Top 10 Customer Service Metrics – An Analytical Perspective

Every customer support team needs to know the critical KPIs that it needs to track. The way in which each KPI is viewed and analyzed becomes a differentiating factor to make better decisions for improving their performance. Here is a list of top customer support metrics that matters for every support team. 

The 3 Step process: Measure, Analyze & Improve

Every support team strives to improve its operational performance in possible ways. A quantitative approach towards capturing the metrics and making data-driven decisions is necessary. Here is the 3 step process.

  1. MeasureWhat gets measured gets managed The first step for creating a sustainable change in your organization to begin by measuring the key performance indicators. This creates an awareness of your current state of operations
  2. Analyze – Every dataset has a story to tell Once the required data is collected around each KPI, the way in which you analyze the KPI will be extremely crucial
  3. Improve –Better decisions lead to better results Based on the perspectives you gain from the analysis, you are able to make data-driven decisions.

Metric 1: First Response Time

Every support team needs to be aware of this critical KPI that measures the speed in which a customer gets their first response.

Measure how fast you respond Once a ticket is created, measure the time taken by an agent to add the first response to the ticket. This first reply time needs to be captured at a ticket level. To aggregate the First Response Time for all tickets, calculate the average of all the First Response Times of each ticket. 

Avg. First Response Time = Sum of First Response Time of all tickets / Total Number of Tickets. 

Analyze the outlier tickets While you have first response time calculated by considering all support requests, it is critical to know the distribution of first response times. I.e How many tickets have First response times in the different time intervals. Let’s take an example.

Histogram View of First Response Times

Response Times is typically measured in hours. If you were to know the count of tickets for each 4-hour  interval of First response time, the above histogram would help you with it. In this example, it shows tickets are primarily responded within 4 to 8 hours or 8 to 12 hours. However, what you begin to realize is the count of that is beyond the 16-hour limit. This gives you an insight into the outliers that exist. 

Improve your SLA performance By drilling down in the final section gives you to see the actual tickets being responded over 16 hours. This enables you to understand special cases and improve your processes. Similarly, looking at the first response time by attributes and dimensions unique to your business would give additional insights.

Metric 2: Ticket Volume

Understanding the volume of tickets is a way to gauge the workload of your agents. It can also help you spot opportunities for your business.

Measure the inflows Measure Ticket volume by the number of customer requests that arrived over a particular time window. It would help you if you are measuring the ticket volume at every instant of the day, week, month, and so on.

Ticket Volume = Total number of tickets over a defined time period

Additionally, for every ticket, you could capture the product, services, or offerings for which the ticket is raised.

Analyze volume by different attributes While it is important to understand the total ticket volume, it is critical to know the ticket volume by the various products, services, or offerings that you serve your customers. Doing this is a fundamental activity while analyzing your support tickets.

True to the phrase ‘A dollar saved is a dollar earned’, you would help your support by spotting ticket types where there is an increased proportion of tickets. Then, for a given ticket type, you need to know the distribution of tickets by the sub-types. A doughnut chart with a drill-down would serve you well.

Improve your deflection rate Once you begin to know the high-density ticket areas of your business, you could make decisions to address each aspect of it. For example, modules where you see repeated tickets of type

  1. “How to” Tickets – Take steps in order to increase user awareness 
  2. Service Requests – Relook at your business process and improve them
  3. Customer Issues or Incidents – Focus on strengthening your product or service.

Metric 3: Agent Replies

Agent replies give you clues about the activity levels of your support team. It enables you to make insightful decisions on your team.

Measure your ticket updates You can start by capturing the number of times an agent has added an update on a ticket. Then, this can be aggregated to find the average number of agent replies. Since this metric is agent-specific, it helps to calculate the average number of agent replies for each agent.

Avg. Agent Replies per ticket per agent = Sum of the number of replies for all tickets by the agent / Total number of tickets handled by the agent.

