Re-Defining Call Quality Monitoring and Enhancement with AI Agents

Customer loyalty in telecommunications is largely influenced by the quality of service provided, especially during phone calls. A single poor call experience, whether it's dropped connections, low audio quality, or disruptions, can quickly lead to customer dissatisfaction and increased churn. As the industry becomes more competitive, ensuring optimal call quality is vital for retaining customers and fostering long-term relationships.

The shift towards digital platforms, accelerated by the pandemic, has heightened the need for telecommunications companies to offer exceptional service across all channels. As customer expectations rise, telecom providers now have the opportunity to utilize advanced technologies like artificial intelligence (AI) to monitor and enhance call quality in real-time. AI agents can detect issues, analyze call data, and make improvements instantly, ensuring smooth communication and minimal disruption.

In this blog, we will explore how AI agents are revolutionizing call quality monitoring and enhancement in telecommunications. From real-time problem detection to personalized improvements, discover how Agentic AI is transforming the way telecom companies optimize their services, reduce customer churn, and improve the overall customer experience.

What is Call Quality Monitoring? 

Call Quality Monitoring means the orderly assessment of voice calls for clarity, connection continuity, and overall customer experience feedback. For many years, this process had a major dependence on human monitoring and evaluation which were time-wasting and error-sensitive processes. The situation changed in modern days with the help of AI where evaluation is done even before the calls are completed.  

AI agents make use of machine learning models to assess the data collected from calls and calls to action where abnormalities are present and how to correct such. Such agents may consider a variety of aspects such as audio, speech, background noise, and also emotion to help the organization identify possible problem areas regarding customer experience. 

A Brief Overview of Call Quality Monitoring in Telecom

Call quality monitoring in telecommunications has been transformed by the use of advanced technologies that can process vast amounts of data in real-time. In the past, random sampling was the standard approach, where only a small percentage of calls were reviewed, potentially missing key patterns or issues that could impact the customer experience. Today, automated systems are capable of analyzing thousands of calls in seconds, providing a more comprehensive and accurate view of call performance across the entire network. This enables companies to identify problems faster and resolve them proactively, ensuring consistent service quality.

Along with improving the accuracy of call quality assessments, automation also leads to significant cost savings. The manual process of reviewing and analyzing calls required considerable resources, making it both time-consuming and expensive. By automating these tasks, companies can streamline their operations, reduce human involvement, and improve the overall efficiency of the monitoring process. This approach not only helps cut costs but also allows for a more extensive evaluation, enabling telecom companies to optimize performance and provide a better experience for their customers.

Traditional vs. Agentic AI Call Quality Monitoring

Akira AI: Multi-Agent in Action 

The application of artificial intelligence to monitor call quality is primarily done in the following chronological order.  

1. Data collection: This encompasses the acquisition of the basic audio component of the interaction, including the relevant attributes (e.g., how long the call lasted, what time it took place, the participant signatures) and user engagement with the system. This data is the cornerstone of every analysis and learning in the future.

2. Preprocessing: At this stage, raw audio is enhanced by eliminating unnecessary sounds, calibrating the sound, and making the speaking more audible. The purpose is to the best possible sound quality that can be achieved for the proceeding stages.

3. Analysis: The system applies advanced algorithms on the audio input for DTV and other assessments: supporting speech-to-text technology, the assessment for audio quality (SNR), emotion and sentiment analysis, transcription of the call, and analysis of overall call effectiveness. It also evaluates the call transcription accuracy and call efficiency.

4. Reporting: Results from the analysis are plotted into graphical presentations in dashboards which project patterns, possible improvements, and indices such as customer satisfaction and agent efficiency. These documents summarize the findings for the audience in a clear manner.

5. Feedback Loop: The application improves with every passing day as it improves its previous interactions and modifies its models. This is very beneficial as the A.I. makes changes to the way it does things and the information it uses to do so.

AI agents such as Google Cloud's Speech to Text or Amazon Transcribe are technologies that power this process by providing a high level of call quality analysis and improvement capabilities. 

Applications of AI Agents in Call Quality Monitoring 

• Proactive Network Management: Call data across the telecom network can be continuously monitored to identify potential issues such as network congestion, latency, or signal degradation. By detecting these problems early, telecom providers can address them before they affect customers, ensuring consistent call quality and minimizing disruptions.

• Real-Time Call Quality Analysis: Call quality can be evaluated in real-time, assessing factors like voice clarity, volume levels, and dropped calls. Issues detected during the call can trigger automatic adjustments or alerts to network engineers for immediate troubleshooting, improving the user experience.

