Have you ever been to an old-fashioned deli, the kind where people line up out the door for their famous pastrami-on-rye sandwiches? It’s busy. It’s noisy. To the untrained eye, the scene might look like complete chaos. And you’re probably asking yourself what this has to do with call center ticket systems. We’ll get to that in just a moment.

But then you hear someone from behind the counter bellow out “number 22!” and things shift into orderly motion. The customer with order number 22 steps forward and grabs their prized lunch parcel. The line inches forward as the next customer approaches the counter. The workers behind the counter hum along assembling sandwiches that are perfectly customized for each hungry patron in line. 

What makes it possible for the chaotic deli to function like a well-oiled machine? A ticketing system. 

What is a ticketing system?

A call center ticketing system works in exactly the same way (albeit with a less delicious final product), helping companies turn a chaotic influx of customer support requests into an organized, manageable queue. When a customer reaches out for help, the call center ticket system creates a virtual file, called a ticket, that documents their request and the resulting activity. All of a customer’s future support interactions on that topic are logged on the customized ticket, creating one continuous thread. When the issue is resolved, an agent can ‘close’ the ticket, signaling the end of that particular inquiry. A ticket can always be reopened in the future if the need arises. 

Ticketing software, also called help desk software, stores tickets alongside the relevant customer data so that all the necessary information on a case is located in one place. This easy-to-use system empowers any agent to jump in and take over a case regardless of whether they’ve worked on it in the past. It also gives managers and administrators a quick debrief on what’s been done so far and where things stand. 

A call center ticket system makes it possible to categorize support requests, route them to the best possible agent, track outcomes and analyze performance. 

Essential features of a tracking system

Here are the most essential of features of a solid ticketing system: 

  • Multichannel accessibility with a complete view of cross-channel interaction history
  • Configurable forms with advanced filtering and field mapping
  • Contact notes and attachment library
  • Full customization of SLAs, triggers, and routing
  • Workflow automation
  • Personalized ticket views based on agent roles
  • Ticket categories and tags
  • A connected knowledge base
  • API call outs to third party systems
  • Performance monitoring and analytics
  • Real-time notifications for status changes and blockers

Tracking metrics and KPIs

To be successful, contact centers need a robust, integrated platform that can generate essential metrics reports. In addition, when metrics change in response to a shift in the business such as a product launch, staff change, or marketing campaign, managers must interpret the data to find what’s not working. 

Below are some of the key report capabilities you’ll need to evaluate the quality and efficacy of a ticketing system. 

1. Total number of tickets your team handles on a daily (or hourly) basis to inform where to put your energy and how to schedule agents.

2. First contact resolution time measures how long it takes your team to resolve an issue after a customer first opens a ticket. You will compare this to internal and industry standards and past performance.

3. Aggregate ticket resolution time tracks the average across all tickets. If there’s a discrepancy between this metric and an individual agent’s average resolution time, you will want to know more about what that agent is doing differently. If this number is higher than your standard, you may have to rethink training, self-serve, and the channels you have available, etc.

4. Net promoter score asks how likely a customer is to refer your business to someone else within their own network. Because this focuses on the intentions of the customer rather than their emotions (like CSAT), you’re more likely to receive accurate answers that are less influenced by people’s current mood.

5. Direction of importance measures two things: 

  •  How the customer feels about your business now
  • Generates predictions about future feelings. Sometimes businesses get a high NPS and still lose customers because the NPS is just about the present. 

To measure DOI, we ask “Is the importance of our business/product increasing, staying about the same, or decreasing?”

6. First response time simply refers to how long it takes your support staff to respond to your customer’s request. This number helps you answer questions like how many agents to schedule and at what times or how much automation can be leveraged.

7. First contact resolution (FCR) differs from the first response time in that it calculates the percentage of support issues that are resolved by the end of the first contact with a customer.

8. Individual agent performance lets you monitor how effective and regulation-compliant each agent is when responding to and resolving tickets.

