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How can we fix the customer experience issue if we can only see 2% of the picture?
This could be for your support team, a new number of complaints, or AI agents that you’re using for customer support.
With all of these, AI-powered monitoring is the only solution because it automatically analyzes 100% of your voice, chat, and email interactions in real time.
So from an operational standpoint, this article will cover how you can implement it and guide you through the process of call center monitoring.
What is call center monitoring?
Call center monitoring is the process of observing, analyzing, recording, and evaluating the interaction between a customer and an agent.
The primary goal of call center monitoring is to ensure that the quality of service meets the company standards, that regulatory compliance is maintained or not, and to identify opportunities for coaching and process improvement.
To help with this, your team's evaluation consistency can be helped by following a structured call audit guide.
Traditional call center monitoring is achieved by plugging in headphones and listening to random phone calls. But today’s monitoring, is through covering voice, chat, and social media, is increasingly augmented by AI.
As per a Gartner report, by 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention, cutting operational costs by 30%.
Types of call center monitoring
Live or real-time monitoring
Real-time monitoring allowed supervisors to listen to the conversation carefully during active customer interaction.
While this method serves as the traditional foundation of quality assurance programs, it remains highly effective today.
Call recording and retrospective review
The most common way to monitor agents is to record calls, and a reviewer listens to them later to grade the agent using a scorecard.
However, there is a major downside to this method of call center monitoring, as listening to calls takes so much time that reviewers can only check a very small percentage of them.
Barge-in, whisper coaching, and silent monitoring
The barge-in technique enables the supervisor to jump into an ongoing call and communicate with the customer face-to-face.
In the whisper coaching technique, the supervisor can guide the agent while he is on an ongoing call, and this communication will not be heard by the customer.
Silent monitoring is passive monitoring where there is no interference in any way.
Automated AI scoring across 100% of interactions
As opposed to reviewing just a small proportion of the calls, today’s AI technology is capable of listening to, transcribing, and scoring all of your customer calls.
The system determines whether the tone, the steps taken by the representative, and the compliance policies were appropriate. Because the software does the heavy lifting, reviewing every single conversation is now actually possible.
And, with this, your quality assurance workflow can be improved by implementing dedicated tools for AI call scoring.
Screen and desktop monitoring
Not only does screen recording include the audio but also includes the activities being performed by the agent in the customer relationship management software, knowledge management database or ticketing system while on the call.
Workflow efficiency across these diverse platforms can be improved by setting up the right call center integrations.
Omnichannel monitoring
As contact volume shifts to chat, email, and messaging apps, monitoring programs that only cover voice calls are missing an increasing share of the customer relationship.
A complete call center monitoring program applies the same scoring framework across every channel.
The sampling problem, why traditional QA misses what matters
The math of 1–3% coverage
A call center receiving 50,000 calls a month and doing 2% manual reviews would have 1,000 calls reviewed. This is to say that 49,000 calls received per month receive no structured feedback whatsoever.
An agent might have one call reviewed every few weeks with a single data point standing in for their entire performance.
Reviewer bias and inter-rater inconsistency
Even though human raters listen to the calls, the scoring process is not always flawless. Two separate raters may listen to the exact same call and rate it in completely opposite ways, particularly when evaluating aspects such as empathy or voice.
However, this is only true unless the company provides training to its raters.
Why are the worst calls the least likely to be reviewed?
When reviewers manually pick which calls to grade, they often choose the short, easy, and routine ones. Long, difficult, or angry calls take too much time and energy to listen to.
And the reason for this is that such decisions require the greatest amount of coaching and pose the greatest amount of risk to the business. This is precisely why the managers miss out on their most important feedback by skipping these calls.
What changes at 100% coverage?
By grading all calls, and not just a few, you start identifying trends which would be hard to notice otherwise. You will easily spot the reasons for the customer anger, the areas in which the agent needs assistance, and any emerging problems.
An agent's entire performance review no longer depends on a reviewer happening to pick one unusually good or bad call.
How to design a QA scorecard that actually drives behavior?
A strong scorecard turns monitoring into action. A bad one just creates meaningless grades and leads to zero improvement.
The four scorecard categories
Most effective scorecards organize criteria into four categories:
- Compliance required disclosures, consent language, regulatory scripting
- Process verification steps, correct tools and systems used, escalation protocol followed
- Communication tone, active listening, clarity, empathy
- Outcome issue resolved, next steps set, customer effort minimized
Weighting auto-fail criteria vs. scored criteria
Not all criteria carry equal weight. Compliance items like skipping a required disclosure are typically treated as auto-fail: a single miss fails the entire call regardless of how well everything else went.
Everything else is scored on a points or percentage basis and rolled into a weighted total. Mixing these into one flat point system tends to bury critical failures under a good overall score.
