LinkedIn as a data source

Bring organic data from your LinkedIn Organization Page to Nekt.

LinkedIn is the world’s largest professional networking platform, connecting professionals and businesses globally. The LinkedIn Community Management API provides access to organic page analytics, follower statistics, post performance, and audience demographics for your organization’s LinkedIn Page.

This connector extracts organic data from your LinkedIn Organization Page. For paid advertising data, see the LinkedIn Ads connector.

Configuring LinkedIn as a Source

In the Sources tab, click on the “Add source” button located on the top right of your screen. Then, select the LinkedIn option from the list of connectors.

Click Next and you’ll be prompted to add your access.

1. Add account access

You’ll need to authorize Nekt to access your LinkedIn Organization data. Click on the LinkedIn Authorization button and log in with your LinkedIn account.

Make sure you’re logged in with a LinkedIn account that has Admin or Content Admin access to the Organization Page you want to extract data from.

Once you authorize your account, add the following information:

• Organization ID: The LinkedIn organization ID you want to extract data from. This is the numeric ID that identifies your company page.

• Start Date: The earliest date from which records will be synced. Note that LinkedIn only returns up to 1 year of historical data for daily reports.

• Lookback Window: (Default: 30 days) Number of days to look back on each incremental run for the posts stream. This ensures post statistics stay up to date for recently published posts. The lookback ensures that posts modified in the last N days have their latest metrics captured, while also preventing data loss during gaps between runs.

Finding Your Organization ID

This is how you can find your Organization ID:

1. From the Admin View URL:
   • Go to your LinkedIn Company Page
   • Click on “Admin tools” or access the admin view
   • Look at the URL - it will contain something like /company/12345678/admin/
   • The number (e.g., 12345678) is your Organization ID

Once you’re done, click Next.

2. Select streams

Choose which data streams you want to sync. For faster extractions, select only the streams that are relevant to your analysis. You can select entire groups of streams or pick specific ones.

Available Streams:

Organization Data:

• Organization Details - Company profile information

Daily Statistics (Time-series):

• Daily Follower Statistics - Daily follower gains
• Daily Page Statistics - Daily page views and visitor analytics
• Daily Share Statistics - Daily aggregate engagement metrics

Lifetime Statistics (Current Snapshot):

• Lifetime Follower Statistics - Total follower count
• Lifetime Page Statistics - Lifetime page views and analytics
• Lifetime Share Statistics - Lifetime engagement metrics

Content:

• Posts - All posts from your page
• Post Statistics - Individual post performance

Tip: The stream can be found more easily by typing its name.

Select the streams and click Next.

3. Configure data streams

Customize how you want your data to appear in your catalog. Select the desired layer where the data will be placed, a folder to organize it inside the layer, a name for each table (which will contain the fetched data) and the type of sync.

• Layer: Choose between the existing layers on your catalog. This is where you will find your new extracted tables as the extraction runs successfully.
• Folder: A folder can be created inside the selected layer to group all tables being created from this new data source.
• Table name: We suggest a name, but feel free to customize it. You have the option to add a prefix to all tables at once and make this process faster!
• Sync Type: You can choose between INCREMENTAL and FULL_TABLE.
  • Incremental: Every time the extraction happens, we’ll get only the new data - which is good if, for example, you want to keep every record ever fetched.
  • Full table: Every time the extraction happens, we’ll get the current state of the data - which is good if, for example, you don’t want to have deleted data in your catalog.

Once you are done configuring, click Next.

4. Configure data source

Describe your data source for easy identification within your organization, not exceeding 140 characters.

To define your Trigger, consider how often you want data to be extracted from this source. This decision usually depends on how frequently you need the new table data updated (every day, once a week, or only at specific times).

Optionally, you can define some additional settings:

• Configure Delta Log Retention and determine for how long we should store old states of this table as it gets updated. Read more about this resource here.
• Determine when to execute an Additional Full Sync. This will complement the incremental data extractions, ensuring that your data is completely synchronized with your source every once in a while.

Once you are ready, click Next to finalize the setup.

