Performance doesn’t improve by accident. It improves when organizations take a disciplined approach to measuring real behaviors in interactions and use those insights to understand trends that drive coaching, training, and change.
Tracking individual call evaluations by memory—or by pen and paper—quickly becomes overwhelming, especially when quality assurance (QA) analysts are responsible for weekly or monthly quotas across 30, 40, or even 50 agents.
With most agents receiving six to eight evaluations per month, that workload adds up fast: a 40-agent team can easily generate nearly 300 QA reviews in a single month.
One call doesn’t tell the story. Multiple evaluations reveal patterns: separating occasional mistakes from consistent behaviors and creating targeted coaching opportunities, such as improving call control to reduce average handle time (AHT) (also see my three-part series on AHT in the May, June, and July issues).
When analysts are moving from call to call and score to score at that volume, it becomes difficult to see patterns over time. Like:
- “Why was Susie marked down for call authentication this week and was it the same issue she missed last time?“
- “Why did Pat fail two evaluations for reasons that seem unrelated on the surface?”
Without structure, those answers get lost while QA data stays fragmented and becomes guesswork. And fragmented data rarely leads to meaningful improvements: if any at all.
As one principle has consistently proven true in call centers: QA data doesn’t fail organization. What fails is treating it as a scorecard instead of a signal.
This is where turning QA activity into insight—rather than just scores—makes all the difference. It brings the QA process full circle; my April article on QA focused on seeing it through the agents’ eyes while in the December issue I will look at integrating QA with training. I will also discuss training here as well.
Start With a Simple, Structured Tracking System
About 10 years ago, when I worked as a team lead, I was responsible for completing weekly QA evaluations for my team of 12 to 16 agents. Like many new leaders, I approached QA the same way everyone else did at the time: complete the required quota and move on to the next task.
Each agent received three evaluations per week: a short call (one to two minutes), a medium call (three to four minutes), and a long call (anything over four minutes). I took detailed notes, explained deductions to agents, and even gave some agents second chances when warranted.
On the surface, it felt thorough, at least to me at the time. But over time, I realized something was missing.
I couldn’t easily answer basic questions, such as:
- Which agents were repeating the same mistakes?
- How often was I overlooking an issue versus coaching it or placing the agent on corrective action for repeat offenders?
- Were the specific problems isolated? Or were they showing up across the team?
Once the week ended, the insights disappeared with it. You know that feeling when the whistle blows, you slide down the dinosaur’s tail, as in the old “Flintstones” cartoon, ready to clock out.
Just as important: don’t only track what went wrong. Track what went right.
That’s when I built a simple tracking system: nothing fancy, just an Excel spreadsheet. (Google Sheets works just as well, especially for smaller centers or teams without the budget for a dedicated QA platform.)
Here’s what I tracked:
- Category of markdown (e.g., Call Authentication, Empathy, Long Hold Without Check-Back).
- Specific reason for the issues (for example: “Agent failed to verify member’s date of birth”). Be specific; two months later you won’t remember what the deduction was really about.
- Call details (channel, date and time, call type [such as an inbound member’s call in Spanish]).
- Agents’ names and call length.
- CSAT scores or other relevant performance indicators, when available.
- Agent scores, color coding those that passed versus those that failed.
That simple structure changed everything. Suddenly, QA stopped being a series of disconnected evaluations and became a source of insight.
What the Data Reveals
If you have a sufficient budget for tracking QAs—but not enterprise-level spend—you can take this a step further. Use SharePoint as a data repository and Power BI for analytics, creating dashboards that reveal trends over time.
If tools like Copilot are available, they can accelerate analysis by surfacing patterns such as: “Agent Terry has failed five QA evaluations in the past 10 months due to improper call disconnects.”
That’s not just data. That’s a signal to investigate call histories and provide targeted coaching before the behaviors escalate or send you down a rabbit hole looking for other instances of inappropriately disconnected calls.
Why Tracking What Goes Right Matters
Just as important: don’t only track what went wrong. Track what went right.
Capturing 100% QA scores helps define what “good” actually sounds like. High-performing calls can serve as learning examples, be included in training, or even be leveraged to identify potential mentors for new hires.
