Quality monitoring call center: complete guide, tools and best practices 2025

You listen to 2-3 calls per month per agent and you call that quality monitoring? 😬

It's time to move on to modern quality monitoring, which uses AI to analyze 100% of interactions, detect problems in real time, and turn your agents into superstars.

In this ultra-complete guide, we'll explore what call center quality monitoring is, why it's critical in 2025, the methods and technologies available, the best software on the market, how to deploy an effective QA program, and best practices for continuously improving the quality of your customer service.

Whether you're launching a QA program or looking to modernize your current approach, this guide will give you all the keys. Let's go! 🚀

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What is quality monitoring for call centers?

Quality monitoring (or QA - Quality Assurance) is the systematic process of evaluating, monitoring and improving the quality of interactions between your agents and your customers.

Main objectives :

• Ensure consistent quality of customer service
• Identify coaching opportunities for agents
• Detect process or training problems
• Ensure compliance (scripts, regulations)
• Improve customer satisfaction (CSAT, NPS)
• Optimize call center performance

Evolution of quality monitoring :

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Why quality monitoring is critical in 2025

The figures speak for themselves:

• Companies with a solid QA program see a 20% improvement in CSAT
• 100% of interactions can be analyzed with AI (vs. 1-3% with manual QA)
• The cost of poor customer service: $75 billion lost every year in the U.S. alone
• 96% of customers leave a company after a bad experience
• Agents who receive regular feedback are 4.6x more committed

But beyond the stats:

1. Quality has a direct impact on revenues

Poor customer service = lost customers = lower sales. QA is an investment, not an expense.

2. Identify targeted coaching opportunities

Rather than training everyone on everything, QA identifies exactly who needs what.

3. Detect systemic problems

If 80% of agents have difficulty with one type of request, the problem isn't the agents: it's the process, the training, or the tool.

4. Ensuring compliance

In some sectors (banking, insurance, healthcare), compliance is regulatory. QA proves that you comply with standards.

5. Improve agent morale

Counter-intuitively, agents WANT constructive feedback. QA done right helps them progress and increases their satisfaction.

To find out more about call center performance, read our guide: Optimizing your call center performance.

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Key elements of a quality monitoring program

1. QA scorecards

Document defining evaluation criteria and their weighting.

Typical elements of a QA grid :

• Welcome and opening (10 points): greeting, identification, empathy
• Active listening and comprehension (20 points): reformulation, clarification questions
• Product knowledge (25 points): accuracy of information, trustworthiness
• Problem solving (30 points): efficiency, appropriateness of solution
• Communication (10 points): clarity, tone, professional language
• Closing (5 points): summary, next steps, thanks

Total score: 100 points

Each company customizes its grid according to its priorities.

2. Sampling (or full analysis)

Traditional approach: Random sampling (2-4 interactions/agent/month)

Modern approach: Automated analysis of 100% of interactions via AI

3. Evaluation and scoring

Manual: Supervisors/QA analysts listen and score

Automated: AI automatically analyzes and scores according to defined criteria

4. Calibration (alignment of evaluators)

Session where several evaluators score the same interaction to ensure consistency.

Objective: Eliminate bias and ensure that all agents are evaluated fairly.

5. Feedback and coaching

Scoring is only the first step. The real impact comes from feedback.

Best practice: 1-on-1 session within 48 hours of the assessment, focusing on a maximum of 1-2 points for improvement.

6. Improvement plans

For agents with recurring low scores: detailed action plan with measurable objectives and timeline.

7. Monitoring and analytics

Dashboards that follow :

• Average scores per agent
• Evolution over time
• Scores by category (home, resolution, etc.)
• Correlation with CSAT and NPS

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Quality monitoring technologies in 2025

1. Speech Analytics (AI-based voice analysis)

AI automatically analyzes 100% of your voice calls.

What it detects:

• Keywords and key phrases
• Feelings and emotions (frustration, satisfaction)
• Compliance (script respected? compulsory information?)
• Agent vs. customer talk time
• Interruptions and overlaps
• Silences (dead air)
• Tone and energy

Example: "AI detects that the agent has not requested recording authorization in 15% of calls → automatic alert"

2. Text Analytics (chat and email analysis)

The same principle applies to written interactions.

Applications :

• Email sentiment analysis
• Inappropriate language detection
• Verification of conformity
• Response time

3. Omnichannel QA

Consistent evaluation across all channels (voice, email, chat, social networks) from a single platform.

4. Real-time monitoring & alerts

Real-time monitoring with instant alerts.

Alert scenarios :

• Very frustrated customer detected → alert supervisor
• Agent uses inappropriate language → immediate alert
• Non-conformity detected → automatic escalation

5. Automated QA scoring

The AI automatically scores each interaction according to your QA grid.

Benefit: Increase from 1-3% of interactions evaluated to 100%.

6. Conversation Intelligence

Beyond scoring: extracting strategic insights.

Questions she answers:

• What are the most frequent reasons for contact?
• What are the most common objections?
• Which products generate the most confusion?
• Which agents perform best (and why)?

