If you have filled even a single role of operations and engineering in the telecom industry, you know it can get quite complex.
These roles are crucial to ensure the smooth operation and development of telecommunication networks and services by fulfilling network design and architecture, network monitoring and maintenance, customer support, capacity planning, quality assurance, emergency response, prototyping and testing, research and development, implementation of new technologies, and more.
All these areas require reliable data and thus robust voice monitoring is becoming the industry’s linchpin, ensuring seamless performance and meeting user expectations. It’s clear that the industry needs to vigilantly monitor VoIP services, with the understanding that issues in this sphere can impact the bottom line.
With this growing need for visibility, I decided to ask several leading telecom professionals— extremely experienced ones, people whose opinions I highly value—for their insights on effective VoIP monitoring. Here’s what they had to say (plus my personal two cents at the end).
Khalil Ayman Badwan, Project Engineer/Digital Platforms & Customer Experience
Khalil is an experienced solution/system engineer working in service delivery, project delivery, 3G, 4G, 5G and cloud computing, and AI. He has been working on network intelligence and network analytics since early 2016, with a rich experience in network protocols. This is what he chose to share with us:
From what I’ve seen, it’s super important for any service provider to have and collect insights about their network performance. Having said that, bear in mind that traditional Network Management (NM) tools are very limited when it comes to providing such details.
In terms of data you have around call quality, it should allow you to get to the root cause of problems. It’s essential to collect the exact metrics and counters that describe the issue and the reason behind it. This capability allows the team to identify both the issue and the solution. Unfortunately, ‘generic’ voice monitoring solutions are simply not enough; it can’t be enough to have insight on each aspect or case faced by the subscriber and internally in the network.
Many industry players are switching their voice services to the cloud. It’s very important to have cloud compatibility since cloud computing is the core technology for modern network architecture nowadays. Having such a step will accommodate various options and capabilities to expand our services. Whether it’s on the cloud or a classic physical network, the most important characteristic of a network is to be visible and monitored at all times.
The most important voice service KPIs to keep track of, in my opinion, are call drop ratio, call setup ratio, Mean Opinion Score (MOS), packet drop, packet loss, and jitter.
Silent calls/one-way-audio is definitely one of the problems my colleagues and I have to deal with the most. In my opinion, the ideal setup is to have a functioning two-directional channel. The second best action is to terminate the call and let the call be re-established between the customers again.
When it comes to VoIP signaling and media monitoring, it’s crucial to care about both and not only one of the two. Let’s assume that your media plane is a bus carrying passengers (which is your VoIP subscriber media) and the signaling plane is the route that the bus is supposed to take to deliver those passengers so both are very critical. The quality of the bus alone is not sufficient if you can’t ensure the quality of the road.
I’m often asked what kind of voice quality information I should expect to get from a voice monitoring solution. My answer to this question is very simple. Make sure it offers:
Prof. Holub’s research interests span various aspects of Measurement Technology, with a focus on subjective quality, intelligibility, and usability testing. He has been an active member of several professional organizations and has chaired organizing and program committees for numerous conferences. In addition to his research pursuits, he has shared his knowledge and expertise through teaching various courses at CTU. He shared the following with us:
While there are many angles to VoIP monitoring I can discuss and hopefully give a piece of useful advice on, I chose to focus my two cents for the telecom community on the role I see for monitoring data in future AI/ML-driven service automation.
I see its potential in multiple domains, primarily in predictive maintenance, dynamic resource allocation, and network security, but also in single-user-tailored QoE.
AI and ML algorithms can analyze RTP data to assess the quality of voice and video streams in real time. By continuously monitoring RTP metrics like jitter, packet loss, and latency, AI can detect performance issues and adjust network configurations or routing to optimize QoS. Also, AI can use historical RTP data to predict when network or service issues are likely to occur. By identifying patterns in RTP metrics that precede high-load periods or even service disruptions, AI can dynamically allocate resources like bandwidth or processors, optimize processes like voice codec or video compression, or even trigger preventive measures or automated maintenance tasks to avoid or at least minimize downtimes. When RTP issues occur, AI-driven systems can automatically perform root cause analysis. By correlating RTP metrics with network configurations and other data sources, AI can rapidly identify the source of problems and suggest corrective actions. AI-based systems that detect anomalous traffic patterns or security threats by monitoring RTP data are already in place. These systems already not only generate alarms but directly respond with security measures.
