Call center quality assurance (QA) is how you check that customer conversations meet your standard and turn what you find into coaching. Most programs still review under 5% of conversations by hand, and that sample drives every coaching and compliance decision. This guide covers how to build the program, design the scorecard, calibrate reviewers, pick metrics and decide how much to automate.
In short
- Call center QA scores conversations against a scorecard and feeds the results into coaching.
- In a BPO, QA also proves quality and SLA adherence to each client.
- Manual programs typically review under 5% of conversations, so most of the work goes unseen.
- A contact center scorecard works best as one weighted core plus a few channel-specific criteria.
- Automation scores every conversation, so reviewers focus on calibration and coaching.
What is QA in a call center?
In a call center, quality assurance (QA) is the process of reviewing and scoring agents’ customer conversations against a defined quality standard, then using the results to coach agents and improve service. In a BPO, the same process also proves to each client that the outsourced team meets the agreed quality standards and SLAs (more on the QA meaning in BPO work).
Call center quality assurance measures whether agents resolve issues, follow process, communicate well and deliver a consistent experience across calls, chats, emails and tickets.
Traditionally, a QA analyst or team lead listens to a few recorded calls or reads a handful of tickets per agent each week and scores them against a rubric. The scores feed coaching, compliance checks and performance reviews. Done well, QA is how a support operation keeps quality high as it scales. Done the old way, it is a slow, subjective process that only ever sees a sliver of the work. If you are looking at tools rather than method, see what to expect from call center QA software.
What is the purpose of QA in a call center?
The purpose of QA in a call center is to make sure customers get correct, consistent service and that every agent knows exactly what to improve. It turns individual conversations into evidence for coaching, compliance and process fixes, and in a BPO it gives each client proof that quality targets are met.
- Check that customers get correct, complete answers.
- Make sure agents follow process, verification and compliance rules.
- Give each agent specific coaching based on real conversations.
- Keep quality consistent across agents, shifts, sites and channels.
- Spot recurring problems in products, policies and processes.
- In a BPO, give each client evidence that quality targets and SLAs are met.
Call center QA vs contact center quality management
The terms are often used interchangeably, with a subtle difference. “Call center” historically means voice, while “contact center” covers every channel: voice, chat, email, messaging and social. “Quality management” (QM) is the broader discipline that includes QA scoring plus calibration, coaching, and often workforce processes. In practice, modern teams run QA across all channels regardless of the label, and a good program treats a chat and a call by the same quality standard.
The distinction still matters for how you build the program, because the channels behave differently. Chat is concurrent, so an agent may be handling three conversations at once. Email is asynchronous and heavily templated. Messaging threads can run for days.
A quality standard designed around a single linear phone call does not describe any of these well, and the channels that go unmeasured are the ones that quietly degrade. The customer who starts on chat, sends an email overnight and calls the next morning experiences one service, so your quality standard has to reflect that.
The evolution of call center QA
Call center QA has moved through three eras: manual sampling in spreadsheets, QA software that made grading consistent but still sampled, and AI-powered QA that scores every conversation. Knowing them explains why the old approach no longer holds up, and why coverage, not effort, is the limit most teams hit.
- Era 1, manual sampling. QA lived in spreadsheets. A supervisor listened to a few calls per agent per week and scored them. Coverage was tiny and scores were subjective.
- Era 2, QA software. Digital scorecards, call recording, calibration and dashboards made grading more consistent and auditable, but a human still graded a small sample, so the coverage ceiling never moved.
- Era 3, AI-powered QA. AI now scores every conversation across channels, and humans move up to calibration and coaching. Coverage jumps from a sample to everything, and QA becomes proactive instead of reactive.
A fourth shift is already underway, where AI does not just grade conversations but handles them, which makes complete QA coverage of human and AI-agent conversations more important than ever.
Why does call center quality assurance matter?
Call center quality assurance matters because it is the difference between knowing your service is good and hoping it is. It protects the customer experience, keeps service consistent across agents, powers coaching, ensures compliance and links quality to customer outcomes. A strong QA program does all five:
- Protects the customer experience by catching poor interactions before they become churn or complaints.
- Drives consistency so every customer gets the same standard, regardless of which agent they reach.
- Powers coaching by turning real conversations into specific, personalized development for each agent.
- Ensures compliance in regulated industries where a missed disclosure carries real risk.
- Links service to outcomes by connecting quality scores to CSAT and retention.
