Call center quality monitoring: Best practices, metrics, and tools
Call center quality monitoring is the process of reviewing and evaluating customer conversations to make sure they meet a defined standard for resolution, accuracy, customer experience, and compliance. It typically combines live call monitoring, post-call review of call recordings and transcripts, and scoring against a consistent set of criteria.
Done well, a quality monitoring program gives supervisors a clear view of how agents handle customers, where coaching will have the most impact, and which processes create friction. It also helps leaders understand their customers more deeply, because the patterns behind good and bad interactions show up in the conversations themselves.
That job has grown. Contact center teams now handle more channels, and many also run AI agents alongside human agents. Below, we cover what to monitor, how to measure quality, the best practices that make a program stick, and how to extend quality monitoring to AI agent conversations.
Why call center quality monitoring matters
The purpose of quality monitoring is to turn individual conversations into decisions: who needs coaching, which processes need fixing, and where the customer experience is breaking down. Here's what a strong program can deliver:
Better agent performance: Regular reviews produce specific, actionable feedback for new and experienced agents, and they show where contact center training and onboarding materials need work.
A deeper understanding of customers: Conversations reveal why customers call, what frustrates them, and what resolves their issue. That context is hard to get from surveys or ticket fields alone.
Compliance and policy adherence: In regulated industries like healthcare and financial services, monitoring helps confirm that agents follow required disclosures, scripts, and data handling policies.
Lower cost of rework: When more issues are resolved correctly the first time, teams can spend less on callbacks, escalations, and repeat contacts.
Data-driven decisions: Over time, quality data gives contact center and business leaders the qualitative and quantitative context to set targets, plan staffing, and prioritize investments.
Quality monitoring vs. quality assurance vs. quality management
These terms are often used interchangeably, but they describe different layers of the same discipline:
Term | What it covers | Example activities |
|---|---|---|
Quality monitoring | Observing and reviewing individual interactions | Listening to live calls, reviewing recordings and transcripts, flagging calls for review |
Evaluating interactions against defined standards | Grading calls with scorecards, checking script and policy adherence, calibrating graders | |
Quality management (QM) | The overall program that uses QA results to improve performance | Setting standards and KPIs, coaching, training, updating processes, reporting to leadership |
In practice, quality monitoring feeds QA, and QA feeds the broader quality management program.
What to monitor: Key call center quality metrics
Most quality monitoring programs track metrics across three areas: customer satisfaction, agent performance, and compliance. The right mix depends on your industry, team size, and goals, but these are the most common starting points:
Metric | What it tells you | What to watch for |
|---|---|---|
How often an issue is resolved in a single interaction | Low FCR often points to knowledge gaps or routing problems | |
How long agents spend on a call, including hold and wrap-up | A short call isn't always a good one; review AHT alongside resolution and CSAT | |
How satisfied customers are after an interaction | Survey responses can represent a small share of customers | |
How likely customers are to recommend you | Reflects the overall relationship more than a single call | |
Whether a conversation trends positive or negative | Sudden negative shifts often mark moments worth reviewing | |
Script and policy adherence | Whether agents follow required steps, disclosures, and compliance rules (for example HIPAA or GDPR requirements) | Manual spot checks can miss issues on calls nobody reviews |
Escalation and transfer rate | How often calls move to a supervisor or another team | Rising escalations can signal training or process gaps |
Service level | The share of calls answered within a target time | The "80/20 rule" (80% of calls answered within 20 seconds) is a common target, though many teams set their own |
How to track CSAT and NPS from call recordings
Traditional CSAT relies on post-call surveys, which only some customers complete. AI can close that gap by analyzing the conversation itself. Dialpad's AI CSAT, for example, estimates customer satisfaction from call transcripts, so teams can see scores across many more calls than surveys capture. AI CSAT explanations go a step further and show why customers were satisfied or frustrated, across categories like agent knowledge, empathy and professionalism, and completeness of resolution.
NPS is still best measured with a survey, since it asks about the overall relationship. Many teams pair periodic NPS surveys with conversation-based CSAT to get both views.
How to measure quality in a call center
The most common way to measure call quality is a QA scorecard: a consistent set of criteria that reviewers use to grade each interaction. A scorecard turns subjective impressions into comparable scores, so you can track performance by agent, team, and time period.
A practical scorecard usually covers the full arc of a conversation:
Scorecard section | Questions to ask |
|---|---|
Opening | Did the agent greet the customer correctly, verify identity if required, and set a helpful tone? |
Identifying the issue | Did the agent ask effective questions and confirm the customer's actual need? |
Listening | Did the agent let the customer explain, without assuming the problem or interrupting? |
Resolution | Did the agent offer an accurate solution and explore other options when needed? |
Compliance | Did the agent follow required scripts, disclosures, and data handling rules? |
Closing | Did the agent confirm the issue was resolved, set expectations for next steps, and document the call? |
Weight the sections that matter most to your business, keep criteria specific enough that two reviewers would score a call the same way, and involve agents when you build the scorecard. Agents who help shape the criteria are more likely to trust the results.
Call center quality monitoring best practices
A quality monitoring program works when it's consistent, fair, and connected to coaching. These practices help:
Build the right QA team: Include supervisors, QA analysts, and experienced agents who understand your customer experience goals and can apply standards consistently.
Set clear quality standards and KPIs: Define what a good interaction looks like and which metrics you'll use to measure it, so reviewers and agents work from the same benchmarks.
Calibrate regularly: Have reviewers score the same calls and compare results. Calibration keeps grading fair as your team and scorecard change.
