You’ve probably heard this before.
“Now you can score 100 percent of your calls.”
Sounds great, but it’s not the point.
Accuracy doesn’t come from scoring every call. It comes from having the right number of calls that actually represent what’s happening in your contact center. There’s real math behind that. Not hype.
The right way to figure out how many calls you need to review is through something called the Cochran formula.
It uses three things: confidence level, margin of error, and sample size.
Your confidence level is how sure you are that your results represent the whole group.
A 95 % confidence level means you can be 95% sure your results are accurate.
Your margin of error is how much those results could vary from the true average.
A 5% margin of error means your results could be 5% higher or lower than the real number.
Your sample size is the total number of interactions you can pull from, like all the calls your contact center handles in a month.
From that total, the Cochran formula uses your chosen confidence level and margin of error to calculate how many of those calls you actually need to review to get statistically reliable results.
The size of your total customer interactions matters.
That’s why the formula gives very different results for a full contact center compared to an individual agent.
If your contact center handles 60,000 or even 450,000 calls a month, and you want a 99% confidence level with a 3% margin of error, you only need to review around 1,800 calls.
What this shows is that as the total number of calls gets larger, the percentage you need to evaluate gets smaller. You can get an accurate view of what’s happening without trying to listen to everything.
An example of this would be when you’re looking for trends across the contact center. Let’s say you just finished a compliance training. You could take a random sample of calls to see how well that training stuck and where agents might still need support.
Now take one agent who handles 1,100 calls a month. To reach that same 99% confidence and 3% margin of error, you’d have to review about 689 calls. That’s not realistic for everyday QA.
For individual agents, the goal isn’t statistical accuracy. It’s about seeing patterns in behavior that help guide coaching. You might review a smaller group of calls to spot those patterns and decide where to focus development.
With AI QA, time is no longer the issue. The real question is how to make coaching efficient and purposeful. It’s about finding the right amount of data to see real change and to guide how coaching happens, not just how often it happens.
So while the math works well for large scale audits and data gathering, at the agent level it’s about using insight to coach better, not measure more.
Scoring every call sounds thorough, but it usually leads to too much data and not enough understanding.
More evaluations don’t always mean better insight. What matters is how you use the information, not how much of it you collect.
The goal is to be confident in what you review, consistent in how you do it, and focused on what actually drives improvement.
The way FiveLumens is built follows this same idea.
Our scoring scorecards were designed for performance evaluations. They measure agent behaviors, capture scores, and identify where coaching can make the biggest impact. This is where you focus on individual improvement and development.
Our audit scorecards were built for large sample reviews. They look and function the same but don’t include scores. They’re made for gathering data, spotting trends, and understanding what’s happening across the contact center as a whole.
With AI QA, the process is faster and easier no matter how large or small the sample is. You can evaluate and audit without losing time, which lets you spend more of your energy where it matters most, coaching.
Quality assurance isn’t about listening to every call.
It’s about listening to the right ones.
With the right sample size, you can trust your data. With the right process, you can help people improve.
That’s what FiveLumens was built for.
Accurate data for your business. Real feedback for your people.