Recent Trends of Customer Relationship Management in AI: A Scientometric Analysis

Maciej Hoffmann, Weronika Marchewka, Bartosz Piotrowski, Marharyta Ratushniak, Angelika Ziółkowska, Magdalena Graczyk-Kucharska

Abstract


Theoretical background: The popularity of AI in recent years has led to its integration with various industrial sectors. This spectrum of AI applications has brought numerous topics that are underexplored and need to be taken up. One such area is the integration of AI with customer relationship management (CRM) systems. In consequence, this research is valuable for the scientific community.

Purpose of the article: The purpose of the research is to conduct scientometric analysis in the field of AI in CRM and to identify the most crucial areas of research, identify motor and niche themes in the AI-CRM and indicate future research trends.

Research methods: The methodology was divided into three parts: data collection, descriptive analysis, scientometric analysis. In the research, “R programming” and “Biblioshiny” were used to conduct scientometric analysis. This made it possible to generate charts and a table based on gathered data and then analyse the results.

Main findings: The results show there is a growth in AI-CRM systems subject since 2019. Keywords like “CRM”, “public relationship” or “data mining” were often used in research articles. Future research trends can be define among others: acceptance and trust for AI powered technology, impact on companies’ environment or relationship with customer.


Keywords


customer relationship management; artificial intelligence; AI-CRM system; customer segmentation

Full Text:

PDF

References


Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007

Burnham, J.F. (2006). Scopus database: A review. Biomedical Digital Libraries, 3(1). https://doi.org/10.1186/1742-5581-3-1

Chatterjee, S., Chaudhuri, R., & Vrontis, D. (2022). AI and digitalization in relationship management: Impact of adopting AI-embedded CRM system. Journal of Business Research, 150, 437–450. https://doi.org/10.1016/j.jbusres.2022.06.033

Chatterjee, S., Chaudhuri, R., Vrontis, D., Thrassou, A., & Ghosh, S.K. (2021). Adoption of artificial intelligence-integrated CRM systems in agile organizations in India. Technological Forecasting and Social Change, 168, 120783. https://doi.org/10.1016/j.techfore.2021.120783

Chatterjee, S., Ghosh, S.K., Chaudhuri, R., & Chaudhuri, S. (2021). Adoption of AI-integrated CRM system by Indian industry: From security and privacy perspective. Information & Computer Security, 29(1), 1–24. https://doi.org/10.1108/ICS-02-2019-0029

Chatterjee, S., Ghosh, S.K., Chaudhuri, R., & Nguyen, B. (2019). Are CRM systems ready for AI integration?: A conceptual framework of organizational readiness for effective AI-CRM integration. The Bottom Line, 32(2), 144–157. https://doi.org/10.1108/BL-02-2019-0069

Chatterjee, S., Rana, N.P., Khorana, S., Mikalef, P., & Sharma, A. (2023). Assessing organizational users’ intentions and behaviour to AI integrated CRM systems: A meta-UTAUT approach. Information Systems Frontiers, 25(4), 1299–1313. https://doi.org/10.1007/s10796-021-10181-1

Chatterjee, S., Rana, N.P., Tamilmani, K., & Sharma, A. (2021). The effect of AI-based CRM on organization performance and competitive advantage: An empirical analysis in the B2B context. Industrial Marketing Management, 97, 205–219. https://doi.org/10.1016/j.indmarman.2021.07.013

Chen, Z.-Y., & Fan, Z.-P. (2013). Dynamic customer lifetime value prediction using longitudinal data: An improved multiple kernel SVR approach. Knowledge-Based Systems, 43, 123–134. https://doi.org/10.1016/j.knosys.2013.01.022

Chen, Z.-Y., Fan, Z.-P., & Sun, M. (2016). A multi-kernel support tensor machine for classification with multitype multiway data and an application to cross-selling recommendations. European Journal of Operational Research, 255(1), 110–120. https://doi.org/10.1016/j.ejor.2016.05.020

Chiang, W.-Y. (2018). Identifying high-value airlines customers for strategies of online marketing systems: An empirical case in Taiwan. Kybernetes, 47(3), 525–538. https://doi.org/10.1108/K-12-2016-0348

Domingos, E., Ojeme, B., & Daramola, O. (2021). Experimental analysis of hyperparameters for deep learning-based churn prediction in the banking sector. Computation, 9(3), 34. https://doi.org/10.3390/computation9030034

Dursun, A., & Caber, M. (2016). Using data mining techniques for profiling profitable hotel customers: An application of RFM analysis. Tourism Management Perspectives, 18, 153–160. https://doi.org/10.1016/j.tmp.2016.03.001

Farquad, M.A.H., Ravi, V., & Raju, S.B. (2014). Churn prediction using comprehensible support vector machine: An analytical CRM application. Applied Soft Computing, 19, 31–40. https://doi.org/10.1016/j.asoc.2014.01.031

Jayasree. (2013). A review on data mining in banking sector. American Journal of Applied Sciences, 10(10), 1160–1165. https://doi.org/10.3844/ajassp.2013.1160.1165

Khobzi, H., & Teimourpour, B. (2015). LCP segmentation: A framework for evaluation of user engagement in online social networks. Computers in Human Behavior, 50, 101–107. https://doi.org/10.1016/j.chb.2015.03.080

