Data to AI™ / Copilot/Google Ads
Give account managers a Copilot agent in Teams that answers Google Ads questions on device performance, shopping products and impression share from synced report data.
Less tokens, better answers.
Microsoft 365 Copilot
Which search campaigns lost the most impression share to budget last month?
Competitor terms lost 44% of its eligible search impressions to budget and Generic - Running shoes lost 31%. Brand - Exact lost only 2%.
| Campaign - Name | Cost Micros (Spend) | Search Impression Share | Search Budget Lost Impression Share |
|---|---|---|---|
| Competitor terms Campaign Report | 5,410 | 38% | 44% |
| Generic - Running shoes Campaign Report | 12,880 | 54% | 31% |
| Brand - Exact Campaign Report | 3,240 | 91% | 2% |
Cost Micros (Spend) was divided by 1,000,000 to match the amounts in the Google Ads interface.
Google Ads data comes as Reports with Metrics, Reports without Metrics and Keyword Planner Historical Metrics. You pick a report type such as campaign, keyword, search term or shopping, then the attributes, segments and metrics to extract.
@Search Ads Desk Compare Conversions Value per cost by device for each campaign last week, with costs converted from micros.
An agency publishes the agent to its account team channel in Teams. Account managers discuss device bid adjustments in the same thread as the answer.
@Search Ads Desk Which Shopping Performance View products by Title had the most Clicks but no Conversions this month?
In Microsoft 365 Copilot, the merchandising team gets the list grouped by Brand and Product Type Level 1 and pastes it into the next product plan.
Use the dataddo tools to describe the Campaign Report entity and add a micros-to-currency conversion for Cost Micros (Spend) in our report loader.
A developer uses GitHub Copilot in Agent mode so the internal dashboard converts micros before values reach anyone, using field names taken straight from Dataddo.
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| Without Dataddo |
With Dataddo
|
Outcome for you | |
|---|---|---|---|
| Many client accounts | Account managers export the same report from each Google Ads account and stitch the files together before every client review. | Multi-account extraction, enabled by the Dataddo Solutions team, pulls the same report from several accounts into one flow the agent can split by account. | Every account in one agent |
| Answers where the team works | Questions about impression share or devices wait for whoever has Google Ads access and time to pull a report. | Anyone in the Teams channel mentions the Copilot Studio agent with @ and gets the answer from synced Google Ads data. | No waiting on an analyst |
| Freshness | A spreadsheet shared in Teams on Monday is still being quoted on Thursday, with no date attached. | Reports with Metrics refresh on your schedule over a relative date range, and the agent can call data_status to say how current they are. | Answers with a date |
| Token consumption | Copilot spends tokens exploring raw Google Ads 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 Google Ads datasets marketing teams use most, with their real field names. Copilot queries them by name through the Dataddo semantic layer.
Reports with Metrics read a relative date range, for example yesterday, that moves forward on every scheduled run; older periods are loaded with a full data re-sync. Reports without Metrics pull all current data on each run.
Google Ads 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 →Attach Google Ads sources from Dataddo, such as the Campaign Report and Shopping Performance View, to a Copilot destination or AI Model. In Copilot Studio, add a Model Context Protocol tool with https://headless.dataddo.com/mcp-data and OAuth 2.0 with Dynamic discovery, sign in, and publish the agent to Teams and Microsoft Copilot.
Yes, through a Copilot Studio agent with Dataddo Data Access as a Model Context Protocol tool. Publish it to Microsoft 365 Copilot and Teams, then mention it with @.
The Google Ads API returns costs in micros. Ask the agent to divide micros fields such as Cost Micros (Spend) by 1,000,000 so answers show currency.
Write a precise server description in Copilot Studio, such as Google Ads campaign, keyword and shopping results. The agent uses it to decide when to call Dataddo.
Yes. Add the server to .vscode/mcp.json with type http and the URL, start it, sign in and use Agent mode. Copilot Business and Enterprise need the MCP servers in Copilot policy enabled.
Yes. Multi-account extraction pulls the same report from several accounts into one flow, and the agent can split answers by account.