How AI Call Analytics Improves Telecom Customer Experience
Discover how Verbix.AI call analytics and VitalPBX integration can help telecom providers improve call quality, identify recurring issues, strengthen QA, and enhance customer experience.
Turning Telecom Support Calls into Actionable Insights
Every customer interaction is an opportunity for telecommunications and internet service providers to deliver top service quality, address technical problems and instill confidence.
Rechargeable broadband off hours, sluggish internet speeds, constantly recurring troubleshooting, and inconsistent support experiences can irritate customers and boost support teams' workloads.
Verbix.AI provided an array of AI-powered tools for telecommunications companies to understand this by leveraging call transcription as well as sentiment analysis, keyword detection, automatic quality scoring, and smart call summaries.
When combined with traditional telephone features as with VitalPBX, these capabilities will help support teams turn recorded conversations into actionable insights identifying common customer problems and bettering the delivery of services.
A case study that analyses how and when AI call analytics can be used to support telecommunications customer experience and quality assurance.
Identifying Customer Problems Hidden in Support Calls
Telecom support teams take on a number of customer issues, ranging from broadband connectivity to Wi-Fi performance issues to VoIP service outages to billing the investigation of technical problems.
Individual agents can – and often do – resolve these issues, but it is much harder to discern patterns across hundreds of calls without systematic analysis.
Common challenges include:
Limited visibility into customer sentiment
Managers may be unaware of which interactions cause customers to become frustrated or unhappy.
Time-consuming quality reviews
Quality teams can only evaluate so many conversations with the feeble method of listening to recorded calls.
Recurring technical issues
Complaints that are similar but not identified as a shared issue may surface during several calls.
Repeated customer contacts
Sometimes customers will have to call again when the original problem is not completely resolved.
Inconsistent agent performance
Support methods and troubleshooting practices throughout workers can have many differences.
Limited customer experience insights
CSAT and NPS feedback may not shed light on the factors contributing customers either to satisfaction or dissatisfaction.
This is one way in which customer support can become reactive. Teams may identify the root of a broader problem too late because of the delays involved, when complaints rise, when more repeat calls start to come in or when customers start to think of switching operators.
Verbix.AI Call Analytics with VitalPBX Integration
Verbix.AI assists in turning customer conversations into searchable transcripts, insights that are organized, and quality indicators which can help managers improve support operations.
Its integration with the recognized processes of regards to the telephony environments we have set up (and for those VitalPBX based services which have been configured), assists enterprises in reviewing the recorded calls instead of having to depend entirely on a laborious manual review.
Automated Call Transcription
Verbix.AI translates recorded customer conversations into searchable text.
Support managers were able to easily see what the customer reported, which troubleshooting steps an agent followed and whether the resolution or next steps were clearly explained.
This allows one to look at previous interactions and identify opportunities to improvement service procedures.
Keyword Analysis for Recurring Telecom Issues
Using AI to analyze keywords and topics as well, teams can pinpoint conversations around recurring technical and service issues, for example:
By looking at calls with some patterns, managers can appreciate whether complaints are isolated incidents or underlying operational issues.
Customer Sentiment Analysis
By applying sentiment analysis, you can identify conversational patterns as positive, neutral, or negative.
Managers can review calls where frustration escalates to see what led to valuable customer dissatisfaction along with possible situations where they should do any more work.
This can relate operational metrics to uncover support experiences which are likely to need attention.
Automated Quality Assurance and Agent Scoring
Verbix.AI is an automated quality monitoring against defined evaluation criteria solution.
The framework for scoring teams around the world may differ, but they can evaluate things like:
Automated scoring can expand quality monitoring to more than just a small roll call, and is useful for supervisors who want to identify opportunities for coaching or to assess performance more evenly.
Intelligent Call Summaries
The AI-generated summaries for customer concern, actions taken and the agreed next steps.
These summaries can reduce the time spent on reviewing conversations and lighten the cognitive load for support people trying to understand past interactions, subject to appropriate access to relevant call records and workflow integration.
Customer Satisfaction and Loyalty Insights
By correlating call analytics with CRM records, customer survey responses, complaint histories, and support outcomes, businesses can identify how customers are reporting positive or negative experiences.
For instance, an example of such would be, a low CSAT score basically meaning the customer has repeated troubleshooting, communication is unclear, or an escalation is pending due to a failure in the service.
Sentiment analysis is not a customer satisfaction or a loyalty score on its own. It is precise to say customer satisfaction and loyalty scores derived from relevant surveys or verified measurement processes.