The catch is coverage. When QA only sees 5% of conversations, all of these benefits rest on an unrepresentative sample. That is the core problem modern QA is built to solve.
There is a hidden cost too. At a large call center, the fully loaded cost of QA reviewers, plus the reporting time around them, can run into the millions per year, all to inspect a fraction of the work. Every hour a senior team lead spends grading calls by hand is an hour not spent coaching.
What does a call center QA analyst do?
A call center QA analyst (or specialist) owns the quality process day to day: scoring conversations against the scorecard, running calibration so reviewers agree, flagging compliance issues and coaching opportunities, and reporting quality trends to team leads. As scoring gets automated, the role shifts from grading toward calibration and coaching.
The role typically involves:
- Reviewing and scoring conversations against the QA scorecard.
- Running calibration sessions so scoring stays consistent between reviewers.
- Flagging compliance issues and coaching opportunities.
- Reporting on quality trends to team leads and operations.
- Refining the scorecard as products, policies and customer needs change.
As automation takes over the manual scoring, the analyst’s role moves up the value chain: less time grading, more time on calibration, coaching and driving improvement. The job does not disappear, it gets more strategic.
What are the 4 types of quality assurance?
The four types of quality assurance in a call center are manual QA, automated QA, peer and self review, and customer-driven QA. Most programs combine them: automation for coverage, human review for calibration and nuance, and customer feedback as an outside check on whether the scores reflect what customers experience.
- Manual QA: a reviewer grades a sample of conversations by hand. High context, very low coverage.
- Automated QA: AI scores every conversation against the scorecard. Full coverage and consistency.
- Peer and self review: agents review their own or colleagues’ conversations to build a shared quality culture.
- Customer-driven QA: CSAT, surveys and feedback bring the customer’s own view of quality into the picture.
Some teams also organize their program around five P’s, People, Process, Product, Policy and Performance, to make sure QA drives action across all of them, not just agent scores.
What does a call center QA program include?
A complete call center QA program has five moving parts, and each one depends on the others. A scorecard without calibration produces scores nobody trusts, and scores without coaching change nothing. Reporting closes the loop by showing whether quality is actually rising. The five parts are:
- A scorecard that defines what a good conversation looks like.
- A monitoring method to evaluate conversations, manual, automated or both.
- Calibration to keep scoring consistent and fair.
- Coaching that turns scores into agent improvement.
- Reporting that tracks quality trends and ties them to business outcomes.
How to build a call center QA program
To build a call center QA program, define what quality means for your team, turn it into a weighted scorecard, choose how conversations get evaluated, calibrate reviewers, route findings into coaching, then report and refine. The six steps below work for a single queue or a whole operation.
- Define quality for your team. Decide what a great conversation looks like across resolution, compliance, communication and efficiency.
- Build a weighted scorecard. Translate that definition into scorecard criteria, weighted by what matters most.
- Choose your monitoring method. Decide how conversations get evaluated. Manual alone caps you at a small sample, so most scaling teams add automation.
- Calibrate. Run calibration sessions so reviewers, and the AI, score the same conversation the same way.
- Coach and close the loop. Route findings into per-agent coaching and track improvement at 30, 60 and 90 days.
- Report and refine. Watch quality trends, tie them to CSAT, and update the scorecard as needs change.
For a longer rollout plan with a starter scorecard, see how to build a QA program.
What goes on a call center QA scorecard?
A call center QA scorecard lists the criteria every conversation is graded on, grouped into a few weighted categories, usually resolution, compliance, communication and efficiency. The weights reflect what matters most to your customers, and every criterion should be specific enough that two reviewers would score it the same way.
| Category | Example criteria | Weight |
|---|---|---|
| Resolution | Issue fully resolved, correct information given | 35% |
| Compliance | Required steps, verification and disclosures followed | 25% |
| Communication | Clear, empathetic, on-brand tone | 25% |
| Efficiency | Handled without unnecessary transfers or repeats | 15% |
A quick call center QA checklist to sanity-check any conversation: Was the customer verified correctly? Was the issue actually resolved? Was the information accurate? Was the tone empathetic and professional? Were the next steps clear? Was policy followed? For a deeper build, see our guide to the QA scorecard.
One core scorecard, plus channel modifiers
The most common mistake in contact center QA is either running one phone-shaped scorecard on everything, or running separate scorecards per channel that cannot be compared. Neither gives you a quality number you can trust across the operation. The workable model is a core plus a modifier: a core of criteria that apply to every conversation regardless of channel, weighted heavily so the overall score stays comparable, plus two or three channel-specific criteria on top.