As the Gartner report says, By 2027, 40% of enterprises will demote or decommission autonomous AI agents due to governance failures.
Writing criteria an AI can evaluate consistently
Criteria written for human judgment are hard for a model to score consistently.
Criteria written for consistent evaluation specify an observable behavior, did the agent use the customer's name at least once, did the agent state the callback number before ending the call, was the required recording disclosure delivered within the first 30 seconds. T
he more behaviorally specific a criterion is, the more reliably it can be scored by a human or a model.
Calibration between AI scores and human reviewers
AI scoring and call center monitoring should not run unchecked. Periodic calibrations where human raters rate a selection of those calls that the machine has rated in order to compare the two sets of scores for early detection of model drift and criteria ambiguity.
Worked example scorecard
The metrics call center monitoring should surface
Monitoring data is only useful if it rolls up into metrics that different stakeholders actually act on.
Compliance monitoring requirements
Call center monitoring on calls isn't just about customer services, it also involves strict legal rules that change based on your location and industry.
- Recording Consent: Different places have different laws. Some areas require everyone on the line to agree to be recorded, so your automated warnings must adjust based on where the caller lives.
- Protecting Payment Info: You cannot save credit card numbers. Your system must automatically pause the recording when a customer gives payment details.
- Mandatory Legal Scripts: In strict industries like healthcare or finance, agents must read specific legal phrases word-for-word. You have to verify those words were actually spoken.
- Data Storage: Strict laws are not personal choice; they dictate exactly how long you must save old recordings and where they can be stored.
- Explaining AI Scores: If AI grades your agents, you cannot just accept a mystery score. You must be able to prove exactly how the AI made its decision.
12 call center monitoring best practices
- Train scorers, both human and artificial intelligence (AI), on a regular basis, and not only at the beginning of operations.
- Provide coaching no later than 48 hours after scoring a call, while the agent still remembers the conversation.
- Enable agents access to their own scores and reasons for scoring, not merely the score alone.
- Monitor the queue as well as interaction patterns and not just individual calls.
- Treat criteria for automatic failures for compliance and scored performance criteria differently.
- Feed information obtained through monitoring back into training materials, not just the performance management cycle.
- Review scorecard criteria quarterly; discard criteria that do not differentiate performance anymore.
- Ongoing sampling checks of AI scores against human reviewers, not only at the start.
- Monitoring of all communication channels, not only of telephone calls.
- Differentiate coaching from disciplinary conversations, even if using the same information in both.
- Monitor performance trends for each agent over time, not single calls.
- Ensure that the supervisor understands the scorecard itself, not just the platform used.
Call center monitoring software: what to evaluate?
McKinsey research, found that traditional contact centers typically analyze less than 2% of voice interactions, making full interaction coverage critical for quality monitoring and compliance.
How does Thunai approach call center monitoring?
Thunai scores and audits every call instantly against custom quality and compliance parameters, rather than a sampled subset.
Supervisors retain live call barge-in and monitoring alongside the automated scoring layer, and agents get real-time assist during the call itself.
In-moment customer resolutions can be improved by implementing a real-time AI agent assist solution to automatically surface relevant knowledge.
Today’s system doesn’t wait for the completion of the call to assess its success, but it keeps track of the customer’s sentiments and problems in real-time.
With Thunai, every call is automatically scored, summarized, and monitored in real time, while AI assists agents with the right knowledge at the right moment.See how Thunai transforms call center monitoring - See Thunai in action today.
FAQs on Call Center Monitoring
What is the difference between call monitoring and call recording?
Call recording is the capture of the audio itself. Call monitoring is the practice of observing and evaluating interactions live or recorded against defined criteria. Recording is a prerequisite for retrospective monitoring, not a substitute for it.
How many calls should you monitor?
Traditionally, 1 to 3% due to the cost of manual review. With AI scoring, monitoring 100% of calls is operationally feasible and is increasingly the standard for programs that can afford the tooling.
Is call monitoring legal?
Yes, when done in accordance with applicable consent laws. Some need one party to agree to the recording, while others need everyone on the call to agree to the recording. Hence, whether the recording is legal or not depends on the location of the parties and the process of disclosure.
What is a good QA score?
There's no universal number; it depends on how the scorecard is weighted and what counts as auto-fail. A more useful benchmark than an absolute score is a consistent, calibrated scoring process and a clear trend line over time for each agent and team.
Can AI replace human QA analysts?
Although AI is capable of grading each call on a scale that cannot be achieved by a human, there are times when calibrations and even coaching require human intervention, especially when it comes to high-risk calls. Most effective applications make use of both types.
What is barge-in in a call center?
Barge-in involves the supervisor interrupting an ongoing conversation between the agent and a customer by directly talking to the customer.