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The best quality monitoring software in 2025

1. AmplifAI

Highlights :

• Named "Gartner Cool Vendor" and Leader in Prism QA/QM Automation 2025
• 100% automated QA powered by AI
• Unifies all contact center data
• Used by 150+ global brands
• Focus on coaching and performance

Weaknesses :

• High price (company)

Ideal for: Large companies and BPOs with high volumes.

2. Genesys Cloud CX

Highlights :

• Sophisticated AI analytics
• Native omnichannel integration
• Real-time monitoring and predictive analytics
• Holistic view of customer interactions
• Complete platform (contact center + QA)

Weaknesses :

• Complex to deploy
• Requires commitment to the complete platform

Ideal for: Companies looking for an all-in-one contact center platform.

3. Enthu.AI

Highlights :

• State-of-the-art speech analytics
• Advanced intelligence conversation
• Automatic scoring of interactions
• Detailed agent-customer analysis
• Modern, intuitive interface

Weaknesses :

• Less track record than established leaders

Ideal for: Companies looking specifically for advanced speech analytics.

4. NICE CXone

Highlights :

• Historic QA market leader
• Complete quality management suite
• Speech + text analytics
• Integrated workforce management
• Very mature and reliable

Weaknesses :

• Aging interface
• High prices

Ideal for : Very large enterprise call centers.

5. Zendesk QA

Highlights :

• Native integration with Zendesk
• Easy to deploy
• AI for auto-scoring
• Clear reporting
• Reasonable price

Weaknesses :

• Less advanced functionalities than QA specialists
• Focused on text-based interactions (chat, email)

Ideal for : Zendesk users looking for integrated QA.

6. Observe.AI

Highlights :

• Powerful conversational AI
• Fully automated QA and coaching
• Real-time analytics
• Excellent for agent coaching

Ideal for : Call centers focused on continuous agent improvement.

7. Scorebuddy

Highlights :

• Specialized QA (not a generalist contact center platform)
• Simple, intuitive interface
• Ultra-customizable evaluation grids
• Excellent for calibration
• Affordable price

Weaknesses :

• Less AI automation than leaders

Ideal for : SMEs looking for a simple, effective, dedicated QA tool.

8. Qualtrics Contact Center

Highlights :

• Exceptional analytical power
• Integration with Qualtrics XM (experience management)
• Lie QA and customer feedback (CSAT, NPS)
• Enterprise-grade

Weaknesses :

• Very high price
• Overkill for SMEs

Ideal for: Large companies with mature CX programs.

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Quick comparison chart

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How to deploy an effective quality monitoring program

Step 1: Define your goals

Questions to ask :

• Why do we do QA? (improve CSAT? compliance? coaching?)
• What are our target KPIs?
• Who will be responsible for the program?

Step 2: Create your evaluation grid

Best practices :

• Involve agents, supervisors and managers in creating
• Focus on what really impacts customer satisfaction
• Limit yourself to 8-12 criteria (not 50)
• Weight by importance
• Make the criteria objective and measurable (avoid "the agent was friendly" → prefer "the agent used the customer's first name at least 2 times").

Step 3: Choose your QA tool

Based on :

• Your volume of interactions
• Your channels (voice-only or omnichannel)
• Your budget
• Your existing integrations
• Your QA maturity level

Step 4: Training evaluators (calibration)

1. Select 5-10 representative interactions
2. All evaluators score them independently
3. Compare results
4. Discuss discrepancies
5. Align on criteria interpretation
6. Repeat until 85%+ consistency is achieved

Stage 5: Pilot (2-4 weeks)

Test with :

• A small team (10-20% of agents)
• Objective: identify problems, adjust grid, train evaluators

Step 6: Full deployment

Rollout to 100% of agents with :

• Clear communication of objectives and processes
• Training agents on what will be assessed
• Official launch

Step 7: Evaluation schedule

Manual approach: Minimum 3-5 interactions/agent/month

Automated approach: 100% of interactions

Step 8: Feedback sessions

Frequency: Weekly or twice-weekly

Format: 1-on-1, 15-30 minutes

Structure :

1. Celebrating strengths
2. Identify 1-2 areas for improvement
3. Listening together 1-2 interactions
4. Co-create an action plan
5. Define next steps

Step 9: Continuous calibration

Quarterly calibration sessions to maintain consistency.

Step 10: Measure and optimize

Metrics to follow :

• Average QA score (evolution)
• Score distribution (number of agents at each level)
• Correlation QA score vs. CSAT/NPS
• Impact of coaching (post-feedback improvement)

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Quality monitoring best practices

1. Analyze 100% of interactions (not 2%)

With AI, it's possible and affordable. Historically, QA teams analyzed 2-4 calls per agent per month, but with speech analytics, organizations can now review up to 100% of voice calls.

2. Combining automated and human QA

• AI: scores 100% of interactions, detects patterns, alerts on anomalies
• Human: in-depth review of complex cases, personalized coaching, calibration

3. Linking QA and customer satisfaction

Analyze the correlation between QA and CSAT/NPS scores to identify which criteria really impact satisfaction.

4. Real-time (or near-real-time) feedback

Feedback delayed by 2 weeks is ineffective. Aim for 24-48 hours maximum.