Illustrative Business Impact: Measuring the Potential Results
The following example illustrates how one could use a telecommunications provider to measure the influence of the introduction of an AI call analytics system and use the resulting insights to refine support processes.
| Performance Indicator | Before | After | Relative Improvement |
|---|---|---|---|
| Customer Satisfaction (CSAT) | 72% | 84% | 17% increase |
| First Contact Resolution | 65% | 78% | 20% increase |
| Repeat Customer Contacts | 30% | 21% | 30% reduction |
| Average Call Handling Time | 8.5 min | 7.2 min | 15% reduction |
| Quality Assurance Score | 70% | 88% | 26% increase |
| Customer Complaint Rate | 12% | 8% | 33% reduction |
| Manual Call Review Effort | 40 hrs/week | 16 hrs/week | 60% reduction |
| Calls Reviewed for Quality | 5% | 95% | Expanded coverage |
All figures are fictitious, not Launtel results. Relative percentage change is calculated in the example using the starting values. NPS is omitted because this table is calculated from starting values until you obtain a relative percentage improvement — you should probably report this as a point change (NPS). Replace these examples with real operational data to get accurate figures presented as results.
How AI Call Analytics Can Improve Telecom Operations
Better Customer Satisfaction
Reviewing customer conversations: Manager can identify issues people repeatedly have talking and how they say things. Solving critical issues makes customers experience a more consistent process.
Improved First Contact Resolution
Call analysis can show where agents need more troubleshooting guidance, product knowledge, or escalation procedures. Focused improvements can reduce the number of customer problems that can be solved on the first contact.
Reduced Repeat Calls
Identifying patterns of recurring complaints and issues left outstanding helps teams answer why customers keep contacting support. Enhancements to troubleshooting and follow-up processes may reduce avoidable duplicate touchpoints.
More Efficient Quality Assurance
Automated transcription and scoring allows for someone to do less reviewing of recorded conversations. Supervisors can focus more on investigating exceptions, coaching agents and improving support procedures.
Stronger Insights from VitalPBX Call Data
Customers who use business VitalPBX integration solutions, can provide a more solid base for evaluating conversations with customers that uses call analytics when paired with telephony records. The usefulness of implementation, access to call recordings and associated metadata, and the quality of analytics workflow, will determine the practical value.
Better Customer Retention Insights
Combining call insights, customer feedback, complaint histories, and cancellation data can identify service experiences that are associated with the likelihood of churn.
These findings provide management teams with a firmer footing to explore customer dissatisfaction and prioritize improvements.
From Conversation Insights to Continuous Improvement
Success when insights from AI call analytics drive operational changes through delivery of measurable value.
This might help a telecommunications provider establish a cycle of continual improvement:
Identify
Collect feedback concerning connectivity (Wi-Fi or cable), voice quality, or support handling.
Investigate
Transcripts, sentiment trends, or calls chosen as an audit if relevant.
Improve
Update troubleshooting instructions, escalation procedures, or agent coaching.
Measure
Changes in repeat-contact rates, quality scores, CSAT, and resolution rates before and after the adjustment.
Optimise
Keep monitoring conversations to see if the intervention is having an impact.
Why Choose Verbix.AI for Telecommunications Call Analytics?
Verbix.AI assists telecommunications and contact centre members in getting more out of their customer conversations through:
For telecom providers, it's not just a matter of funneling more calls through analytical tools. It is earlier solutions for customer plight, maintaining consistency support, resulting in more informed operational decisions.
Customer Perspective
“Verbix.AI is powered by our VTL integration solutions, which allow us to bring the telephone industry support into the call analytics world. Below, take a look at how conversational insights can quickly provide context around any customer interaction and help you identify opportunities for improving the quality of calls and more consistently achieving service delivery. ”
— Matthew Cettolin, Customer Support
Frequently Asked Questions
Clear answers to help telecommunications providers evaluate AI call analytics and VitalPBX integration.
AI call analytics is the use of call analytics, and it utilizes technologies like speech recognition, natural language processing and automated scoring to evaluate customer support calls. For telecom operators, it can help them find out the most common issues they face, measure the quality of service, and gauge customer sentiment.
It can identify an ongoing source of pain, communication gaps, and unresolved support issues. Labs can learn from this to enhance troubleshooting, coaching, and customer service.
Verbix.AI is able to integrate with the supported telephony environments. Medicalicina s should specifically confirm the VitalPBX compatibility and the configuration needed for the specific deployment before making claims of successful implementation.
Yes. Keyword analysis, searchable transcripts, as well as topic identification can help teams find linked references to slow speeds, disconnections, Wi-Fi, and other technical concerns throughout recorded conversations.
No. Sentiment Analysis Assesses Conversational Tone CSAT and NPS on particular customer feedback and scoring methods. Together these sources give a fuller picture of the customer experience.
Business activities can compare analyses of validated baseline and post-implementation metrics using high-fidelity metrics that include manual review hours, repeat contact rates, first contact resolution, quality scores, average handling time and customer satisfaction. All ROI calculations should factor in implementation costs and ongoing operating expenses.