When analysing support ticket metrics, it’s common to notice a discrepancy between the number of tickets created and those solved within the same time period — such as a specific month. This can raise questions, especially when trying to measure team performance or workload trends.
The simple explanation lies in the natural lifecycle of support tickets.
- Not all tickets created in a given month are solved in the same month.
We're going to explore why this happens and what factors contribute to the difference by covering the following topics:
- The lifecycle of a ticket
- Understanding the time lag
- Factors influencing the discrepancy
- How to interpret ticket metrics
The Lifecycle of a Ticket
A support ticket goes through several stages before it’s considered resolved. From creation to resolution, factors such as ticket complexity, customer responsiveness, and operational workflows can affect the time it takes to close a ticket.
Tickets created in one month don’t necessarily align with tickets solved in the same month because:
- Tickets created late in the month are more likely to carry over and be resolved in the following month.
- Complex tickets may require input from multiple teams or extended investigation, leading to longer resolution times.
- Pending tickets, such as those awaiting customer responses, can remain unresolved for weeks or even months.
Understanding the Time Lag
Here’s a simplified example to illustrate the time lag:
- In Month A, 1,000 tickets are created, and 800 tickets are resolved.
- Of the 800 solved tickets, 650 were created in Month A, and the remaining 150 were unresolved tickets from the previous month (Month A-1).
- The remaining 350 tickets created in Month A are likely to be resolved in Month B or later.
This overlap of ticket lifecycles across time periods explains why the numbers don’t match.
Factors Influencing the Discrepancy
- Ticket Complexity:
- Straightforward tickets can be resolved quickly, while more complex ones—such as technical investigations or escalations—take longer.
- Volume Spikes:
- Events like sales promotions or product launches often generate a surge in ticket volume, creating a backlog that takes weeks to address.
- Customer Delays:
- Sometimes, the delay isn’t on your team’s side. Tickets waiting on customer responses or approvals remain unresolved until the customer takes action.
- Operational Constraints:
- Introducing Self-Service Deflection: Shifting simpler queries to self-service tools (like knowledge base articles or chatbots) can reduce the volume of easily solvable tickets handled by agents. However, this might leave the support team handling a higher proportion of complex tickets, leading to longer resolution times.
- Changes in Prioritization: Reprioritizing ticket categories—such as focusing on critical tickets first—may result in less urgent tickets remaining unresolved longer.
- Process Overhauls: Introducing new workflows or tools might temporarily disrupt ticket resolution as teams adjust.
How to Interpret Ticket Metrics
When reviewing ticket metrics, it’s essential to remember that:
- Tickets created in a month represent new demand for your support team.
- Tickets solved reflect the team’s output, but not necessarily their ability to keep up with current demand due to carryover effects.
By examining these metrics over time and factoring in the time lag, you can get a more accurate picture of your team’s performance and workload.
The gap between tickets created and tickets solved in a given time period is a natural outcome of the rolling lifecycle of support tickets. Understanding this dynamic helps set realistic expectations for your team’s performance and provides deeper insight into the overall support process.
By tracking and analysing these metrics over time, you can better identify trends, address bottlenecks, and optimise your support operations.