A Customer Experience Centric Approach To AI-Driven Service Assurance in 4G/5G Mobile Networks

Achieving an in-depth understanding of 4G/5G mobile network's operational efficiency and its direct impact on customer satisfaction necessitates a multi-faceted approach to data collection, analysis, and integration. This comprehensive approach is pivotal in ensuring that network performance aligns with business objectives and customer expectations.

A Customer Experience Centric Approach To AI-Driven Service Assurance in 4G/5G Mobile Networks

In this blog, we discuss weaving three types of datasets to provide full observability of your services based on customer centricity. This capability helps service providers minimize outages and ensure their customers are getting the best digital experience.

Firstly, Network Elements KPIs, Alarms & Configurations - The journey begins with an extensive gathering of data from a variety of network elements. This stage is critical and involves collecting a wide array of Key Performance Indicators (KPIs), metrics, alarms, and configurations from the network infrastructure ranging from the eNodeB, gNodeB, Mobile Backhaul (Microwave Nodes or Optical-Line Terminals), IP Backhaul and Core as well as Mobile Core network elements. This foundational data collection serves as the backbone for understanding the basic operational status of the network and identifying potential issues that might disrupt performance. It provides a preliminary but limited view of the network's capability to support services and customer needs. However, to fully grasp the network's operational effectiveness and its impact on business success, merely collecting this data is insufficient.

Secondly, Service Level KPIs & Alarms - The collected data is then enriched with critical end-to-end service level KPIs. This enrichment process specifically targets 4G/5G service level metrics that have a direct impact on customer experience. 4G/5G Service level KPIs categories, as classically defined (Accessibility, Retainability, Availability, Mobility, Integrity & Utilization) shed light on how effectively the network supports these vital business operations, providing insights into areas where network performance directly influences the organization's revenue streams. This step enhances the analysis from basic network functionality to understanding its role in impacting the service performance.

Finally, Customer Experience Metrics - Incorporating customer experience metrics into the network performance analysis elevates the insights to a new level that is defined by customer centricity. This integration is accomplished by ingesting Call Detail Records (CDRs) on a large scale, whether from Deep Packet Inspection (DPI) or Network Probes. CDRs offer a granular view of service usage and customer interactions with the network, enabling a detailed analysis of individual experiences. Correlating this customer-centric data with the previously collected network and service performance metrics allows operators to pinpoint specific service quality issues affecting customer satisfaction. This targeted approach facilitates more effective problem resolution, enhancing customer experience and loyalty.

The GreySkies AI-Driven Service Assurance platform intelligently blends, enriches and standardizes disparate data sets, creating a comprehensive foundation that propels customer-centric service assurance within AIOps framework. This starts with the platforms’ ability to infer and deduce the network and service topology for the 4G/5G mobile data services. Such topologies are not only visualizable but also allow for individual network or service elements to be accentuated with their respective metrics and KPIs, complemented by extensive, operator-specific drilldowns. A particularly strong application of this functionality is the visualization of per-customer QoE metrics, especially useful for diagnosing issues in customers whose transactions pass through these network or service elements

While many platforms offer detailed insights into customer quality of experience (QoE), they often stop short of providing actionable intelligence. The question then arises, "What's next? Which service or network element is causing the QoE degradation?" The GreySkies platform excels here, merging customer experience data with network and service KPIs, thus enabling operators to pinpoint, for any customer and time frame, the exact network and service elements involved in their transactions. This integration is vividly demonstrated through the Dynamic Topology widget of GreySkies.

By unifying these elements, the GreySkies AIOps platform facilitates rapid correlation between customer QoE downturns and network or service KPI degradations, thereby pinpointing the root cause swiftly to prevent customer impact.

Are you ready to prioritize customer centricity in Service Assurance with GreySkies? Contact us today to get started!