First contact resolution calculator
Calculate the share of issues solved in the first contact with its margin of error, check whether a change between two periods or teams is real, and see how many repeat contacts, agent hours and money a higher FCR would save. Free, no sign-up, nothing leaves your browser.
Made by Anatolii Ulitovskyi 
Fact-checked by RH Fardin Updated Solve it the first time
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The method
How to calculate first contact resolution
Issues solved in one contact, divided by all resolved issues.
Count issues, not contacts: one issue may take several calls, chats or emails. An issue counts as solved in the first contact only if the customer did not have to come back about the same problem. Use one window to spot repeat contacts, for example 7 days, and keep it the same every month.
70%
280 ÷ 400 = 70%
Range 65.3% to 74.3%.
1.4
(280 + 160 + 120) ÷ 400
At least 160 repeat contacts.
$2,160
70% → 78% on 3,000 issues
A month, from 360 fewer repeat contacts.
Measurement
How to measure FCR
Customers know best whether their issue was solved.
SQM Group recommends asking customers right after a contact whether the issue was resolved and how many times they contacted you about it. Systems that only track repeat calls from the same number miss customers who switch channel, and internal reviews tend to overstate FCR. If you use help desk data, link contacts by customer and topic, not only by ticket.
Impact
Why FCR matters
Every repeat contact costs twice: in money and in satisfaction.
SQM Group's research found that for every 1% improvement in FCR, a contact center cuts operating costs by 1% and customer satisfaction rises by 1%, and that each repeat contact lowers top-box satisfaction by about 15%. The Savings tab turns your own numbers into repeat contacts, agent hours and money saved.
Comparison
Did FCR really improve?
Monthly numbers move by chance.
The Compare two tab runs a two-proportion z-test. Going from 280 of 400 issues (70%) to 330 of 420 (78.6%) is a rise of 8.6 points with a p-value of 0.005: very likely a real improvement. With 100 issues per period, a few points of change is usually noise.
Spreadsheets
FCR formulas for Excel and Google Sheets
One issue per row, number of contacts in column B, from B2 to B401.
First contact resolution
Contacts per issue
Monthly saving from a higher FCR
Issues in C2, FCR now in D2 and target in E2 as decimals, extra contacts per unsolved issue in F2, cost per contact in G2.
FAQ
First contact resolution questions
How do you calculate first contact resolution?
Divide the issues solved in the first contact by all resolved issues. 280 of 400 is an FCR of 70%.
What is a good FCR rate?
It depends on how complex your product is and how you measure it: customer surveys usually give lower numbers than internal tracking. Compare your own trend over time, and compare channels and teams measured the same way.
What is the difference between FCR and first call resolution?
First call resolution counts only phone calls. First contact resolution covers every channel, which matters when customers start in chat and follow up by email.
How long should the repeat-contact window be?
Long enough to catch customers who come back about the same issue; many teams use 7 days. Whatever you choose, keep it the same so months can be compared.
Should issues passed to another team count?
If the customer had to contact you again or wait for another team, the issue was not solved in the first contact. Internal handovers that the customer never notices can count.
How can I improve FCR?
Give agents complete answers and the right permissions, route issues to the right person at once, and let an AI agent answer the common questions completely from your help content.
Does the calculator store my data?
No. Everything runs in your browser. The share link keeps your numbers in the address after the # sign, which is not sent to our server.
References
Sources
- FCR measurement and impact: Mike Desmarais, SQM Group, "First Call Resolution – Defining, Research, Success Stories and Best Practices", 2012
- Confidence range: Edwin B. Wilson, "Probable Inference, the Law of Succession, and Statistical Inference", Journal of the American Statistical Association, 1927
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