Multiple announcements right before and during Mobile World Congress show that AI/ML -driven solutions are poised to help CSPs improve Quality of Experience and provide more relevant and personalized offers.

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Notable news from Mobile World Congress

Multiple announcements right before and during Mobile World Congress show that AI/ML-driven solutions are poised to help communications service providers (CSPs) improve quality of experience and provide more relevant and personalized offers. The hope is that doing so—both reactively and proactively—will lead to lower churn and increased ARPU.

  • On February 20, Nokia announced AVA Customer and Mobile Network Insights: “…a cloud-native analytics software solution that simplifies the collection and analysis of 5G network data to provide CSPs with stronger and more cost effective analytical capabilities.  The solution delivers “intelligence everywhere” through AI and machine learning tools that support intelligent and automated decision making based on correlated reports generated from data across 5G networks.”  
  • On February 26, Microsoft announced a public preview of Azure Operator Insights: “…enables the collection and analysis of massive quantities of network data gathered from complex multi-part or multi-vendor network functions. It delivers insights for operator-specific workloads to help operators understand the health of their networks and the quality of their subscribers' experiences.”
  • On February 27, Amdocs and Microsoft announced the Intelligent Customer Engagement Platform: “The Customer Engagement Platform will be integrated with Amdocs’ end-to-end set of solutions, from customer experience to monetization products to network automation while fully exploiting the capabilities of Dynamics 365, the Microsoft Power Platform, and the Microsoft Cloud.” It will also leverage Amdocs’ Data and AI solution to “enable cross-domain data input (from network, billing, and transactional data) to be fed in to drive insight-driven recommendations.” When asked how this product relates to Azure Operator Insights, Microsoft said that the Intelligent Customer Engagement Platform focuses on the OSS/BSS domain, while Operator Insights is focused on the infrastructure layer.
  • Also on February 27, Google Cloud announced Telecom Subscriber Insights: “…Telecom Subscriber Insights, a new service designed to help CSPs accelerate subscriber growth, engagement, and retention. It does so by taking insights from CSPs’ existing permissible data sources such as usage records, subscriber plan/billing information, customer relationship management, app usage statistics, and others.”  

These solutions, to varying degrees, collect information from disparate OSS, network devices, billing, and other systems and apply AI/ML processing to correlate events and deliver insights to help improve the customer experience. That two of the announcements came from hyperscalers should not be surprising given the immense processing power that will be needed to support AI/ML at scale—not to mention the analytical tools the hyperscalers have developed for their cloud businesses.

The significance of these announcements

Even without widespread 5G, CSPs are awash with data. More troubling is that even with 5G, revenues are not growing commensurate with investment. All of the mature markets are saturated, so growth mostly comes from poaching others’ customers. Better to extract more from existing customers. Best way to do that is to improve the quality of experience.

CSPs have long had systems to help Customer Service Representatives (CSRs) upsell customers and billing departments manage churn. What’s changed more recently is AI/ML. From a care perspective, AI and ML help systems make recommendations more quickly, more personalized to the subscriber and based on more types of data. AI/ML allow CSPs to do more sophisticated correlation of network and service performance and predictive/proactive maintenance to minimize or altogether avoid service interruptions or degradation.


The challenges of implementing AI/ML-solutions

As with most data-related initiatives, the challenges with implementing AI/ML-driven customer experience solutions relate to data integrity and organizational issues. CSPs have accumulated petabtyes of data over the years, and few, if any, have a good sense for how accurate and complete the data is. The introduction of real-time data into the mix only exacerbates the issue. In some cases, CSPs will ingest data from third party sources, which again, adds a layer of complexity and risk. It almost goes without saying that privacy and data sovereignty issues must be considered anytime subscriber data is in play. And while it is ideal to collect data from numerous sources from across the organization, getting the different departments to agree to share their data is often difficult, and must be mandated from the most senior levels to bring everyone on-board. Indeed, such initiatives need executive sponsorship to ensure compliance and participation. Without that, these projects are unlikely to reach their full potential and deliver maximum value to the business. The journey to a data-driven organization is fraught with challenges and pitfalls, but if planned and managed correctly, it will be more than worth it in the end. The solutions announced at MWC can play an important role in enabling CSPs to reach their goals.

More to come on this topic

Omdia will be writing a report on analytics and AI for customer experience management (CEM) later in 2023. This report will address how analytics and AI are being used to support CEM. It will highlight the use cases that are driving demand for analytics and AI in the CEM domain as well as the suppliers providing CEM solutions that leverage analytics and AI.



Roz Roseboro, Principal Analyst, Service Provider Transformation