Recognition, when grounded in data, becomes meaningful rather than subjective. What gets recognized gets repeated.
When I joined as a call center supervisor and trainer in my new role, I was asked to help with QA and I brought back my old tracking system. I began noticing that agents handling payment-related calls performed well on straightforward questions:
- “How much do I owe?”
- “When is my payment due?”
But when callers asked more complex questions — such as why their monthly payments fluctuated — performance dropped sharply. Agents redirected callers unnecessarily, guessed at explanations, or provided vague responses that created confusion rather than clarity.
As you can imagine, callers were calling back, which led to a drop in first call resolution (FCR), higher AHT, and, you guessed it, a reduction in CSAT; it’s a domino effect most of us reading this don’t like. QA data made the knowledge gap that had developed impossible to ignore.
We addressed the gap through targeted microlearning and updated knowledge articles in our knowledge management system (KMS).
When leaders have built rapport, agents are often willing to share what’s really happening.
For new hires, we revised instructor-led training (ILT) to explain the most common reasons premiums change clearly. We also added knowledge checks to ensure agents retained the material.
In reality, the explanations were straightforward:
- Changes in household income.
- Partial or missed premium payments or excess payments in prior months.
But without proper training, agents were improvising and the callers paid the price.
After rolling out the updated training, QA monitoring showed steady improvement. A few agents needed one-on-one coaching, but before long, the entire center was delivering consistent and accurate explanations.
The difference was night and day.
QA tracking didn’t just improve scores; it aligned behavior, strengthened confidence, and ensured customers received clear and correct information on each call.
And that’s the real value of structured QA tracking: not compliance for compliance’s sake, but for clarity, consistency, and confidence across the floor.
Using QA Data to Spot Risks
Once QA data is consistently captured and structured, the next step is turning it into something your leadership team can act on.
Visual charts and dashboards, whether in Excel, Power BI, AI, or another analytics tool, help surface patterns that are easy to miss when reviewing individual scorecards in isolation.
In my experience, one of the most valuable uses of this data is identifying agents who may be approaching burnout.
QA scores often provide the first signal. These are agents who consistently hover at “meets standards,” fluctuate between passing and failing, or continue to miss expectations despite coaching or corrective action. While QA alone doesn’t diagnose burnout, it can raise a vital flag that warrants closer attention.
To understand the whole picture, QA data should be reviewed alongside other operational indicators, including:
- AHT
- Schedule adherence
- CSAT
- Transfer and escalation rates.
- The ratio of calls documented to calls handled.
When several of these metrics begin to trend downward together — or show sustained inconsistency — it may indicate fatigue, disengagement, or cognitive overload, rather than a skills issue.
At that point, the most crucial step isn’t another scorecard: it’s a conversation. When leaders have built rapport, agents are often willing to share what’s really happening.
Sometimes the solution is operational: moving an agent from voice to chat or email, rotating them to a different call type, or giving them time off the phones to support new hires or assist with quality initiatives. These changes can help restore purpose and reduce emotional strain without sacrificing performance.
In other cases, the challenge may be personal rather than work-related. While managers don’t need to solve those problems, they can help by partnering with Human Resources (HR) to explore options such as flexible scheduling, shift changes, or temporary accommodations.
Many of these situations are short-term, and thoughtful support can make the difference between losing a strong performer and helping them regain their footing.
Used this way, QA data becomes more than a compliance tool: it becomes an early warning system and a bridge to meaningful engagement.
And when QA is treated as a living system — not a static scorecard or an item on a checklist — it becomes one of the most potent tools a contact or call center has.
Structured tracking turns volume into visibility. Visibility turns patterns into insight. And insight, when paired with thoughtful training and leadership, turns everyday interactions into consistent, confident customer experiences (CXs).
When QA insights reduce repeat calls, rework, and agent frustration, the payoff goes far beyond quality metrics. Handle times come down, retention improves, retraining decreases, and leaders gain clearer insights into where automation or AI can help agents succeed.
The real value of QA isn’t found, then, in the scores themselves. Instead, it’s found in what organizations choose to do with the story behind it.
This article appears in November 2026.