9. Agent utilization tells you the ratio of time spent on tickets to time spent on other internal tasks. Aim for a higher ratio for a more effective team. If this ratio is low, it could be that agents are burdened by time-consuming tasks that could be automated, or that agents need to be trained on more efficient practices.

10. Ticket volume by channel determines where customer engagement is happening by looking at where tickets are originating from—social, email, chat, voice, or SMS. Metrics will likely vary by channel. Use them to inform decision-making about the overall customer experience strategy.

11. Cost per ticket is a metric that gives you a granular understanding of how much customer engagement costs you given your current systems. It is based on the cost of the staffing, technology, and other expenses involved in resolving a ticket, broken down per minute and then multiplied by the number of minutes it takes to receive and resolve a ticket.

Want to learn more about successful ticketing systems? Read our blog here

12. Helpdesk staff engagement metrics help you build a strong, reliable team. When your workers are happy with their work, managers, and the company they work for, they’re much more likely to work harder, perform better, and be more loyal.

13. Average resolution time from your call center ticket system should be low or it could mean that your team is understaffed or undertrained. If the metric is based on average time to fully resolve tickets, including multiple times opening and closing them, you can look for patterns in particular agents, training, or your knowledge base that might be causing barriers to efficient solutions.

14. SLA success rate tracks the percentage of incidents resolved within the agreed timeframe for completing the service.

15. Complaint escalation rate tells you how well your frontline agents are handling complaints.

16. Number of support tickets generally reflects the quality of your products or services and the self-serve support of those products.

17. Number of tickets backlogged pertains to customer support requests that stay unresolved during a particular period or beyond the usual response time you set. If this number is high or suddenly changes, you will be looking for a weak link in your system.

18. Ticket volume by support channel informs you what additional training may be needed on a particular channel. Each channel is used differently, has different compliance regulations, and requires different training. If tickets are all coming through on voice and none on SMS, you might improve your ticketing system by making SMS more available, since it can reduce staffing costs and improve customer experience.

19. Average time in queue alerts you to the likelihood of dissatisfied customers. Research shows that most customers are not willing to wait for more than a minute on hold.

20. After-call work time tells you how long it takes to fully resolve an issue, even after the customer gets off the phone. If agents are spending more than half the length of the call wrapping up and reporting, a reexamination of your processes is in order.

21. Abandonment rate refers to customers discontinuing their contact while waiting for an agent. If the percentage is anything significant, it may be worth exploring other channels for your customer engagement strategy.

22. Self-serve views to ticket ratio tells you how many people tried to deal with their problems without an agent and gave up and filed a ticket. Looking at which self-serve options customers choose most often and which they abandon can help you improve your offerings.

The most important metrics for call-based support should assess how well support addresses customer needs. None of these customer experience KPIs should be viewed in a silo. A skilled manager will look at all of the data and understand the story told by the various metrics in combination with one another, like putting pieces of a puzzle into a coherent, recognizable picture.

MIS reports for ticketing systems 

An MIS report is a management information system report. Executives and senior management use MIS reports to organize, compare, and analyze data related to daily tasks and overall business processes.

MIS reports help contact center leaders identify problems in processes by providing a concise view of the overall operation. MIS reports can contain variance data and show deltas between targets, projections, and performance. There are many kinds of MIS reports: sales, budget, production, inventory, human resources, service time, service fluctuations, etc. 

MIS reports generated by pulling information in from your ticketing system help leaders to identify trends and conduct deep analysis that will inform future projections. 

Call to ticket ratio formula

To calculate your call to ticket ratio all you need is basic math. Add up all of your calls on a given day and divide them by the number of tickets created as a result of those interactions. Similarly, to determine the number of tickets created from calls in comparison with the number of tickets created by customers by filling out a web form, simply divide the two. That is your call to ticket ratio formula.

Finding your average help desk tickets per user

You can calculate the average help desk tickets received by agent users by adding up all of the tickets in your queue and dividing by the total number of agent users on staff. Normally your ticketing system will tell you who is assigned to what ticket case so you shouldn’t have to manually determine this figure. 

