Abstract
Customer relationship management (CRM) is a set of technologies, methods, and practices that companies adopt to analyze and manage customer interactions and data throughout their engagement with them. The primary purpose behind using CRM is to enhance the business and improve interaction with the customer at the service point. It also helps in increasing the revenue of suppliers or manufacturers and is more useful in customer retention. The efficient operation of the CRM system needs to collect information from several contact points with the consumer, such as direct phone calls, live chat, marketing materials, the company’s website, chatbot, social media, and mail communication. In addition, the CRM system also collects and provides detailed information about the customers’ personal information, preferences, history of purchase, and various concerns with the employees from the marketing side. Thus, the CRM system rapidly processes a huge volume of data from the consumer and uploads numerous data from the supplier or manufacturer. In such cases, an efficient way of handling this voluminous data is highly needed. The advent of Data Mining techniques and their rapid growth makes it easier to handle this large volume of CRM data more effectively. The present work discusses various Data Mining techniques and their application to CRM. It also outlines how effective it is to make a contemporary CRM from a conventional one.
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References
Buttle F (2008) Customer relationship management. Routledge
Kumar V (2010) Customer relationship management. Wiley international encyclopedia of marketing
Payne A, Frow P (2005) A strategic framework for customer relationship management. J Mark 69(4):167–176
Winer RS (2001) A framework for customer relationship management. Calif Manage Rev 43(4):89–105
Knox S, Payne A, Ryals L, Maklan S, Peppard J (2007) Customer relationship management. Routledge
Chen IJ, Popovich K (2003) Understanding customer relationship management (CRM). Bus Process Manag J. https://doi.org/10.1108/14637150310496758
Bhat SA, Darzi MA (2016) Customer relationship management. Int J Bank Mark. https://doi.org/10.1108/IJBM-11-2014-0160
Baashar Y, Alhussian H, Patel A, Alkawsi G, Alzahrani AI, Alfarraj O, Hayder G (2020) Customer relationship management systems (CRMS) in the healthcare environment: a systematic literature review. Comput Stand Interfaces 71:103442
Dewnarain S, Ramkissoon H, Mavondo F (2021) Social customer relationship management: a customer perspective. J Hosp Mark Manag. https://doi.org/10.1080/19368623.2021.1884162
Gil-Gomez H, Guerola-Navarro V, Oltra-Badenes R, Lozano-Quilis JA (2020) Customer relationship management: digital transformation and sustainable business model innovation. Eco Res-Ekonomska Istraživanja 33(1):2733–2750
Mitchell R, Michalski J, Carbonell T (2013) An artificial intelligence approach. Springer, Berlin
Schoen S, Sykes W, Little AD (1987) Putting artificial intelligence to work: evaluating and implementing business applications
Reitman WR (Ed) (1984) Artificial intelligence applications for business: proceedings of the NYU symposium, May, 1983. Intellect Books
Min H (2010) Artificial intelligence in supply chain management: theory and applications. Int J Log Res Appl 13(1):13–39
Muthusamy V, Slominski A, Ishakian V (2018) Towards enterprise-ready AI deployments minimizing the risk of consuming AI models in business applications. In: 2018 first international conference on artificial intelligence for industries (AI4I), IEEE, pp 108–109
Jordan MI, Mitchell TM (2015) Machine learning: trends, perspectives, and prospects. Science 349(6245):255–260
Bose I, Mahapatra RK (2001) Business data mining—a machine learning perspective. Inf Manag 39(3):211–225
Finlay S (2021) Artificial intelligence and machine learning for business: a no-nonsense guide to data driven technologies, 4th ed. Relativistic
Canhoto AI, Clear F (2020) Artificial intelligence and machine learning as business tools: a framework for diagnosing value destruction potential. Bus Horiz 63(2):183–193
Khan WA, Chung SH, Awan MU, Wen X (2019) Machine learning facilitated business intelligence (Part I): neural networks learning algorithms and applications. Ind Manag Data Syst 120(1):164–195
Goodfellow I, Bengio Y, Courville A, Bengio Y (2016) Deep learning, vol 1. MIT press, Cambridge
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444
Kraus M, Feuerriegel S, Oztekin A (2020) Deep learning in business analytics and operations research: models, applications and managerial implications. Eur J Oper Res 281(3):628–641
