Customer service metrics to track in 2023 [+ How to use them]

What are customer service metrics? 

Customer service metrics are performance indicators that help measure the quality and efficiency of your customer support, and how your team’s performance fares against industry standards and benchmarks. These metrics help you gauge the quality of customer interactions and identify where your team can perform better.

In addition to that, customer service metrics are a mark of how well your company meets customers’ needs and expectations, and how well you are progressing toward achieving organizational goals. These metrics are critical to gain the insights needed to improve customer loyalty, help customer support teams track whether or not they have happy customers and ultimately drive more sales.  

Why is measuring customer service metrics important?

Customer service metrics indicate how well your customer support team is doing and can guide your support teams to achieve goals and reach set benchmarks. Good customer service is a part of efficient customer experience management and is also vital for retaining customers and driving sales — without which your business will likely suffer.

To quickly summarize some of the benefits of measuring customer service metrics:  

• Astute performance evaluation for measuring and quantifying performance 

• Optimal resource allocation for maximum efficiency 

• Data-driven decision-making on the back of real insights 

• Proactive crisis detection by identifying and flagging recurring issues

Also, many companies are yet to understand the connection between sales and customer service and, when done well, how it can become a powerful driver of both customer satisfaction and revenue.

For example, a popular American bank had a very poorly optimized customer service process, which in turn reflected in their customer service metrics. Due to the fragmented nature of handling customer queries, their support team usually responded to queries within two hours or more — which clearly indicated that there was scope to improve their customer response times.

Also, they noticed their inbound ticket volume had grown year on year by about 25%, which they handled by leveraging AI and automation to triage incoming customer queries. By identifying these issues and fixing them, they were able to significantly improve customer satisfaction and in turn revenue, since customers were more than willing to pay for better customer service. 

Experiential metrics vs. operational metrics

A multitude of metrics are generally used to analyze customer service, but they all fall into two broad categories: experience and operational. Let’s see how they’re different and how they reflect your standards of customer service.

Experiential metrics focus on the overall satisfaction and experience of the customers with your support teams and tools. These metrics provide insights into customer interactions, which you can use to evaluate your support experience and focus on improving it. Customer satisfaction score (CSAT) and customer retention rate (CRR) are two good examples of experiential customer service metrics.

Operational metrics, on the other hand, focus on the efficiency and effectiveness of your support processes and workflows, which shows how well your support teams are functioning internally. Operational metrics include parameters like total number of tickets, call abandonment rate and average wait time (AWT). 

Learn more: Top 7 customer experience metrics to track 

21 key customer service metrics to track

How you measure customer service is essential to your company’s continuous improvement. The metrics you choose need to help you determine how satisfied your customers are with your service, how well your team is performing and what improvements are necessary. 

Here are some of the key customer service metrics that you need to track for your business:

Let’s have a look at some of the customer service metrics that are standard across most industries and the buckets they fall into. 

Types of customer service metrics

Apart from the two broad categories we discussed before (operational and experiential), customer service metrics are also differentiated based on several other aspects. Let’s take a quick look at other types of groupings that are usually followed by businesses worldwide. 

I. Quality metrics

Quality metrics primarily indicate the accuracy and effectiveness of interactions in your call center or contact center. Here are some of the most important metrics under this category:

Customer satisfaction score (CSAT)

Customer satisfaction scores are an indication of your customer’s overall satisfaction with your service or product, and CSAT surveys are a simple way to gather info about customer satisfaction. A survey question can be something like this — “On a scale of 1-10, how satisfied are you with your experience today?” A higher CSAT score indicates a more satisfied customer.

Ensure your survey is short, simple and easy to understand to get the best results. Surveys that are long or complicated often turn customers away, making it difficult to get objective feedback. Time your surveys strategically during key milestones in the customer journey. For example, a survey can be used after a customer buys a product, before a customer renews subscriptions or after speaking with customer service.  

Net promoter score (NPS)

Net promoter score indicates how likely your customers are to return to your company and how likely they are to promote it. Essentially, it measures your customer loyalty. A single-question survey is often used to determine NPS: “On a scale from 1-10, how likely are you to recommend [product/service/company] to someone you know?” 

• Those who answer 9-10 are very satisfied and are likely to recommend you to people they know. 

• Those who answer 7-8 are satisfied but will most likely not recommend you to others. 

• Those who answer 6 or below are not satisfied and may steer people away from your business. 

Customer effort score (CES)

Customer effort score measures the amount of effort a customer has to put in to resolve an issue, complete tasks or talk to a support agent. CES is usually measured on a 7-point scale, and is calculated with the following formula: 

Customer effort score = [Number of top-scoring responses (5, 6 or 7) / Total number of responses] x 100

Many companies also ask questions at the end of shopping sessions about how easy it was to place an order or complete an exchange. They also ask questions about how an agent helped the customer handle an issue. These questions gauge how easy it is for customers to use your services, navigate your website or get their issues resolved.

