Artificial Intelligence-Driven Customer Experience Optimization in Fixed Broadband Networks

Abstract

The rapid growth of fiber broadband and home Wi-Fi services has transformed customer expectations regarding service quality, reliability, and digital experiences. Traditional customer experience management approaches in fixed broadband networks primarily rely on reactive troubleshooting and historical performance reporting, which often fail to address service degradation before customers are affected. Recent advances in Artificial Intelligence (AI), Machine Learning (ML), predictive analytics, and automation have enabled telecom operators to shift from reactive operations toward proactive and customer-centric network management. This paper examines the role of AI in optimizing customer experience within modern fixed broadband environments. It explores how AI technologies leverage large-scale operational, network, and customer datasets to predict service issues, automate root cause analysis, improve fault management, personalize customer support, and enhance service quality. The study synthesizes existing literature on AI-driven telecom operations and develops a conceptual framework linking AI capabilities to customer experience outcomes. Particular attention is given to fixed broadband networks, including Fiber-to-the-Home (FTTH), Gigabit Passive Optical Networks (GPON), Wi-Fi ecosystems, and Customer Experience Management (CEM) platforms. The findings suggest that AI-driven customer experience optimization significantly improves service reliability, network performance, customer satisfaction, and operational efficiency. However, challenges related to data quality, model transparency, privacy, and organizational readiness remain critical considerations. The paper concludes by outlining future re-search directions involving autonomous networks, explainable AI, and digital experience assurance frameworks for next-generation broadband ecosystems.

Introduction

The text discusses how Artificial Intelligence (AI) can improve customer experience in fixed broadband telecommunications networks, which are becoming increasingly important due to rising digital demand, streaming, remote work, and smart home usage. As customer expectations for speed, reliability, and service quality increase, traditional network-focused monitoring is no longer sufficient, leading to a shift toward customer-centric service management.

Despite major investments in broadband infrastructure, operators still face issues like service degradation, Wi-Fi problems, delayed fault resolution, and customer dissatisfaction. These problems are often handled reactively, which increases complaints, churn, and operational costs. The study addresses how AI can enable proactive and intelligent management of customer experience.

The paper explores how AI technologies such as machine learning, predictive analytics, natural language processing, and intelligent automation can analyze network and customer data in real time. These tools help predict service failures, detect faults, analyze customer behavior, automate support, and optimize network performance.

A key contribution is a conceptual framework with four layers: data collection (network and customer data), intelligence (AI processing), decision-making (experience scoring and recommendations), and action (automated fixes and optimization). This creates a closed-loop system for continuous customer experience improvement.

The literature review highlights that Customer Experience Management (CEM) has evolved from simple surveys to advanced AI-driven systems that integrate network performance and user behavior. Research shows AI improves predictive maintenance, churn reduction, fault management, and personalized services, though challenges remain in explainability, data integration, and standardization.

Conclusion

Artificial Intelligence is fundamentally transforming customer experience management in fixed broadband networks. By leveraging machine learning, predictive analytics, natural language processing, and intelligent automation, telecom operators can transition from reactive service management toward proactive and predictive customer experience optimization. The conceptual framework proposed in this paper demonstrates how AI integrates operational, network, and customer intelligence to improve service quality and operational efficiency. The analysis indicates that AI-driven customer experience optimization enhances fault management, Wi-Fi performance, customer support, and service assurance while contributing to higher customer satisfaction and reduced churn. However, achieving these benefits requires addressing challenges related to data quality, explainability, privacy, and organizational readiness. Future broadband ecosystems will increasingly rely on autonomous operations, digital experience assurance platforms, and explainable AI models capable of delivering personalized and proactive customer experiences. Future research should focus on developing standardized customer experience metrics, evaluating real-world AI deployments, and exploring the integration of generative AI within telecom service assurance frameworks.

References

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Copyright

Copyright © 2026 Mohammad Mustafa, Mohammed Hussain Moheet, Mohammed Abdul Moheet, Safi Ammar Mohammed, Mohammed Hassan Ali Habeeb. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.