Call Quality Monitoring Guide for Contact Centers
TL;DR: AI-powered quality management (QM) evaluates every conversation automatically, connects agent behaviors to business outcomes, and surfaces coaching opportunities that manual sampling misses. Organizations making this shift move from scorecards built on executive assumptions to data-driven frameworks based on what actually drives resolution, satisfaction, and revenue.
Most contact centers review 1-2% of conversations through manual sampling, which means decisions about agent performance, compliance posture, and customer experience are based on extrapolations from a tiny slice of reality. The agents know their performance is being judged on a handful of calls that may not represent their actual work, and your compliance team knows it too, even if they don't say it out loud during audits.
The deeper problem isn't just coverage. Traditional QM programs build scorecards based on what executives think matters rather than what the data shows actually drives outcomes. Without the ability to correlate behaviors with results across thousands of conversations, you're guessing. Your top performers might be doing something specific that makes them successful, but you can't see it in a 1-2% sample.
This guide walks through the eight decisions that shape an effective QM program, from defining goals and designing scorecards to calibrating evaluators and building coaching workflows.
Step 1: Define your quality goals
Quality monitoring fails when you skip the foundational question: what are you actually trying to accomplish? Start by identifying which outcome categories matter most to your operation: customer experience improvement, operational efficiency, compliance adherence, or business impact.
Once you know what matters, build your scorecard around those priorities. A scorecard measuring compliance tells you nothing about revenue performance. Tracking empathy provides limited insight into whether agents follow required data security protocols. The metrics you choose determine what behaviors your team prioritizes.
If you're like most contact center leaders, you're juggling pressure to hit efficiency targets while your CX team demands higher satisfaction scores. Contact center agent turnover runs between 30-45% annually according to Cresta's 2024 State of the Agent Report, with each departure costing $10,000-$21,000 in recruitment, training, and lost productivity. These competing demands feel even more acute when you're constantly rebuilding your team. Your quality monitoring program needs to acknowledge these tensions explicitly rather than pretending you can maximize everything simultaneously.
Step 2: Identify what "good" looks like on calls
The behaviors that drive positive outcomes don't always match what executives think matters. But it's worth distinguishing what "behaviors" means in this context, because the term covers more ground than most scorecards reflect.
Process adherence and regulatory compliance are your baseline. Authentication steps, required disclosures, data security protocols. Most organizations already track these. Behavioral competencies are different. These are the conversational choices that influence how a customer feels about the interaction and whether the issue actually gets resolved.
Advocacy language, for example, demonstrates a bigger impact on customer satisfaction than any other single agent behavior. When an agent says "Let me take ownership of this so you don't have to call back" instead of a scripted apology, customers respond differently. Setting appropriate expectations has a similar effect. An agent who tells a customer "You should see the credit within 48 hours" reduces repeat contacts because the customer leaves knowing what to expect.
Connecting behaviors to business outcomes
Contact centers have traditionally built scorecards based on what executives thought mattered, creating wishlists of behaviors that felt important without evidence that they actually drove results. Modern approaches use conversation data to identify behaviors that statistically correlate with resolution, satisfaction, or conversion.
This is where outcome inference becomes critical. The most advanced platforms can classify whether a sale was made, whether a conversation was resolved, and what the customer satisfaction score was, directly from conversation transcripts rather than relying on post-call surveys or CRM data entry. This capability separates platforms that track keywords and disclosures from those that actually understand what drives results.
When you can correlate specific agent behaviors with verified outcomes across all your conversations, your scorecard stops being a wishlist and becomes a data-driven framework of what actually moves the needle.
Step 3: Design your quality scorecards
The biggest mistake in scorecard design is measuring too many things. Overloaded scorecards confuse priorities and slow down evaluations. Even when AI auto-scores the majority of your criteria, too many items create more analysis work on the backend for leaders trying to separate signal from noise and important criteria from everything else.
Weighting that reflects your priorities
Weighting signals what your organization actually values. Consider allocating the largest portion to customer outcome metrics, including first contact resolution and issue resolution quality. Communication skills deserve the second largest allocation. Compliance and policy adherence merit attention for regulatory requirements. Process efficiency gets the smallest allocation.