The core usually covers resolution (was the actual issue solved, not just answered), accuracy, process and compliance, communication, tone and empathy, and ownership (did the agent take the next step, or leave it with the customer). The modifiers cover what is genuinely different per channel:
| Channel | What makes it different | Criteria to add | Common failure mode |
|---|---|---|---|
| Voice | Live, linear, tone carries most of the message | Hold and transfer handling, verification, call control, summarising back | Dead air, unexplained holds, blind transfers |
| Live chat | Concurrent, fast, customer is waiting in real time | Response gaps within the chat, appropriate use of canned replies, closing the loop before ending | Long silences while the agent handles other chats, copy-paste answers that miss the question |
| Asynchronous, written record, often templated | Completeness of the first reply, structure and readability, correct use of templates | Answering one of three questions asked, forcing an avoidable second round trip | |
| Messaging and social | Threads run for days, short turns, public in some cases | Context retention across the thread, channel-appropriate brevity, escalation to a private channel | Losing the thread history and asking the customer to repeat themselves |
| AI agents | High volume, perfectly consistent, cannot self-report a failure | Factual grounding, escalation triggers, scope discipline, handover quality | Confident wrong answers, refusing to hand over to a human when it should |
Most of these criteria are objective and checkable against the transcript, which is what makes consistent scoring possible at all. Every criterion should pass one test: two reasonable reviewers reading the same conversation would reach the same answer. Kaizo’s scorecards follow the same model, one weighted core plus criteria for the channel, applied to human and AI-agent conversations on your own scorecard.
What is a good call center QA score?
There is no universal pass mark, because a good score depends on how demanding your scorecard is. That said, most mature programs set a target band, often around 85 to 90% and up, and treat anything below it as a coaching trigger. Two nuances matter more than the headline number:
- Auto-fail criteria. Some misses, like a skipped identity verification or a compliance breach, should fail the whole conversation regardless of the rest of the score. Define these explicitly.
- Trend over absolute. A single score means little. What matters is the trend per agent and per team over time, and whether it correlates with CSAT.
Calibration is what makes any score trustworthy. Run regular calibration sessions where reviewers, and your AI scoring, grade the same conversations and reconcile differences. Without calibration, a “good score” is just one reviewer’s opinion. With it, the number becomes a fair, shared standard your agents will actually accept.
How do you calibrate QA across reviewers and channels?
You calibrate QA by having reviewers score the same conversations independently, comparing the results, and rewriting any criterion they disagreed on so the disagreement cannot recur. In a contact center, calibrate across channels as well as within them, so a score of 90 means the same thing on chat as it does on voice.
The mechanics of a session are simple. Pick two or three conversations. Everyone scores them independently, without discussion. Compare the results, focus only on the criteria where people disagreed, and argue until you reach a shared reading. Then, and this is the step teams skip, rewrite the criterion. A session that ends in verbal agreement but leaves the wording untouched will produce the same disagreement next quarter. The full mechanics are in how to run QA calibration sessions.
Reviewers who specialise in one channel drift towards that channel’s norms, so a chat reviewer starts marking brevity as efficient while a voice reviewer marks the same brevity as curt. Include at least one cross-channel conversation in every session.
Agents also need a dispute route. Every score should link back to the specific evidence that produced it, and agents should be able to challenge one and get a human answer. A score an agent cannot trace or contest is a score they will dismiss, and a dismissed score coaches nobody. The dispute rate is a useful diagnostic too: a spike on one criterion almost always means the criterion is ambiguous.
Call center QA best practices
The best call center QA programs score for coverage rather than samples, keep the scorecard short, calibrate on a schedule and treat QA as coaching rather than policing. Six practices separate programs that agents trust from programs that agents learn to ignore:
- Score for coverage, not just samples. A 5% sample hides systemic problems. Aim for full coverage.
- Keep the scorecard focused. Score what predicts customer outcomes, not everything you could measure.
- Calibrate regularly. Calibration is what keeps scores fair and trusted by agents.
- Make QA a coaching engine, not a policing tool. Frame it around development, and agents engage with it.
- Tie quality to CSAT. Prove that better scores mean happier customers.
- Standardize across teams and BPOs. One standard everywhere keeps quality even.
