In this blog we will discuss:
- Traditional Call Center Metrics
- Average Handle Time
- Average Speed of Answer
- Service Level
- Service Level Success Rate / Compliance Rate
- Agent Utilization Rate
- Adherence to Schedule
- Customer Survey Scores
- Call Quality Performance
- Net Promoter Score
- AI Call Center Metrics and KPIs
- AI Efficiency and Effectiveness Metrics
- AI-to-Human Escalation Rate
- Virtual Agent Resolution Time
- AI Accuracy Score
- AI Agent Assistance Metrics
- AI Assist Utilization Rate
- AI-Assisted Handle Time Reduction
- Knowledge Retrieval Time
- Agent Augmentation Value
- AI Quality and Analytics Metrics
- Auto QA Coverage and Accuracy
- Deeper Call Analytics
- Sentiment Shift Analysis
- Strategic Business Impact Metrics
- Cost Per Resolution
- Operational Cost Impact
- AI Efficiency and Effectiveness Metrics
The call center is an important point of contact where customers meet with the organization. Customer calls or written requests to the organization are met here. Information, service, and complaint requests are managed here as well. It is often the front door of a business. A miscommunication can destroy the entire image of the business.
The effective and efficient operation of the call center is one of the factors that determine the organization’s stance towards customers. Call center management may seem easy, but the math behind it is not that simple. Received calls have a certain process until they reach the agent. Managing this process well is only possible with the correct interpretation of call center metrics.
The customer dials the call center’s number, enters the IVR, reaches the representative, waits during the call, call time after reaching the agent, hold time, processing time, survey dialing, and so on. All of these are processes that create an algorithm in itself.
Many contact centers today are beginning their AI journey, but most are still human-staffed.
Explore More: How Post Call Analysis Improves AI Voice Accuracy, Reliability, and ROI
Traditional Call Center Metrics
Many contact centers today are beginning their AI journey, but most are still human-staffed. Traditional metrics include
- Average Handle Time
- Average Speed of Answer
- Service Level
- Service Level Success Rate / Compliance Rate
- Agent Utilization Rate
- Adherence to Schedule
- Customer Survey Scores
- Call Quality Performance
- Net Promoter Score
- And many more
Average Handle Time
Average Call Handling Time (AHT) covers the time from opening to closing. It shows how long transactions take on average in a call center. This period includes talking on the phone, holding the caller, and post-call processing.
Average Speed of Answer
Average Speed of Answer displays the average call answering speed. It refers to the average time it takes for calls to be answered after waiting in the queue. It does not include missed calls. It shows how long answered calls wait before being answered.
Service Level
The most important metric to monitor workforce and production management is called service level.
Service level is the response rate of calls to the call center within the target time. The target time is called acceptable waiting time. The holding time should be an acceptable amount of time depending on the business and customer segment. This metric can be generated correctly only if the optimum time for each operation target is well determined.
Service level is the ratio of the calls answered before the specified target time to the number of all incoming calls.
Service Level (SL) = (Calls Answered Before Target Time ÷ Total Answered Calls) × 100
This is the main formula for service level calculation, but not the only one. Different formulas can be used according to the operation objectives.
The formula can be changed by dividing the answered calls within the target time by the total calls answered, not total calls received, but you should know the difference.
Service Level=(Total Answered Calls within or before targeted time ÷ Calls Answered Within Target Time)×100
Let’s discuss an example. You are a call center manager. You set the target of receiving 80% of the calls before 20 seconds. 100 customers called your call center today. 10 customers dropped off the line. 60 customers were answered within the targeted time you said. 30 customers were answered by waiting for more than 20 seconds.
In this case there are 100 incoming calls and 60 of them are answered within the 20 seconds target. According to the result of the first formula the service level is 60%.
SL=(100/60) x 100
SL=60%
In the second formula the answered calls within the target time should be divided by the total answered calls, not the total received calls. There are 10 abandoned calls then 60 calls are divided by 90 answered calls. According to the result of this formula the service level becomes 66%.