5. Check your new source

You can view your new source on the Sources page. If needed, manually trigger the source extraction by clicking on the arrow button. Once executed, your data will appear in your Catalog.

For you to be able to see it on your Catalog, you need at least one successful source run.

Streams and Fields

Below you’ll find all available data streams from LinkedIn and their corresponding fields:

Organization Details

Company profile information for your LinkedIn organization.

Field	Type	Description
id	Integer	The LinkedIn numeric organization ID
urn	String	URN for the organization
vanityName	String	The LinkedIn public (vanity) name
localizedName	String	The localized name of the organization
localizedDescription	String	Localized description of the organization
organizationType	String	The organization type (e.g., PRIVATELY_HELD)
primaryOrganizationType	String	Primary type (e.g., BRAND, NONE)
staffCountRange	String	Organization staff count range
foundedOn	Object	Founding information with year
name	Object	Structured name data with localized variants
description	Object	Structured description with localized variants
website	Object	Structured website URLs with localized variants
localizedWebsite	String	Localized website URL
specialties	Array	List of specialties for the organization
localizedSpecialties	Array	Localized specialties
alternativeNames	Array	Alternative names for the organization
locations	Array	Array of organization locations with address and type
logoV2	Object	Organization logo (cropped, original, cropInfo)
coverPhotoV2	Object	Cover photo (cropped, original, cropInfo)
defaultLocale	Object	Default locale (country, language)
created	Object	Creation metadata (actor, time)
lastModified	Object	Last modification metadata (actor, time)
versionTag	String	Version tag of the record
autoCreated	Boolean	Whether the organization was auto-created
groups	Array	List of group URNs associated with this organization

Daily Follower Statistics

Daily follower statistics for your organization, including follower gains.

Field	Type	Description
date	Date	The date of the report (replication key)
organizationalEntity	String	The organization URN
timeRange	Object	Time range for the statistics (start/end timestamps)
followerGains	Object	Daily follower changes with organic and paid counts
followerCountsByAssociationType	Array	Followers by relationship (employee, etc.)
followerCountsByGeoCountry	Array	Followers by country
followerCountsByFunction	Array	Followers by job function
followerCountsByIndustry	Array	Followers by industry
followerCountsByGeo	Array	Followers by market area/region
followerCountsBySeniority	Array	Followers by seniority level
followerCountsByStaffCountRange	Array	Followers by company size

Lifetime Follower Statistics

Total follower count for your organization (current snapshot).

Field	Type	Description
date	Date	The date of the report
firstDegreeSize	Integer	Total number of followers (network size)

This stream provides the current total follower count, not historical data. Use Daily Follower Statistics for time-series follower growth data.

Daily Page Statistics

Daily page view and click statistics for your organization’s LinkedIn page, with breakdowns by visitor demographics.

Field	Type	Description
date	Date	The date of the report (replication key)
organization	String	The organization URN
timeRange	Object	Time range for the statistics (start/end timestamps)
totalPageStatistics	Object	Aggregate statistics with views and clicks
allPageViews	Object	Total page views across all devices
allDesktopPageViews	Object	All desktop page views
allMobilePageViews	Object	All mobile page views
overviewPageViews	Object	Overview/home tab views (all devices)
desktopOverviewPageViews	Object	Desktop overview views
mobileOverviewPageViews	Object	Mobile overview views
aboutPageViews	Object	About tab views (all devices)
desktopAboutPageViews	Object	Desktop about views
mobileAboutPageViews	Object	Mobile about views
jobsPageViews	Object	Jobs tab views (all devices)
desktopJobsPageViews	Object	Desktop jobs views
mobileJobsPageViews	Object	Mobile jobs views
careersPageViews	Object	Careers tab views (all devices)
desktopCareersPageViews	Object	Desktop careers views
mobileCareersPageViews	Object	Mobile careers views
lifeAtPageViews	Object	Life at [Company] views (all devices)
desktopLifeAtPageViews	Object	Desktop life at views
mobileLifeAtPageViews	Object	Mobile life at views
productsPageViews	Object	Products tab views (all devices)
desktopProductsPageViews	Object	Desktop products views
mobileProductsPageViews	Object	Mobile products views
peoplePageViews	Object	People tab views (all devices)
desktopPeoplePageViews	Object	Desktop people views
mobilePeoplePageViews	Object	Mobile people views
insightsPageViews	Object	Insights tab views (all devices)
desktopInsightsPageViews	Object	Desktop insights views
mobileInsightsPageViews	Object	Mobile insights views
pageStatisticsByGeoCountry	Array	Views by country
pageStatisticsByFunction	Array	Views by job function
pageStatisticsByIndustryV2	Array	Views by industry (v2)
pageStatisticsByGeo	Array	Views by region/market area
pageStatisticsBySeniority	Array	Views by seniority
pageStatisticsByStaffCountRange	Array	Views by company size
pageStatisticsByTargetedContent	Array	Views by targeted content