DeLotell PJ, Millam LA, Reinhardt MM (2010) The use of deep learning strategies in online business courses to impact student retention. Am J Bus Educ 3(12):49–56
Howard J (2013) The business impact of deep learning. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 1135–1135
Mehdiyev N, Evermann J, Fettke P (2017) A multi-stage deep learning approach for business process event prediction. In: 2017 IEEE 19th conference on business informatics (CBI), vol 1, IEEE, pp 119–128
Raj EFI, Balaji M (2021) Analysis and classification of faults in switched reluctance motors using deep learning neural networks. Arab J Sci Eng 46(2):1313–1332
Raj EFI, Kamaraj V (2013) Neural network based control for switched reluctance motor drive. In: 2013 IEEE international conference on emerging trends in computing, communication and nanotechnology (ICECCN), IEEE, pp 678–682
Gampala V, Kumar MS, Sushama C, Raj EFI (2020) Deep learning based image processing approaches for image deblurring. Materials Today: Proceedings
Agarwal P, Ch MA, Kharate DS, Raj EFI, Balamuralitharan S (2021) Parameter estimation of COVID-19 second wave BHRP transmission model by using principle component analysis. Annals of the Romanian Society for Cell Biology, pp 446–457
Hand DJ, Adams NM (2014) Data mining. Wiley StatsRef: statistics reference online, pp 1–7
Chung HM, Gray P (1999) Data mining. J Manag Inf Syst 16(1):11–16
Larose DT, Larose CD (2014) Discovering knowledge in data: an introduction to data mining, vol 4. John Wiley & Sons
Chen MS, Han J, Yu PS (1996) Data mining: an overview from a database perspective. IEEE Trans Knowl Data Eng 8(6):866–883
Rygielski C, Wang JC, Yen DC (2002) Data mining techniques for customer relationship management. Technol Soc 24(4):483–502
Berry MJ, Linoff GS (2004) Data mining techniques: for marketing, sales, and customer relationship management. John Wiley & Sons, Hoboken
Sharma S, Goyal DP, Mittal RK (2008) Data mining research for customer relationship management systems: a framework and analysis. Int J Bus Inf Syst 3(5):549–565
Chen Y, Zhang G, Hu D, Wang S (2006) Customer segmentation in customer relationship management based on data mining. In: international conference on programming languages for manufacturing. Springer, Boston, MA, pp 288–293
Shokouhyar S, Shokoohyar S, Raja N, Gupta V (2021) Promoting fashion customer relationship management dimensions based on customer tendency to outfit matching: mining customer orientation and buying behaviour. Int J Appl Decis Sci 14(1):1–23
González-Serrano L, Talón-Ballestero P, Muñoz-Romero S, Soguero-Ruiz C, Rojo-Álvarez JL (2021) A big data approach to customer relationship management strategy in hospitality using multiple correspondence domain description. Appl Sci 11(1):256
Pacha NH, Khebazi FZ, Mazouz N (2021) Data mining and its contribution to decision-making in business organizations. In: Sedkaoui S, Khelfaoui M, Kadi N (eds) Big data analytics. Apple Academic Press, NE Palm Bay, pp 67–80
Hernández-Nieves E, Parra-Domínguez J, Chamoso P, Rodríguez-González S, Corchado JM (2021) A data mining and analysis platform for investment recommendations. Electronics 10(7):859
Baloch S, Muhammad MS (2021) An intelligent data mining-based fault detection and classification strategy for microgrid. IEEE Access 9:22470–22479
Zhao Y, Chang C, Hannum M, Lee J, Shen R (2021) Bayesian network-driven clustering analysis with feature selection for high-dimensional multi-modal molecular data. Sci Rep 11(1):1–11
Omuya EO, Okeyo GO, Kimwele MW (2021) Feature selection for classification using principal component analysis and information gain. Expert Syst Appl 174:114765
Neelakandan S, Rene Beulah J, Prathiba L, Murthy GLN, Irudaya Raj EF, Arulkumar N (2022) Blockchain with deep learning-enabled secure healthcare data transmission and diagnostic model. Int J Model Simul Sci Comput. https://doi.org/10.1142/S1793962322410069
Pradana MG, Ha HT (2021) Maximizing strategy improvement in mall customer segmentation using K-means clustering. J Appl Data Sci 2(1):19–25
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Raj, E.F.I. (2024). Applications of Data Science and Artificial Intelligence Methodologies in Customer Relationship Management. In: Kautish, S., Chatterjee, P., Pamucar, D., Pradeep, N., Singh, D. (eds) Computational Intelligence for Modern Business Systems . Disruptive Technologies and Digital Transformations for Society 5.0. Springer, Singapore. https://doi.org/10.1007/978-981-99-5354-7_12
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