Service level agreement (SLA) compliance

Service level agreement (SLA) compliance is defined as the percentage of cases that meet the defined SLA criteria. It is used to determine your performance levels against benchmark response and resolution times set for customer inquiries or issues. 

SLA compliance = (Total number of tickets resolved within SLA criteria / Total number of tickets received) x 100

SLA compliance throws light on your operational efficiency and helps identify areas for improvement, ultimately helping you develop better customer relationships and maintain support quality. It ensures you provide consistent and timely service, satisfying customers’ expectations and increasing their trust in you.

First contact resolution rate (FCR)

First contact resolution refers to the percentage of incoming tickets or calls that are resolved right at the customer’s first interaction with your brand. FCR measures how efficient your agents are at solving issues without the need for follow-up calls/interactions.  

Gross FCR is calculated using the below formula: 

First contact resolution rate = (Total number of tickets resolved at first contact / Total number of tickets received) x 100

A quick first contact resolution rate increases your agent's productivity over time. When an issue isn't solved in the first interaction, customers are forced to call back repeatedly. This takes a lot of time from your team members that they could use for other tasks or to help other customers. Fewer repeat calls mean fewer calls overall.

Average ticket resolution time

Ticket resolution time is the total amount of time an agent takes to resolve an issue and close a ticket once it is assigned to them. A team-wide or org-wide average of this duration, called average ticket resolution time, is widely considered as an important customer service metric.

Average ticket resolution time = (Total time spent on ticket resolution (for agent/team) / Total number of tickets resolved)

Customer satisfaction relies heavily on quick resolution time as customers want simple and smooth support experiences. Average resolution time indicates how efficient your service staff is. A quick response time is important, but taking too much time to resolve customer issues hurts the customer experience. 

If the average resolution time is high, investigate the underlying problem. Common issues are inefficient communication between the service and technical teams or inadequate training on more complex issues. 

II. Performance metrics

Performance metrics focus on the overall effectiveness of your customer service and the direct impact it has on your customers. Popular performance metrics used in customer service quality management are as follows.

Total number of tickets

The total number of tickets indicates the total number of queries you receive daily, weekly or monthly across all your channels. This information will help you determine when and where you're getting the most complaints and help you allocate resources better. For example, you can see how often specific issues occur, and determine if they can be solved with automated systems to save your agents’ time.

If you experience high ticket volumes after product releases or big sales, you can manage your customer service workforce and staffing needs proactively.

Customer retention rate (CRR)

Customer retention rate is the percentage of customers a brand has retained over a certain amount of time. The probability of selling to existing customers is 60%-70% while selling to new customers is 5-20%. This is why knowing your CRR is so essential. 

Customer retention comes from positive customer experience and stellar customer service. Providing predictable, consistent and quality experiences to your customers can increase your retention rate. Likewise, providing quick customer support during and post-purchase can keep customers loyal to your brand. Retaining customers long-term shows that your business has built trust and loyalty with its customers. 

You can calculate CRR for your brand with this formula: 

CRR = (No. of customers at end of period — New customers acquired in period / No. of customers at the beginning of period) x 100

A rising retention rate shows that your support team is doing well, while a declining rate indicates that there is a scope for improvement in your support operations and in the quality of customer service you provide. 

Customer churn

Customer churn is the opposite of customer retention rate. It determines how many customers you have lost in a specific timeframe. It’s calculated with the formula below: 

Churn rate = [(Customers at beginning of timeframe — Customers at end) / Customers at the beginning of timeframe] x 100

Customer churn can be detrimental to companies since it’s challenging to get customers back after they leave for competitors. When a customer leaves, they might even persuade others to avoid your business, depending on their reasons for leaving. Obtaining new customers is more difficult and expensive than retaining old ones, so reducing churn should be a top priority for all companies. 

Number of upsells and cross-sells

Upselling involves convincing your customers to opt for a more expensive version of your product. Upselling works best for customers who need the additional features of the higher-priced product. Customers are unlikely to buy the more expensive version if its features don’t add much value for them.

Cross-selling convinces clients to purchase additional products to complement their already purchased product or service. For example, an organization buying a customer service solution will benefit from a CRM platform to manage customer details easily. So, if there’s a potential need for a CRM solution and your brand provides one, you can pitch it to your client as an additional purchase, boosting your overall revenue.  

Higher upselling and cross-selling rates show that your customer support teams are doing a great job at convincing customers to buy additional or higher-priced products. This eventually increases the customer lifetime value. Lower rates show that your support staff may need additional training to understand best-selling techniques. 

Since cross-sells and upsells are dynamic, it's important that you monitor them in real time using a point-of-sale (POS) system, e-commerce platform or CRM software to record and analyze transaction data. This data will help you measure the effectiveness of your upselling and cross-selling strategies and refine them as needed. 