An agent who resolves the customer's issue completely while taking slightly longer than the target handle time delivers more value than an agent who rushes through required steps while leaving the problem only partially addressed. The scorecard structure should reinforce this priority hierarchy.
Choosing your scoring scale
Weighted point scales using 0-5 ratings provide flexibility but introduce subjectivity that needs careful calibration, whether the evaluator is a human analyst or an AI model. Binary pass/fail scoring reduces subjectivity and works best for non-negotiable compliance items. The most sophisticated scorecards use a hybrid approach with weighted scales for behavioral categories and binary scoring for compliance items, maintaining 8-12 evaluation criteria.
The scorecard must distinguish between behaviors that occur on every call and situational behaviors. Authentication requirements happen on every interaction. Objection handling only matters when customers raise objections, which means the scorecard needs a "not applicable" option for evaluations where that behavior never had a chance to occur.
Step 4: Choose your monitoring approach
You face a fundamental decision about coverage philosophy. Traditional manual quality management reviews only 1-2% of interactions due to the resource constraints of having humans listen to every conversation. That sampling approach creates significant blind spots where compliance violations and performance patterns go undetected.
The coverage gap creates particular problems for regulated industries. A financial services contact center manually sampling a small fraction of calls leaves the vast majority of conversations unmonitored for required disclosures. A collections team subject to the Fair Debt Collection Practices Act (FDCPA), for example, needs agents to deliver specific language on every call. Sampling 1-2% means you can't verify whether that happened on the other 98%. Can you really defend your compliance program to auditors when you've only reviewed 1-2% of conversations?
The human plus AI approach
AI-powered quality monitoring evaluates all conversations by using natural language processing to automatically score interactions against defined criteria. But the most effective programs combine AI-powered monitoring of all interactions with human oversight for calibration, complex cases, and developmental coaching. This human plus AI model uses automation for complete coverage at scale while preserving the human expertise needed for agent development.
CVS Health, the largest pharmacy healthcare provider in the U.S., faced exactly this challenge while coordinating AI strategy across multiple large, diverse business lines. Their traditional survey-based feedback suffered from low response rates and delayed signals. After implementing AI-powered conversation intelligence, CVS Health moved from scoring just 5% of calls to 100% call scoring with AI. The platform provided predictive customer satisfaction (CSAT) on every call and dramatically faster time to insight, from weeks to minutes.
The accuracy of AI-powered monitoring depends critically on transcription quality. When speech-to-text systems misunderstand words or fail to distinguish between speakers, downstream AI models produce unreliable results. Cresta's custom Automatic Speech Recognition (ASR) delivers over 92% transcription accuracy through models fine-tuned on customer audio and business-specific vocabulary.
AI systems automatically score all conversations against defined scorecard criteria. Supervisors or dedicated QM teams then manually evaluate selected conversations, typically 5-6 per agent monthly, for validation, calibration of AI scoring rubrics, agent development, and dispute resolution. Research establishes this minimum threshold to achieve a statistically meaningful assessment. Deliver evaluation feedback promptly while interaction details remain vivid for both agent and coach.
Prioritizing what gets evaluated
High-risk interactions receive priority evaluation, including compliance-sensitive calls, escalations, high-value customer accounts, and new agent interactions during the first 90 days. Cresta's platform automatically queues conversations for manual review based on criteria you define, so if an interaction contains high-risk keywords, involves a specific topic, or meets other metadata conditions, it gets routed to your QM team without anyone having to go looking for it. Representative sampling distributed across all interaction types prevents blind spots. Targeted monitoring focuses on specific agent improvement areas.
Oportun, a mission-driven fintech serving 2 million members, faced a common challenge: limited resources, largely untapped QM data, and an inability to identify coaching opportunities in real time. After implementing Cresta's platform, Oportun achieved 100% quality analysis (QA) coverage while simultaneously reducing QM workload by 50%. This combination of complete visibility and reduced administrative burden freed their team to focus on coaching conversations rather than the time-consuming sample selection process.