SL=(90/60)×100
SL≈66.67%
You need to determine well which formula to use for tracking this metric, learn the math behind it, and see the differences by simulating different days.
In some service level formulas fast abandonment calls can be omitted from the incoming call, so if the customer hung up by waiting for 5 seconds you can remove it from the target, then your formula would be like this.
Service Level = [Calls Answered Within Target Time ÷ (Total Incoming Calls − Fast Abandoned Calls)] × 100
SL=[60 ÷ (100-5)]×100
=63.16%
Service Level Success Rate / Compliance Rate
After the service level is figured it is followed by the success rate. This value can be calculated daily or monthly. The compliance rate of an operation that has achieved the service level target on 18 days of the month is calculated as 60%.
Compliance Rate=(Days Target Achieved ÷ Total Days)×100
Compliance Rate=(18÷30)×100=60%
If this situation is not periodic we need more radical measures or updates on the target.
By the same logic, looking at the compliance rate on a shift basis is important for capacity planning and the management of this value as well.
Agent Utilization Rate
Agent utilization rate is another metric used in call centers. The workforce in the call center is human labor. The call center manager should make sure that the staff uses time efficiently.
Agent utilization rate is simply the ratio of work produced in the call center divided by work capacity. With this metric, call centers can evaluate the productivity of their workforce.
Agent Utilization Rate = (Work Produced ÷ Work Capacity) × 100
If an agent works 6 hours in an 8 hour day the agent utilization rate is 75%.
Adherence to Schedule
It shows how compliantly the agents manage the time they spend at the login time. When commitment to the program is high it means that the plan you have prepared is acted upon. This is how the adherence to schedule formula works.
First Call Resolution
First call resolution or FCR reflects the average number of calls in which questions or complaints received by the call center are resolved on the first contact.
Quality metrics
Metrics related to quality management are customer survey scores, call quality performance, and net promoter score or NPS. In addition, the complaint rates of the call center should be monitored.
You can also use calibration to monitor the performance of the quality team. You can evaluate the same call with a quality specialist on different dates or evaluate it with another quality specialist to determine the success rate. The difference between these two ratios is the calibration ratio.
However, with AI becoming part of contact center operations, new types of metrics must be introduced.
AI Call Center Metrics and KPIs
For AI call centers, traditional call center metrics are not enough and they need to be evolved to properly evaluate the effectiveness of AI. Let’s discuss the important AI call center metrics of the following domains:
- AI Efficiency and Effectiveness Metrics
- AI Agent Assistance Metrics
- AI Quality and Analytics Metrics
- Sentiment Shift Analysis
- Strategic Business Impact Metrics
AI Efficiency and Effectiveness Metrics
These metrics focus on evaluating how effective AI systems are compared to traditional agent-based metrics.
Virtual Agent Containment Rate
The percentage of interactions fully resolved by a virtual agent or bot without any human interaction. This provides a benchmark of what percentage of interactions are handled entirely by AI.
Virtual Agent Containment Rate= (Interactions resolved by virtual agents ÷ Total interactions) X 100
AI-to-Human Escalation Rate
The percentage of interactions that begin with a virtual agent but require escalation to a human agent. This metric helps identify situations where bots are failing to resolve issues and allows deeper analytics into problem areas.
AI-to-Human Escalation Rate = (Number of AI interactions escalated to human agents ÷ Total number of AI-initiated interactions) × 100
A typical starting benchmark today may be around 25% to 40% of calls requiring human escalation.
Virtual Agent Resolution Time
Similar to average handle time, but for virtual agents. It measures how long customers spend interacting with the AI system. Ideally, these interactions should be significantly shorter than human-handled calls, although longer interactions may still be acceptable because virtual agents are less expensive.
AI Accuracy Score
AI systems should also undergo quality assurance just like human agents.
An AI accuracy score evaluates whether the AI provides correct responses and accurate information at scale. Organizations can use Auto QA platforms to randomly sample or review AI interactions to measure accuracy.
AI Agent Assistance Metrics
AI tools are increasingly used to support agents during live interactions. These tools must also be evaluated.
