Data to AI™ / ChatGPT/LinkedIn Organic
Explore who follows your LinkedIn company page and how candidates use your careers pages in ChatGPT, confirming every query so you know which data shaped the answer.
Better answers, fewer tokens.
ChatGPT
Which of our company pages gained the most followers in September, and how much of it was paid?
The three pages gained 2,090 followers in September. Northwind Group added the most, 1,240, and 31% of its gain came from Paid Follower Gain; the careers page grew almost entirely organically.
| Organization Name | Organic Follower Gain | Paid Follower Gain |
|---|---|---|
| Northwind Group Company Daily Follower Metrics | 860 | 380 |
| Northwind Careers Company Daily Follower Metrics | 540 | 12 |
| Northwind Labs Company Daily Follower Metrics | 290 | 8 |
Organic Follower Gain and Paid Follower Gain are kept apart as LinkedIn reports them, read at Month granularity for each company page.
LinkedIn Organic covers the LinkedIn company pages you administer: followers by day and by audience segment, page views, page and post engagement, and video analytics. Dataddo extracts them per company page, so marketing can ask about audience growth and content performance in plain language. Paid campaigns are a separate connector, LinkedIn Ads.
Compare Organic Follower Count by Seniority and by Function Name with our target buyers, senior marketing and IT leaders. What share of followers fits?
Before planning next quarter's content, the head of marketing approves each lookup and learns whether the page speaks to decision makers or mostly to peers and students.
Run data_status on the LinkedIn Organic flow, then show weekly Careers Page Views, Jobs Page Views and Life At Page Views for the last three months.
Marketing and the recruiting lead review how the careers tabs performed during a hiring push, and agree which tab to refresh before the next opening goes live.
What share of All Page Views came from All Mobile Page Views each month this year, and is the share growing?
A web designer gets a clear answer on whether the company page cover, overview text and links have to be written for a phone screen first.
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.
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| Without Dataddo |
With Dataddo
|
Outcome for you | |
|---|---|---|---|
| Follower demographics | Someone screenshots the follower demographics chart in LinkedIn page analytics and uploads the image, and ChatGPT guesses the numbers from the picture. | ChatGPT queries Organic Follower Count and Paid Follower Count by Seniority and Function Name from the latest extraction. | Exact audience numbers |
| Careers page traffic | Recruiting asks marketing for a visitor export, which arrives a week later as one total with no split between careers, jobs and life tabs. | Company Daily Views Metrics carries Careers Page Views, Jobs Page Views and Life At Page Views per day, so HR gets the split on request. | Recruiting answers same day |
| Control over tools | Nobody can tell which rows of an uploaded file shaped the summary ChatGPT produced. | ChatGPT asks you to confirm each tool call, and tools such as refresh can be switched off in the app settings. | Every query approved |
| Token consumption | ChatGPT spends tokens exploring raw LinkedIn Organic 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 LinkedIn Organic 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
|
|---|---|---|
| Company Daily Follower Metrics |
|
On which days did Organic Follower Gain spike last month, and did Paid Follower Gain rise on the same days? |
| Company Daily Engagement Metrics |
|
Which days last month had the highest Comment Count and Share Count on our page updates? |
| Company Daily Views Metrics |
|
How did Careers Page Views and Jobs Page Views change during our hiring campaign in September? |
| Company Lifetime Follower Metrics by Seniority |
|
Which Seniority level has the highest Paid Follower Count relative to its Organic Follower Count? |
| Company Lifetime Follower Metrics by Function |
|
Which Function Name groups have the highest ratio of Paid Follower Count to Organic Follower Count? |
| Post Lifetime Engagement Metrics |
|
Which recent posts had a high Impression Count but a low Click Count? |
| Posts Metadata |
|
Which posts went out last week, with their Content Type and URL of the post? |
| Video Analytics |
|
How does TIME_WATCHED compare with VIDEO_VIEW for each Post ID over the last 30 days? |
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 →Extract LinkedIn Organic in Dataddo with datasets such as Company Lifetime Follower Metrics by Seniority and Company Daily Views Metrics, and attach the flow 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 pick it in chats.
Developer mode on the web, which is available on Pro, Plus, Business, Enterprise and Education. On Business and Enterprise, a workspace admin may need to allow it first.
Yes. ChatGPT asks you to confirm every tool call, so you approve each lookup of follower, views or post data before any result appears in the chat.
The lifetime follower datasets by Seniority, Function, Country, Industry and Staff Count Range pull the current totals on every run. For change over time, use Company Daily Follower Metrics, which reads a date window.
Choose LinkedIn and approve access, which Dataddo recommends, or LinkedIn Custom with your own app credentials. The account needs ADMINISTRATOR permissions on the company page so the page appears in the source.
One source can include several company pages of the authorized account. To extract the same data from multiple LinkedIn accounts, ask the Dataddo Solutions team to enable multi-account extraction.
ChatGPT can call known_gaps, which lists what the attached data cannot answer. If you did not extract Video Analytics, for example, questions about watch time on video posts are out of reach.
