From Reactive to Proactive: AI-Driven CCaaS Solutions in Predictive Customer Service
DOI:
https://doi.org/10.32628/CSEIT25112843Keywords:
AI, CCaaS, Predictive Analytics, Customer Service, Machine Learning, Conversational AI, Customer SatisfactionAbstract
Customer service is no longer the answer to a complaint but a personal, accurate, timely interaction that is expected to be highly influenced by AI. AI is being integrated into CCaaS solutions as more and more of these platforms aim to provide better ways to engage with customers while anticipating their needs and even providing personalized updates. The role of advanced intelligent CCaaS solutions in the context of predictive customer experience transformation is the focus of this paper. These areas are: shift from traditional reactive systems to proactive AI, use of Predictive Analytics, Machine Learning Models and Conversational AI for customer satisfaction. In so doing, this article outlines a comprehensive approach to a literature review and case study analyses to stress the significance of embedding AI into CCaaS for long-term competitive advantage. It calls for the adoption of predictive customer service as critical to organizations that are determined to strive in the current volatile business environment.
Downloads
References
Mohanty, A., Mohanty, J., Naveen, L., Mohanty, S., & Acharya, A. Artificial Intelligence Transforming Customer Service Management: Embracing The Future.
Saberi, M., Khadeer Hussain, O., & Chang, E. (2017). Past, present and future of contact centers: a literature review. Business Process Management Journal, 23(3), 574-597.
Allie Delos Santos, What Is CCaaS and the CCaaS Cloud? Our Guide, unity communications, online. https://unity-connect.com/our-resources/blog/ccaas-cloud/
Shakerkhan, K. O., & Abilmazhinov, E. T. (2019). Development of a Method for Choosing Cloud Computing on the Platform of Paas for Servicing the State Agencies. International Journal of Modern Education & Computer Science, 11(9).
The Evolution of CCaaS: From Monolithic Stacks to Cloud-Native Platforms, cxtoday, online. https://www.cxtoday.com/contact-centre/the-evolution-of-ccaas-from-monolithic-stacks-to-cloud-native-platforms/
Bravo, C., Saputelli, L., Rivas, F., Pérez, A. G., Nikolaou, M., Zangl, G., ... & Nunez, G. (2014). State of the art of artificial intelligence and predictive analytics in the E&P industry: a technology survey. Spe Journal, 19(04), 547-563.
Badmus, O., Rajput, S. A., Arogundade, J. B., & Williams, M. (2024). AI-driven business analytics and decision making.
Sonkar, S., & Dixit, A. K. Artificial Intelligence for Predictive Customer Service in Marketing.
Avancha, S., Aggarwal, A., & Goel, P. (2024). Data-driven decision making in IT service enhancement. Journal of Quantum Science and Technology, 1(3), 10-24.
Stampfl, G., Prügl, R., & Osterloh, V. (2013). An explorative model of business model scalability. International Journal of Product Development, 18(3-4), 226-248.
Johansson, L. (1994). Numerical simulation of contact pressure evolution in fretting.
Marshall, M. B., Lewis, R., Dwyer-Joyce, R. S., Olofsson, U., & Björklund, S. (2006). Experimental characterization of wheel-rail contact patches evolution.
Daqar, M. A. A., & Smoudy, A. K. (2019). The role of artificial intelligence on enhancing customer experience. International Review of Management and Marketing, 9(4), 22.
Xu, Y., Shieh, C. H., van Esch, P., & Ling, I. L. (2020). AI customer service: Task complexity, problem-solving ability, and usage intention. Australasian marketing journal, 28(4), 189-199.
Barker, W. O., Lane, J. R., Holbrook, D. P., Vadrevu, N. R., & Padalino, L. T. (2005). Preventative maintenance: A proactive customer service. Bell Labs Technical Journal, 9(4), 187-200.
Wang, J., de Moraes, R. M., & Bari, A. (2020, August). A predictive analytics framework to anomaly detection. In 2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService) (pp. 104-108). IEEE.
TRANSFORMING CUSTOMER EXPERIENCE: THE ROLE OF AI IN CCaaS, International Journal of Computer Engineering and Technology (IJCET), Volume 15, Issue 6, Nov-Dec 2024, pp. 211-225, https://iaeme.com/MasterAdmin/Journal_uploads/IJCET/VOLUME_15_ISSUE_6/IJCET_15_06_018.pdf
Tang, B., Cai, L., Yang, S., Xu, J., & Yu, Y. (2022). Evolutionary Computation for Sparse Synthesis Optimization of CCAAs: An Enhanced Whale Optimization Algorithm Method. Future Internet, 14(12), 347.
The Future of Customer Contact: What to Expect from CCaaS Companies, GRANITE, online. https://granite.tech/blog/future-of-ccaas-companies/
Khushbu Raval, 10 Challenges When Adopting a CCaaS (+Solutions), Martechview, 2024. online. https://martechview.com/10-challenges-when-adopting-a-ccaas-solutions/
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.