AI Assist Utilization Rate
Measures how frequently agents use AI tools during interactions. This helps determine whether the tool is providing value and whether agents are properly trained to use it.
AI-Assisted Handle Time Reduction
Measures how much time AI tools save during interactions. Typical expectations from agent-assist tools range from 15% to 25% reduction in handle time, depending on call complexity. Agent assist tools are especially valuable in environments with complex knowledge requirements, such as companies with thousands of product SKUs.
Knowledge Retrieval Time
It measures how long it takes agents to find necessary information. If agents spend significant time searching for information, AI-powered knowledge retrieval systems can reduce this time and improve efficiency.
Agent Augmentation Value
It evaluates the overall impact of AI tools on agent performance by examining improvements in:
- Quality scores
- Handle time
- First call resolution
AI Quality and Analytics Metrics
AI tools enable deeper quality analysis than traditional QA systems. Let’s discuss some of the most important AI Quality and Analytics Metrics below:
Auto QA Coverage and Accuracy
Auto QA systems can analyze calls at scale and score them question by question, identifying whether agents followed correct procedures. A reliable Auto QA system should typically achieve 95–96% accuracy.
Deeper Call Analytics
Auto QA tools allow organizations to analyze large numbers of interactions to identify patterns such as:
- Reasons customers cancel services
- Mentions of competitors
- Product complaints
- Marketing insights
These analytics provide insights that were previously impossible to obtain at scale.
Sentiment Shift Analysis
AI enables deeper analysis of customer sentiment throughout a call.
Organizations can evaluate customer sentiment at the start of the interaction and at the end of the interaction and find out whether agents successfully convert negative sentiment into positive sentiment. These sentiment shifts help measure the true outcome of the customer experience.
Strategic Business Impact Metrics
The ultimate goal of an AI call center is to demonstrate measurable business value. It can be calculated via following metrics:
Cost Per Resolution
It measures how much it costs to resolve an issue through a human agent versus a virtual agent. This helps determine the financial impact of AI deployments.
Operational Cost Impact
AI can reduce costs through lower handle times, higher first call resolution, and reduced need for human agents. Organizations can assign dollar values to these improvements to evaluate the real impact of AI.
Conclusion
Contact centers must begin correlating traditional human-based metrics with new AI-driven metrics to determine the overall impact on customer experience, quality outcomes, sentiment scores, operational costs, CSAT and NPS These AI call center metrics are still evolving, but they provide a foundation for measuring whether AI is truly improving contact center performance.
FAQs about Call Center Metrics
1. What are traditional call center metrics?
They are kind of key performance indicators used to measure the performance of traditional or human based call centers. These KPIs include:
- Average Handle Time
- Average Speed of Answer
- Service Level
- Service Level Success Rate / Compliance Rate
- Agent Utilization Rate
- Adherence to Schedule
- Customer Survey Scores
- Call Quality Performance
- Net Promoter Score
- And many more
2. Why Traditional Call center metrics are not enough for AI or automated call centers?
AI call centers are based on AI agents, natural language understanding, Machine learning, reinforcement learning etc along with AI models like LLM, STT, TTS, and resonance generation models. To measure the performance of AI call centers we have to work out how efficiently these models are working. And to measure their efficiency we need a special set of KPIs either different or somehow similar to traditional call center metrics.
3. What are the most common AI call center metrics?
The major AI call center metrics with respect to their domains are as follow:
AI Efficiency and Effectiveness Metrics
- AI-to-Human Escalation Rate
- Virtual Agent Resolution Time
- AI Accuracy Score
AI Agent Assistance Metrics
- AI Assist Utilization Rate
- AI-Assisted Handle Time Reduction
- Knowledge Retrieval Time
- Agent Augmentation Value
AI Quality and Analytics Metrics
- Auto QA Coverage and Accuracy
- Deeper Call Analytics
Sentiment Shift Analysis
Strategic Business Impact Metrics
- Cost Per Resolution
- Operational Cost Impact