Lifetime Page Statistics

Lifetime page view statistics for your organization’s LinkedIn page. Same structure as Daily Page Statistics but represents cumulative/lifetime data.

Field	Type	Description
date	Date	The date of the report (replication key)
organization	String	The organization URN

Contains the same fields as Daily Page Statistics (totalPageStatistics, all page view breakdowns, and demographic breakdowns), but represents lifetime cumulative data rather than daily increments.

Daily Share Statistics

Daily aggregate share and engagement statistics for your organization’s content.

Field	Type	Description
date	Date	The date of the report (replication key)
organizationalEntity	String	The organization URN
share	String	The share URN (blank for aggregate statistics)
ugcPost	String	The UGC post URN (blank for aggregate statistics)
timeRange	Object	Time range for the statistics (start/end timestamps)
totalShareStatistics	Object	Aggregate engagement metrics with clicks, comments, impressions, likes, shares, and engagement rate

Lifetime Share Statistics

Lifetime aggregate share and engagement statistics for your organization’s content.

Field	Type	Description
date	Date	The date of the report (replication key)
organizationalEntity	String	The organization URN
share	String	The share URN (blank for aggregate statistics)
ugcPost	String	The UGC post URN (blank for aggregate statistics)
timeRange	Object	Time range for the statistics (start/end timestamps)
totalShareStatistics	Object	Cumulative engagement metrics with clicks, comments, impressions, likes, shares, and engagement rate

Posts

All posts published by your organization on LinkedIn.

Field	Type	Description
id	String	The post ID (URN)
author	String	The author URN (organization)
createdAt	Integer	Creation timestamp (epoch milliseconds)
publishedAt	Integer	Publication timestamp (epoch milliseconds)
lastModifiedAt	Integer	Last modification timestamp (replication key)
lifecycleState	String	Post state (PUBLISHED, DRAFT, etc.)
commentary	String	The post text/caption
content	Object	The attached content object (media, article, poll, etc.)
contentCallToActionLabel	String	CTA button label
contentLandingPage	String	CTA landing page URL
visibility	String	Visibility setting (PUBLIC, CONNECTIONS, etc.)
distribution	Object	Distribution settings including feed distribution and targeting
adContext	Object	Ad information if post is sponsored
reshareContext	Object	If this is a reshare, contains parent/root references
isReshareDisabledByAuthor	Boolean	Whether resharing is disabled
lifecycleStateInfo	Object	Additional state information

Post Statistics

Individual post performance metrics. This stream is a child of Posts, providing statistics for each post.

Field	Type	Description
postLastModifiedAt	Integer	The last modified time of the post (replication key)
organizationalEntity	String	The organization URN
postId	String	The post ID (URN) - links to the Posts stream
share	String	The share URN (for legacy shares)
ugcPost	String	The UGC post URN
timeRange	Object	Time range for the statistics (start/end timestamps)
totalShareStatistics	Object	Post performance metrics including clicks, comments, engagement rate, impressions, likes, shares, and unique impressions

Data Model

The following diagram illustrates the relationships between the core data streams in LinkedIn. The arrows indicate the join keys that link the different entities.

Use Cases for Data Analysis

This guide outlines valuable business intelligence use cases when consolidating LinkedIn organic data, along with ready-to-use SQL queries that you can run on Explorer.