One could say that all these applications were already available in existing monitoring systems, but the major benefit of AI/ML deployment is opening the possibility of continuous improvement and automated self-learning by using RTP data to identify areas for enhancement in real-time communication services, leading to ongoing service optimization.
Another fruitful AI application is perhaps user behavior and preferences analysis based on tracked user interactions and/or sentiment analysis from audio and video data where patterns acquired from RTP monitoring play an essential role. Also here AI can provide personalized recommendations or features to enhance the user experience.
Liju Mathew, Director—Solutions & Systems
With over 15 years of professional experience in the telecom industry, Liju has a robust background spanning various sectors, including manufacturing, software development, and telecom service provision in 2G, 3G, LTE, and NGN technologies. His expertise encompass a deep understanding of signaling, switching, network planning, billing systems, revenue assurance, service provisioning, network management, service costing, OSS/BSS, risk assessment, and mitigation. Here is what he told us:
It is critical to have the ability to generate data-driven insights through your voice monitoring solution. Here’s why: Voice over 4G and 5G technologies will remain one of the main services offered to the customers and it is critical and very important to be able to monitor and measure the technical parameters that influence and impact the setup, call quality, speech impairments like noise, silence, one-way call, etc. Effective monitoring of the signaling and RTP media is required to ensure top-quality voice services demanded by your customers.
It is also very important to make sure the data you have around call quality allows you to get to the root cause of problems. The first thing is to detect that there is poor quality in voice service experienced by the customers. With effective monitoring of different legs of the network (access, core) and with proper knowledge of the network architecture and flow of the traffic within the network it is possible to determine the source of quality issues and conclude the root cause analysis.
The most important voice service KPIs I highly recommend you keep track of are latency and packet loss potentially resulting in MOS value degradation, one way speech, and silent calls, as those are the ones that are impacting your customers’ experience the most.
Unfortunately, with Voice over IP networks in 4G and 5G mobile networks, the generic signalling-based voice monitoring solutions are not enough and it is absolutely necessary to invest in media quality monitoring solutions.
Livio Pogliano, Head of Roaming & Carrier Operations
With 28 years of experience within global telecommunications companies, Livio is a highly skilled and accomplished executive known for pioneering new technologies, conceptualizing innovative services, and advocating continuous efficiency enhancements. A proactive change agent, he specializes in implementing innovative strategies that optimize resource allocation while upholding cutting-edge quality benchmarks. According to him:
In a world where IP-protocol is driving many types of communications from data to voice, it becomes very important to have a clear and detailed description of the service perceived by the customer, and it has to be possible to do that in real-time. Service monitoring moves to the next level where it is not enough to verify that network nodes are up and running, but multiple sets of KPIs can provide the real description and potentially create “service alarms.”
A wider and open approach is required to create KPIs and to potentially detect subjective effects, such as voice call quality, in an objective way. Great effort is required to have a consistent and accepted-by-standards Mean Opinion Score algorithm to verify that traffic quality can reach good levels for the human ears. It means that working on media contents, and not just IP signaling, is needed, taking always into account privacy constraints.
Moreover, migrating the infrastructure into the Cloud increases the focus on service performance for Operations teams, driving engineers to a new learning phase in their careers.
At last, all these service evolutions and new requests are highly demanding when it comes to computation and analytical algorithms: a strong knowledge of the service implementation and technical architecture is needed to create the right and customizable tools.
This future-oriented and high expertise is really the key factor for the definition and choice of providers for voice-over-IP service measurement systems.
Markus Monka, Hacking telco since 2003
Our long-time customer and friend Markus from sipgate GmbH told us this:
Generating data-driven insights through a voice monitoring solution is paramount to our continuous improvement in the telecommunications industry. These insights allow us to spot trends, pinpoint quality issues, and proactively enhance customer satisfaction. This data-driven approach is pivotal to our service optimization.
Our ability to track problems down to their root causes is made possible by the wealth of call-quality data at our disposal. It’s not just about addressing surface-level issues; it’s about eradicating underlying causes for sustainable service quality and minimal disruptions.
As more industry players migrate their voice services to the cloud, careful consideration is required. Robust voice monitoring becomes essential to ensure the performance of cloud-hosted services meets expectations.
In my opinion, key voice service KPIs to monitor include call quality, delay, jitter, packet loss, and call drop rates. These metrics are crucial for ensuring smooth communication and customer satisfaction.
Both VoIP signaling and media monitoring deserve attention as problems at either level can affect service quality. A comprehensive approach that considers both aspects is advisable.