Analyze against benchmarks As you gauge the number of replies across agents, it helps you compare agents against a common benchmark. This benchmark could be the average number of agent replies considering your support team. Having a bar chart with a benchmark added would give you that perspective.

Configurable Benchmarks helping you segment data

Improve your agent skills Once you identify the agents who are above and below the benchmark, you get to know the behavior and style of each agent. You could then work with them to understand the root cause of their high or low number and optimize your customer interaction process.

Metric 4: Customer Satisfaction (CSAT)

Customer Satisfaction (CSAT) is one of the most popular metrics to gauge customer satisfaction. It is a fundamental tool for every support team to understand the customer experience.

Measure customer satisfaction You can measure Customer Satisfaction Score in a number of ways. Typically, it is done through customer satisfaction surveys as part of the ticket resolution process. You can trigger surveys either at the end of ticket resolution or during every update of the ticket.

Average CSAT score = Total number satisfaction ratings received / Total number of ratings provided

Analyze Customer Segments Considering feedback from all your customers may give you an initial understanding of Customer Satisfaction. As you cater to different customers across departments, regions, types, it is important to analyze CSAT scores by different segments. An example of this is shown below.

Map Chart enabling you to spot variation across regions

Improve mind share with customers With a clear distribution of your CSAT scores, you can now begin to devise strategies to uniquely address business needs for each customer segment. This helps you enrich the service experience, increase customer loyalty and retention rates.

Metric 5: Time in Status

Time in status gives you the ground reality of your operational efficiency. It enables you to gauge your performance at different stages of your business process.

Measure Average Time in each Ticket Status Consider that tickets in your business processes move through a specific set of statuses. During the ticket lifecycle, every ticket will traverse through one or more statuses. You will need to capture the amount of time a ticket spends in each status.

Avg Time in Status X = Sum of time spent by tickets in status X / Total number of tickets.

Analyze averages and percentiles Being able to view the aggregated values of Time in status across each ticket status gives enormous insights into your business process. Additionally, it is important to go beyond average values. As the number of tickets increases, there is always a set of outlier values that skew the averages. While outliers need to be separately handled, it is a good practice to understand this KPI from the perspective of percentile also.

Data Tiles giving you the right numbers

Improve your cycle times As you begin to spot ticket statuses that take more than expected times, you can take corrective actions to reduce the time spent in a particular step. It gives an opportunity to dive into the root causes, go through sample tickets, and bring about changes to improve efficiencies. Here in this example, we can 

  1. Look at why the customer is made to wait for an average of 25.31 mins
  2. Implement better assignment to quickly move tickets from ‘New’ status to ‘WIP’.
  3. Perform Root cause analysis of why it takes around 2 hours in ‘WIP’ status
  4. Explore automation opportunities to ‘Auto-close’ resolved tickets.

Metric 6: Agent Resolution Time

There is always variability in the speed in which each support agent resolves tickets. Understanding agent resolution time will help you improve the performance of your customer service team.

Measure Resolution Time of each agent You need to measure the time taken by each agent to resolve their tickets. This may translate to the time taken to mark the status as ‘resolved’ or the required status defined by you.

Average Resolution Time of an agent = (Total time taken by an agent to resolve the tickets) / (Total Number of Tickets)

Typically there would a small fraction of outlier tickets that took an undue amount of time to resolve due to special reasons. You may want to additionally consider the 90th percentile of the resolution time of each agent for a deeper understanding.

Analyze resolution time with additional dimensions As there is variability in ticket volume handled by each agent, it is important to view resolution times in comparison to the ticket volume. A decision-making matrix would enable you to achieve this

Decision Making Matrix enabled through a segmentation chart

Improve your agent skills Using the decision-making matrix, you get to classify your agents into High performers, Bottlenecked resources, resources with available capacity, and those who need training. Based on this, you can take the right decisions to improve your agent performance through focussed training sessions, capacity optimizations, and the right incentives.

Metric 7: Resolution Time

Every organization wants to reduce its resolution time. If only they were able to identify the right bottlenecks, they could effectively do that.

