How QA Volume Impacts Call Center Agent Performance

Our research shows more QA visibility drives stronger agent performance—see how AI Agents for Operations evaluate 100% of calls, no sampling required.

“This call is being recorded for quality assurance purposes.” We’ve all heard this message before, but we know the sad truth:

That tradeoff between coverage and cost is exactly what we set out to measure below. As you’ll see, more QA visibility consistently correlates with stronger agent performance—which raises an obvious question: what happens when you no longer have to choose between sampling a handful of calls and evaluating everything? Today, AI Agents for Operations, part of Observe.AI’s agentic AI platform, can automatically evaluate 100% of customer interactions, eliminating the QA volume tradeoff this research identifies rather than just narrowing it.

The vast majority of contact centers only evaluate 2% of customer conversations, even though they know those conversations hold valuable, business-altering insights.

According to our survey of 300+ contact center leaders, 99% of respondents say they use insights from customer conversations to inform business decisions.

However, only about 1 in 3 respondents were completely satisfied with their level of visibility into those conversations.

Without visibility into what’s actually happening on the front line of your contact center, it’s nearly impossible to make data-driven decisions to improve performance and drive better business outcomes.

How do you know what’s driving sales conversions?
How do you know which agents are outperforming or underperforming?
How do you provide targeted coaching to agents who need it?

To better understand how more visibility affects business outcomes, we analyzed how QA volume impacts agent performance.

We’re big believers in the power of visibility, so, while we aren’t surprised by these numbers, the data is striking.

How QA Volume Impacts Agent Performance

In our analysis, the value of visibility was evident.

When contact centers increased QA volume from 0.1% of calls to 19.15%, agent fail rates decreased by 20%.

Why?

When you have full visibility into what’s happening in customer conversations, leaders have a better understanding where to focus training and coaching.

This data-driven approach leads to better business outcomes as teams focus on the metrics and behavior that matter most.

Agents are the lifeblood of every contact center.

How they perform directly correlates to how the contact center performs.

Poor agent performance leads to low operational efficiency, poor customer satisfaction, and stalled revenue growth.

The opposite is true for high performing agents.

And the more you invest in targeted coaching for agents, the better the result.

According to our State of the Contact Center Conversation Intelligence report, companies with top performing agents are 4x more likely to feel prepared for the future.

But what’s the key to top performing agents? 80% of respondents with top performing agents said they had a formal coaching program, 4x more likely than those that do not.

Targeted agent coaching is key, and it starts with visibility into what’s actually happening on the front lines.

4 Ways to Increase QA Volume

If most contact centers only QA 2% of customer conversations, increasing to 20% of calls may seem daunting (although we have seen our customers get to 100%).

We get it.

But we’ve also seen our customers make significant headway and see massive business results.

Here are some ways we’ve seen contact center leaders, just like you, increase their QA volume and gain more visibility.

1. Increase QA Headcount

This may seem like a no-brainer, but it’s true. If you need to increase QA volume, one way to do it is to grow your QA team. Depending on the health of the business and whether or not your industry is considered “recession-proof,” the viability of this option will vary.

2. Refine Your QA Forms

Many teams’ QA forms have ballooned over the years for one reason or another. We see it all the time. Sometimes teams will be using templatized QA forms just because it’s industry-standard and they haven’t stopped to ask why they’re asking these questions and whether they impact the business.

By limiting the size and scope of your QA forms to only the questions that matter most to your business, you can reduce the time it takes for a QA manager to get through it.

3. Adopt QA Technology

For years, QA teams were using disconnected, manual, and inefficient spreadsheets to evaluate customer conversations. This is a painstakingly long process that is prone to inaccuracies. It’s time to take QA into the 21st century and embrace online workflows that are not only more user-friendly, but also connect to reporting and training interfaces as well.

We’re not the only ones that think so either. According to our report, quality assurance technology was ranked as the single most important technology for contact center leaders.

4. Automate Your QA Process

We have seen customers’ QA go from 0% to 100% by using automated quality management software, like Auto QA. I’m obviously biased, so I’m not going to sell you too hard on this, but you can learn more about it here, schedule a chat with one of our experts, and read how Figo is saving $700K a year with Auto QA..

AI Agents for Operations: QA Coverage Without the Tradeoff

The four tactics above—adding headcount, refining forms, adopting QA technology, and automating scoring—were the right moves for a world where evaluating every conversation wasn’t realistic. Even automated QA tools were typically applied to a defined sample, because evaluating 100% of interactions with legacy technology meant more infrastructure, more review time, and more cost. That constraint is what created the volume tradeoff this data illustrates in the first place.

AI Agents for Operations removes that constraint. It’s one of the Purpose-Built AI Agents on Observe.AI’s One CX Platform, and rather than scoring a sample and extrapolating, it autonomously evaluates every voice and digital interaction against your QA criteria, consistently and at scale. It works alongside VoiceAI Agents and ChatAI Agents that handle conversations directly, and the Companion Agent (the evolution of real-time agent assist) that guides human agents live—giving contact centers full visibility into 100% of interactions instead of the 2% most teams sample today.

Evaluates 100% of voice and digital interactions automatically, not a small sample
Applies QA criteria and scoring consistently across every conversation and every agent
Surfaces root-cause trends and targeted coaching opportunities without manual review
Frees QA analysts to spend time coaching agents instead of scoring calls
Complements the Companion Agent, which supports agents in real time while AI Agents for Operations evaluates outcomes after each interaction

Get More Contact Center Insights

For more contact center insights like these, subscribe to our newsletter and check out these resources:

‍

No items found.

Want more like this straight to your inbox?
Subscribe to our newsletter.

Thanks for subscribing. We've sent a confirmation email to your inbox.

Oops! Something went wrong while submitting the form.

Frequently Answered Questions

What is contact center quality management software?

Contact center quality management software evaluates customer interactions against defined criteria to measure agent performance, compliance, and customer experience. Traditionally, that meant QA teams manually listening to and scoring a small sample of calls. Modern platforms, including Observe.AI’s AI Agents for Operations, extend the same quality management workflow to 100% of interactions automatically, removing the sampling limits legacy tools required.

How much of our call volume should we QA to see results?

Our analysis found that increasing QA volume from 0.1% to 19.15% of calls reduced agent fail rates by 20%—the more conversations you evaluate, the more accurately you can identify coaching opportunities and performance trends. With AI Agents for Operations, contact centers no longer have to weigh volume against resourcing; they can evaluate 100% of interactions from day one.

How is AI Agents for Operations different from traditional automated QA?

Traditional automated QA tools were a major step beyond manual scoring, but most implementations still applied evaluation to a defined sample of calls. AI Agents for Operations is built to autonomously evaluate every voice and digital interaction, applying consistent criteria across 100% of conversations and surfacing coaching insights without adding headcount.