Silent calls and one-way audio are serious issues affecting customer satisfaction. Vigilant monitoring and testing are crucial for early detection and swift resolution of such problems to maintain uninterrupted communication.
From a Voice Monitoring solution, we expect detailed information on call quality, including MOS scores, packet loss, jitter, and latency. This data is instrumental in our continuous quality enhancement efforts.
Michael Wallbaum (that’s me), Director of Product Marketing
What can I add to the insights of the seasoned telco professionals who contributed to this article? In 1999 I was part of a research team that demonstrated voice and data transmission over an HSCSD mobile data connection at the Telecom ‘99 in Geneva. Back then, voice over IP was new and exciting, but also flakey and we were happy for every demo that went without a glitch. A long time has passed since and the telco industry has now almost completed its transition to IP. Any form of “glitch” is no longer acceptable as we all rely on phone services for our daily lives. Still, everyone has experienced the inability to set up a call, call drops, silent calls, or simply bad voice quality. How can this happen and why does it often take so long to fix?
Having worked with many different CSPs I would argue that the main reason for today’s “glitches” is lack of information in many different forms. It’s not that there is a lack of data—quite the opposite. There are, however, issues with the data itself and how it’s organized and processed.
First of all, while CSPs have tons of data, it is often inadequate. Traditionally, there is a strong focus on signaling performance monitoring, but the in-call user experience is frequently neglected. Still, every monitoring solution vendor and CSP will claim that “MOS is available,” but few actually understand (or admit) that this media quality information is not accurate, because it is not obtained from direct measurements of the RTP streams. One symptom of this data quality issue, is that only in 2019 ETSI proposed a set of timeslice media quality KPIs in TR 103 639 to complement well-known signaling KPIs. Only now is it possible to calculate media quality for calls, trunks, routes and other aggregates in a meaningful and standardized way.
Secondly, even if information on the voice service performance is accurate, comprehensive and reliable, all too often relevant data is not accessible to all stakeholders. Data is still stored in silos, defined either by the licensing and export policies of vendors or by organizational boundaries. Typically, the larger the CSP the more fragmented the data space. Identifying the stakeholders and putting all the puzzle pieces together can be cumbersome and time-consuming, which greatly adds to the stress of incident management. Exporting all available data into a so-called data lake is the recommended approach to break down the silos, but I have yet to see more than measly data ponds.
Lastly, even when all the data is accessible, relevant and accurate, there is a lack of tools and best practices helping to make sense of the vast amounts of data, in addition to a lack of human resources caused by low margins and stiff competition in the telecommunications market. Artificial intelligence and machine learning technology are promising to automate the process of filtering, correlating and analyzing the available data to speed up root cause analysis and other operational tasks. The holy grail is zero-touch automation (ZTO), i.e., fully automated voice service management. However, without reliable data from many different sources ZTO remains a pretty bold vision.
To wrap it up: the industry needs to focus on the quality and accessibility of data, to enable more automation leading to better service quality.
Final Words
In the ever-evolving landscape of telecommunications, industry leaders have shared invaluable insights concerning the complexities, necessities, and benefits of effective VoIP monitoring. These professionals, with years of experience under their belts, emphasize the importance of generating data-driven insights.
As professionals deeply entrenched in the industry, we now all recognize that voice monitoring goes beyond the conventional checks of network node functionality; it should delve into the intricate details of the service quality experienced by our users, examining issues in both VoIP signaling and media quality. Generating insights through comprehensive data analysis is not merely a best practice; it’s the cornerstone of service optimization strategies.
We should meticulously track an array of key performance indicators, from call quality and delay to jitter, packet loss, and call drop rates. By deciphering this data, we not only address surface-level issues but also uncover the root causes, ensuring sustained service quality and enhancing customer satisfaction. On top of that we can lay the foundation for trustworthy service automation technologies.
Through continuous monitoring efforts, we can swiftly identify and resolve challenges such as silent calls and one-way audio, maintaining uninterrupted communication for our users. Voice monitoring solutions available in the market should all provide us with detailed insights, including MOS scores, packet loss, jitter, and latency, enabling us to drive continuous quality enhancement initiatives.
In this transformative landscape, our commitment to accurate monitoring and proactive resolution remains unwavering, ensuring superior user experiences and driving the future of telecommunications.
These roles are crucial to ensure the smooth operation and development of telecommunication networks and services by fulfilling network design and architecture, network monitoring and maintenance, customer support, capacity planning, quality assurance, emergency response, prototyping and testing, research and development, implementation of new technologies, and more.
