Automated Analytics and Multi-level Diagnostics for VoIP and VoLTE Voice Services
NETSCOUT's Automated Analytics Voice applies automation and AI/ML technology to reduce the complexity associated with the problem isolation of next-generation voice services for VoIP and VoLTE. Interoperability challenges centered around handovers, device proliferation, associated software versioning, and coverage increase the amount of information that service providers track when evaluating and assuring their subscribers’ experience with SIP and RTP-based voice service.
A new approach is necessary to effectively manage the daily deluge of data, KPIs and improve focus on and dedication of resources to the performance issues with real business impact.
With Automated Analytics Voice, automation is applied to voice services with modern AI/ML techniques to identify users and populations of users experiencing issues such as registration, setup latency. call attempts, call drops, MOS, and audio gaps, just to name a few. Correlated information about specific populations experiencing variances in performance are categorized as outliers and subjected to next-level computer-based diagnostics.
Resulting information supports guided investigation of the performance issues experienced alongside evaluations of potential contributing sources (RAN, Core, IMS, Handset Type, Software version, etc.). Armed with evidence and localization of problems, service providers can prioritize troubleshooting resources where resolution has the most business impact.
Address Service and Network Complexity
Deliver seamless voice services across multiple domains, network topologies and devices with a solution embedded with decades of VoIP and VoLTE knowledge.
Prioritize the Subscriber Experience
Accelerate understanding of business impacts with visibility to qualify voice service experience and machine learning to highlight performance outliers within a population.
Analyze Millions of Voice Calls
Continuously and automatically monitor and categorize all aspects of voice traffic on the network for at-a-glance evaluation.
React with Purpose and Evidence
Drill-through investigation of reported diagnostics engages in-depth, cross-corelating analysis to report concise findings and relevance for immediate action.
Automated Analytics Voice Understands the Business Impact of Audio Gaps
In addition to network components, other factors such as handset or even the time of day can contribute to a poor voice experience. Automated Analytics Voice is uniquely equipped to account for non-network factors with an embedded ability to consider and evaluate both handset models and software versions in use.
Leveraging the voice service expertise that is evident in ISNGs ability to collect and calculate complex metrics around audio gaps (beginning gap, middle gap, short gaps, gap duration), NETSCOUT’s Automated Analytics Voice identifies the users impacted by audio gaps and then groups them by software version.
Once grouped, Automated Analytics Voice machine learning techniques are applied to tag the impacted users running software versions with performance that deviates from those around them (outlier identification).
Performance outliers undergo next-level, deeper analytics using available data sets (Core and RAN when available) from nGenius Business Analytics to deliver evidence and smart outcomes that localize specific network infrastructure and calling scenarios contributing to reported business impacts.