Customer lifetime value (CLV)

Customer lifetime value measures how much money customers are expected to spend over the course of their relationship with your company. CLV is a great way to measure success in customer relationships as it shows that your offerings are valuable to your customers.

Customer lifetime value = (Revenue per year x No. of active years) — Acquisition & retention cost

CLV will rise over time if your company continues to meet customer needs. A decline in CLV can be rectified by identifying opportunities for product/service improvement for an enhanced user experience. Better customer retention strategies like loyalty programs, personalized offers and targeted campaigns can also help improve customer satisfaction, and thereby your CLV.  

III. Efficiency metrics

Efficiency metrics help assess the utilization of resources in your support operations and streamlines your processes for maximum efficiency. Below are some examples of efficiency-related metrics.

Call abandonment rate

Call abandonment rate measures the percentage of calls dropped or disconnected before they reach an agent or are resolved by an automated system, compared to the number of calls your customer support team receives. One of the most important efficiency metrics in customer service, the call abandonment rate, is calculated by: 

Call abandonment rate = (No. of abandoned calls / Total no. of received call) x 100

A higher rate of missed customer calls indicates a large number of customers with negative experiences and low efficiency among customer support staff. When customers cannot talk with customer support agents, they are more likely to get frustrated and have a negative opinion of your business, in turn affecting customer satisfaction and retention in the long run.

First response time (FRT)

First response time refers to the duration taken to provide an initial reply to a customer's inquiry or concern. FRT is measured from the moment a customer initiates contact (e.g., sends a message) to when they receive the first meaningful response.

FRT ideally excludes automated responses and considers only responses sent by the agent. But modern contact center solutions have advanced automation capabilities that help take tickets to resolution without any agent intervention at all, bringing automated responses back into the picture. Keeping FRT low enhances customer engagement, prevents frustration and demonstrates a commitment to timely support. 

5 Ways to Improve Customer Response Times — and Your Bottom Line

READ MORE

Average wait time (AWT)

Average wait time is the average of all the time callers are not speaking with a customer service representative. This includes the time customers spend on hold in a queue, being on hold while waiting for the representative to complete a task, etc. It is calculated by using the following formula: 

Average wait time = Total time customers spend in queue or on hold / Total number of answered calls

No one likes long hold times so the longer a person is on hold, the more irritated they will become. This not only makes them less likely to return to your company, but also more likely to tell others about their negative experience.

Average wait times can be improved by:

• Optimizing your queue. Ineffective interactive voice response (IVR) workflows are a big issue in customer wait times. Ensure your IVR quickly connects people to the right department. This reduces the time customers spend waiting for representatives who often end up transferring their calls anyway.  

• Changing your workforce management strategy. Representatives who can quickly and effectively handle calls are better at resolving caller issues. This reduces the time customers spend waiting while representatives find solutions to their problems.  

• Hiring more staff. If your current staff is working well enough, it may be time to consider adding new members to the customer service team to keep up with the number of calls they are receiving. 

Average after-call work time (ACWT)

ACWT tracks how long it takes customer service agents to complete all of the call-related tasks after the call has ended. This includes inputting data, filing paperwork, updating databases etc. Understanding how your support staff uses their time is vital for creating efficient workflows. 

Agents need to spend enough time to complete their work thoroughly, but if the average after-call work time is too high, there might be other undetected issues in your workflow (like high amounts of paperwork). It is essential for you to keep after-call activities to the bare minimum so that your agents are available for the most time.

Average handle time (AHT)

Average handle time is the average duration your agents spend with a customer on a call resolving an issue. This duration includes the total time taken to resolve a particular issue, including wait/hold times, after-call work etc. Average handle time is calculated by: 

Average handle time = (Talk time + Hold or wait durations + After-call work time) / Total number of calls handled

Average handle time and average ticket handling time are two terms that are used interchangeably in the industry — average ticket handling time is mostly not used for call-based resolutions, whereas average handle time is widely used as a call center-specific metric.

If handle times are high, identify the root cause, then coach agents where needed. For example, if agents are receiving a high number of supervisor escalations, coaching agents on de-escalation tactics may reduce these escalations.

Sometimes, handling time is affected by factors that are out of the agent’s control. An example of this would be a digital marketer working on multiple tickets at once. Issues outside of agents’ control should also be considered.

If not used properly, average handle time can be misleading. If your support agents attempt to hang up calls quickly rather than fully resolve issues, average handle time can be harmful. Customers will be dissatisfied if they feel like they're being rushed or if their issue isn't properly resolved. It’s crucial to find the right balance between quality and speed.

