Data to AI™ / ChatGPT/Google Ad Manager
Check demand mix, viewability and Ad Exchange pricing in a ChatGPT conversation, confirming each lookup, instead of downloading another Ad Manager report.
Better answers, fewer tokens.
ChatGPT
Which ad units earned the most last week, and where did inventory go unfilled?
video_preroll earned the most, 6,840 from 380,000 impressions, an eCPM of 18.00. article_sidebar served the most impressions but left 412,000 unfilled.
| Ad Unit Name | Total Impressions | Total Line Item Level All Revenue | Total Inventory Level Unfilled Impressions |
|---|---|---|---|
| video_preroll Google Ad Manager | 380,000 | 6,840 | 21,000 |
| article_sidebar Google Ad Manager | 2,310,000 | 5,544 | 412,000 |
| homepage_leaderboard Google Ad Manager | 1,240,000 | 4,960 | 86,000 |
Last week is re-read on every scheduled run, so its rows are replaced, not added twice.
Google Ad Manager is Google's ad serving platform for publishers and advertisers. Dataddo extracts one flexible report dataset per network: choose from 101 standard metrics, 100 dimensions and line item attributes.
What share of Total Line Item Level All Revenue came from each Demand Channel Name last month, compared with the month before?
A yield manager confirms each tool call as ChatGPT runs it, then uses the shift between demand channels to decide where pricing rules need another look.
Rank Placement Name by Total Active View Viewable Impressions Rate for the last 30 days and give me the five strongest placements.
Before an advertiser meeting, the publisher's sales team builds its pitch around placements that are actually seen, with figures taken from the network's own reporting.
Check data_status, then show Ad Exchange Ad eCPM by Country Name week by week for the last 8 weeks and flag countries down more than 15%.
A programmatic lead knows when the flow last ran before raising a falling market with the team, so a missing day is not mistaken for a price drop.
Data to AI™ is priced per seat, like the AI assistants your team already uses, with ~25% off on annual billing. Need tailored contracts or volume pricing? Talk to us about Enterprise.
Serve governed business data to Claude, ChatGPT and other AI tools over MCP.
per seat, billed monthly per seat, billed annually
Flexibly deploy Dataddo in any cloud or hybrid environment.
We have a payment model that works for you
| Without Dataddo |
With Dataddo
|
Outcome for you | |
|---|---|---|---|
| Getting reports into the chat | Run an Ad Manager report query, download it, and upload the file to ChatGPT, then do it again when the next breakdown is needed. | The Dataddo developer-mode app reads the scheduled Ad Manager source on request, and you confirm each tool call as it happens. | No uploads, every call approved |
| Knowing how current revenue is | Nobody in the conversation can tell when the uploaded report was pulled or whether yesterday is already included. | ChatGPT can call data_status to state when the Ad Manager flow last ran before it quotes yesterday's revenue or eCPM. | Freshness on every answer |
| Older reporting | Trend questions stop at whatever date range the last download happened to cover. | A full data re-sync loads older weeks once, and the relative date range then re-reads the recent window on every scheduled run. | Trends over full history |
| Token consumption | ChatGPT spends tokens exploring raw Google Ad Manager 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. | Fewer 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 Google Ad Manager datasets marketing teams use most, with their real field names. ChatGPT queries them by name through the Dataddo semantic layer.
Runs on the schedule you set with a relative date range, for example yesterday, that moves forward on every run. Older reporting is loaded once with a full data re-sync.
Google Ad Manager 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 →Build a Google Ad Manager source in Dataddo with metrics such as Ad Exchange Ad eCPM and Total Active View Viewable Impressions Rate by Placement Name, and attach it to a ChatGPT destination or AI Model. In ChatGPT on the web, turn on developer mode, create an app for https://headless.dataddo.com/mcp-data and sign in.
Any plan with developer mode: Pro, Plus, Business, Enterprise and Education, in ChatGPT on the web. Business and Enterprise admins may need to allow it first.
In developer mode ChatGPT asks you to confirm each tool call. You see whether it is listing entities, describing fields or running a query before any revenue data is returned.
Yes, if you extract both. Fields keep their Ad Manager names, such as Ad Server CPM and CPC Revenue and Ad Exchange Estimated Revenue, so ChatGPT can query them separately.
Some Ad Manager metrics cannot be queried together with some dimensions. The Dataddo source wizard flags this when you build the source, and Test on Sample Data previews the result first.
Yes. In the app settings you can turn individual Dataddo tools on or off, so you can keep query and describe_entity while disabling refresh.