Data to AI™ / ChatGPT/Facebook Public Data
Watch competitors' Facebook posting rhythm, format mix and most shared posts from a ChatGPT conversation, approving every lookup of the public data Dataddo extracts.
Better answers, fewer tokens.
ChatGPT
How does our Facebook Page compare with two competitors on audience and buzz?
Bluebird Roasters has the largest audience, but Kettle & Co gets people talking: its Talking About Count is 4.1% of its Fan Count, against 1.6% for Bluebird and 2.3% for our Page.
| Name | Fan Count | Talking About Count |
|---|---|---|
| Northwind Coffee (us) Profile | 48,200 | 1,110 |
| Bluebird Roasters Profile | 93,500 | 1,490 |
| Kettle & Co Profile | 31,800 | 1,300 |
Each Page is its own source with its own Page Identifier; Profile values are current as of the last extraction, not summed over time.
Facebook Public Data reads the public profile and posts of any Facebook Page you name by its Page Identifier, including competitors' Pages. Marketing teams use it to benchmark audience size, posting cadence and post engagement against other brands.
Count Posts per competitor by weekday and hour of Created Time over the last eight weeks, and tell me when each Page usually publishes.
A social media manager confirms the query in ChatGPT and adjusts the team's own publishing slots to avoid clashing with the two busiest competitors.
For each From value, show the share of posts by Media Type and Status Type this quarter, and the average Shares Count per Media Type.
The content lead sees that one rival has moved most of its output to video and that video posts are shared more, and takes that into the next format test.
Run data_status on the Kettle & Co flow, then list its Posts from the last 14 days whose Message mentions cold brew, with Created Time and Permalink URL.
A product marketer checks how a competitor announced a new product line before her own launch, opening each post through its link to read the full context.
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| Without Dataddo |
With Dataddo
|
Outcome for you | |
|---|---|---|---|
| Posting rhythm | The team guesses how often competitors post by scrolling their Pages, and the guess changes with whoever looked last. | Every post carries Created Time, so ChatGPT counts posts per week, weekday and hour for each Page from the latest extraction. | Cadence counted, not guessed |
| Format choices | Notes on which formats rivals favour live in a slide that is rebuilt by hand before each content review. | Posts carries Media Type and Status Type next to Shares Count, so the format mix and its response are queried directly. | Format mix on demand |
| Control over lookups | A pasted export gives no trace of which rows shaped ChatGPT's summary of the competition. | ChatGPT asks you to confirm each tool call, and individual Dataddo tools can be turned off in the app settings. | Every query approved |
| Token consumption | ChatGPT spends tokens exploring raw Facebook Public Data 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 Public Data 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
|
|---|---|---|
| Posts |
|
How many posts did each competitor Page publish per week this quarter, based on Created Time? |
| Profile |
|
Which competitor Page has the highest Talking About Count relative to its Fan Count right now? |
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 Facebook Public Data in Dataddo with one source per competitor Page Identifier and the Posts dataset, then attach the flows to a ChatGPT destination or AI Model. In ChatGPT on the web, enable Developer mode, create a Dataddo Data Access app with https://headless.dataddo.com/mcp-data and OAuth, and sign in.
Developer mode in ChatGPT on the web, available on Pro, Plus, Business, Enterprise and Education. On Business and Enterprise, a workspace admin may need to allow developer mode first.
Posts has no date range in Dataddo. Every run pulls all posts currently available from the Page, so ChatGPT can compare this month's posting cadence with earlier months from the same data.
No. Posts holds counts such as Comments Total Count and Likes Total Count, plus the post's own Message and From. It does not include the names or text of individual commenters.
Create one more Facebook Public Data source in Dataddo with that Page's Page Identifier and add the flow to your ChatGPT destination or AI Model. Each source covers a single Page.
ChatGPT asks you to confirm each tool call before it runs, so you approve every read of Posts or Profile. You can also switch off tools you do not want ChatGPT to use.
No. ChatGPT reads extracted data through the semantic layer by entity and field name. Dataddo never accepts SQL from the model and nothing is written back to Facebook.
