big Data 7(1):9

    Article  Google Scholar 

  6. Amnur H (2017) Customer relationship management and machine learning technology for identifying the customer. JOIV Int J Inform Vis 1(1):12–15

    Google Scholar 

  7. Analytics M (2018) Analytics comes of age. McKinsey & Company, New York

    Google Scholar 

  8. Ascarza E, Neslin SA, Netzer O, Anderson Z, Fader PS, Gupta S (2018) In pursuit of enhanced customer retention management: Review, key issues, and future directions. Customer Needs Solut 5(1–2):65–81

    Article  Google Scholar 

  9. Asthana P (2018) A comparison of machine learning techniques for customer churn prediction. Int J Pure Appl Math 119(10):1149–1169

    Google Scholar 

  10. Ayodele TO (2010) Types of machine learning algorithms. In New advances in machine learning. IntechOpen.

  11. Bernat JR, Koning AJ, Fok D (2018) Modelling customer lifetime value in a continuous, non-contractual time setting.

  12. Boutaba R, Salahuddin MA, Limam N, Ayoubi S, Shahriar N, Estrada-Solano F, Caicedo OM (2018) A comprehensive survey on machine learning for networking: evolution, applications and research opportunities. J Internet Serv Appl 9(1):16

    Article  Google Scholar 

  13. De Caigny A, Coussement K, De Bock KW (2018) A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees. Eur J Oper Res 269(2):760–772

    Article  Google Scholar 

  14. Castanedo F, Valverde G, Zaratiegui J, Vazquez A (2016) Using deep learning to predict customer churn in a mobile telecommunication network

  15. Chagas BNR, Viana JAN, Reinhold O, Lobato F, Jacob AF, Alt R (2018) Current applications of machine learning techniques in CRM: a literature review and practical implications. In: 2018 IEEE/WIC/ACM international conference on web intelligence (WI), pp. 452–458. IEEE

  16. Chamberlain BP, Cardoso A, Liu CH, Pagliari R, Deisenroth MP (2017) Customer lifetime value prediction using embeddings. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1753–1762. ACM

  17. Chen S (2018) Estimating customer lifetime value using machine learning techniques. In: Data mining, p 17, BoD–Books on Demand

  18. Chen ZY, Fan ZP, Sun M (2012) A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data. Eur J Oper Res 223(2):461–472

    Article  Google Scholar 

  19. Chen PP, Guitart A, del Río AF, Periáñez Á (2018) Customer lifetime value in video games using deep learning and parametric models. In: 2018 IEEE international conference on big data (big data), pp 2134–2140. IEEE

  20. Christy AJ, Umamakeswari A, Priyatharsini L, Neyaa A (2018) RFM ranking–an effective approach to customer segmentation. J King Saud Univ Comput Inf Sci

  21. duToit J, Davimes R, Mohamed A, Patel K, Nye J (2016) Customer segmentation using unsupervised learning on daily energy load profiles. J Adv Inform Technol 7(2)

  22. Do Ruibin K, Borglöv T (2018) Predicting customer lifetime value: understanding its accuracy and drivers from a frequent flyer program perspective

  23. Dullaghan C, Rozaki E (2017) Integration of machine learning techniques to evaluate dynamic customer segmentation analysis for mobile customers. arXiv preprint

  24. Emtiyaz S, Keyvanpour M (2012) Customers behavior modeling by semi-supervised learning in customer relationship management. arXiv preprint

  25. Fares N, Lebbar M, Sbihi N (2018) A customer profiling’machine learning approach, for in-store sales in fast fashion. In: International conference on advanced intelligent systems for sustainable development, pp 586–591, Springer, Cham

  26. Farquad MAH, Ravia V, Raju SB (2014) Churn prediction using comprehensible support vector machine: an analytical CRM application. Appl Soft Comput J.

    Article  Google Scholar 

  27. Fu GS, Levin-Schwartz Y, Lin QH, Zhang D (2019) Machine learning for medical imaging. J Healthcare Eng

  28. Glas F (2015) Machine-learning techniques for customer recommendations. LU-CS-EX 2015–17

  29. Hu K, Li Z, Liu Y, Cheng L, Yang Q, Li Y (2018) A framework in CRM customer lifecycle: identify downward trend and potential issues detection. arXiv preprint

  30. Jangid C, Kothari T, Spear J, Wadsworth E (2014) CustoVal: estimating customer lifetime value using machine learning techniques

  31. Jasek P, Vrana L, Sperkova L, Smutny Z, Kobulsky M (2018) Modeling and application of customer lifetime value in online retail. In: Informatics, vol 5, p 2. Multidisciplinary Digital Publishing Institute

  32. Kamthania D, Pawa A, Madhavan SS (2018) Market segmentation analysis and visualization using K-mode clustering algorithm for E-commerce business. J Comput Inf Technol 26(1):57–68

    Article  Google Scholar 

  33. Khodabandehlou S, ZivariRahman M (2017) Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behavior. J Syst Inf Technol 19(1/2):65–93.

