How do you improve call center agent performance?
Short answer
· 5 min read
What are the steps to improve call center agent performance?
A working program runs on a repeatable sequence, not a one-time scorecard rollout.
Define a QA scorecard rubric tied to outcomes like resolution and customer effort, not just call mechanics. Criteria only earn trust when they predict whether the issue actually got solved.
Score every conversation rather than a sample. A small sample gives you an estimate with a wide margin of error. Full coverage shows the real pattern across every agent and call type.
Calibrate reviewers on a fixed cadence so the same call earns the same score. Have reviewers independently score identical recordings, then reconcile any gaps in their reasoning.
Diagnose the specific behaviors behind low scores, not the score itself. Listening to the call tells you whether the agent skipped verification, missed a chance at one-contact resolution, or used language that raised customer effort.
Coach on real examples pulled from the agent's own conversations. A manager and agent reviewing one actual call changes behavior more reliably than a generic training module.
Support agents live, in the moment they need it. Catching a struggling interaction while it happens prevents a bad outcome instead of explaining one after the fact.
Re-score the same criteria to confirm the behavior changed. The rubric from step one and the measurement here must match, or you cannot tell if coaching worked.
Which call center agent performance metrics should you track, and which mislead you?
Track these metrics together. Any one becomes unreliable the moment it becomes the goal instead of a signal.
Metric | What it tells you | How it misleads as a target |
|---|---|---|
QA score | How well a call matched the rubric's defined behaviors | Inflates when reviewers are uncalibrated; a high score may reflect a lenient grader, not a strong call |
Whether the issue was solved without a repeat contact | Definitions drift between teams, so it's only reliable compared against itself over time | |
How the customer felt about that interaction | Response rates skew toward extremes, overrepresenting very happy and very frustrated customers | |
How long calls take, useful for staffing and routing | Pressure to hit a target pushes agents to close contacts early, driving up repeat contacts | |
Schedule adherence | Whether an agent was logged in during scheduled hours | Measures presence, not the quality of the time spent on calls |
Transfer rate | How often an agent moves a contact elsewhere | Falls when agents keep contacts they should have escalated, trading a clean metric for a worse outcome |
First contact resolution and satisfaction move together: SQM Group's benchmarking research found that each 1% gain in first contact resolution corresponds to roughly a 1% gain in customer satisfaction, a link that only holds when resolution is defined and scored consistently across every call, the same calibration discipline a QA rubric requires.
Why does sampling a few calls miss what you need to fix?
Most manual QA programs review only a small slice of total volume, too little evidence to separate a real agent habit from noise. A 15-agent team making hundreds of calls a day might review roughly 1% of total contacts, not enough to tell whether one agent repeats a mistake or a reviewer pulled two unusual calls.
That gap creates specific problems. A thin sample cannot distinguish a habit from a bad day. It also cannot tell whether a low score reflects agent behavior or a broken process, like a knowledge article that sends every agent down the wrong path. And because reviews happen weeks later, a policy breach sits undetected until the next sampling cycle, if it catches it at all.
100% conversation scoring changes what becomes visible. Patterns emerge per agent and per issue type instead of per sampled call. Agent coaching starts from examples the agent actually remembers. Policy and compliance criteria can trigger alerts alongside routine scoring instead of waiting for a monthly review. The same rubric should score AI agents and human agents on one scorecard, so a drop in resolution quality can be traced to whichever one handled the contact. This loop coaches individual behavior; comparing agents against each other isn't the point.
How does OnClarity help with this?
OnClarity (www.onclarity.com) builds this loop into one workflow. AI Agent QA runs 100% conversation scoring across AI and human agents, measured against the team's own rubric, with evidence quoted for each score. Coaching starts from a real example instead of an average, because a manager can see exactly what the agent said. Alerts can be set on policy and compliance criteria alongside routine scoring, so a breach surfaces next to the conversation rather than weeks later. AI Agent Assist drafts replies and guidance inside the agent's workflow and helps new hires ramp up faster. OnClarity connects to the existing contact center. It is SOC 2 Type II certified, GDPR and HIPAA Ready, and Saudi PDPL aligned.
Frequently asked questions
How often should you coach a call center agent?
Coach continuously, tied to specific conversations, rather than on a fixed monthly schedule. Waiting for a scheduled review lets a behavior repeat for weeks before anyone addresses it. Pulling evidence from recent calls as soon as a pattern appears, then following up with a short re-check, keeps feedback close enough to the original interaction that the agent still remembers it.
What should a call center QA scorecard include?
A QA scorecard should tie its criteria to outcomes like resolution and customer effort, not just mechanics such as greeting language or hold procedures. It should include policy and compliance checks alongside routine quality criteria, and the same rubric should apply whether a human agent or an AI agent handled the conversation.
Is average handle time a good measure of agent performance?
Average handle time is useful as context, not as a performance target. It tells you how long conversations take, which matters for staffing and routing. But when agents are pushed to hit a handle time goal, they tend to close contacts early, which increases repeat contacts and undermines first contact resolution, the metric that actually predicts satisfaction.
Key terms
Average handle time
Average handle time is the mean time an agent spends on a single contact, including talk or chat time and after-contact work. Because the definition varies between teams, comparisons are only meaningful when both sides confirm whether wrap-up time is included inside the calculation being reported.
CSAT
CSAT, or customer satisfaction score, is a post-contact rating averaged across responses and usually collected with a single question immediately after an interaction closes. Because only a small fraction of customers respond, CSAT reflects the experience of people motivated to answer rather than the whole contact population.
First contact resolution
First contact resolution is the share of issues fully resolved during a customer’s first interaction, with no follow-up contact about the same problem. It correlates strongly with satisfaction and cost, but only when measured over a defined reopen window rather than at ticket close.
Quality assurance
Quality assurance is the structured review of conversations against a scorecard, applied to human agents and AI agents alike. Traditional QA samples a handful of conversations per agent each month; automated QA scores every conversation, which changes the exercise from spot-checking into actual measurement.
