How to Review More Customer Calls Without Hiring More QA Staff

Customer service teams want to evaluate more calls so they can understand performance, coach more effectively, and improve customer experience. But manual QA is time-consuming, expensive, and difficult to scale — especially for small and mid-sized teams. As call volumes grow, so does the backlog, leaving supervisors with the impossible task of reviewing a meaningful portion of conversations.

Here’s how teams can dramatically increase their call coverage without hiring more QA reviewers or sacrificing quality.

1. Stop Relying on Random Sampling

Most teams review 1–3% of calls, and those calls may not reflect the reality of what’s happening day to day.

Random sampling leads to:

• coaching based on outliers
• inconsistent insight
• limited visibility into customer experience
• slow detection of recurring issues
• blind spots across agents and topics

Increasing coverage begins with replacing random sampling with systematic insight.

2. Automate the First Pass of Call Evaluation

The most time-consuming part of QA is the initial review. AI can automate this step by:

• evaluating every call
• identifying missed steps
• detecting customer confusion
• scoring core behaviors
• highlighting unusual or high-risk interactions

Reviewers can then focus on what matters — the calls that need attention.

This saves time without lowering standards.

3. Use AI to Identify High-Impact Calls

Not all calls are equal. Some conversations contain:

• friction that needs analysis
• early signs of dissatisfaction
• policy misunderstandings
• tone or sentiment shifts
• coaching moments for new agents

AI can automatically flag these calls so supervisors spend time where it counts, not digging through dozens of low-impact interactions.

4. Simplify and Standardize Your Quality Scorecard

QA teams often use complex scorecards with dozens of criteria.

These take longer to complete and discourage consistency.

Instead:

• focus on the behaviors that matter most
• reduce ambiguity in criteria
• make scores easier to compare across agents
• align scoring with what customers actually care about

Streamlined scorecards speed up review and improve consistency — even before automation is added.

5. Combine AI Evaluation With Human Judgment

Automation doesn’t replace human QA — it multiplies its effectiveness.

A modern approach looks like this:

• AI handles: reviewing every call, surface-level scoring, pattern detection
• Humans handle: nuance, coaching, edge cases, final decisions

This hybrid model gives teams full coverage without needing to hire additional staff.

6. Turn Insights Into Coaching Workflows

Reviewing more calls is only valuable if it drives improvement.

Teams can accelerate agent development by using insights for:

• weekly coaching themes
• targeted training plans
• peer review sessions
• real-time feedback loops
• onboarding for new agents

More coverage = more visibility = more opportunity to grow.

7. Close the Loop With Trends and Patterns

Increasing call review coverage also unlocks trend-level insights:

• which topics cause friction
• where processes break down
• which behaviors predict success
• which issues recur across customers
• how call drivers shift over time

These insights help leaders make stronger operational decisions — well beyond the quality team.

Why It Matters

You don’t need to hire a large QA team to review more calls.

With automation and smarter workflows, teams can:

• expand coverage
• improve consistency
• accelerate coaching
• catch issues earlier
• deliver better customer experiences

The path forward isn’t more headcount — it’s more clarity.

What’s Next

Future Insights will explore how automated QA connects to call scoring, real-time analysis, and customer signals to give teams a complete understanding of their conversations.