You can drill down further and calculate your average resolution rate by following a similar formula. That is, take your total number of closed tickets in a given time period and divide that figure by the total number of the tickets opened in that same time period and multiply by 100. Then you’ll know on average how long cases stay open. 

Ticket analysis tools

Your ticketing system will come equipped with many kinds of reports that will be customizable so you can see all of your ticket sources, such as the channels which customers used to contact you, ticket volumes, ticket resolution rates, ticket open rates, and more. You can determine whether tickets are most often created via Email, entered manually by an agent or via the contact form on your website.

NICE’s Integrated Ticketing lets you combine multiple channel sources to collect all customer interactions in one place and keep your workflow organized. You can regularly run a new ticket report to spot the trends and see how your support work can impact this number in other areas. For instance, you may identify that the same ticket type is opened multiple times, maybe for password resets, and set up an automated trigger via email that resets the customer’s password.

Another great ticket analysis tool available with NICE Ticketing is at-a-glance historical reporting. You can see the last seven days, last thirty days, and beyond to see overarching trends and improvements. These historical reports provide a summary of your progress in resolving tickets.

Data extracted from ticketing tools can serve as input for metrics 

You can use the metrics mentioned above to refine or establish new KPIs for your support teams. For example, use your resolution rate to assess whether your customer service agents are solving customer tickets in a timely manner in accordance with service level agreements. Or, determine the overall performance of your customer service team. Knowing your resolution rate especially lets you swiftly decide whether or not you’re meeting your service level agreement (SLA) that you’ve established for the highest priority customers, which in turn will open the door to higher CSAT scores! 

How to do ticket analysis

You shouldn’t have to manually analyze customer support tickets, that’s what integrated ticketing is for. But, if you’re interested, this is how you’d go about performing ticket analysis. 

First, you need to collect your data and store it in a call center ticket system database. Then you need to decide how you’re going to organize that data, so you’ll need to decide on a categorization schema or taxonomy. For example, ticket types, channel of origin, customer type, etc. Then you’ll want to go one level down and assign a ticket theme for each case, e.g. “password reset” or “order delay.” You can even go another step and log the time periods for ticket types. 

Now that your data is collected and categorized in your spreadsheet, you can begin looking for trends and patterns by running different formulas or applying filters for each ticket type. We don’t recommend doing this – it’s a lot of work and the margin for error is high. That’s why it’s important to make sure your ticketing software has built-in analytics available.

Why every call center should use a ticketing system 

By now it should be clear how a customer support ticketing system is used and how its performance can be measured. Here are the top reasons every contact center should have a ticket system. 

Manage call volume

The creation of a queue is a basic function of any call center software, but an advanced call center ticket system takes queueing one step further with an algorithm that prioritizes requests. Based on a set of rules, calls can be routed to agents based on their subject matter, customer value, level of urgency, and more. This ensures that the most important tickets reach an agent first, while other lower-urgency items can be directed to options like live chats where an agent can manage more than one request at a time.

Automate support requests

Because an integrated ticketing system is connected to your phone system, many incoming support calls can be deflected to self-service options, reducing wait times. Options like ‘press one to check the status of your order’ might route to an automated shipping update that tells the customer everything they need to know; no agent is required, but the ticketing system still keeps a record of the call. In cases where speaking with an agent is required, the ticketing software can compile the necessary customer information ahead of time so the agent doesn’t have to ask the customer for it. 

Improve scheduling

A call center ticket system gives managers the full range of metrics they need to optimize how many agents are at work at any given time. We touched on many of these in the metrics section above.

MIS reports for ticketing system 

An MIS report is a management information system report. Executives and senior management use MIS reports to organize, compare, and analyze data related to daily tasks and overall business processes.

MIS reports help contact center leaders identify problems in processes by providing a concise view of the overall operation. MIS reports can contain variance data and show deltas between targets, projections, and performance. There are many kinds of MIS reports: sales, budget, production, inventory, human resources, service time, service fluctuations, etc. 

MIS reports generated by pulling information in from your ticketing system help leaders to identify trends and conduct deep analysis that will inform future projections.