CX stats in 2023: average support tickets per day, per user and other important benchmarks

CX stats in 2023: average support tickets per day, per user and other important benchmarks

TL: DR

  • Understanding the number of support tickets per user and per day can help you make better decisions to increase efficiency and customer satisfaction.
  • The average number of tickets will vary depending on the nature of the business.
  • Knowing these average statistics will help you understand how much pressure your support agents are under and what areas need more attention or automation in order to increase efficiency and provide better customer experiences.

Regardless of your industry, customer service plays a vital role in the success of your business. Keeping track of how many support tickets are created on a daily basis and how many tickets per user per month can be essential to understanding customer satisfaction levels. This statistic is especially useful for tracking changes over time so that you can make necessary adjustments to improve performance.

Each business is different, and so is the complexity of the requests coming in, but knowing what the average numbers are across industries, or even for specific industries, is still very helpful. In this article, we'll look at some industry averages when it comes to supporting tickets per day and tickets per user per month.

Average number of tickets per user per day

The average number of tickets per user per day is a measure of how many customer service tickets are submitted by an average customer each day. This metric is useful for gauging the level of customer support needed by a business. It can also help to identify potential problem areas in the customer experience. 

According to live agent, the average company has 578 tickets per day, 3,991 per week, and 17,630 per month. The ticket volume in working hours comes out to a total of 6,594.

A high number of tickets may indicate that customers are struggling to use the product or service or that they are encountering significant delays or issues with the customer service team. Conversely, a low number of tickets may suggest that customers are happy with the product or service and are not experiencing any major problems. Businesses can use this metric to adjust their customer service strategy accordingly.

Average time to reach a live agent

The average time to reach a live agent is the average amount of time it takes for someone to answer your call. This number is important because it allows you to gauge how long your callers are waiting on hold. If the average time to reach a live agent is too high, it may be an indication that your callers are getting frustrated and may hang up before they reach an agent. On the other hand, if the average time to reach a live agent is too low, it may mean that your agents are not taking enough time with each caller.

The average first response time for live chat is 47 seconds, which is relatively fast compared to other channels such as phone or email. This is likely due to the immediacy of chat and its ability to provide a more personal level of communication with customers.

Average overall resolution rate

When a company talks about its "resolution rate," they are referring to the percentage of customer complaints that they have been able to resolve successfully. 

The average overall resolution rate is 76.2%, which means that for every 100 tickets received, 76 are successfully closed within the given timeframe. This is a good indicator of how efficiently a customer service team is working and whether they need to improve any processes or add more resources.

In general, the larger and more well-established a company is, the higher its resolution rate is likely to be. This is because larger companies usually have more resources and experience in dealing with customer complaints. They also tend to have better systems and processes in place for handling customer service issues.

Average live chat hold time

The average live chat hold time is the average amount of time that a customer spends on hold during a live chat session. This metric is important because it can help you understand how long your customers are willing to wait for a response. If the average hold time is too long, it may be an indication that your live chat team is not providing timely responses. On the other hand, if the average hold time is too short, it may be an indication that your team is not providing enough information or adequately resolving the customer query. 

The average chat hold time for responses is 160 seconds. This is a bit longer than the average first response time but is still relatively fast, depending on the number of tickets being sent in.

Average handling time

Average Handling Time (AHT) is a metric used in contact centers to measure the average amount of time an agent spends dealing with a single customer interaction. This includes both the time spent on the phone with the customer as well as any post-call work that needs to be done, such as logging notes or updating records. 

If agents are regularly going over the AHT goal, it may be necessary to provide more training or additional resources. Conversely, if agents are consistently meeting or exceeding the AHT goal, it may be possible to increase call volume without negatively impacting customer satisfaction. 

The average handling time is 525 seconds for telecommunications, 282 seconds for business, IT, and financial services, and 324 seconds for retail.

The problem with relying on averages

While averages are a good way to get an idea of how well your customer support team is doing, they should not be used as the only metric for measuring performance. Here are a couple of reasons why:

Not all tickets are equal

Averages don't take into account that some tickets can be more complex than others and therefore require more time to resolve. For example, a customer with a technical issue may require more time to troubleshoot than one with a billing inquiry.

Team structure makes a difference

Depending on how the team is structured, different teams may have different levels of expertise or access to resources. This means that one team may be able to resolve a ticket faster than another, even if both teams are working on the same type of ticket.

Averaging out different ticket types can be misleading

While averages can provide a good starting point for understanding how well your customer service team is performing, they can also be misleading. This is because averaging out different ticket types does not take into account that some tickets may require more time or resources to resolve than others. For example, a level three support request may take significantly longer to resolve than a level one request.

It doesn't take into account customer satisfaction

Lastly, averages don't take into account customer satisfaction. While a low average response time may indicate that the team is performing efficiently, it doesn't tell you if customers are actually happy with the service they receive. To truly gauge customer satisfaction and provide an accurate view of team performance, other metrics such as resolution rate, customer satisfaction rating, and customer feedback should be considered.

How to use metrics in your business

The right metrics can be essential for making sound business decisions. For example, if you run a customer support operation, you might track the number of tickets submitted per month and the number of tickets handled by each agent. This information can help you predict when you need to expand your headcount in order to keep up with customer demand.

Alternatively, it might show that you're already operating efficiently and that you can invest in new technologies instead. For example, Fullview provides the tools your business needs to provide excellent customer service. With Fullview Replays, customer support agents can quickly review a user's latest sessions in their app to see bugs and issues in context. With Fullview Console, customer support agents can see relevant and real-time console information like user steps, errors, and warnings to quickly and painlessly diagnose issues without having to ask the user for screenshots or to inspect an element or explain.

Author

Shifa Rahaman

Content Marketing Manager

Contributor