    Article  Google Scholar 

  34. Koch R (2011) The 80/20 principle: the secret of achieving more with less: updated 20th anniversary edition of the productivity and business classic. Hachette, UK

  35. Korpusik M, Sakaki S, Chen F, Chen YY (2016) Recurrent neural networks for customer purchase prediction on twitter. CBRecSys 1673:47–50

    Google Scholar 

  36. Laaksonen A (2020) The use of artificial intelligence in customer relationship management.

  37. Lv D, Yuan S, Li M, Xiang Y (2019) An empirical study of machine learning algorithms for stock daily trading strategy. Math Prob Eng

  38. Martínez A, Schmuck C, Pereverzyev S Jr, Pirker C, Haltmeier M (2020) A machine learning framework for customer purchase prediction in the non-contractual setting. Eur J Oper Res 281(3):588–596

    Article  Google Scholar 

  39. Meinzer S, Jensen U, Thamm A, Hornegger J, Eskofier BM (2017) Can machine learning techniques predict customer dissatisfaction? A feasibility study for the automotive industry. Artif Intell Research 6(1):80–90

    Article  Google Scholar 

  40. Mishra A, Reddy US (2017) A novel approach for churn prediction using deep learning. In: 2017 IEEE international conference on computational intelligence and computing research (ICCIC), pp 1–4. IEEE

  41. Ngai EW, Xiu L, Chau DC (2009) Application of data mining techniques in customer relationship management: a literature review and classification. Expert Syst Appl 36(2):2592–2602

    Article  Google Scholar 

  42. Van Nguyen T, Zhou L, Chong AYL, Li B, Pu X (2020) Predicting customer demand for remanufactured products: a data-mining approach. Eur J Oper Res 281(3):543–558

    Article  Google Scholar 

  43. Norlin, P., &Paulsrud, V. (2017). Identifying New Customers Using Machine Learning: A case study on B2B-sales in the Swedish IT-consulting sector.

  44. Sabbeh SF (2018). Machine-learning techniques for customer retention: a comparative study. Int J Adv Comput Sci Appl 9(2)

  45. Salehinejad H, Rahnamayan S (2016) Customer shopping pattern prediction: a recurrent neural network approach. In: 2016 IEEE symposium series on computational intelligence (SSCI), pp 1–6), IEEE

  46. Sandy, (2019). Investigating Starbucks Customers Segmentation using Unsupervised Machine Learning, online accessed on 12 July 2019.

  47. Singh P, Agrawal R (2018) A customer centric best connected channel model for heterogeneous and IoT networks. J Org End User Comput (JOEUC) 30(4):32–50

    Article  Google Scholar 

  48. Singh N, Gupta M, Dash SK (2018) A study on impact of key factors affecting buying behaviour of residential apartments: a case study of Noida and Greater Noida. Int J Indian Cult Bus Manag 17(4):403–416

    Article  Google Scholar 

  49. Singh N, Singh P, Singh KK, Singh A (2020a) Diagnosing of disease using machine learning, accepted for machine learning & internet of medical things in healthcare. Elsevier Publications, Amsterdam (In Press)

    Google Scholar 

  50. Singh N, Singh P, Singh KK, Singh A (2020b) Machine learning based classification and segmentation techniques for CRM: a customer analytics. J. Bus Forecast Mark Intell, Int.

    Google Scholar 

  51. Singh P, Agrawal V (2019) A collaborative model for customer retention on user service experience. In: Advances in computer communication and computational sciences, pp 55–64. Springer, Singapore

  52. Spanoudes P, Nguyen T (2017) Deep learning in customer churn prediction: unsupervised feature learning on abstract company independent feature vectors. arXiv preprint

  53. Tong L, Wang Y, Wen F, Li X (2017) The research of customer loyalty improvement in telecom industry based on NPS data mining. China Commun. 14(11):260–268.

    Article  Google Scholar 

  54. Vafeiadis T, Diamantaras KI, Sarigiannidis G, Chatzisavvas KC (2015) A comparison of machine learning techniques for customer churn prediction. Simul Model Practice Theory 55:1–9

    Article  Google Scholar 

  55. Vanderveld A, Pandey A, Han A, Parekh R (2016) An engagement-based customer lifetime value system for e-commerce. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 293–302. ACM

  56. Wassouf WN, Alkhatib R, Salloum K, Balloul S (2020) Predictive analytics using big data for increased customer loyalty: Syriatel telecom company case study. J Big Data 7:1–24

    Article  Google Scholar 

  57. Wen Z, Yan J, Zhou L, Liu Y, Zhu K, Guo Z, Zhang F (2018) Customer churn warning with machine learning. In: The Euro-China conference on intelligent data analysis and applications, pp 343–350. Springer, Cham

  58. Yadav NS, Gupta M, Singh P (2018) Factors affecting buying behavior & CRM in real estate sector: a literature survey. Asian J Res Bus Econ Manag 8(6):32–39

    Article  Google Scholar 

  59. Yin Z, Sulieman LM, Malin BA (2019) A systematic literature review of machine learning in online personal health data. J Am Med Informat Assoc 26(6):561–576

    Article  Google Scholar 

  60. Zhu X (2007) Semi-supervised learning tutorial. In: International conference on machine learning (ICML), pp 1–135

Download references

Author information



Corresponding author

Correspondence to Narendra Singh.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Singh, N., Singh, P. & Gupta, M. An inclusive survey on machine learning for CRM: a paradigm shift. Decision 47, 447–457 (2020).

Download citation


  • CRM
  • Machine learning
  • Churning
  • Decision tree
  • SVM
  • Deep learning