How do you turn QA scores into coaching?
You turn QA scores into coaching by routing each agent’s specific gaps, with the conversations that show them, into regular one-to-one sessions, then checking whether the score moves at 30, 60 and 90 days. A QA score that does not change behavior is wasted effort, because the point of measuring quality is to improve it.
The problem with manual QA is that preparing coaching, pulling examples, spotting patterns and writing it up, eats the very time team leads should spend actually coaching. Automated QA flips this: because every conversation is scored, the system can surface each agent’s specific strengths and gaps and generate coaching automatically, so the lead walks into a session with the evidence already gathered.
The best programs make coaching continuous rather than a monthly event and tie it to the specific behaviors the scorecard measures. That feedback loop, score, coach, measure, improve, is what separates a QA program that reports on quality from one that raises it. See how AI coaching automates this loop.
Which call center QA metrics should you track?
Track the call center QA metrics that connect quality to the business: the quality score trend, its correlation with CSAT, first contact resolution and handle time alongside it, coaching impact, auto-fail rate, coverage and reviewer agreement. Always quote coverage next to the score, or the score says very little.
- Quality (QA) score trend across the whole team, ideally on 100% of conversations.
- QA-to-CSAT correlation, to prove scores reflect real customer experience.
- First contact resolution and average handle time, watched alongside quality so speed never comes at its expense.
- Coaching impact, score movement per agent over 30, 60 and 90 days.
- Scorecard automation rate, the share scored without human grading, as a maturity measure.
- Auto-fail rate, the share of conversations that breached something critical. Often a more actionable number than the average score.
- Coverage, the share of conversations actually scored. Quote it every time you quote a quality score: 92% on 2% of conversations and 92% on all of them are completely different claims.
- Reviewer agreement, how closely reviewers (and the AI) agree on the same conversation. If this drops, every other number becomes unreliable.
For the metrics that matter most, see the call center QA metrics that predict CSAT, and for the wider set, our guide to customer service metrics and KPIs.
How call center QA improves your core metrics
Done right, call center QA moves the numbers a support leader is measured on: CSAT, first contact resolution, agent ramp time and attrition. It does so by showing which behaviors drive each number, so coaching targets what actually changes it rather than what happens to be visible in a small sample.
- CSAT. Scoring every conversation reveals the specific behaviors that drive satisfaction, so coaching targets what actually raises the score.
- First contact resolution. Complete coverage exposes the resolution gaps a small sample misses, from skipped verification to wrong information.
- Agent ramp time. New agents are coached from real conversations immediately, shortening the path to full productivity.
- Attrition. Fair, consistent, development-focused QA raises agent engagement and lowers the churn that plagues call centers.
Because scores tie back to CSAT, you can prove the link between quality work and customer outcomes rather than asserting it.
Manual vs automated call center QA
Manual call center QA has a reviewer grade a small sample by hand, typically under 5% of conversations. Automated QA scores every conversation against the same scorecard, so reviewers spend their time on calibration and coaching instead. How much to automate is the single biggest decision in a modern QA program.
| Manual QA | Automated QA | |
|---|---|---|
| Coverage | Under 5% of conversations | 100% of conversations |
| Consistency | Reviewer-to-reviewer bias | One consistent standard |
| Reviewer time | Hours of grading weekly | Redeployed to coaching |
| Coaching input | Prepared by hand | Auto-generated per agent |
Automated QA does not remove the human, it removes the manual grading, freeing analysts for calibration and coaching. Read the full automated QA approach, or learn how AI coaching turns scores into agent development.
100% of QA automated at UiPath, with 200% ROI and +8% quality score per quarter
75% less coaching-prep time at EverHelp, across 16 support domains
100% of conversations scored automatically, versus the under-5% manual-sampling average
What modern call center QA looks like in practice
In practice, modern call center QA means every conversation in scope is scored against one scorecard, reviewers spend their time on calibration and coaching instead of grading, and quality is tied to resolution and CSAT. Three Kaizo customers show what that looks like on their support conversations: UiPath, EverHelp and Foot Locker Europe.
UiPath, a large enterprise support operation, automated its QA: the result was 100% of QA automated, 200% ROI, and a quality score climbing around 8% per quarter, because the team stopped spending its week grading and started acting on complete data. EverHelp, an outsourcer running support across 16 domains, cut coaching-preparation time by 75%, freeing team leads to coach far more people.