Measure time across your business processes Every support team is generally involved in helping a number of customers across a range of products, processes, and services. This requires them to interact with multiple internal and external stakeholders. The ability to identify the bottlenecks across the various operational processes is crucial to reduce the overall cycle time.

Average Resolution Time = (Total Time taken to resolve the tickets)/(Total Number of tickets).

Analyze by spotting your bottlenecks in the business process By being able to view the time spent across your business process for various departments or categories of your tickets, you are able to identify the hot spots where an undue amount of time is being consumed. These are the bottlenecks in your operations that need immediate attention.

Spotting your bottlenecks through a heat map

Improve by removing your bottlenecks Once you identify your bottlenecks, you could take decisions to address them. For example, you work with the required departments or teams who are slow in their resolution process, relook at operations when the ticket takes too long in a particular status, and reallocate capacity towards bottlenecked areas.

Metric 8: Call Times

Optimizing call times can happen through focussed agent conversations. It is linked to higher customer satisfaction.

Measure your Talk Time and Wait Times As your agents spend time in phone calls, it is important to capture every minute spent by the customer either waiting to talk or actually talking to an agent.

Average Wait Time = (Time spent waiting to talk an agent by all customers)/(total number of calls)

Average Talk Time = (Time spent talking to customers)/(total number of calls)

Analyze the trends over time As your support operations service customer over time, you will need to gauge the KPIs over time. Looking at a trend of call times will enable you to detect patterns and behaviors.

Time Trending Analysis showing you patterns over time

Improve your efficiency By looking at trends, you could better understand the variations of call times over the weeks or months. Then, through effective root cause analysis and retrospective meetings with agents, you can take corrective action in processes and agent training in order to optimize the call times.

Metric 9: Number of Process Automations

Process automation is already transforming the way in which customer support is done. Focusing on automation as definitive KPI is critical.

Measure the current extent of automation Whether you have just begun your automation journey or well into its maturity, measuring the extent to which your customer support processes are automated is vital to your success.

Extent of Automation = (Count of processes automated) / (Total number of processes that you are supporting)

Analyze the frequent actions that your agents perform In order to maximize automation in your customer support processes, you can begin by looking at the most frequent and repetitive actions that each agent performs. eg. it could be data entry, adding standard replies to customers, or triggering an approval process. Analyzing this would open up windows of opportunities to automate your business processes.

Detecting the actions performed by agent

Improve your operations As you identify the most frequent actions performed by your agents, you can implement workflow automation for reducing manual effort and standardizing your business processes. It enables your support team to focus on critical items rather than performing mundane tasks. This results in increased productivity and operational efficiency. Additionally, you could also look at providing a robust knowledge base for your customers to increase self-service.

Metric 10: First Contact Resolution

Customers who get their issues resolved in the first contact with the support team are likely to return as happier customers.

Measure your first contact resolution (FCR) Whether you are using a telephony system or a ticketing system, you need to be aware of the extent to which resolution is provided in the first contact. This reveals a lot about your support team’s effectiveness and the quality of your product or service.

First Contact Resolution Rate = (Number of calls/tickets resolved on first interaction) / (Total number of calls/tickets).

Analyze your agent performance Efficient agents have higher first contact resolution rates. Hence, it helps to analyze this KPI across your team members.

Word Cloud giving you perspectives on data

Improve your customer handling process The best practices followed by top agents who are able to efficiently resolve calls/tickets in the first attempt needs to be emulated by the entire team. You could further dig deep into the customer data and understand segments that cause higher and lower rates of first contact resolution. This would enable you to strategically approach each customer type or segment-leading to increased customer retention, reduced churn rate, and satisfied customers.


HappyFox BI empowers you to analyze all the above mentioned KPIs in an effortless manner. You could achieve more by employing decision tools, creating powerful visualizations using comprehensive datasets through a truly integrated analytics platform exclusively for customer support resulting in enhanced customer relationships, better support experience, and higher business value.

Get a demo of HappyFox BI and accelerate your analytics journey.