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What is First Contact Resolution?

Share of issues resolved in a single interaction.

How to calculate it

Calculate First Contact Resolution as: Issues resolved on first contact / Total issues × 100. Pull the inputs from your connected data and track the trend over time in your dashboard.

Examples

Example 1

350 of 500 tickets are solved on first contact -> 70% FCR.

Example 2

350 of 500 tickets are resolved on first contact -> 70% FCR. Cutting repeat contacts on the remaining 30% would lift CSAT and reduce cost.

Why it matters

First contact resolution (FCR) is the share of issues resolved in a single interaction and is a strong driver of both satisfaction and cost efficiency. Resolving issues the first time avoids repeat contacts that frustrate customers and burden the team. Counting reopened tickets as resolved inflates FCR and hides the real customer experience.

Benchmark context

70-75% is considered good; the right target depends on issue complexity, with simpler products able to push higher.

Common pitfalls

Reopened tickets counted as resolved.

The complete guide

Understanding First Contact Resolution

First contact resolution (FCR) is the share of issues resolved in a single interaction and is a strong driver of both satisfaction and cost efficiency. Resolving issues the first time avoids repeat contacts that frustrate customers and burden the team. Counting reopened tickets as resolved inflates FCR and hides the real customer experience. For context, 70-75% is considered good; the right target depends on issue complexity, with simpler products able to push higher. 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 First Contact Resolution alongside Average Handle Time, CSAT and Ticket Backlog; together these measures provide a more balanced view and reduce the risk of optimizing one number at the expense of the wider outcome.

Make sure the numerator and denominator cover compatible populations and periods, and avoid dividing by a partial or unusually small base. 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: reopened tickets counted as resolved. 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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