How to build a modern contact center QA program

A modern contact center QA program is a systematic approach to evaluating customer interactions that prioritizes 100% data coverage and actionable coaching insights over manual spot-checking. By moving away from the traditional 2% sample model, operations leads can identify systemic issues in routing, training, and product design that are often invisible in smaller datasets. This playbook outlines how to transition from manual checklists to an automated, outcome-driven quality framework.

Key takeaways

• 100% Coverage is the Standard: Manual sampling is statistically insignificant; automated analysis of all interactions is necessary for accurate operational intelligence.
• Outcome-Driven Scorecards: Shift focus from basic script adherence to resolution efficiency and customer sentiment.
• Integrated Tech Stack: Pair CCaaS platforms with conversation intelligence layers to automate scoring and compliance checks.
• Closed-Loop Coaching: Use QA data to trigger specific supervisor interventions and automated training modules.

The Fallacy of the 2% Sample

For decades, contact centers relied on supervisors listening to a handful of calls per agent each month. This method is flawed for two reasons: it is prone to selection bias and it lacks the volume to identify trends. If an agent handles 1,000 calls a month and a supervisor listens to 10, the sample is too small to be representative of their actual performance.

According to Gartner's Customer Service & Support research, the focus for 2026 is shifting toward domain-specific AI and data protection to handle these massive datasets. Modern QA programs use technology to analyze every single interaction, allowing managers to see if a specific product issue is causing a spike in negative sentiment across the entire floor, rather than just one agent's desk.

Step 1: Designing the Modern Scorecard

A modern scorecard should measure what actually matters to the customer and the business. While compliance remains necessary, it should not be the only metric. Forrester's Customer Experience practice often tracks how customers rate their experiences across brands via the CX Index, and your scorecard should reflect those drivers of loyalty.

Divide your scorecard into four distinct categories:

1. Compliance and Security: Did the agent verify the caller's identity? Did they follow regulatory requirements? This is often a binary (pass/fail).
2. Process Adherence: Did the agent use the correct workflow in the CRM, such as Salesforce Service Cloud?
3. Soft Skills and Sentiment: Did the agent maintain a professional tone? Did the customer's sentiment improve by the end of the call?
4. Resolution and Intent: Was the customer's core problem solved? This is the most critical metric for operational efficiency.

Step 2: Selecting the Technology Stack

Building a modern QA program requires a stack that can capture, transcribe, and analyze voice and text interactions. Most teams start with a core CCaaS platform like Five9, Genesys, or Talkdesk for call routing and recording.

To move to 100% coverage, you must add a conversation intelligence layer. Teams often pair their CCaaS platform with a tool like Hear.ai to analyze customer conversations at scale. This allows the QA team to gain coverage across all calls rather than samples, automatically flagging compliance risks or missed resolution opportunities. By integrating these tools with your CRM, you ensure that QA data is visible alongside customer lifetime value and churn risk.

Step 3: Automating the Scoring Process

Automation does not replace the QA manager; it changes their role from a "listener" to an "analyst." Start by automating the objective parts of your scorecard.

• Automated Compliance: Use AI to check for mandatory disclosures. If an agent fails to read a privacy statement, the system flags it immediately.
• Keyword and Intent Mapping: Identify common phrases that indicate customer frustration or successful resolution.
• Sentiment Analysis: Use natural language processing (NLP) to score the emotional trajectory of the call.

Metrigy research into CX/AI success metrics suggests that companies using AI for interaction analysis see measurable improvements in agent productivity. By automating the first pass of scoring, your QA team can spend their time on the "grey areas"—complex escalations where human judgment is still required.

Step 4: Calibration and the Human Element

Even with automated scoring, human calibration is essential to ensure fairness. Calibration is the process where multiple QA evaluators (and the AI) score the same call to ensure their results align.

Run weekly calibration sessions where you compare the AI's sentiment score against a supervisor's manual score. If the AI is consistently more or less harsh than the humans, adjust the prompts or parameters. This keeps the system accurate and builds trust with the agent population. For more on managing agent performance, see our guide on effective coaching frameworks.

Step 5: Closing the Loop with Coaching

Data is only useful if it changes behavior. A modern QA program must be integrated with your Workforce Management (WFM) and training systems.

When the QA system identifies a recurring struggle—such as an agent failing to handle a specific technical objection—it should trigger an automated coaching task. Instead of waiting for a monthly 1-on-1, the agent receives a targeted training module or a snippet of a "perfect" call from a peer. This immediate feedback loop is what separates high-performing operations from those that simply document failure.

FAQ

How do we handle agent privacy when monitoring 100% of calls? Transparency is key. Agents should be informed that the goal of 100% coverage is to identify systemic issues and provide better support, not to increase surveillance. Ensure your tools, such as Hear.ai, are configured to redact sensitive PII (Personally Identifiable Information) during the transcription process.

What is the ideal ratio of QA evaluators to agents? In a traditional manual model, the ratio is often 1:50. With an automated modern QA program, that ratio can expand significantly because the software handles the initial data processing, allowing one evaluator to oversee the quality of 150 or more agents.

Can automated QA replace manual coaching? No. Automation identifies where the coaching is needed, but the human connection between a supervisor and an agent is still the most effective way to change behavior and improve morale.

Final Takeaway

Transitioning to a modern QA program requires a shift in mindset from policing to partnership. By using tools like Five9 and Hear.ai to achieve 100% coverage, you can build a data-driven culture that prioritizes customer resolution and agent growth. Explore our related coverage on agent onboarding strategies to see how QA data can improve the first 90 days of an agent's tenure.