All these areas require reliable data and thus robust voice monitoring is becoming the industry’s linchpin, ensuring seamless performance and meeting user expectations. It’s clear that the industry needs to vigilantly monitor VoIP services, with the understanding that issues in this sphere can impact the bottom line.
With this growing need for visibility, I decided to ask several leading telecom professionals— extremely experienced ones, people whose opinions I highly value—for their insights on effective VoIP monitoring. Here’s what they had to say (plus my personal two cents at the end).
Khalil Ayman Badwan, Project Engineer/Digital Platforms & Customer Experience
Khalil is an experienced solution/system engineer working in service delivery, project delivery, 3G, 4G, 5G and cloud computing, and AI. He has been working on network intelligence and network analytics since early 2016, with a rich experience in network protocols. This is what he chose to share with us:
From what I’ve seen, it’s super important for any service provider to have and collect insights about their network performance. Having said that, bear in mind that traditional Network Management (NM) tools are very limited when it comes to providing such details.
In terms of data you have around call quality, it should allow you to get to the root cause of problems. It’s essential to collect the exact metrics and counters that describe the issue and the reason behind it. This capability allows the team to identify both the issue and the solution. Unfortunately, ‘generic’ voice monitoring solutions are simply not enough; it can’t be enough to have insight on each aspect or case faced by the subscriber and internally in the network.
Many industry players are switching their voice services to the cloud. It’s very important to have cloud compatibility since cloud computing is the core technology for modern network architecture nowadays. Having such a step will accommodate various options and capabilities to expand our services. Whether it’s on the cloud or a classic physical network, the most important characteristic of a network is to be visible and monitored at all times.
The most important voice service KPIs to keep track of, in my opinion, are call drop ratio, call setup ratio, Mean Opinion Score (MOS), packet drop, packet loss, and jitter.
Silent calls/one-way-audio is definitely one of the problems my colleagues and I have to deal with the most. In my opinion, the ideal setup is to have a functioning two-directional channel. The second best action is to terminate the call and let the call be re-established between the customers again.
When it comes to VoIP signaling and media monitoring, it’s crucial to care about both and not only one of the two. Let’s assume that your media plane is a bus carrying passengers (which is your VoIP subscriber media) and the signaling plane is the route that the bus is supposed to take to deliver those passengers so both are very critical. The quality of the bus alone is not sufficient if you can’t ensure the quality of the road.
I’m often asked what kind of voice quality information I should expect to get from a voice monitoring solution. My answer to this question is very simple. Make sure it offers:
- An end-to-end analysis capability as well as packet status (transport, loss, delivered, etc.)
- Call information (termination, release cause, drop rate, setup time, etc.)
- How would I know exactly what the actual customer experience is (as a real value) using your solution?
- How would you help me maximize and increase my customers’ experience?
- How can we prevent issues and problems from happening in the first place, using your tool?
Prof. Holub’s research interests span various aspects of Measurement Technology, with a focus on subjective quality, intelligibility, and usability testing. He has been an active member of several professional organizations and has chaired organizing and program committees for numerous conferences. In addition to his research pursuits, he has shared his knowledge and expertise through teaching various courses at CTU. He shared the following with us:
While there are many angles to VoIP monitoring I can discuss and hopefully give a piece of useful advice on, I chose to focus my two cents for the telecom community on the role I see for monitoring data in future AI/ML-driven service automation.
I see its potential in multiple domains, primarily in predictive maintenance, dynamic resource allocation, and network security, but also in single-user-tailored QoE.
AI and ML algorithms can analyze RTP data to assess the quality of voice and video streams in real time. By continuously monitoring RTP metrics like jitter, packet loss, and latency, AI can detect performance issues and adjust network configurations or routing to optimize QoS. Also, AI can use historical RTP data to predict when network or service issues are likely to occur. By identifying patterns in RTP metrics that precede high-load periods or even service disruptions, AI can dynamically allocate resources like bandwidth or processors, optimize processes like voice codec or video compression, or even trigger preventive measures or automated maintenance tasks to avoid or at least minimize downtimes. When RTP issues occur, AI-driven systems can automatically perform root cause analysis. By correlating RTP metrics with network configurations and other data sources, AI can rapidly identify the source of problems and suggest corrective actions. AI-based systems that detect anomalous traffic patterns or security threats by monitoring RTP data are already in place. These systems already not only generate alarms but directly respond with security measures.
