Tracking the right metrics is the difference between a call centre that improves and one that stays stuck. With so many numbers to choose from, managers often end up measuring the easy things rather than the important things.
Here are the 15 metrics that actually matter, grouped by what they tell you, with benchmarks and notes on how AI auditing makes them easier to track.
The percentage of calls where the customer's issue is fully resolved without needing a follow-up call or callback. FCR is one of the strongest predictors of customer satisfaction. Every unresolved call costs money and erodes trust.
Industry benchmark: 70 to 75% is considered good. Best-in-class operations hit 80%+.
Typically collected via post-call surveys, CSAT measures how satisfied customers were with the interaction. However, survey response rates are low (5 to 15%), which is why AI sentiment analysis on the call itself is increasingly used as a proxy.
Benchmark: 85%+ satisfied is a strong result for most industries.
Measures whether customers would recommend your company based on their service experience. While traditionally survey-based, AI can now detect NPS-like signals from call transcripts by analysing language and sentiment patterns.
Benchmark: NPS above 50 is excellent for contact centre operations.
How hard did the customer have to work to get their issue resolved? High-effort experiences drive churn. AI can flag calls where customers had to repeat information, were transferred multiple times, or expressed frustration.
Benchmark: Lower is better. Score of 2 or below (on a 1 to 5 scale) is the target.
The overall score an agent receives on a quality audit. Typically a weighted average of script adherence, tone, objection handling, compliance, and resolution. This is the core metric that AI auditing tools like Bolo Aur Likho calculate automatically for every call.
Benchmark: 85%+ is the target for most teams. Below 70% triggers immediate coaching.
What percentage of the required script elements did the agent cover? This is especially critical in regulated industries where specific disclosures are legally required. AI tracks this automatically by detecting key phrases and required statements in the transcript.
Benchmark: 90%+ for compliance-sensitive calls in BFSI, insurance, and telecom.
How professional, empathetic, and calm was the agent throughout the call? Sentiment analysis tools can detect aggression, impatience, or over-enthusiasm, all of which negatively affect customer experience.
Benchmark: Agent positive sentiment should be above 70% of call duration.
When a customer raised an objection or concern, how often did the agent address it effectively before closing or escalating? This metric is particularly valuable for outbound sales teams where objection handling directly drives conversion.
Benchmark: Top-performing sales agents handle 60%+ of objections successfully.
The average duration of a call including hold time and after-call work. Shorter is generally better for efficiency, but AHT must be balanced against quality. Agents who rush through calls often have lower FCR and CSAT scores.
Benchmark: Varies by industry. For outbound sales, 3 to 6 minutes is typical.
The percentage of calls where a required disclosure, consent statement, or regulatory requirement was missed. In BFSI, insurance, and telecom, a single mis-selling or disclosure failure can result in regulatory fines. AI auditing flags these automatically on 100% of calls.
Benchmark: Target 0%. Any violation rate above 1% requires immediate programme intervention.
How often are agents making claims that are not supported by product documentation, or using pressure tactics that violate sales ethics guidelines? AI can flag specific phrases that indicate mis-selling risk.
Benchmark: Zero tolerance. Any instance requires immediate review and remediation.
What percentage of calls are escalated to a supervisor or senior agent? A high escalation rate points to training gaps or product knowledge deficiencies. AI can identify which call topics or objection types most frequently trigger escalations.
Benchmark: Below 5% for well-trained teams. Above 10% signals a systemic training issue.
The percentage of inbound callers who hang up before reaching an agent. High abandonment rates indicate staffing or routing problems. While this is not a quality metric per se, it affects customer experience significantly.
Benchmark: Below 5% is good. Above 10% is a red flag.
The percentage of outbound sales calls that result in a sale, sign-up, or agreed next step. AI auditing helps identify which agent behaviours and script patterns correlate with higher conversion rates, enabling data-driven coaching.
Benchmark: Highly industry-specific. Improvement of 2 to 5 percentage points is typical after structured QA programmes.
How often does the same customer call back within a short window (typically 7 days) about the same issue? High repeat call rates indicate failed first call resolution. AI can link call transcripts by customer ID to automatically track this.
Benchmark: Below 10% repeat rate within 7 days for customer service teams.
💡 You do not need to track all 15 from day one. Start with QA Score, Script Adherence, and FCR. These three give you the most insight with the least complexity.
Manually tracking even 5 of these metrics across hundreds of calls per day is not realistic. AI call auditing tools like Bolo Aur Likho automate the measurement by transcribing every call and analysing it against your quality criteria.
Upload a call recording and get instant output on:
No setup fee. No subscription. Try it free on your next call.
Upload a call recording and get instant AI analysis. No signup required.
Audit a Call Free →