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Customer Support

What is Ticket Backlog?

Number of open, unresolved support tickets.

How to calculate it

Calculate Ticket Backlog as: Count of unresolved tickets at period end. Pull the inputs from your connected data and track the trend over time in your dashboard.

Examples

Example 1

320 tickets remain open at month end, up from 250 -- a capacity-strain warning.

Example 2

The backlog grows from 250 to 320 open tickets in a month, and a quarter of them are over a week old -> a clear capacity-strain warning.

Why it matters

Ticket backlog is the number of open, unresolved support tickets and signals capacity strain and SLA risk. A growing backlog is an early warning that demand is outpacing the team's ability to respond. Ignoring the age distribution within the backlog hides whether old tickets are festering while new ones are handled.

Benchmark context

The trend should be flat or declining; a steadily rising backlog indicates understaffing or a process bottleneck that needs addressing.

Common pitfalls

Ignoring ticket age within the backlog.

The complete guide

Understanding Ticket Backlog

Ticket backlog is the number of open, unresolved support tickets and signals capacity strain and SLA risk. A growing backlog is an early warning that demand is outpacing the team's ability to respond. Ignoring the age distribution within the backlog hides whether old tickets are festering while new ones are handled. For context, the trend should be flat or declining; a steadily rising backlog indicates understaffing or a process bottleneck that needs addressing. Treat an external benchmark as a reference point rather than a universal target: company stage, sector, customer mix, pricing model and measurement definitions can materially change what “good” looks like. Read Ticket Backlog alongside Resolution Time and SLA Compliance; together these measures provide a more balanced view and reduce the risk of optimizing one number at the expense of the wider outcome.

Define every included record and value consistently, and reconcile the total to its source system before comparing periods. Document business-hours rules, bot interactions, reopened tickets, pending time and the event that marks a case as resolved. Split the measure by channel, priority, issue type, customer tier and business-hours policy so that ticket mix does not create a misleading average. A dashboard should show the current value, prior-period comparison and a trend rather than presenting one isolated reading.

Improve the measure by diagnosing demand and flow: remove repeat contact causes, strengthen knowledge content, route accurately, balance staffing to arrival patterns and coach from quality reviews. Common pitfalls: ignoring ticket age within the backlog. Record the definition beside the dashboard, review it when systems or processes change, and annotate unusual events so future readers can distinguish a genuine performance shift from a data or timing effect.

Sources and further reading

  1. ITSM metrics: what to measure and why it matters

    Zendesk Accessed

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