Escalation rate

Escalation rate refers to the percentage of tickets that are escalated to a higher support tier from the total number of tickets that are received. The calculation for escalation rate is simple: 

Escalation rate = (No. of tickets that were escalated to higher levels / Total number of tickets received) x 100

Escalation rate serves as an important indicator of agent efficiency — if you have a greater proportion of escalations, it means that your agents are struggling to resolve issues. High escalation rates usually indicate a knowledge gap, either on the customer end (when the product/service is too technical and not user-friendly) or on the agent end (which requires personalized training and upskilling).

Agent utilization

Agent utilization measures the efficiency of customer service representatives by tracking the time they spend actively assisting customers compared to their available work hours. It is measured as the ratio of actual customer interaction time to total available work time.

Agent utilization = Total duration of actual customer interactions / Total available work time

Measuring agent utilization is critical to optimizing your resources, ensuring agents have minimal idle times when they are at their desks. Higher utilization rates signify effective agent engagement, streamlined operations and, in turn, improved customer service delivery. 

Backlog

The backlog refers to the number of customer service requests accumulated over a given amount of time. There are many reasons customer requests remain unsolved — but experiencing a high volume of query requests with too little staff is one of the primary reasons.

It’s also possible that your team is taking longer to resolve issues than expected, for which you might need extra training, more time or extra employees. Every company should strive to keep this number as low as possible — the bigger your backlog queue is, the higher the number of angry customers you’ll have to deal with. 

IV. Employee satisfaction metrics

As mentioned before, employee happiness is a critical metric when it comes to improving your customer service. Two of the most important metrics related to this category are as follows:

Agent satisfaction/happiness

Agent satisfaction is a measure of how happy your agents feel at their work. It is determined by assessing factors like job fulfillment, the presence of a supportive work environment and team dynamics. There’s no direct formula to gauge agent satisfaction, but extensive questionnaires and surveys are commonly used to evaluate this metric.

For your customer service levels to be at their best, it’s critical that your agents are also feeling their best during their work hours. Agent happiness is exactly about that — it gauges the contentment and morale of customer service representatives in their roles. Happy agents are more likely to provide better service, leading to enhanced customer experiences.

Although it isn’t a proper customer service metric, agent happiness is still recognized by organizations across all industries as an important factor in delivering quality support. Positive agent satisfaction is directly linked to lower turnover rates and increased productivity — positively impacting overall customer satisfaction. 

Employee net promoter score (eNPS)

Employee net promoter score is simply an agent version of NPS; it evaluates agent happiness and the likeliness of them being advocates for your brand based on a simple 10-point scale.  

eNPS is determined with a simple survey question like NPS, namely “How likely are you to recommend our brand to your friends and family?”, which is answered by agents on a scale of 1-10. The respondents are divided into three groups — promoters (scores 9 or 10), passives (7 or 8) and detractors (6 and below). The effective eNPS score is calculated by using the formula: 

Employee net promoter score (eNPS) = [(No. of promoters — no. of detractors) / Total no. of respondents] x 100

Promoters are considered the happiest group of employees who can serve as brand advocates, and detractors are considered the most vulnerable to attrition and low performance. eNPS serves as an indicator of employee happiness and loyalty and is a very important metric in improving agent retention.

Read more: The call center guide to agent retention

How to use customer service metrics for your business: A real-world example 

Background: In 2022, boAt, one of India’s largest audio and wearable tech brands in India, was working on the process of improving their customer service quality to industry-best levels. They wanted to create a highly engaging, rewarding customer experience for their users by identifying and streamlining their end-to-end issue resolution process.

Objective: boAt’s primary focus was on a few critical customer service metrics — improving CSAT, handling/resolution times and escalation rates.

Solution: That’s when they came across Sprinklr Service as a potential CCaaS solution that could help them monitor and improve their support processes. Right from the get-go, Sprinklr took over and helped their agents perform remarkably better with cutting-edge support capabilities like: 

• AI-powered Predicted CSAT: Agents were able to understand in real time if their responses helped the customers. It also helped them take quick corrective actions in case the predicted score dropped, thereby keeping their customers happy and satisfied. 

• Real-time performance insights: Handling and resolution times were readily available on the platform for anyone in the team to view. Supervisors had a holistic view of critical performance metrics across teams and for all agents.

• Efficient escalation management: Initially, their team was also managing escalations on spreadsheets, leading to reduced transparency and collaboration. With Sprinklr, the agents were able to easily collaborate in real time within the case window itself — enabling quicker, more efficient resolutions that improved customer satisfaction and averted potential escalations. 

Accessing and connecting all the data from across your different contact center point solutions can get complicated. Sprinklr Service can serve as comprehensive customer service reporting and analytics software, unifying data from across 30+ channels to deliver real-time insights on the quality of your customer service. With fully integrated dashboards and highly customizable reports, you can gain complete visibility into critical customer service metrics and identify how you can optimize your support operations.  

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