How Does the LinkedIn Algorithm Work? Our Top 10 Programmatic Posts 🍿
Our little “Programmatic Advertising” series has grown into quite a collection. So, taking a cue from Netflix, we decided to publish our ten most-viewed posts. 🍿 TUDUM! But the ranking also raised a question: what actually drives organic reach on LinkedIn?
1. GreyFox Ads’ Top 10 programmatic advertising posts
Here is our own French AdTech top ten, ranked by the number of organic impressions each post generated—not by clicks, likes, comments or sales. Each original LinkedIn post is linked below, alongside related articles where available.
- The three building blocks of the Open Web. Original post · Explore Open Web fragmentation
- The end of Xandr DSP. Original post
- CTV-washing. Original post · Read our CTV-washing analysis
- Advertisers’ first-party data. Original post · First-party and 1.5-party data
- Season 01 recap. Original post · Season recap article
- AI and programmatic: the end of Media Traders? Original post · AI-washing in AdTech
- Programmatic advertising and Christmas dinner. Original post · Read the Christmas special
- Throttling. Original post · What is bid-request throttling?
- MFA risks. Original post · Why MFA sites matter
- CTV-washing on Open Garden. Original post
2. How was this LinkedIn ranking calculated?
The ranking reflects organic impressions reported for our own posts. It is not a comparison of unique readers or engagement rates, and we have not published the impression count for each entry in this source post.
We also observed that our average reach roughly halved following what we perceived as a LinkedIn algorithm change in September 2025 (an account-level observation, not independent proof of a platform-wide update or its cause). That makes comparisons between posts published at different times less straightforward.
3. Is average reading time the secret to LinkedIn reach?
Our leading theory is that average reading time—or dwell time—matters a great deal. It is not directly exposed as a simple post-level metric in the interface we use, so we cannot measure its weight or confirm it is the most important signal.
The logic is appealing: if readers stop scrolling and spend time with a post, that behavior could signal interest. But reading time alone cannot establish how LinkedIn ranks content, and long dwell time does not necessarily mean someone found the post useful.
4. Does LinkedIn distribute posts in successive waves?
Our working model is a staged distribution process: a post is initially seen by a relatively small, relevant audience; if its early signals are encouraging, it may reach a broader audience, and then another. This is a hypothesis based on the patterns we see in our analytics, not a confirmed technical specification.
In our experience, most organic impressions arrive within the first 48 hours, with occasional additional exposure afterward (the timing will vary by post, account and audience).
5. Which LinkedIn interactions could help a post travel further?
When we inspect our best-performing posts, we pay attention to several types of interaction:
- Saves: a sign that someone may want to revisit a useful analysis.
- Public shares and private messages: ways a post can reach new people, though private sharing is not fully observable in our analytics.
- Substantive comments: a possible sign of deeper discussion.
- Likes: a lightweight signal of interest.
This order reflects our interpretation, not a proven LinkedIn weighting formula. We cannot infer how much each action contributes to distribution from our account data alone.
6. Why does the image matter so much?
On a mobile feed, an image can occupy a substantial share of the visible screen—roughly 25% to 60% in the layouts we had in mind. It is often the first thing someone notices, before they read a word of your copy.
We have seen cases where an underwhelming visual coincided with reach around three times lower than stronger creative executions (an informal comparison, not a controlled experiment). It is a reminder that even excellent research can disappear in a busy feed if the hook does not invite people in.
Three articles we think deserved another chance:
- The challenge of advertising attribution · Related analysis
- Publisher-integrated creative strategy · Read the article
- The limits of GEO · Read the GEO analysis
7. What can B2B and AdTech creators learn from this?
Start with a question your audience genuinely cares about. Make the first visual immediately understandable. Offer an original observation, a practical example or a useful challenge to accepted wisdom. And measure more than likes: compare impressions, meaningful interactions and what your readers actually do next.
Above all, treat every claim about “the algorithm” as a testable hypothesis. Correlation between a strong image and strong reach is interesting; it is not proof that the image alone caused the result. Our own content experiments are no exception. 😉
8. What does this have to do with GreyFox Ads?
Quite a lot, actually. The same principle applies to programmatic media buying: creative quality and the environment in which a message appears both matter. At GreyFox Ads, our Master Curated Deal focuses on quality Open Web inventory in France, direct publisher supply paths, MFA exclusions, brand safety, screen-share optimisation, privacy-appropriate targeting and transparent fees.
We want campaigns that people actually notice—not simply impressions that look good in a dashboard. Discover the GreyFox Ads Master Deal.
Want to discuss quality programmatic advertising?
If you are looking for a partner to activate effective display and video campaigns on the French Open Web, contact GreyFox Ads.
Original LinkedIn post: France’s Top 10 and our LinkedIn algorithm hypotheses.