Conversations per seat a month
- Typical546
- Good856
- Best1,355
Each seat handles a quarter more conversations than two years ago.
Every conversation counts, including ones an AI agent handled. A seat is a licence, not a working hour.
The Dixa Customer Service Benchmark 2026
See how your customer service team compares
Enter five numbers you already have. See where your team sits against 316 customer service teams on Dixa: typical, good and best, for teams your size or in your industry.
- 316 teams on Dixa
- Platform data, not a survey
- July to September 2026
- Free, no sign-up to compare
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All six findings, the charts and how we measured it. 21 pages, free to download.
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21 pages: the six findings, the charts and how we measured it.
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How it works
Two minutes, five numbers from your own reporting, and a clear answer on where you stand.
- 01
Enter your numbers
Conversations a month, licensed seats, email first reply time, CSAT and the share of conversations your AI agent handles. Use July to September if you can.
- 02
Pick who to compare with
Teams your size, teams in your industry, or all 316 teams. Your seats decide your size group.
- 03
See your tier on each number
Typical is the middle team, good is better than three in four, best is better than nine in ten. Each card shows how far you are from the next tier.
- 04
Share it with your boss
Copy your results as plain text, with the source line, and paste them into an email, a deck or your 2027 plan.
The five numbers, across all 316 teams
The tool compares you with your own size group or industry. These are the figures for all teams, July to September 2026.
Email first reply time
- Typical20.2 h
- Good11.2 h
Replies got almost a quarter faster this year, from 26.1 hours.
First reply by a person. Good is the fastest quarter of teams.
CSAT
- Typical81%
- Good88%
- Best91%
Phone scores highest (88) and email lowest (78).
Share of positive survey answers, teams with 30 or more answers.
Share of conversations the AI agent handles
- Typical7.6%
- Good19.7%
- Best46.3%
A third of teams run an AI agent. At these teams it handles 61% of chat and 11% of email.
Teams that run an AI agent. Includes conversations it handed to a person.
AI resolution rate
- Typical62%
- Good78%
- Best87%
Plan your business case on six in ten. On email, the typical AI resolves under half.
Conversations the AI closed without a person, teams with 50 or more AI outcomes.
Want to go through your numbers with us?
We'll show you which number moves first, and what teams in the good tier do differently.
Questions people ask about this data
What is a good AI resolution rate for customer service?
About 62% is typical. The best quarter of teams pass 78%. Measured on 76 teams on Dixa, July to September 2026.
How much of customer service do AI agents handle?
7.6% of all conversations at the typical team that runs one. Across those teams, AI handles 61% of chat but 11% of email.
Does AI lower customer satisfaction?
Not measurably. Teams that switched on an AI agent moved about one point, the same as teams that didn't.
What is a good email first reply time?
About 20 hours is typical. The good tier replies within 11 hours.
What is a good CSAT score?
81% positive is typical, 88% is good and 91% is best. Phone scores highest (88) and email lowest (78).
How many conversations does a support agent handle a month?
About 546 per licensed seat at the typical team. The good tier handles 856.
How much does customer service volume rise in Q4?
The typical team's busiest month is 15% above normal. For a quarter of ecommerce teams it is 70% or more.
Where does this data come from?
Platform data from 316 customer service teams on Dixa, mostly European and in ecommerce. It is not a survey.
How we measured it: teams with 100 or more conversations in every month of July to September 2026; test, demo and internal accounts excluded. Typical is the median team, good the 75th percentile and best the 90th. Groups under 10 teams are not shown, and every group under 200 teams should be read as direction. Every definition was written down before any number was computed, and an independent second run reproduced the figures.