Data to AI™ / ChatGPT/YouTube
Ask ChatGPT which videos win or lose subscribers, which caption languages hold viewers and how long members stay, approving each Dataddo lookup as it happens.
Less tokens, better answers.
ChatGPT
Which traffic sources brought the most watch time to our September uploads?
Suggested videos drove 63% of watch time across these three sources, about twice as much as YouTube search, and held viewers longest at 46% average view duration.
| Traffic Source Type | Views | Watch Time (Minutes) | Average View Duration (%) |
|---|---|---|---|
| Suggested videos Owned Channel Videos Traffic Sources | 48,200 | 212,100 | 46% |
| YouTube search Owned Channel Videos Traffic Sources | 31,500 | 101,000 | 38% |
| External Owned Channel Videos Traffic Sources | 9,800 | 21,600 | 27% |
Views and Watch Time (Minutes) are summed per traffic source; Average View Duration (%) is weighted by Views, not a plain average of videos.
YouTube Organic extracts your channel's non-paid performance: views, watch time, subscribers, traffic sources, audience, comments and playlists, per video and per day.
Which uploads in September had more Subscribers Lost than Subscribers Gained in Owned Channel Videos Basic User Activity? Add each Title.
You confirm each lookup ChatGPT makes, then compare the list with titles and publish dates to see which topics push subscribers away before the next month is planned.
Which Subtitle Language carries the most Watch Time (Minutes) outside the US in Owned Channel Videos Subtitles Usage this quarter?
The localization owner sees which caption languages viewers already rely on, country by country, and plans the next round of translations around that answer instead of guesses.
Count members by Highest Accessible Level Display Name and give the average Member Total Duration (Months) for each level.
Before a discussion about membership perks, the channel manager gets a short split of members per level and how long each level stays, ready to paste into the meeting notes.
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| Without Dataddo |
With Dataddo
|
Outcome for you | |
|---|---|---|---|
| Getting the data into ChatGPT | YouTube Studio reports are downloaded and uploaded to the chat again every time a question needs a different breakdown. | The developer-mode app queries the YouTube Organic flow on demand, and ChatGPT asks you to confirm each tool call. | Fewer exports, visible lookups |
| Member data | A members export with Display Name and Channel URL ends up stored in a chat history next to everything else. | Leave Members Metadata out or untick Display Name when refining attributes, so ChatGPT only works with levels and tenure. | Personal fields stay out |
| Subtitle figures | Subtitle view totals look low next to channel views, and nobody in the chat can explain the gap. | The dataset description states that views with captions mostly turned off are excluded, so ChatGPT can explain the difference. | Subtitle numbers explained |
| Data age | An uploaded file says nothing about when it was exported, so the newest uploads may be missing. | Each run pulls all currently available YouTube Organic data, and data_status tells ChatGPT when the flow last ran. | Known data age |
| Token consumption | ChatGPT spends tokens exploring raw YouTube columns, sampling rows and retrying until it understands the data. | Field definitions and relations are supplied up front, so the model skips the exploration and goes straight to the answer. | Less tokens per answer |
| Answer quality | Prone to wrong joins, invented fields and numbers that are hard to check. | Answers grounded in defined fields. In the Dataddo benchmark, 78.5% of questions were answered correctly, against 65.5% with plain CSV files. | Better answers |
The YouTube datasets marketing teams use most, with their real field names. ChatGPT queries them by name through the Dataddo semantic layer.
List of curated datasets
Need a dataset, metric, or attribute you don't see?
Tell us what's missing and we'll add it to the connector.
Runs on the schedule you set, for example daily. YouTube Organic datasets do not use a date range, so every run pulls all currently available data.
YouTube connector →We asked 43 pre-registered questions of three real datasets - Google Search Console, HubSpot deals and Google Ads - and delivered the same rows to the same model in four ways. Only the data layer changed.
of questions answered correctly - the best of the four delivery methods tested
fewer confidently wrong answers - 14.0% of runs against 24.0% and 26.3%
more correct answers on messy CRM data - better on 8 questions, worse on none
Same model, prompts and rows in every condition; 759 graded runs, ground truth frozen before any run. On Google Search Console alone, documented CSV files did as well as Data to AI™ - the advantage comes from messier data such as HubSpot deals.
Read the benchmark →Create a YouTube Organic source in Dataddo with Owned Channel Videos Basic User Activity and Videos Metadata, and attach it to a ChatGPT destination or AI Model. Enable developer mode under Settings > Security and login, add a Dataddo Data Access app with https://headless.dataddo.com/mcp-data, and sign in with OAuth.
Developer mode is available on Pro, Plus, Business, Enterprise and Education, on the web only. Business and Enterprise admins may need to allow it first.
ChatGPT confirms every tool call before it runs. Reading Subtitles Usage and then Videos Metadata, for example, shows up as two separate steps you approve.
Yes. Leave Members Metadata out of the source, or untick fields such as Display Name when you refine the attributes. ChatGPT only sees what the attached flow contains.
They reflect the flow's last scheduled run, and each run pulls all currently available YouTube Organic data. Ask ChatGPT to call data_status to see how current it is.
Ask it to call report_problem, which reports a suspicious answer to Dataddo, and to check data_status so you can rule out stale data first.