Combatting Revenue Leakage with AI in 5G Roaming
Juniper Research has highlighted the transformative role of Artificial Intelligence (AI) in reducing “revenue leakage” in 5G roaming connections, a significant challenge in the telecom industry. Through the deployment of AI solutions, the average revenue leakage per 5G roaming connection is expected to drop from $1.72 to $1.20. This notable decrease is credited to telecom operators adopting AI-based segmentation strategies, a critical move in effectively monetizing data-centric users.
The Role of AI in Resource Allocation and Pricing
Revenue leakage refers to the loss incurred when services are provided but not fully monetized. Juniper Research emphasizes that AI-based segmentation is instrumental in addressing this issue, particularly in 5G standalone networks. AI’s ability to categorize traffic types and segments in real-time allows operators to enhance resource allocation and develop innovative pricing models, leading to more effective monetization of emerging roaming services.
Enhancing Enterprise Traffic Billing with Machine Learning
By utilizing machine learning models, telecom operators can distinguish enterprise traffic based on specific use cases. This differentiation enables them to implement premium billing for mission-critical 5G standalone connections, thereby minimizing revenue leakage. Alex Webb, the author of the research, underscores the significance of AI-based segmentation in differentiating enterprise traffic and facilitating appropriate billing strategies.
A Strategic Call to Action for Operators
The report, titled ‘Global Roaming Clearing Market: 2023-2028,’ urges operators to embrace AI segmentation tools to address the challenges of revenue leakage in 5G roaming, particularly on standalone networks. The necessity of this approach stems from the unique infrastructure of 5G standalone networks, which utilize a dedicated 5G core, unlike the 4G infrastructure employed by non-standalone networks. This distinction necessitates tailored pricing strategies for each network type, ensuring pricing reflects the enhanced Quality of Service (QoS) such as higher throughput and lower latency offered by these networks.
AI-Driven Segmentation: A Key to Optimizing 5G Networks
In conclusion, the adoption of AI-driven segmentation tools is emerging as a vital strategy for telecom operators. This approach not only aids in optimizing network resource distribution but also in identifying enterprise traffic suitable for dedicated network slices. Ultimately, it represents a forward-thinking solution to curb revenue leakage in the burgeoning area of 5G roaming connections.