LinkedIn engagement research by B2B market
Explore three published studies of public LinkedIn activity in specific B2B categories. Each report defines its sample, time window and measurement.
How to read these reports
The revenue-tooling and ecommerce customer-service reports compare engagement per vendor post. The AI agent platform report compares the roles of people engaging with eight platforms. These measures answer different questions.
What these studies measure
The reports start with people who reacted to or commented on selected vendors' public posts. Role filters and activity-based sampling narrow that pool for each study.
For the vendor tables, we divide qualifying reactions and comments by the vendor's post count in the same window. The result compares observed engagement per post within one sample. It does not measure total reach, market share or product quality.
Creator tables count unique people who engaged with each account. See /data/method for the definitions and sampling limitations.
Revenue tooling: LinkedIn engagement in the Nordics
Nordics pull, 27 August 2026. 90 day window. 170 most active of 642 sampled people who engaged the category's posts.
Gong took 327 market reactions in the window, Outreach 264. Attention recorded zero qualifying reactions from the sample across 24 posts. The most-reacted post in the whole category covered sales practice: Gong on the biggest sales call mistake, 74 market reactions from the 170. And the account with the highest creator overlap belongs to an account executive at Gong who writes a newsletter, ahead of every CEO in the category. 29 of the 170 engaged him.
Full table: /markets/revenue-tooling
Ecommerce customer-service AI: LinkedIn engagement
London pull, August 2026. 90 day window. 170 most active of 264 sampled people.
Kustomer recorded 6.6 reactions per post and Gorgias 0.8 within this sample. The CX Professionals Association had engagement from 50 of the 170 sampled people. These findings suggest checking relevant institutions as well as vendor pages when researching the category.
Full table: /markets/ecommerce-customer-service-ai
AI agent platforms: who is in the crowd
UK pull, 20 August 2026. Eight anchor platforms, 90 days, 4,449 raw reactors. This table is a ratio rather than a rate.
Across all eight platforms, 79 people whose title suggests they sign, and 1,523 builders and practitioners. Every anchor skews the same way and the only difference is by how much. 27 people show up around two or more platforms, so the crowds barely overlap.
Full table: /markets/ai-agent-platforms
Next markets we are counting
These are being built now. We publish no numbers until a pull is finished.
- German HR software
- ERP for the Mittelstand
- B2B payments
- Developer tools
- Cybersecurity
Each one gets the same treatment. Competitor engagers first, then a title filter, then 90 days of counting.
Questions to test in your own market
The published studies suggest several useful tests:
- Compare posts about the work customers do with product announcements.
- Measure engagement with employee and founder accounts as well as company pages.
- Include relevant professional associations in creator research.
- Review more than one event or community before planning distribution.
These are research directions, not universal performance rules. For example, Postelix's own highest creator overlap was 4.5% in a 200-person sample; a single creator may not be a useful route into every market.
How we count, in five lines
- A market is the people who engaged competitors' posts in the window, filtered by title.
- Market reactions per post equals reactions and comments from that sampled market on a vendor's public posts, divided by the vendor's posts in the same 90 days.
- The sample and the window are printed on every table.
- Zero is printed as zero, with the vendor's post count next to it.
- Role filters help define relevance; they do not prove buying authority.
The long version, including where the method is weak: /data/method
Questions
We start with the people who reacted to or commented on the posts of the vendors in a category during the window. That pool gets filtered by title and ranked by how often each person engages. The tables use the most active slice, and when we run this for a company, that company strikes out the fits that are wrong.
Total reactions rewards posting volume. A vendor publishing four times a week will out-total a vendor publishing twice a month even when each individual post lands worse. Dividing by posts in the window makes two vendors comparable, and we print the totals as well so you can read both.
The vendors in these tables are public companies and every number comes from their public posts. Nothing here comes from a private profile or a private list. We never name a person who reacted, and the companies whose readouts produced these numbers stay anonymous unless they ask to be named.
Yes. We need the category and the vendors you compete with, then we build the named market and run the same count over 90 days. The output is the table, plus the voices the market follows and the rooms it gathers in.
Book a demo. Then run it on your own market for 7 days.
The demo is with the founder and shows the loop in action. A work email is enough, no card.