Ask LinkedIn Pages Questions in ChatGPT
Ask LinkedIn Pages questions in ChatGPT using OWOX Data Marts. Collect LinkedIn Pages data into a governed data mart, then get answers in ChatGPT via OWOX's MCP server — every number traces back to analyst-written SQL.
LinkedIn Pages endpoints
2 LinkedIn Pages endpoints you can import. Each lands in its own table in your warehouse.
LinkedIn Follower Statistics
Lifetime follower counts broken down by segment — seniority, industry, function, geography, and association type.
6 fieldslinkedin_pages_follower_statisticsLinkedIn Time-Bound Follower Statistics
Daily follower gains broken down by organic and paid — tracks how your audience grows over time.
12 fieldsTime serieslinkedin_pages_follower_statistics_time_bound
LinkedIn Pages data schema
LinkedIn Follower Statistics 6 fields · linkedin_pages_follower_statistics
| Field | Type | Description |
|---|---|---|
organization_urn | STRING | Organization URN |
category_type | STRING | Type of follower category (followerCountsByAssociationType, followerCountsBySeniority, etc.) |
segment_name | STRING | Name of the segment identifier (associationType, seniority, industry, function, staffCountRange, geo) |
segment_value | STRING | Value of the segment identifier (e.g., urn:li:seniority:3, EMPLOYEE, etc.) |
organic_follower_count | INTEGER | Count of organic followers in this segment |
paid_follower_count | INTEGER | Count of paid followers in this segment |
LinkedIn Time-Bound Follower Statistics 12 fields · linkedin_pages_follower_statistics_time_bound
| Field | Type | Description |
|---|---|---|
organization_urn | STRING | Organization URN |
time_range_start | TIMESTAMP | Start timestamp of the time range |
time_range_end | TIMESTAMP | End timestamp of the time range |
organic_follower_gain | INTEGER | Number of organic followers gained during the time period |
paid_follower_gain | INTEGER | Number of paid followers gained during the time period |
follower_counts_by_association_type | OBJECT | Followers segmented by association type (empty in time-bound responses) |
follower_counts_by_seniority | OBJECT | Followers segmented by seniority (empty in time-bound responses) |
follower_counts_by_industry | OBJECT | Followers segmented by industry (empty in time-bound responses) |
follower_counts_by_function | OBJECT | Followers segmented by function (empty in time-bound responses) |
follower_counts_by_staff_count_range | OBJECT | Followers segmented by staff count range (empty in time-bound responses) |
follower_counts_by_geo_country | OBJECT | Followers segmented by country (empty in time-bound responses) |
follower_counts_by_geo | OBJECT | Followers segmented by geographic area (empty in time-bound responses) |
Ask LinkedIn Pages questions in ChatGPT
- You
How fast is our LinkedIn page gaining followers, and how much of that is paid?
- ChatGPT
From Time-Bound Follower Statistics (linkedin_pages_follower_statistics_time_bound) I summed organic_follower_gain and paid_follower_gain by time_range_start, week by week, so you see both lines and the share of each.
This comes from your governed LinkedIn Pages Data Mart through the OWOX MCP server, so it is as fresh as that Data Mart's last scheduled refresh.
- You
Which industries do our followers come from?
- ChatGPT
follower_counts_by_industry breaks the audience down by industry. I ranked industries by follower count so you can check whether the page reaches the market you sell to.
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Every claim has a receipt.
Real things real customers said — each quote pinned to a specific claim, straight from the quotes database.
“AI, by the nature of the models, will hallucinate. And because of that, you need something which will create guardrails to ensure that there are no hallucinations, that you can trust your data.”
“We had six or seven different channels and no single source of truth — it was almost impossible, a lot of guesswork.”
“We regained time. And time is the one resource that never comes back.”
Turn LinkedIn Pages Data into Answers in ChatGPT
See how OWOX Data Marts helps you ask LinkedIn Pages questions in ChatGPT, blend it with other sources, and scale reporting without rebuilding logic.
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