Data to AI™ / ChatGPT/Facebook Ads
Check budget pacing, audience efficiency and video performance in a ChatGPT conversation that reads your scheduled Meta ad data, with every lookup confirmed by you.
Better answers, fewer tokens.
ChatGPT
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.
Run data_status on the Facebook Ads flow, then list active campaigns from Campaigns Insights with Daily Budget, Budget Remaining and yesterday's Spend.
You approve each tool call as it runs, see that yesterday is already loaded, and move budget away from campaigns burning through their plan before the month is half over.
In Age Gender Breakdowns Ad Insights, which Age and Gender groups took a growing share of Spend last month while Purchase Roas stayed flat?
ChatGPT spots segments that absorb budget without returning it, then drafts targeting notes in the same conversation for the next ad set review with the paid social team.
Rank videos in Video Asset Ad Insights by Cost Per Thruplay and show which still hold viewers at Video P100 Watched Actions.
The answer separates cheap views from videos people actually finish, so the video team gets short, specific feedback on which openings and lengths to reuse.
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.
$10.49/month
per seat
3 seats minimum
Flexibly deploy Dataddo in any cloud or hybrid environment.
Custom
We have a payment model that works for you
| Without Dataddo |
With Dataddo
|
Outcome for you | |
|---|---|---|---|
| Pacing data in the chat | Someone downloads campaign budgets and spend from Ads Manager, uploads the file, and the pacing answer is only as recent as that upload. | ChatGPT reads Campaigns Insights and Facebook Ads Insights from the scheduled flow, and data_status tells you when the last run finished. | Pacing on current spend |
| Control over each lookup | A pasted export shares every column and account at once, including data the question never needed. | Developer mode asks you to confirm each tool call, and you can switch off Dataddo tools you do not want ChatGPT to use. | You approve every query |
| Demographic accuracy | A file that mixes age, gender and region breakdowns invites ChatGPT to add up distorted absolute values. | Age Gender Breakdowns Ad Insights keeps one supported breakdown, and known_gaps flags what the data does not cover before you rely on it. | Fewer misleading segment totals |
| Token consumption | ChatGPT 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. | 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 Facebook Ads datasets marketing teams use most, with their real field names. ChatGPT queries them by name through the Dataddo semantic layer.
| Dataset | Metrics and attributes |
Ask ChatGPT
|
|---|---|---|
| Facebook Ads Insights |
|
Which ad sets have the highest Cost per purchase this week, and how much did they spend? |
| Platform Position Device Ad Insights |
|
Is Purchase Roas better on Instagram or Facebook when comparing by Publisher Platform? |
| Age Gender Breakdowns Ad Insights |
|
Which age groups did our Facebook ads Reach most often last month? |
| Video Asset Ad Insights |
|
Which Video Asset keeps viewers to the end, based on Video P100 Watched Actions? |
| Ad Creatives (by Ads) |
|
Which ads by Ad Name reuse the same Creative Title with a different Body? |
| Campaigns Insights |
|
How is our total Daily Budget split across each campaign Objective? |
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 →Sync Facebook Ads Insights, Campaigns Insights and Age Gender Breakdowns Ad Insights with a Dataddo source and attach them to a ChatGPT destination or AI Model. Switch on developer mode in ChatGPT, create the Dataddo Data Access app with https://headless.dataddo.com/mcp-data and OAuth, then select it from the + menu.
Pro, Plus, Business, Enterprise and Education, because the connection uses developer mode, which runs on ChatGPT web only. On Business and Enterprise an admin may need to allow it first.
ChatGPT confirms every tool call in developer mode. You see each query against your ad data before it runs, and you can switch off Dataddo tools you do not want ChatGPT to use.
Yes. Ask it to run data_status for freshness and known_gaps for anything the data does not cover. That is useful for pacing questions, where yesterday's Spend must already be in the flow.
No. An upload is a one-off file. Here Dataddo refreshes the Facebook Ads source on its schedule, and ChatGPT queries the current data by field name through the semantic layer.
The Facebook Ads API does not allow every breakdown combination, so Dataddo only offers valid ones. If a supported combination is missing, contact the Dataddo Solutions team.
Yes, if they are in the extracted data. Multi-account extraction pulls the same datasets from every ad account you can access. Contact the Dataddo Solutions team to turn it on for your Facebook Ads source.
