Data to AI™ / Claude/Google Ad Manager
Ask Claude where ad revenue comes from, which line items are behind and which ad units go unfilled, using the Ad Manager fields you already report on.
Better answers, fewer tokens.
Claude
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.
Which line items have a Line Item End Date Time this month, and how many Total Line Item Level Impressions has each delivered so far?
An ad ops manager sees which campaigns are close to their end date with delivery still low, and takes the slowest ones to sales before the flight closes.
Query Ad Exchange Ad eCPM by Ad Unit Name and Country Name through the dataddo server and write this week's summary to reports/yield.md.
A revenue analyst runs Claude Code next to the team's reporting scripts, so the weekly yield note lands in Git beside the code that produced earlier ones.
Rank Ad Unit Name by Total Inventory Level Unfilled Impressions for the last 14 days and show Ad Exchange Coverage next to each.
A monetization lead finds the placements that most often go empty and decides where opening more demand would recover the most revenue.
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
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We have a payment model that works for you
| Without Dataddo |
With Dataddo
|
Outcome for you | |
|---|---|---|---|
| Running a new breakdown | Every question about a different ad unit, country or advertiser means another report query in Ad Manager, another download and another merge. | Claude queries the fields already extracted from your network by name, and asks for a new breakdown in the same conversation. | Follow-ups in seconds |
| Look-alike revenue metrics | Ad server, Ad Exchange and AdSense revenue columns sit side by side with names that are easy to confuse in a spreadsheet. | Fields keep their Ad Manager labels with descriptions from the semantic layer, so Claude can tell Ad Server CPM and CPC Revenue from Ad Exchange Estimated Revenue. | The right revenue line |
| Field combinations | A hand-built report asks for a metric and dimension that cannot be combined and fails, or returns rows nobody can explain. | The Dataddo source wizard flags incompatible fields when the source is created, so Claude only ever queries combinations that ran. | Queries that run |
| Token consumption | Claude 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. Claude 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 →Create a Google Ad Manager source in Dataddo for your network, choose metrics such as Total Impressions and Total Line Item Level All Revenue by Line Item Name or Ad Unit Name, and attach it to a Claude destination or AI Model. Then add https://headless.dataddo.com/mcp-data as a Claude custom connector, or run claude mcp add.
Dataddo runs your configured Ad Manager report on a schedule and serves it through the Data Access MCP server. Claude signs in there, not to Ad Manager.
No. Claude only queries extracted report data by entity and field name. Dataddo never accepts SQL from the model, and nothing is written back to your network.
Not every metric can be paired with every dimension. The source wizard flags fields that cannot be combined, so fix the selection there before the flow runs.
Yes. Add the server with claude mcp add, sign in with /mcp, and Claude Code queries the same Ad Manager entity while you work on reporting code.
Whatever the flow holds. A new source starts from its relative date range, so run a full data re-sync once to load older weeks before asking Claude about trends.
No. An Owner adds Dataddo Data Access once, and each member signs in with their own Dataddo account, so they only reach data that account can see.