Content Performance Analysis

1. Post Engagement Overview

Track the performance of your LinkedIn posts to identify top-performing content.

Business Value:

• Identify which content types drive the most engagement
• Understand optimal posting patterns
• Inform content strategy with data-driven insights

SQL query

AWS

WITH post_metrics AS (
    SELECT
        p.id AS post_id,
        p.commentary AS post_text,
        FROM_UNIXTIME(p."publishedAt" / 1000) AS published_date,
        p."lifecycleState" AS status,
        CASE
            WHEN p.content.media IS NOT NULL THEN 'MEDIA'
            WHEN p.content.article IS NOT NULL THEN 'ARTICLE'
            WHEN p.content.poll IS NOT NULL THEN 'POLL'
            WHEN p.content."multiImage" IS NOT NULL THEN 'MULTI_IMAGE'
            WHEN p.content.carousel IS NOT NULL THEN 'CAROUSEL'
            ELSE 'TEXT_ONLY'
        END AS content_type,
        ps."totalShareStatistics"."impressionCount" AS impressions,
        ps."totalShareStatistics"."uniqueImpressionsCount" AS unique_impressions,
        ps."totalShareStatistics"."clickCount" AS clicks,
        ps."totalShareStatistics"."likeCount" AS likes,
        ps."totalShareStatistics"."commentCount" AS comments,
        ps."totalShareStatistics"."shareCount" AS shares,
        ps."totalShareStatistics".engagement AS engagement_rate
    FROM
        nekt_raw.linkedin_posts p
        LEFT JOIN nekt_raw.linkedin_post_statistics ps 
            ON p.id = ps."postId"
    WHERE
        p."lifecycleState" = 'PUBLISHED'
)
SELECT
    post_id,
    SUBSTRING(post_text, 1, 100) AS post_preview,
    published_date,
    content_type,
    impressions,
    unique_impressions,
    clicks,
    likes,
    comments,
    shares,
    ROUND(engagement_rate * 100, 2) AS engagement_rate_pct,
    (likes + comments + shares) AS total_engagements
FROM
    post_metrics
ORDER BY
    total_engagements DESC
LIMIT 20

GCP

WITH post_metrics AS (
    SELECT
        p.id AS post_id,
        p.commentary AS post_text,
        TIMESTAMP_MILLIS(CAST(p.publishedAt AS INT64)) AS published_date,
        p.lifecycleState AS status,
        CASE
            WHEN p.content.media IS NOT NULL THEN 'MEDIA'
            WHEN p.content.article IS NOT NULL THEN 'ARTICLE'
            WHEN p.content.poll IS NOT NULL THEN 'POLL'
            WHEN p.content.multiImage IS NOT NULL THEN 'MULTI_IMAGE'
            WHEN p.content.carousel IS NOT NULL THEN 'CAROUSEL'
            ELSE 'TEXT_ONLY'
        END AS content_type,
        ps.totalShareStatistics.impressionCount AS impressions,
        ps.totalShareStatistics.uniqueImpressionsCount AS unique_impressions,
        ps.totalShareStatistics.clickCount AS clicks,
        ps.totalShareStatistics.likeCount AS likes,
        ps.totalShareStatistics.commentCount AS comments,
        ps.totalShareStatistics.shareCount AS shares,
        ps.totalShareStatistics.engagement AS engagement_rate
    FROM
        `nekt_raw.linkedin_posts` p
        LEFT JOIN `nekt_raw.linkedin_post_statistics` ps 
            ON p.id = ps.postId
    WHERE
        p.lifecycleState = 'PUBLISHED'
)
SELECT
    post_id,
    SUBSTR(post_text, 1, 100) AS post_preview,
    published_date,
    content_type,
    impressions,
    unique_impressions,
    clicks,
    likes,
    comments,
    shares,
    ROUND(engagement_rate * 100, 2) AS engagement_rate_pct,
    (likes + comments + shares) AS total_engagements
FROM
    post_metrics
ORDER BY
    total_engagements DESC
LIMIT 20