Step 5: Calibrate evaluators and AI scoring
Calibration keeps your evaluators aligned on how to score calls. Without regular calibration, different evaluators gradually drift in their scoring approaches, creating unfair situations where agents receive different ratings for similar performance.
Calibration sessions bring together QM evaluators, supervisors, and agents to rate and discuss calls together. Each participant independently scores the same interaction, then the group examines inconsistencies. When three evaluators score the same objection handling attempt as a 3, 4, and 5 on a 5-point scale, the variance signals that the scorecard criteria need clarification.
AI scoring calibration needs periodic audits where both AI and human evaluators score identical interactions. When AI systems consistently rate differently from human evaluators, investigate whether humans are applying inconsistent standards or whether the AI model needs retraining. You can also track how often human analysts override AI scores on specific criteria. If a particular rule is getting overridden frequently, that's an early signal the criteria need refinement before you get to a formal calibration session.
Automated scoring significantly reduces the variability found in manual review, providing more consistent evaluation across interactions. However, consistency alone doesn't ensure fairness. True fairness needs complementary practices, including evaluator alignment, agent participation in the QM process, and formal dispute resolution mechanisms that let agents challenge results they believe are inaccurate.
Step 6: Turn QM results into coaching
Quality monitoring only drives value when you convert evaluation data into coaching that actually changes agent behavior. Many contact centers invest significant resources in evaluation infrastructure but fail to establish systematic processes for turning insights into development conversations.
Using analytics to drive coaching
Picture this scenario. Sarah, one of your agents, has a lower sales conversion rate than her peers. You open her coaching profile and the platform shows you, based on analysis of her QM scorecards, that she doesn't consistently ask discovery questions when customers raise a price objection. The data also tells you that asking discovery questions in response to price objections is highly correlated with conversion across your team.
Instead of randomly selecting calls to review, you pull up every conversation where Sarah faced a price objection and filter by instances where she did and didn't ask discovery questions. Now you have specific examples of both behaviors to walk through with her, grounded in her actual conversations and tied to an outcome she cares about. That's the difference between coaching that feels arbitrary and coaching that feels actionable.
This precision matters because generic coaching doesn't work. Research from Cresta's 2024 State of the Agent Report reveals that 49% of contact center agents report receiving highly effective coaching, and much of that gap comes down to personalization. Agents who receive personalized coaching say it's nearly 3x more effective than one-size-fits-all approaches. The downstream effects are significant: 91% of agents with personalized coaching report being happy at work, compared to only 57% of those with standard coaching. When your QM program can pinpoint exactly what each agent needs to improve and surface the evidence to support it, you're not just coaching more effectively. You're building the kind of environment where agents actually want to stay.
Structured feedback conversations follow a balanced framework that starts with positive recognition, provides constructive feedback in the middle, and ends with another positive note. Limit focus to one or two key behaviors to avoid overwhelming agents. End with a clear agreement on a single action plan and scheduled follow-up.
Step 7: Balance real-time guidance with post-call analysis
Real-time agent guidance and post-call quality review serve fundamentally different purposes, and you need both working together.
Real-time systems monitor interactions as they happen, providing immediate suggestions based on customer sentiment, conversation context, and compliance needs. This reduces errors and boosts first-call resolution. Real-time guidance works best for compliance-critical interactions where immediate prompts prevent costly violations, high-stakes situations where intervention can prevent escalation, and new agent support.
Post-call analytics reveal patterns, trends, and areas of strength or weakness over time. This supports developmental coaching that builds long-term agent capabilities and root cause analysis for recurring issues.
While real-time assistance provides valuable performance support, its ability to develop agents and build long-term capabilities on its own is limited. Agents do internalize guidance they see repeatedly, but structured post-call coaching is what builds deeper skill development. Real-time guidance handles immediate error prevention. Post-call analysis handles the broader picture of agent growth.
