Scalable AI and ML-Powered Customer Support for Telecom Enterprises: Optimizing Engagement for 200 Million+ Customers
Keywords:
operational, solution, omnichannelAbstract
The telecom industry faces unprecedented challenges in managing large-scale customer support operations for user bases exceeding hundreds of millions. Traditional human-led models are increasingly inefficient, leading to long wait times, poor customer satisfaction, and high churn rates. This paper proposes a highly scalable AI and Machine Learning (ML) framework tailored for telecom enterprises with extensive customer bases. The solution leverages intent recognition, sentiment-aware routing, predictive analytics, and AI-driven omnichannel self-service to deliver responsive, personalized, and cost-effective support. With insights from large-scale telecom implementations, the proposed system demonstrates tangible improvements in First Contact Resolution (FCR), Net Promoter Score (NPS), and operational efficiency.
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References
Google Research, “Language Models for Dialog Applications,” 2022
McKinsey, “AI Adoption in Telecom: Scaling Personalization for 200M+ Customers,” 2023
Hugging Face Transformers Documentation, 2023
Telecom Regulatory Authority of India (TRAI) Reports, 2022–2024
Zendesk CX Trends Report, 2022
Rasa, “Conversational AI Infrastructure at Scale,” Whitepaper, 2023
Goyal, M. K., Gadam, H., & Sundaramoorthy, P. (2023). Real-Time Supply Chain Resilience: Predictive Analytics for Global Food Security and Perishable Goods. Available at SSRN 5272929.
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