Sample Result

post_id	post_preview	published_date	content_type	impressions	unique_impressions	clicks	likes	comments	shares	engagement_rate_pct	total_engagements
urn:li:share:123456	We’re excited to announce our new product…	2024-11-15 14:30:00	MEDIA	45,230	38,450	1,234	892	156	78	4.82	1,126
urn:li:ugcPost:789012	Join us for our upcoming webinar on…	2024-11-12 09:00:00	ARTICLE	32,150	28,320	956	543	89	45	3.21	677
urn:li:share:345678	Proud to share that our team has been…	2024-11-10 16:45:00	MULTI_IMAGE	28,900	24,670	445	678	234	123	5.12	1,035

2. Follower Growth Analysis

Track your organization’s follower growth over time and understand the source of new followers.

Business Value:

• Monitor organic vs paid follower acquisition
• Track growth trends over time
• Measure the impact of content and campaigns on follower growth

SQL query

AWS

WITH daily_followers AS (
    SELECT
        DATE(date) AS report_date,
        "followerGains"."organicFollowerGain" AS organic_gained,
        "followerGains"."paidFollowerGain" AS paid_gained,
        ("followerGains"."organicFollowerGain" + "followerGains"."paidFollowerGain") AS total_gained
    FROM
        nekt_raw.linkedin_daily_follower_statistics
    WHERE
        date >= CURRENT_DATE - INTERVAL '30' DAY
),
running_totals AS (
    SELECT
        report_date,
        organic_gained,
        paid_gained,
        total_gained,
        SUM(total_gained) OVER (ORDER BY report_date) AS cumulative_growth,
        AVG(total_gained) OVER (
            ORDER BY report_date 
            ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
        ) AS seven_day_avg
    FROM
        daily_followers
)
SELECT
    report_date,
    organic_gained,
    paid_gained,
    total_gained,
    cumulative_growth,
    ROUND(seven_day_avg, 1) AS seven_day_moving_avg,
    ROUND(organic_gained * 100.0 / NULLIF(total_gained, 0), 1) AS organic_pct
FROM
    running_totals
ORDER BY
    report_date DESC

GCP

WITH daily_followers AS (
    SELECT
        DATE(date) AS report_date,
        followerGains.organicFollowerGain AS organic_gained,
        followerGains.paidFollowerGain AS paid_gained,
        (followerGains.organicFollowerGain + followerGains.paidFollowerGain) AS total_gained
    FROM
        `nekt_raw.linkedin_daily_follower_statistics`
    WHERE
        date >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
),
running_totals AS (
    SELECT
        report_date,
        organic_gained,
        paid_gained,
        total_gained,
        SUM(total_gained) OVER (ORDER BY report_date) AS cumulative_growth,
        AVG(total_gained) OVER (
            ORDER BY report_date 
            ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
        ) AS seven_day_avg
    FROM
        daily_followers
)
SELECT
    report_date,
    organic_gained,
    paid_gained,
    total_gained,
    cumulative_growth,
    ROUND(seven_day_avg, 1) AS seven_day_moving_avg,
    ROUND(SAFE_DIVIDE(organic_gained * 100.0, total_gained), 1) AS organic_pct
FROM
    running_totals
ORDER BY
    report_date DESC

Sample Result

report_date	organic_gained	paid_gained	total_gained	cumulative_growth	seven_day_moving_avg	organic_pct
2024-11-27	45	12	57	1,234	48.3	78.9
2024-11-26	38	8	46	1,177	46.7	82.6
2024-11-25	52	15	67	1,131	45.1	77.6
2024-11-24	28	5	33	1,064	43.9	84.8
2024-11-23	31	7	38	1,031	44.2	81.6

3. Page Visitor Demographics

Analyze who is visiting your LinkedIn page to understand your audience.

