A QA analyst is the person in a customer service team responsible for reviewing conversations against a quality scorecard and turning what they find into feedback and improvement. They evaluate how agents handle tickets, track quality trends, and flag coaching opportunities and process gaps. As QA becomes automated, the role shifts away from manual grading toward coaching and insight.
In short
- A QA analyst reviews customer conversations against a scorecard and reports on quality across agents and teams.
- Traditionally the job is dominated by manual reviewing, sampling a small percentage of tickets by hand.
- The analyst also maintains the scorecard, runs calibration, and surfaces trends and coaching moments to team leads.
- When QA is automated, the grading grunt work is removed and the role moves toward coaching and analysis.
- The highest-value work of a QA analyst is interpreting quality data and driving change, not reading tickets one by one.
What a QA analyst does
A QA analyst owns the quality signal for a support operation. Day to day, that covers a few core responsibilities:
Reviewing conversations
The analyst reads or listens to interactions and scores them against the quality scorecard, judging tone, accuracy, resolution and process adherence.
Maintaining the scorecard
They keep the criteria current, run calibration so reviewers stay aligned, and rewrite rules that grade inconsistently.
Reporting and coaching
They turn scores into trends, flag where agents need help, and feed specific, evidence-based coaching moments to team leads.
How the role changes when QA is automated
The traditional bottleneck for a QA analyst is time. Reading tickets by hand caps coverage at a few percent, so most of the analyst’s week goes to grading rather than acting on what the grades reveal. Automation removes that ceiling.
| Dimension | Manual QA analyst | With automated QA |
|---|---|---|
| Main activity | Reading and scoring tickets by hand | Interpreting results and coaching |
| Coverage they work from | A 2% to 5% sample | 100% of conversations |
| Time spent grading | Most of the week | Near zero, handled automatically |
| Value delivered | A score on a fraction of tickets | Insight and improvement across everything |
From grading to coaching
When scoring is automated, the analyst stops being a human review engine and becomes the person who makes the data matter. Kaizo scores 100% of conversations against your scorecard automatically, with each score linked to the evidence in the transcript, and generates a coaching card per agent. That means the analyst walks in with complete data and specific moments to coach on, rather than a spreadsheet of hand-graded samples. At UiPath, Kaizo automated 100% of QA with 200% ROI, freeing the QA function to focus on raising quality rather than measuring a sliver of it.
Frequently asked questions
What skills does a QA analyst need?
Attention to detail, a firm grasp of the quality scorecard and what good service looks like, and the ability to turn scores into clear, actionable coaching. As QA automates, data interpretation and coaching skills matter more than raw reviewing speed.
What is the difference between a QA analyst and a team lead?
A QA analyst measures quality and surfaces where agents need help. A team lead usually owns the coaching and performance of the agents directly. The two work closely, and automated QA hands both of them the same complete, evidence-linked data.
Does automated QA replace the QA analyst?
No. It replaces the manual grading work, not the person. The analyst shifts from reading a small sample by hand to acting on complete data, coaching agents and improving processes, which is the higher-value part of the role.
Related terms
Give your QA analysts complete data to work from
Bring a week of your real conversations and we will show you 100% coverage and the coaching cards your analysts would get on Monday.