One could say that all these applications were already available in existing monitoring systems, but the major benefit of AI/ML deployment is opening the possibility of continuous improvement and automated self-learning by using RTP data to identify areas for enhancement in real-time communication services, leading to ongoing service optimization.
Another fruitful AI application is perhaps user behavior and preferences analysis based on tracked user interactions and/or sentiment analysis from audio and video data where patterns acquired from RTP monitoring play an essential role. Also here AI can provide personalized recommendations or features to enhance the user experience.
Liju Mathew, Director—Solutions & Systems
With over 15 years of professional experience in the telecom industry, Liju has a robust background spanning various sectors, including manufacturing, software development, and telecom service provision in 2G, 3G, LTE, and NGN technologies. His expertise encompass a deep understanding of signaling, switching, network planning, billing systems, revenue assurance, service provisioning, network management, service costing, OSS/BSS, risk assessment, and mitigation. Here is what he told us:
It is critical to have the ability to generate data-driven insights through your voice monitoring solution. Here’s why: Voice over 4G and 5G technologies will remain one of the main services offered to the customers and it is critical and very important to be able to monitor and measure the technical parameters that influence and impact the setup, call quality, speech impairments like noise, silence, one-way call, etc. Effective monitoring of the signaling and RTP media is required to ensure top-quality voice services demanded by your customers.
It is also very important to make sure the data you have around call quality allows you to get to the root cause of problems. The first thing is to detect that there is poor quality in voice service experienced by the customers. With effective monitoring of different legs of the network (access, core) and with proper knowledge of the network architecture and flow of the traffic within the network it is possible to determine the source of quality issues and conclude the root cause analysis.
The most important voice service KPIs I highly recommend you keep track of are latency and packet loss potentially resulting in MOS value degradation, one way speech, and silent calls, as those are the ones that are impacting your customers’ experience the most.
Unfortunately, with Voice over IP networks in 4G and 5G mobile networks, the generic signalling-based voice monitoring solutions are not enough and it is absolutely necessary to invest in media quality monitoring solutions.
Livio Pogliano, Head of Roaming & Carrier Operations
With 28 years of experience within global telecommunications companies, Livio is a highly skilled and accomplished executive known for pioneering new technologies, conceptualizing innovative services, and advocating continuous efficiency enhancements. A proactive change agent, he specializes in implementing innovative strategies that optimize resource allocation while upholding cutting-edge quality benchmarks. According to him:
In a world where IP-protocol is driving many types of communications from data to voice, it becomes very important to have a clear and detailed description of the service perceived by the customer, and it has to be possible to do that in real-time. Service monitoring moves to the next level where it is not enough to verify that network nodes are up and running, but multiple sets of KPIs can provide the real description and potentially create “service alarms.”
A wider and open approach is required to create KPIs and to potentially detect subjective effects, such as voice call quality, in an objective way. Great effort is required to have a consistent and accepted-by-standards Mean Opinion Score algorithm to verify that traffic quality can reach good levels for the human ears. It means that working on media contents, and not just IP signaling, is needed, taking always into account privacy constraints.
Moreover, migrating the infrastructure into the Cloud increases the focus on service performance for Operations teams, driving engineers to a new learning phase in their careers.
At last, all these service evolutions and new requests are highly demanding when it comes to computation and analytical algorithms: a strong knowledge of the service implementation and technical architecture is needed to create the right and customizable tools.
This future-oriented and high expertise is really the key factor for the definition and choice of providers for voice-over-IP service measurement systems.
Markus Monka, Hacking telco since 2003
Our long-time customer and friend Markus from sipgate GmbH told us this:
Generating data-driven insights through a voice monitoring solution is paramount to our continuous improvement in the telecommunications industry. These insights allow us to spot trends, pinpoint quality issues, and proactively enhance customer satisfaction. This data-driven approach is pivotal to our service optimization.
Our ability to track problems down to their root causes is made possible by the wealth of call-quality data at our disposal. It’s not just about addressing surface-level issues; it’s about eradicating underlying causes for sustainable service quality and minimal disruptions.
As more industry players migrate their voice services to the cloud, careful consideration is required. Robust voice monitoring becomes essential to ensure the performance of cloud-hosted services meets expectations.
In my opinion, key voice service KPIs to monitor include call quality, delay, jitter, packet loss, and call drop rates. These metrics are crucial for ensuring smooth communication and customer satisfaction.
