QA teams have more scores than ever, and it's somehow harder to know where to start. Every call is graded, every agent is on the scorecard, and Monday still begins with the same question: where does coaching time go this week?
Auto QA solved the scoring. It didn't solve the next question: which team, which behavior, which calls? One QA leader told us their scorecard was "pretty, but it means nothing." So coaches fall back on scrolling the grid and pulling a few calls that look bad.
That's what a QA heatmap is for. Instead of a wall of scores, it shows you the few places that are off and the calls behind them, so coaching starts with the problem instead of a search for it.
What is a QA heatmap?
A QA heatmap is a view built on top of QA scoring that plots your scorecard items or behaviors against your teams and agents and colors each cell against its target. Green where a team or behavior is on track, red where it isn't. You read it the way you read a weather map instead of the way you read a spreadsheet: pattern first, numbers second.
The value is in what you stop doing. You stop reading scores row by row and start seeing patterns: the one behavior slipping across three teams, the site that's been trending down for a month, the agent who's strong on everything except one step. A few thousand scores become a handful of places worth your time.
That alone changes how a coaching week gets planned. But seeing where the problem is only gets you halfway. What matters most is what happens when you click the red cell.
A red cell should be a starting point, not a conclusion
Spotting the red cell is usually where the easy part ends. You still have to find the calls behind it, read through them to figure out what went wrong, and decide what coaching the agent actually needs. That's hours of work, and it's the part most QA teams never get enough time for.
In Reddy, a red cell is three clicks from a fix:
- See the calls. Click the cell and you get the actual conversations behind the score, not a sample.
- Find the pattern. Ask a question in plain English, like "what do these calls have in common?", and Reddy answers from those conversations. You get something specific, like a skipped verification step, instead of a vague "communication" score.
- Coach it and practice it. Turn what you found into coaching for the agent, then assign a simulation on that one gap in the same systems they use on live calls.
That's where the bigger impact comes from. The heatmap doesn't just show you where something is off. It takes you from the score to the cause to coaching and practice, all in one place.
Did the coaching work?
Most coaching never gets a follow-up. A supervisor has the conversation, the agent says they'll work on it, and everyone moves on to the next thing. Nobody goes back to check.
The heatmap does the checking for you. The same cell that showed the problem shows whether the coaching landed. If it's moving back toward green, it worked. If it isn't, the cell shows you that too, so you can try something different before another month goes by.
That's the loop most QA programs never close: score, coach, practice, then score again to see if it stuck. (More on the loop)
What it looks like in practice
ISG, with more than 800 agents, had the problem this post started with. Its QA team could only review a small fraction of calls by hand, which made it hard to spot trends early or coach consistently across the team. With Reddy scoring every call and assigning coaching from those scores, ISG lifted call quality 135% in its first 90 days. (Read the ISG story)
Where coaching time should go
Back to the question every QA lead starts the week with: where does coaching time go? With a heatmap, it's no longer a guess. You can see what's off, who it's off for, and which calls prove it, then coach the fix and watch whether it holds.
See it. Find it. Coach it.