Review more than a small sample: Manually reviewing a handful of calls per agent each week can miss patterns. AI scoring and keyword detection make it practical to evaluate a much larger share of conversations.
Combine live monitoring with post-call review: Live monitoring helps supervisors step in on difficult calls, while post-call review supports deeper coaching and trend analysis.
Coach quickly, and without blame: Share findings while the call is still fresh, highlight what went well, and treat gaps as training or process issues first. A drop in a metric can have causes outside an agent's control, like a product launch that drives more complex calls.
Monitor every channel customers use: Apply the same standards to calls, chat, SMS, and email so the customer experience stays consistent wherever customers reach you.
Include AI agents in the program: If AI agents handle part of your customer conversations, they need quality monitoring too. More on that below.
Close the loop: Quality data only helps if it leads to action. Use it to update training, knowledge base content, routing, and scripts, and track whether the changes work.
How to monitor AI agent conversations
As more contact centers use AI agents to handle routine requests, quality monitoring needs to cover those conversations as well. An AI agent that resolves an order status request but leaves the customer frustrated isn't a success, even if it never escalates.
When you evaluate AI agents, look beyond automation volume:
Resolution rate: How often the AI agent actually resolves the customer's issue, not just how often it avoids a handoff.
Deflection and escalation: How many conversations the AI agent handles on its own, and why the rest move to a human.
Customer satisfaction: Whether customers who interact with the AI agent leave satisfied. A high deflection rate paired with low satisfaction is a warning sign, not a win.
Handoff quality: Whether human agents receive enough context to pick up where the AI agent left off, without asking customers to repeat themselves.
Accuracy and safety: Whether responses stay accurate, on policy, and within the actions the AI agent is allowed to take.
Dialpad Resolve, AI agents for voice and digital interactions, is built with this kind of oversight in mind. Its AI Agentic Analytics report shows deflection rate, escalations, resolution rate, AI CSAT, and handle time across voice and digital channels, along with how often each agentic skill is used. When an AI agent escalates, the human agent can see context such as the reason for the call, the escalation reason, troubleshooting steps already attempted, and customer sentiment. Dialpad Resolve also includes a Guardian safety layer that monitors for issues like hallucinations and abuse.
Quality monitoring of human conversations can inform AI agents, too. Skill Mining analyzes contact center call transcripts between customers and human agents, then recommends skills an AI agent could take on, with an estimate of the share of calls each skill could handle. When human agents and AI agents run on the same platform, what your best agents do well can shape how your AI agents resolve the next customer's request.
Call center quality monitoring tools and software
The right tools make it practical to monitor more conversations with less manual effort. When you evaluate call center quality monitoring software, look for these capabilities:
Live call monitoring
Supervisors need to see what's happening on calls in real time and step in when it matters. In Dialpad Support for contact centers, supervisors can listen in on live calls, whisper guidance to an agent without the customer hearing, barge into the call, or take it over. Live call views also show AI-detected sentiment and call purpose, so supervisors can spot the conversations that need attention.
Automated scoring
Manual grading limits how many calls a team can review. With AI Scorecards, Dialpad AI pre-selects answers to AI questions based on the conversation, and supervisors review the AI-assigned grade instead of scoring every question by hand. Digital interactions can be evaluated with scorecards as well.
Now, we can score a card in under a minute, so we can score 20 to 30 cards per person, compared to only four per person previously.
- Clare McDevitt, Director of Sales, New City Moving
Real-time coaching
Quality monitoring is more effective when guidance reaches agents during the call, not only after it. AI Live Coach surfaces talking points and relevant information when specific topics come up, which can help newer agents handle complex questions with more confidence.
Keyword tracking and adherence
Rather than listening to every recording, teams can flag the conversations that matter. Custom Moments can flag calls where phrases like "cancel" or "refund" come up, and AI Playbooks help supervisors see whether agents follow required steps and talking points.
Transcripts, summaries, and analytics
AI transcripts and AI Recaps give reviewers a quick view of each conversation, with key details and action items already captured. Contact center analytics dashboards then show metrics like AHT, CSAT, and scorecard results across agents and teams, so leaders can spot trends and act on them.
Coaching workflows
Findings need a path back to agents. Coaching playlists let supervisors group noteworthy calls, such as strong examples or common mistakes, to use in training sessions.
The payoff can be significant for the people who run QA. According to an IDC Business Value Study of Dialpad customers, Dialpad helped call center managers become 24% more productive.
Who's involved in call center quality monitoring?
Quality monitoring works best as a shared responsibility:
Supervisors and managers: Run the review process, set benchmarks for agents and teams, and turn findings into coaching.
QA analysts: Grade interactions, calibrate scoring, and report on trends.
Agents: Review their own performance data and call recordings to see what's working and where to improve. Some teams add friendly competition with leaderboards, but gauge your team first, since it can add stress.
Executives: Connect quality goals to business objectives and use quality data to guide budgets and investments.
Turn quality monitoring into operational insight
Quality monitoring delivers the most value when the data doesn't sit in a dashboard. When customer conversations, scoring, coaching, and analytics all live in one place, each reviewed interaction can become part of the conversation intelligence your team uses to understand customers and make better decisions.
That's the idea behind Dialpad's AI platform for customer experience, where human agents, AI agents, and the insights from their conversations run on the same platform.
Improve call center quality with Dialpad
See how Dialpad helps supervisors monitor more conversations, coach agents in real time, and evaluate both human and AI agent interactions in one platform.