Khrais, L.T. (2020). Role of artificial intelligence in shaping consumer demand in e-commerce. Future Internet, 12(12), 226. https://doi.org/10.3390/fi12120226

Kozak, J., Kania, K., Juszczuk, P., & Mitręga, M. (2021). Swarm intelligence goal-oriented approach to data-driven innovation in customer churn management. International Journal of Information Management, 60, 102357. https://doi.org/10.1016/j.ijinfomgt.2021.102357

Krafft, M., Sajtos, L., & Haenlein, M. (2020). Challenges and opportunities for marketing scholars in times of the Fourth Industrial Revolution. Journal of Interactive Marketing, 51, 1–8. https://doi.org/10.1016/j.intmar.2020.06.001

Kumar, B.S., & Ravi, V. (2016). A survey of the applications of text mining in financial domain. Knowledge-Based Systems, 114, 128–147. https://doi.org/10.1016/j.knosys.2016.10.003

Kumar, P., Sharma, S.K., & Dutot, V. (2023). Artificial intelligence (AI)-enabled CRM capability in healthcare: The impact on service innovation. International Journal of Information Management, 69, 102598. https://doi.org/10.1016/j.ijinfomgt.2022.102598

Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4), 135–155. https://doi.org/10.1177/0008125619859317

Kumar, V., Ramachandran, D., & Kumar, B. (2021). Influence of new-age technologies on marketing: A research agenda. Journal of Business Research, 125, 864–877. https://doi.org/10.1016/j.jbusres.2020.01.007

Lam, H.Y., Ho, G.T.S., Wu, C.H., & Choy, K.L. (2014). Customer relationship mining system for effective strategies formulation. Industrial Management & Data Systems, 114(5), 711–733. https://doi.org/10.1108/imds-08-2013-0329

Lamrhari, S., Ghazi, H.E., Oubrich, M., & Faker, A.E. (2022). A social CRM analytic framework for improving customer retention, acquisition, and conversion. Technological Forecasting and Social Change, 174, 121275. https://doi.org/10.1016/j.techfore.2021.121275

Lee, C.K.H., Choy, K.L., Ho, G.T.S., & Lin, C. (2016). A cloud-based responsive replenishment system in a franchise business model using a fuzzy logic approach. Expert Systems, 33(1), 14–29. https://doi.org/10.1111/exsy.12117

Libai, B., Bart, Y., Gensler, S., Hofacker, C. F., Kaplan, A., Kötterheinrich, K., & Kroll, E.B. (2020). Brave new world? On AI and the management of customer relationships. Journal of Interactive Marketing, 51, 44–56. https://doi.org/10.1016/j.intmar.2020.04.002

Pang, K.-W., & Chan, H.-L. (2017). Data mining-based algorithm for storage location assignment in a randomised warehouse. International Journal of Production Research, 55(14), 4035–4052. https://doi.org/10.1080/00207543.2016.1244615

Ramos, G. (2022). A.I.’s impact on jobs, skills, and the future of work: The UNESCO perspective on key policy issues and the ethical debate. New England Journal of Public Policy, 34(1), Article 3.

Sohrabi, C., Franchi, T., Mathew, G., Kerwan, A., Nicola, M., Griffin, M., Agha, M., & Agha, R. (2021). PRISMA 2020 statement: What’s new and the importance of reporting guidelines. International Journal of Surgery, 88, 105918. https://doi.org/10.1016/j.ijsu.2021.105918

Thakkar, D., Kumar, N., & Sambasivan, N. (2020). Towards an AI-powered future that works for vocational workers. In Proceedings of the 2020 CHI Conference on Human Factors in Computing System (pp. 1–13). https://doi.org/10.1145/3313831.3376674

Wassouf, W. N., Alkhatib, R., Salloum, K., & Balloul, S. (2020). Predictive analytics using big data for increased customer loyalty: Syriatel Telecom Company case study. Journal of Big Data, 7(1), 29. https://doi.org/10.1186/s40537-020-00290-0

Wei, J.-T., Lee, M.-C., Chen, H.-K., & Wu, H.-H. (2013). Customer relationship management in the hairdressing industry: An application of data mining techniques. Expert Systems with Applications, 40(18), 7513–7518. https://doi.org/10.1016/j.eswa.2013.07.053

Youn, S., & Jin, S.V. (2021). “In A.I. we trust?” The effects of parasocial interaction and technopian versus luddite ideological views on chatbot-based customer relationship management in the emerging “feeling economy”. Computers in Human Behavior, 119, 106721. https://doi.org/10.1016/j.chb.2021.106721

Zdravković, M., Panetto, H., & Weichhart, G. (2021). AI-enabled Enterprise Information Systems for Manufacturing. Enterprise Information Systems, 16(4), 668–720. https://doi.org/10.1080/17517575.2021.1941275

Zhu, J., & Liu, W. (2020). A tale of two databases: The use of Web of Science and Scopus in academic papers. Scientometrics, 123(1), 321–335. https://doi.org/10.1007/s11192-020-03387-8




DOI: http://dx.doi.org/10.17951/h.2024.58.3.181-202
Date of publication: 2024-07-12 06:50:13
Date of submission: 2024-03-21 22:38:39


Statistics


Total abstract view - 813
Downloads (from 2020-06-17) - PDF - 0

Indicators



Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Bartosz Piotrowski

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.