Meeting data, published with its method

Charts drawn from 1,609,703 booked meetings and 1,171,388 attendee time zones in meetergo's production database. Aggregated, non-identifiable and free to cite.

Every figure on this page comes from a query run on August 20, 2026. Where a number needs a filter to be true, and most of them do, the filter is in the subhead rather than in a footnote.

Lead finding

Europe's lunch break, measured in a million meetings

Five European markets, five midday dips, and France's arrives a full hour after everybody else's.

Read the report

Five markets, five lunch breaks, and they do not all happen at once

% of that market's own day

Local start hour

Share of each market's own day by local starting hour. Domestic meetings, 365 days, cancellations excluded. Berlin n = 983,062, Vienna 39,369, Zurich 18,286, Amsterdam 13,186, Paris 6,553.

Normalised to each market's own total: Berlin is 93% of the sample, and plotting raw counts would flatten the other four onto the axis. Copenhagen and every cross-timezone market are excluded.

Source: meetergo, meetergo.com/en/data/europes-lunch-breakData as a table

Reports

One recurring index, the meeting clock, plus occasional one-offs. Each report states its sample size, its window, and the confound it removed before the finding was believed.

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The data behind it

Meetings analysed: 1,609,703; Across every booking channel, all time.
Attendee time zones: 1,171,388; 180 days. All but 11 of them carry a real local time zone.
German domestic meetings: 983,062; 365 days, attendee and host on the same clock, cancellations excluded.
European markets charted: 5; Nothing below 3,000 domestic meetings gets a line.

Method and privacy

How we collect this data

meetergo is scheduling software used across Europe. The reports here are a by-product of running it: aggregate patterns in when meetings get booked and when they start.

01
Production data, not a survey
Every figure is a query against the live appointment, attendee and host tables. Nobody was asked what time they prefer to meet: these are the meetings that actually happened.
02
The method travels with the number
Sample size, window and filter sit in the subhead of every chart, and the SQL sits at the bottom of every report. Run it against your own book if you have one.
03
Re-run each quarter
Working hours are seasonal: summer hours, the December collapse, the January surge. The meeting clock is re-queried rather than republished unchanged.
04
Aggregated and non-identifiable
meetergo sells GDPR compliance, so no cut is fine enough to trace back to one customer's booking volume. Every market shown clears a minimum sample of 3,000 meetings.

How complete the fields are

Attendee time zone coverage
100.0%
1,171,388 attendees over 180 days. The strongest field in the dataset, and the one the meeting clock is built on.
Referrer coverage
43.3%
268,005 bookings since attribution shipped. The weakest field in the dataset, and the reason nothing here is built on referrers.

Four checks before every chart

What we don't publish

Four chart ideas were tested against this data. One survived unchanged. These are the four checks that killed the others, and they now run before any design work starts.

Coverage before design
A field filled in on 43% of rows cannot carry a claim, however good the chart looks. Referrer coverage killed an entire concept after the design was finished. The fill rate gets checked first now.
What the field measures, not what it is called
A no-show flag that a host ticks by hand measures how diligent hosts are, not how many people showed up. We publish attendance when it is measured, not when it is asserted.
Confound removed before the finding is believed
Without the domestic filter, an attendee's local meeting hour is set by the host's calendar rather than by local habit. It changed the answer here, which is exactly why it is stated on the chart.
Margin checked against sample size
An afternoon peak leading by 30 bookings on a sample of 13,186 is not a peak. One finding in this set was cut in review for exactly that.