Business Value:

• Understand your audience composition
• Identify high-value visitor segments
• Inform content targeting decisions

SQL query

AWS

WITH seniority_stats AS (
    SELECT
        s.seniority,
        SUM(s."pageStatistics".views."allPageViews"."pageViews") AS total_views,
        SUM(s."pageStatistics".views."allPageViews"."uniquePageViews") AS unique_views
    FROM
        nekt_raw.linkedin_daily_page_statistics ps
        CROSS JOIN UNNEST(ps."pageStatisticsBySeniority") AS t(s)
    WHERE
        DATE(ps.date) >= CURRENT_DATE - INTERVAL '30' DAY
    GROUP BY
        s.seniority
),
total AS (
    SELECT SUM(total_views) AS grand_total FROM seniority_stats
)
SELECT
    seniority,
    total_views,
    unique_views,
    ROUND(total_views * 100.0 / grand_total, 1) AS pct_of_total
FROM
    seniority_stats, total
ORDER BY
    total_views DESC

GCP

WITH seniority_stats AS (
    SELECT
        s.seniority,
        SUM(s.pageStatistics.views.allPageViews.pageViews) AS total_views,
        SUM(s.pageStatistics.views.allPageViews.uniquePageViews) AS unique_views
    FROM
        `nekt_raw.linkedin_daily_page_statistics` ps,
        UNNEST(ps.pageStatisticsBySeniority) AS s
    WHERE
        DATE(ps.date) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
    GROUP BY
        s.seniority
),
total AS (
    SELECT SUM(total_views) AS grand_total FROM seniority_stats
)
SELECT
    seniority,
    total_views,
    unique_views,
    ROUND(SAFE_DIVIDE(total_views * 100.0, grand_total), 1) AS pct_of_total
FROM
    seniority_stats, total
ORDER BY
    total_views DESC

Sample Result

seniority	total_views	unique_views	pct_of_total
urn:li:seniority:5	12,450	9,230	28.4
urn:li:seniority:6	9,870	7,456	22.5
urn:li:seniority:4	8,234	6,123	18.8
urn:li:seniority:3	6,543	4,890	14.9
urn:li:seniority:7	4,321	3,210	9.9

Implementation Notes

Data Freshness

LinkedIn analytics data typically has a delay of 24-48 hours. The most recent data available may be from 1-2 days ago.

• Daily reports: Updated once per day
• Post statistics: Updated as engagement occurs, with some delay
• Lifetime demographics: Represent current state, not historical

Historical Data Limits

LinkedIn limits historical data access:

• Daily statistics (Follower, Page, Share): Up to 1 year of historical data
• Posts: All published posts are available
• Post statistics: Available for all posts, but metrics are cumulative (not daily)

API Permissions

This connector requires the following LinkedIn permissions:

• r_organization_social - Read organization posts and social data
• Access to Community Management API

The LinkedIn account used for authorization must have Admin or Content Admin access to the organization page.

URN Format

LinkedIn uses URNs (Uniform Resource Names) to identify entities:

• Organizations: urn:li:organization:{id}
• Posts: urn:li:share:{id} or urn:li:ugcPost:{id}
• Seniority levels: urn:li:seniority:{id}
• Industries: urn:li:industry:{id}
• Functions: urn:li:function:{id}
• Countries: urn:li:geo:{id} or urn:li:country:{code}

These URNs are used as identifiers and can be decoded to get the underlying IDs.

Engagement Rate Calculation

LinkedIn’s engagement field is calculated as:

engagement = (clicks + likes + comments + shares) / impressions

This is returned as a decimal (e.g., 0.0482 = 4.82%). Multiply by 100 to get the percentage.

Incremental Sync with Lookback

The Posts and Post Statistics streams now support incremental sync with a lookback window:

• Posts: Uses lastModifiedAt as the replication key for incremental sync
• Post Statistics: Uses postLastModifiedAt as the replication key, derived from the parent post’s modification time
• Lookback Window: Configurable number of days (default: 30) to look back on each run to ensure recently modified posts have up-to-date statistics
• API Rate Limiting: The connector handles LinkedIn’s daily rate limits gracefully, stopping extraction when limits are reached and resuming from the current state on the next run

This ensures that post performance metrics stay current for recently published content while maintaining efficient incremental syncs.

Skills for agents

Download LinkedIn skills file

LinkedIn connector documentation as plain markdown, for use in AI agent contexts.