Connect YouTube Analytics to Airtable
Pull channel and video performance — views, watch time, subscriber changes, traffic sources and demographics — into your reporting.
Coming soon — join the list to get early access
What you can pull from YouTube Analytics
YouTube Analytics exposes 2 objects you can pull, each as its own job. Every run will pull the records into a flat table, so nested fields arrive as ordinary columns that Airtable can sort, filter and total without further work.
Daily performance
Views, watch time and subscriber change per day.
| Column | Type | Notes |
|---|---|---|
| day | dimension | — |
| views | metric | — |
| estimatedMinutesWatched | metric | — |
| averageViewDuration | metric | Seconds |
| subscribersGained | metric | — |
Top videos
Best-performing videos for the period, sorted by views.
| Column | Type | Notes |
|---|---|---|
| video | dimension | Video ID, not title — see the gotcha below |
| views | metric | — |
| estimatedMinutesWatched | metric | — |
| likes | metric | — |
Options you can set
- date_range
- yesterday, last_7_days, last_28_days, last_90_days or custom
- dimensions
- Prefilled by the preset, and editable
- metrics
- Prefilled by the preset, and editable
How it lands in Airtable
Airtable accepts append, upsert writes. Upsert is the one that matters on a schedule: it merges on a key you choose, so matching rows are updated in place and only genuinely new records are created. Append instead, and a daily job multiplies your data.
Upsert merges on up to three key fields. Matching records are updated in place; only genuinely new rows are created.
- At most 3 merge fields - Airtable rejects more.
- Writes go 10 records per request, at 4 requests per second by default (Airtable’s own ceiling is 5) - roughly 2,400 records a minute.
- typecast is on by default, letting Airtable coerce strings into select, number and date fields rather than rejecting the write.
- unknown_fields defaults to error, so a renamed source column fails loudly instead of silently dropping data. Set it to skip if you would rather drop.
Authentication
You connect YouTube Analytics once through OAuth and the credential is stored server-side, never pasted into a cell or a formula. Only the scopes the connector actually calls are requested — the table below lists each one and what it is for.
What YouTube Analytics is asked for
- yt-analytics.readonly non-sensitive
- Read channel and video performance. Read-only
- userinfo.email non-sensitive
- Label the connection
What is deliberately not requested
youtube.readonly — It would be needed to resolve video titles through the Data API v3, which nothing here calls. Requesting it would be asking for a permission no code uses - so reports return video IDs rather than titles.
Keeping runs incremental
Yes. Each run pulls a rolling date window rather than the whole history.
Pair a narrowed read with an upsert write and a re-run costs almost nothing: the rows it already knows are updated, and nothing is duplicated. That combination is what makes a frequent schedule affordable.
Limits and pacing
These are the provider-side ceilings that shape a schedule, not ours. Knowing them up front is the difference between a job that runs quietly every morning and one that starts failing the week your data grows.
| Limit | Value | Applies to |
|---|---|---|
| Report types Dimension/metric pairs outside it are rejected by the API. | YouTube's documented matrix | YouTube Analytics |
Common gotchas
Most of what goes wrong with a scheduled sync is not a bug — it is a detail of how one side behaves that nobody wrote down. These are the ones that come up for this pair.
Video IDs, not titles
The Analytics API does not expose titles, and the scope that would resolve them is deliberately not requested.
Dimension and metric pairs must match the supported matrix
The presets mirror YouTube's documented report types. Changing a dimension without checking that matrix produces a query the API rejects outright on first use.
Do not key an upsert on email
People change their email address. When they do, upsert cannot match, so it creates a second record - the exact duplicate you were trying to prevent, plus a stale one. Use a stable ID from the source system.
The key field has to be written
If you restrict which columns get written and the key field is not among them, there is nothing to match on and every row is treated as new.
A row that is a fact per period needs a compound key
For a daily history, one row is one entity per day. Key on the entity alone and each day overwrites the last, leaving no history at all.
You will need your own title column
The Analytics API returns video IDs, not titles. Keep a title field in the base and key the upsert on the video ID - the ID is the stable half.
Set it up in four steps
- 1
Connect YouTube Analytics
Connect the Google account that owns the channel.
- 2
Connect Airtable
Connect your Airtable account once, then pick a base and table.
- 3
Shape the data
Select the columns you want, filter rows, cast types and add computed fields. Everything else is dropped before it reaches the destination.
- 4
Schedule it
Run once, or on a cron. Every run refreshes Airtable with the latest YouTube Analytics data.
Do I need an add-on or extension for this?
No. The job runs server-side and writes into Airtable through its API, so there is nothing installed in the destination itself. It keeps running when nobody has the file open, and a copy of the file does not need anything installed to work.
How often does the data refresh?
On whatever schedule you set with a cron expression — hourly, daily, or a specific time on specific days. Each run pulls the latest from YouTube Analytics.
Do I need to write any code?
No. You connect both sides, map the fields in a wizard and set a schedule. Computed fields accept small expressions — abs, round, min, max, len — but there is nothing to host or maintain.
Why are my videos showing as IDs?
The YouTube Analytics API returns bare video IDs. Resolving them to titles needs the Data API v3 and the youtube.readonly scope, which is deliberately not requested — asking for a permission nothing calls would be worse than the inconvenience.
How do I stop duplicate records appearing?
Use upsert rather than append, and key it on a stable identifier from the source. Append creates new records every run, so a daily job leaves seven copies of every row by the end of the week.
How fast can it write?
Airtable accepts 10 records per request, and writes are paced at 4 requests a second by default against a ceiling of 5 - roughly 2,400 records a minute. That is comfortable for thousands of rows and worth planning around for hundreds of thousands.
When will YouTube Analytics be available?
YouTube Analytics is built and waiting on verification. Join the early-access list and we will let you know the moment it opens.
Other sources into Airtable
Related reading
Airtable upsert: stop creating duplicate records
Appending on every run turns a tidy base into six copies of every row. Here is how upsert works in Airtable, and how to choose a merge key that holds up.
Export Stripe data to Google Sheets
Stripe's dashboard exports are manual and stale the moment you download them. Here is how to keep customers, invoices and charges fresh in a sheet instead.
Import a CSV from a URL into Airtable
Point a job at a CSV link and it re-reads it on your schedule — no download, no manual import. Here is the setup, and the field-type trap to avoid first.