Both VoIP signaling and media monitoring deserve attention as problems at either level can affect service quality. A comprehensive approach that considers both aspects is advisable.
Silent calls and one-way audio are serious issues affecting customer satisfaction. Vigilant monitoring and testing are crucial for early detection and swift resolution of such problems to maintain uninterrupted communication.
From a Voice Monitoring solution, we expect detailed information on call quality, including MOS scores, packet loss, jitter, and latency. This data is instrumental in our continuous quality enhancement efforts.
Michael Wallbaum (that’s me), Director of Product Marketing
What can I add to the insights of the seasoned telco professionals who contributed to this article? In 1999 I was part of a research team that demonstrated voice and data transmission over an HSCSD mobile data connection at the Telecom ‘99 in Geneva. Back then, voice over IP was new and exciting, but also flakey and we were happy for every demo that went without a glitch. A long time has passed since and the telco industry has now almost completed its transition to IP. Any form of “glitch” is no longer acceptable as we all rely on phone services for our daily lives. Still, everyone has experienced the inability to set up a call, call drops, silent calls, or simply bad voice quality. How can this happen and why does it often take so long to fix?
Having worked with many different CSPs I would argue that the main reason for today’s “glitches” is lack of information in many different forms. It’s not that there is a lack of data—quite the opposite. There are, however, issues with the data itself and how it’s organized and processed.
First of all, while CSPs have tons of data, it is often inadequate. Traditionally, there is a strong focus on signaling performance monitoring, but the in-call user experience is frequently neglected. Still, every monitoring solution vendor and CSP will claim that “MOS is available,” but few actually understand (or admit) that this media quality information is not accurate, because it is not obtained from direct measurements of the RTP streams. One symptom of this data quality issue, is that only in 2019 ETSI proposed a set of timeslice media quality KPIs in TR 103 639 to complement well-known signaling KPIs. Only now is it possible to calculate media quality for calls, trunks, routes and other aggregates in a meaningful and standardized way.
Secondly, even if information on the voice service performance is accurate, comprehensive and reliable, all too often relevant data is not accessible to all stakeholders. Data is still stored in silos, defined either by the licensing and export policies of vendors or by organizational boundaries. Typically, the larger the CSP the more fragmented the data space. Identifying the stakeholders and putting all the puzzle pieces together can be cumbersome and time-consuming, which greatly adds to the stress of incident management. Exporting all available data into a so-called data lake is the recommended approach to break down the silos, but I have yet to see more than measly data ponds.
Lastly, even when all the data is accessible, relevant and accurate, there is a lack of tools and best practices helping to make sense of the vast amounts of data, in addition to a lack of human resources caused by low margins and stiff competition in the telecommunications market. Artificial intelligence and machine learning technology are promising to automate the process of filtering, correlating and analyzing the available data to speed up root cause analysis and other operational tasks. The holy grail is zero-touch automation (ZTO), i.e., fully automated voice service management. However, without reliable data from many different sources ZTO remains a pretty bold vision.
To wrap it up: the industry needs to focus on the quality and accessibility of data, to enable more automation leading to better service quality.
Final Words
In the ever-evolving landscape of telecommunications, industry leaders have shared invaluable insights concerning the complexities, necessities, and benefits of effective VoIP monitoring. These professionals, with years of experience under their belts, emphasize the importance of generating data-driven insights.
As professionals deeply entrenched in the industry, we now all recognize that voice monitoring goes beyond the conventional checks of network node functionality; it should delve into the intricate details of the service quality experienced by our users, examining issues in both VoIP signaling and media quality. Generating insights through comprehensive data analysis is not merely a best practice; it’s the cornerstone of service optimization strategies.
We should meticulously track an array of key performance indicators, from call quality and delay to jitter, packet loss, and call drop rates. By deciphering this data, we not only address surface-level issues but also uncover the root causes, ensuring sustained service quality and enhancing customer satisfaction. On top of that we can lay the foundation for trustworthy service automation technologies.
Through continuous monitoring efforts, we can swiftly identify and resolve challenges such as silent calls and one-way audio, maintaining uninterrupted communication for our users. Voice monitoring solutions available in the market should all provide us with detailed insights, including MOS scores, packet loss, jitter, and latency, enabling us to drive continuous quality enhancement initiatives.
In this transformative landscape, our commitment to accurate monitoring and proactive resolution remains unwavering, ensuring superior user experiences and driving the future of telecommunications.