Foot Locker Europe, whose care center saw volumes more than double in 2020, used Kaizo on top of Zendesk to see performance at team and agent level across several languages, and cut resolution time by 75% and reply time by 37% between 2020 and 2021.
The pattern is the same at UiPath and EverHelp: coverage goes from a sample to everything, senior time shifts from inspection to improvement, and quality rises because coaching is finally based on the full picture. The QA team does not disappear, its work becomes calibration and coaching, the higher-value roles manual grading never left time for.
See it on your own conversations. Kaizo scores 100% of your chat, email and ticket conversations and turns the findings into coaching. Let’s talk.
Building the business case for better QA
The business case for better QA has to convince three people at once: the quality manager, who cares about coverage and fairness; the head of support or operations, who cares about ROI and reviewer time; and team leads, who care about coaching. The strongest case speaks to all three.
- The QA or quality manager cares about coverage and fairness, moving from under 5% to 100% and removing reviewer-to-reviewer bias.
- The head of support or operations cares about ROI and time, reviewer hours reclaimed, faster reporting, and payback measured in weeks.
- Team leads care about coaching, less prep and more time developing agents with impact they can see.
Frame a pilot around a single team or queue so the before-and-after is easy to measure, then extrapolate the reclaimed hours and score improvement across the operation to size the full opportunity. Combine a hard cost saving from reviewer time with a quality and CSAT gain from coaching on complete data, and the case makes itself.
Voice vs digital channel QA
Voice and digital channel QA differ because QA started in the voice world, where reviewers listened to recorded calls, while for many companies chat, email and messaging now carry more volume than the phone. Modern quality assurance has to grade every channel on one consistent standard, so quality is comparable across the operation.
That creates a problem for voice-era QA tools, which are strong on calls but weaker on digital conversations. When evaluating a QA approach, check that it treats digital channels as first-class, not as an afterthought bolted onto a voice product. For digital-first and omnichannel teams, that multichannel QA coverage is often the deciding factor.
How to choose call center QA software
To choose call center QA software, check five things: true 100% coverage, custom weighted scorecards, automatic per-agent coaching with tracked impact, integrations with your helpdesk, and fast time to value. Then test one thing in a demo rather than taking it on trust: whether every score links to its evidence.
- True 100% coverage with an always-on mode, not an AI bolt-on to sampling.
- Custom, weighted scorecards that match your definition of quality.
- Automatic per-agent coaching with tracked impact.
- Platform-agnostic integrations (Zendesk, Salesforce and more) so it fits your stack.
- Fast time to value, live in days rather than months.
Every automated score should link to the exact evidence in the transcript that produced it, so it can be checked, coached on or overturned on your own conversations, rather than accepted because a vendor published an accuracy figure. Ask to see one score taken apart line by line against the transcript.
Compare options in our customer service QA software guide.
Call center QA by industry
Call center QA works the same way in every industry, but the value of full coverage shows up differently. BPOs need proof for each client, regulated teams need compliance on every interaction, retail needs stability through peaks, SaaS needs steady quality while headcount grows, and telecom needs scale without a huge QA team.
- BPOs and outsourcers. Standardize quality across many clients with unified evaluations, and prove SLA adherence with evidence from every conversation rather than a sample.
- Fintech and regulated support. Get compliance coverage on 100% of interactions, not 3%, with a consistent, auditable standard.
- Ecommerce and retail. Protect CSAT through seasonal volume spikes, when manual QA coverage would otherwise collapse under peak load.
- SaaS and tech support. Keep quality steady while headcount scales fast, and ramp new agents from real conversations.
- Telecom and utilities. Manage huge volumes and strict process requirements without a proportionally huge QA team.
Call center QA and compliance
In regulated industries, call center QA is about risk as much as experience. A missed identity verification, an omitted disclosure or incorrect regulated advice can carry real financial and legal consequences. If you review 3% of calls by hand, you are blind to compliance on the other 97%.
Automated QA changes the equation by checking every conversation for the required steps, flagging violations the moment they happen rather than during a quarterly audit. That shift from sampling to full coverage is often the single strongest argument for automating QA in fintech, healthcare, insurance and other regulated support environments, because it turns compliance from a spot check into continuous assurance.
Signs your call center QA program needs an upgrade
Your call center QA program needs an upgrade when the sample is too small to trust, agents dispute scores, supervisors lose hours to grading, or QA cannot keep pace with new headcount, sites, channels or BPO partners. If three or more of the signs below are true, manual QA is already the bottleneck.