• Quality Assurance in VoIP Services: For providers offering VoIP services, call quality can be assessed in terms of jitter, packet loss, and delay. This helps maintain a clear and uninterrupted voice experience, even on internet-based networks that might face bandwidth limitations.

• Compliance and Regulatory Monitoring: Telecom providers must often comply with regulations regarding call quality. Continuous monitoring can ensure that calls meet standards for clarity, latency, and service reliability, helping telecom companies avoid penalties for service failures.

• Churn Prevention: By analyzing call quality patterns, companies can identify when poor service leads to customer frustration. Providers can then intervene with targeted solutions, such as technical fixes or compensation, to reduce churn and maintain customer loyalty.

The Operational Benefits of AI Agents in Call Quality Monitoring 

Employing AI workflow in the process of quality monitoring of calls helps improve operations in several ways: 

• Enhanced Operational Efficiency: Automated call monitoring reduces the time spent on manual reviews, boosting overall operational efficiency by up to 30%. This allows human resources to focus on higher-level tasks such as strategic decision-making and improving customer experiences.

• Faster Issue Resolution: Call quality problems can be detected and addressed much more quickly, improving issue resolution times by up to 25%. This leads to fewer disruptions and a more reliable service, benefiting both customers and the business.

• Better Resource Allocation: By automating routine tasks, employees are freed up to focus on more critical functions, such as customer support or strategic planning. This optimal allocation of resources increases overall productivity and strengthens the company’s performance.

• Improved Customer Experience: The faster and more accurate monitoring process leads to reduced wait times and quicker issue resolution, which enhances the overall customer experience. This results in higher customer satisfaction and stronger loyalty.

• Proactive Quality Assurance: Continuous monitoring enables companies to identify and address issues before they affect customers, preventing disruptions and maintaining high service standards. This proactive approach helps ensure a seamless experience for users.

• Scalability and Flexibility: As telecom providers expand, automated call monitoring systems can scale to handle increased call volumes without compromising service quality, eliminating the need for additional resources or staff.

Technologies Transforming Call Quality Monitoring 

Multiple technologies are central to the idea of improving the quality monitoring of calls through Artificial Intelligence: 

1. NLP (Natural Language Processing): NLP examines patron talk to understand the emotion – tone and the reason for the call – and improve response and routing.

2. Procedural Learning Strategies: These strategies enhance anomaly detection and predictive analytics by remembering things from previous encounters, as well as recognizing odd-looking issues and predicting how the customer will act next.

3. Voice Processing: Speech-to-text and content analysis performance review tools measure the level of accuracy and emotional, compliance of the call content enhancing the understanding of the customer better.

4. Emotion Recognizing tools: These tools determine the emotional state of the customer on the other end, thus helping know how satisfied the customer is and how soon or how much one should respond to the individual depending on their levels of agitation or anger.

5. Internet of Things (IoT): IoT is described as high-capacity data imaging where ingestion, storage, and or processing of the data occurs in seconds allowing better call data analytics and faster service delivery by the organization. 

The Future Trends of Call Quality Monitoring in Telecom 

1. Greater Automation Levels: The need for manual intervention will be reduced, streamlining processes and increasing operational efficiency. Fewer specialists will be required for routine tasks, enabling businesses to allocate resources to more complex or strategic activities.

2. Personalized Customer Engagement: By leveraging real-time customer data, businesses can go beyond just responding to service requests. This enables more personalized interactions, providing tailored feedback and proactive solutions based on up-to-date information about each customer.

3. Enhanced Integration: Automation systems can seamlessly integrate with various software tools, such as CRMs, to streamline customer information management. This integration ensures that data flows smoothly between platforms, improving service delivery and data accuracy.

4. Smarter Insights: Systems will not only track and monitor data but also predict trends and customer behavior. This predictive capability will enable businesses to make informed decisions and anticipate customer needs, ultimately improving strategy and outcomes.

5. User Privacy Protection: With the increasing use of automated systems, protecting customer privacy will become even more critical. Businesses will need to implement robust security measures and ensure transparency in how customer data is handled, maintaining trust while delivering personalized services.
   
   

Conclusion: Call Quality Management in Telecommunications

Effective call quality monitoring is vital for telecom providers aiming to deliver exceptional customer experiences while streamlining operations. As networks become more complex and digital services increase, maintaining clear communication is crucial for customer satisfaction and loyalty. The integration of automated systems and predictive analytics allows for early issue detection and proactive resolution, ensuring continuous service improvement. By investing in advanced call quality monitoring solutions, telecom companies can maintain high service standards, reduce churn, and enhance their competitive position in the market. This approach not only strengthens customer relationships but also helps shape the future of the telecom industry.