5. Make QA visible and transparent

• Agents must have access to their scores in real time
• Leaderboards (tactfully)
• Public celebrations for top performers

6. Using QA to detect systemic problems

If 80% of agents fail on the same criterion, the problem isn't the agents: it's the process, the training, or the tool.

7. Gamification

Make the QA engaging:

• Badges for milestones achieved
• Monthly challenges
• Awards for top performers

8. Peer coaching

The best agents coach others. It's a powerful thing.

9. Regular calibration

At least quarterly to maintain consistency.

10. Focus on coaching, not punishment

AQ is a tool for development, not police surveillance. This mentality is crucial.

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Common mistakes to avoid

Mistake #1: Scoring without giving feedback

The score alone is useless. Feedback and coaching are the essence of AQ.

Mistake #2: Overly complex evaluation grid

50 criteria = total confusion. Limit to 8-12 essential criteria.

Error #3: Lack of calibration

Uncalibrated raters = inconsistent scores = frustrated agents.

Error #4: Biased sampling

Evaluating only "bad" agents or only short calls = huge bias.

Mistake #5: Ignoring the context

A difficult call with an aggressive customer should not be scored in the same way as a routine call.

Mistake #6: Using AQ as a disciplinary tool

"Your QA score is low, you're on probation" = wrong approach. QA should be a development tool.

Mistake #7: Never reviewing the grid

Your business is changing. Your QA grid must evolve too. Minimum annual review.

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The impact of AI on quality monitoring

AI has revolutionized AQ in 2025.

Before AI :

• 1-3% of interactions evaluated
• Delayed feedback for weeks
• Subjectivity of evaluators
• High cost (human time)
• No large-scale pattern detection

With AI :

• 100% of interactions evaluated
• Real-time scoring
• Objectivity and consistency
• Reduced cost (automation)
• Large-scale insights (patterns, trends)

Klark is a case in point:

Our generative AI automatically analyzes each support interaction and :

• Detects customer frustration signals in real time
• Suggests optimal responses to agents (improves quality)
• Identifies coaching opportunities
• Measures empathy, clarity and efficiency

Result: +20% CSAT and 2x better agent performance.

Find out more in : AI in customer relations.

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Essential quality monitoring KPIs

1. Average QA score

Formula: Average of all agent scores

Good score: 85%+ (varies by industry)

2. Compliance rate

Formula: % of standard-compliant interactions

Target: 95%+ (especially in regulated sectors)

3. Score distribution

• How many 90%+ agents?
• Combien d'agents à < 70% (nécessitent coaching intensif) ?

4. QA-CSAT correlation

Objective: Strong positive correlation (r > 0.7)

If there is no correlation, your QA grid is not evaluating the right criteria.

5. Feedback time

Formula: Time between interaction and feedback session

Objectif : < 48h

6. Post-coaching improvement

Formula: Evolution of score after coaching session

Target: +10-20 points on average

7. Coverage

Formula: % of interactions evaluated

Target: 100% (with AI) or minimum 3-5 interactions/agent/month (manual)

The future of quality monitoring

1. Predictive AQ

AI will predict quality problems before they occur: "This agent is showing signs of burnout, his score will probably drop."

2. Real-time coaching

The AI will suggest corrections in real time during the interaction (already being tested by some leaders).

3. Emotional AQ

Beyond words: analysis of micro-expressions, vocal tone, energy to assess real empathy.

4. Auto-QA

Agents will be able to self-evaluate their interactions with instant AI feedback.

5. Holistic AQ

Full integration with CX: link QA, CSAT, NPS, CES, behavioral data for a 360° view.

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Why Klark improves your quality monitoring

Quality monitoring measures quality. Klark improves it at source.

How?

• Generative AI that suggests optimal responses in real time → automatically enhanced quality
• Integrated sentiment analysis → proactive detection of frustrated customers
• AI-powered knowledge base → agents always have the right info
• Automated coaching → identification of improvement opportunities

Result:

• QA scores increased by 15-25%.
• CSAT 20% improvement
• Guaranteed compliance (AI follows your scripts and processes)

You don't just measure quality. You're actively boosting it. 🚀

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Ready to transform your quality monitoring?

Modern quality monitoring has nothing to do with manually listening to 2 calls a month.

Key points to remember :

• Go from 1-3% to 100% evaluated interactions thanks to AI
• Combine automated QA (AI) and human QA (coaching)
• Create a focused evaluation grid (8-12 criteria max)
• Give feedback within 48 hours
• Calibrate your evaluators regularly
• Use QA to detect systemic problems
• Best tools 2025: AmplifAI, Genesys Cloud, Enthu.AI, NICE CXone
• Aim for a strong correlation between QA score and CSAT

Want to not only measure quality, but radically improve it? Request a Klark demo and discover how our generative AI turns your agents into superstars.

Because the best quality monitoring is the one that makes monitoring almost unnecessary 😉

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About Klark

Klark is a generative AI platform that helps customer service agents respond faster and more accurately, without changing their tools or habits. Deployable in minutes, Klark is already used by over 50 brands and 2,000 agents.