Data to AI™ / Copilot/Facebook Ads
Give the paid social team an agent in Teams that answers spend, placement and audience questions from Meta ad data, so nobody waits for the weekly media report.
Less tokens, better answers.
Microsoft 365 Copilot
Which placements returned the most purchase value per dollar spent last month?
Instagram Reels returned 4.1 in purchase value per unit of spend, the best of the three, on about a third of the budget. Facebook Feed took the largest share at 3.2.
| Placement | Spend | Purchase Roas | Ctr |
|---|---|---|---|
| Instagram Reels Platform Position Device Ad Insights | $8,400 | 4.1 | 1.32% |
| Facebook Feed Platform Position Device Ad Insights | $12,600 | 3.2 | 1.05% |
| Instagram Stories Platform Position Device Ad Insights | $5,200 | 2.4 | 0.71% |
Spend and Purchase Roas come from one placement breakdown the Facebook Ads API supports, not from separate exports matched by hand.
Facebook Ads extracts paid performance from Meta ad accounts, from account down to ad level: spend, delivery, conversions and ROAS, broken down by placement, device, demographics or creative asset.
@Paid Social What did each campaign spend yesterday in Facebook Ads Insights, and which one had the lowest Cost per purchase?
Account managers mention the agent in the team channel each morning instead of logging in to every ad account, and discuss the outliers right there in the thread.
@Paid Social Split last month's Spend by Publisher Platform and Platform Position, with Purchase Roas, as a table I can paste into PowerPoint.
In Microsoft 365 Copilot, the agent returns a placement table while you write the monthly client report, so the slide uses the same numbers the team saw in Teams.
Use the dataddo tools to describe Facebook Ads Insights and map Spend, Impressions and CTR into our metrics config file.
In VS Code, GitHub Copilot in Agent mode reads real field names and sample rows, so marketing engineers build internal tooling without waiting for someone to send an export.
Data to AI™ is free while in private pre-release. We onboard teams in waves and shape the product with their feedback. At launch, it will be priced like the AI assistants your team already uses. Need tailored contracts or volume pricing? Talk to us about Enterprise.
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| Without Dataddo |
With Dataddo
|
Outcome for you | |
|---|---|---|---|
| Answers in the team channel | The media buyer exports Ads Manager reports, shares them in Teams, and every follow-up question waits for the next export. | The Copilot Studio agent sits in the Teams channel and answers spend and placement questions from the scheduled Facebook Ads flow. | No waiting for the media report |
| Many ad accounts | Agencies repeat the same export for each client ad account, then merge the files before anyone can compare them. | Multi-account extraction, enabled by the Dataddo Solutions team, pulls the same datasets from every ad account into one source. | One agent, every account |
| When the agent calls Dataddo | A general assistant has no idea which questions need Meta ad data, so people paste numbers into prompts by hand. | The server description in Copilot Studio tells the agent to call Dataddo for paid social spend, ROAS and placement questions. | Ad questions routed to ad data |
| Token consumption | Copilot spends tokens exploring raw Facebook 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 Facebook Ads datasets marketing teams use most, with their real field names. Copilot 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 your schedule with a relative date range that moves forward on every run. The Facebook Ads API returns up to 37 months of history, loaded in batches of up to 90 days.
Facebook 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 →Set up a Facebook Ads source in Dataddo with Facebook Ads Insights and Platform Position Device Ad Insights and attach it to a Copilot destination or AI Model. In Copilot Studio, add https://headless.dataddo.com/mcp-data as a Model Context Protocol tool with OAuth 2.0, then publish the agent to Teams and Microsoft 365 Copilot.
Yes, Dataddo connects as an MCP tool on a Copilot Studio agent. Once published to Microsoft 365 Copilot and Teams, people reach the Facebook Ads data by mentioning that agent with @.
Write a clear server description in Copilot Studio. It tells the agent when to call Dataddo, for example only for paid social spend, ROAS and placement questions.
The agent reaches every flow or Context attached to an AI destination or AI Model in the signed-in Dataddo account. Attach only the Facebook Ads flows that team should query.
Yes. Add the Dataddo server to .vscode/mcp.json, start it, sign in and use Copilot Chat in Agent mode. On Business and Enterprise an admin first enables the MCP servers in Copilot policy.
Daily Reach values cannot be added up, because each day counts unique people. Extract a weekly breakdown in its own source and flow and point the agent to that data.
No. The agent reads Facebook Ads data that Dataddo has extracted and answers questions about it. Budget and status changes still happen in Meta Ads Manager.