Effective contact centers deploy both as complementary workflows. Cresta's real-time capabilities are a genuine differentiator here. Even technologically advanced competitors struggle with firing the right hint at the right moment or tracking anomalies as they happen.
Step 8: Measure and iterate on your QM program
Root cause analysis protocols systematically analyze underlying causes of recurring problems. Effective programs must diagnose whether performance gaps result from training deficiencies, process design flaws, system limitations, or environmental factors. Rather than treating symptoms by coaching individual agents on issues that stem from broken processes, organizations that identify the true source of problems achieve sustainable improvements.
The most sophisticated organizations use conversation data to continuously refine their quality monitoring programs. Platforms that correlate agent behaviors to business outcomes provide data to identify high-impact coaching areas. This lets QM programs evolve based on measured results rather than assumptions.
Scorecard refinement should occur when performance data reveals inconsistent scoring patterns, when business objectives shift, or when agent feedback identifies gaps between scorecard items and actual service requirements. Conduct formal reviews minimum every six months with agent involvement to ensure criteria reflect operational realities.
How Cresta supports your quality monitoring journey
Cresta's unified platform is built around outcome inference: the ability to classify whether a sale was made, whether a conversation was resolved, and what the customer satisfaction score was, directly from conversation transcripts. When you can correlate specific agent behaviors with verified business outcomes across all conversations, scorecards become data-driven rather than executive wishlists.
The three capabilities discussed throughout this guide, real-time guidance, coaching workflows, and outcome analysis, determine whether a QM platform actually improves performance or just generates reports. Forrester evaluated vendors on exactly these criteria and named Cresta a Leader in The Forrester Wave™: Conversation Intelligence Solutions for Contact Centers, Q2 2025, with the highest Current Offering score and perfect marks in all three.
Cresta Conversation Intelligence analyzes all interactions to identify which behaviors actually drive results. Cresta Coach surfaces specific coaching actions for each agent with evidence from their conversations. Cresta Agent Assist reinforces coaching in real time during live interactions. And because Cresta has spent seven to eight years building these QM and coaching tools for human agents, the same infrastructure now applies to AI agent oversight, providing mature capabilities that newer market entrants lack.
Visit our resource library to explore more quality monitoring approaches, or request a demo to see how the platform works in practice.
Frequently asked questions about call quality monitoring
How do we get agents to trust the quality monitoring process?
Trust comes from transparency and participation. Include agents in calibration sessions so they gain direct exposure to quality standards. Implement formal dispute resolution procedures that let agents challenge results they believe are inaccurate. Share evaluation results directly through the QM platform. When agents participate in the evaluation process rather than viewing it as something done to them, they develop a genuine partnership in performance improvement.
How do we measure whether our quality monitoring program is actually working?
Correlate internal quality scores with customer satisfaction metrics to validate effectiveness. Track improvement velocity, meaning the rate of performance enhancement following coaching initiatives. Cresta's Coaching Reports take this a step further by tracking your coaches themselves, plotting coaching activity against coaching effectiveness so you can see who needs to coach more often, who needs to coach differently, and what your best coaches do that others don't. The goal is to continuously refine your approach based on which behaviors actually correlate with business outcomes, not assumptions about what should matter.
How do we handle quality monitoring for AI agents, not just human agents?
Generative AI agents behave non-deterministically, which means they require the same oversight infrastructure as human agents. You can apply the same QM frameworks, scorecards, and calibration processes to AI agent conversations.
The key is having a platform that provides unified visibility across both human and AI interactions. Organizations that built QM programs only for human agents often discover significant blind spots when they deploy AI agents without equivalent monitoring.
What's the difference between tracking sentiment and tracking outcomes?
Sentiment analysis tells you how customers felt during a conversation. Outcome inference tells you what actually happened: whether the issue was resolved, whether a sale was made, or whether the customer churned. Many platforms stop at sentiment and keyword tracking, which means your scorecard becomes a wishlist of behaviors that seem important rather than a data-driven framework of what actually drives results. Platforms with true outcome inference can correlate specific agent behaviors with verified business outcomes, so you know which behaviors to reinforce in coaching.