- Reviewers can only reach a small percentage of conversations, and you suspect the sample is not representative.
- Agents push back on scores as subjective or inconsistent between reviewers.
- Supervisors spend hours each week grading and preparing coaching by hand.
- You are scaling headcount and QA cannot keep pace without hiring more reviewers.
- You operate across multiple sites, channels or BPO partners and struggle to apply one standard.
- Compliance depends on a sample, leaving most conversations unchecked.
When automated QA is not the right fit
Automated call center QA is not the right choice for every team. If you handle a few dozen conversations a week, one reviewer can read them all and a spreadsheet scorecard is enough. If you have no agreed definition of quality yet, write the scorecard and calibrate it by hand first, because automation only scales a standard you already have.
The same goes for channels. If most of your quality risk sits on the phone, confirm in a demo which channels a tool scores today, on your own conversations, before you commit.
Common call center QA mistakes to avoid
The most common call center QA mistakes come down to three things: measuring too little (a tiny sample, no coverage figure), measuring the wrong way (an overloaded or phone-shaped scorecard, no calibration) and doing nothing with the result (scoring without coaching, or using QA to punish).
- Grading a tiny sample and treating it as representative.
- An overloaded scorecard that dilutes the signal that matters.
- Skipping calibration, which erodes agent trust in scores.
- Scoring without coaching, so nothing actually improves.
- Using QA to punish rather than develop, which kills engagement.
- Running a phone scorecard on every channel, where criteria like call control and hold handling are meaningless on email and quietly distort the score.
- Running unconnected scorecards per channel, the opposite failure, which leaves you with no operation-level quality number at all.
- Quoting a quality score without its coverage, which hides how little of the work the number describes.
- Leaving AI-handled conversations out of scope, so the fastest-growing part of your contact volume is the only part nobody inspects.
Call center QA for AI and digital channels
Two shifts are reshaping call center QA. Support is going digital-first, with chat, email and messaging overtaking voice at many companies, so QA has to cover every channel on one standard. And AI is starting to handle conversations directly, so those conversations need grading too, side by side with human ones on the same scorecard.
For digital-first teams, that combination of full coverage and one scorecard for human and AI conversations is quickly becoming the norm for modern quality management.
The bottom line
Call center quality assurance has outgrown the spreadsheet. Reviewing 5% of conversations by hand cannot keep pace with the volume, the channels or the rise of AI-handled interactions. The programs that win measure quality on 100% of conversations, keep humans focused on calibration and coaching, and tie every score back to the customer.
Build the scorecard, automate the grading, coach from complete data, and QA stops being a bottleneck and becomes an engine for better service. For the wider view beyond the contact center, read our guide to customer care quality. When you are ready to compare tools, see what to look for in call center QA software, or book a meeting to see automated QA on your own conversations.
Frequently asked questions
What does a call center quality manager do?
A call center quality manager owns the QA program: the scorecard, calibration, coaching, reporting, and the decision of how much to automate. Their job is to keep quality high as the team scales.
What skills do you need for call center quality assurance?
You need analytical judgment, knowledge of the product and policies, coaching and communication skills, and comfort with QA tools and data. As scoring automates, calibration and coaching skills matter most.
What is the difference between contact center QA and call center QA?
Call center QA evaluates phone conversations. Contact center QA evaluates every channel the operation runs on, so it has to handle the differences between a live call, a concurrent chat, an asynchronous email and a messaging thread that runs for days. In practice that means one core scorecard shared across channels plus a small set of channel-specific criteria.
How do you measure call center quality?
You measure it with a weighted QA scorecard applied to conversations, a quality-score trend, QA-to-CSAT correlation and coaching-impact tracking. Automation lets you measure on 100% of conversations rather than a sample.
Can call center QA be automated?
Yes. Automated QA uses AI to score every conversation against your scorecard and generate coaching, moving coverage from under 5% to 100%. Humans stay in the loop for calibration.
What are the 5 P’s of quality assurance?
There is no single fixed standard, but many teams frame QA around five P’s: People, Process, Product, Policy and Performance. The framing makes sure quality work drives action across all of them, not just individual agent scores.
How many calls should QA review per agent?
Manual programs typically manage three to five per agent per week, which is under 5% of volume. Automated QA removes that limit by scoring 100% of conversations, so the question shifts from how many to sample to what to do with complete coverage.
