Decoding the LinkedIn Algorithm: Insights from Former Insider Alicia Teltz

In a recent LinkedIn post, former LinkedIn employee Alicia Teltz offers an insider’s perspective on how the platform’s algorithm determines content visibility, debunking common myths and explaining the actual mechanics.

Teltz begins by emphasizing the inherent opacity of the algorithm, stating:

“Firstly, nobody knows exactly how the algorithm works. That includes me. Anyone claiming to know either works inside LinkedIn’s engineering team, is liaised with someone who does, or is outright lying.”

She further clarifies that even during her tenure at LinkedIn, direct access to distribution data was restricted to maintain fairness.

Debunking Algorithm Myths

Teltz addresses several prevalent theories about why content reach might be declining, labeling them as myths:

Myth 1: Lowered Reach to Sell “Boosts”

While seemingly plausible, Teltz dismisses this, arguing that eroding user trust would ultimately harm the platform itself.

Myth 2: Video Prioritization

The idea that text posts are intentionally throttled in favor of video is also debunked. Teltz points out that if this were the case, video impression numbers would be significantly higher than they currently are.

Myth 3: Silencing Women

While acknowledging potential biases in LinkedIn’s new Large Language Model (LLM), Teltz asserts there is no commercial incentive for the platform to suppress women’s reach.

Myth 4: Impact of Engagement Pod Crackdowns

While engagement pods are indeed being addressed, Teltz explains that this measure alone cannot account for a platform-wide decrease in impressions.

Myth 5: The Role of SSI Score

Contrary to popular belief, Teltz clarifies that the Social Selling Index (SSI) score is not a direct determinant of content distribution but rather a spam-prevention metric.

The Real Drivers of Content Distribution

According to Teltz, the LinkedIn feed is designed to align with the company’s monetization goals, especially given its ownership by Microsoft and the need for sustainable revenue. With a dramatic increase in content volume, LinkedIn has shifted towards a more personalized user experience, akin to platforms like TikTok.

Teltz outlines a three-phase process for how content is evaluated:

1. Phase 1: Analyze: Upon posting, LinkedIn assesses the content’s topic, the author’s qualification to discuss it, and the potential audience interest. The post is then tested on a small segment of the author’s network and a similar demographic.
2. Phase 2: Evaluate: Over approximately one to two hours, LinkedIn monitors user interaction. Meaningful comments, saves, reposts, and reshares from the target audience are weighted more heavily than simple likes.
3. Phase 3: Expand: If the content receives strong, positive engagement from the right audience, LinkedIn expands its reach beyond the author’s immediate network. Teltz notes that in such cases, 70-90% of impressions can come from outside the author’s network. Conversely, low engagement results in the content being primarily shown within the existing network, with only 10-20% of impressions coming from outside.

Teltz emphasizes the importance of genuine engagement:

“Saves, reposts, reshares, and meaningful comments carry more weight than Likes, especially when they come from the right audience.”

Strategies for Enhanced Visibility

To improve content visibility, Teltz recommends several key actions:

• Optimize your profile with relevant keywords and skills.
• Create high-quality content that resonates with your target audience.
• Engage intentionally with relevant individuals and communities.

While acknowledging the complexity of the algorithm, Teltz offers further insights through her specialized LinkedIn cohort program, designed to provide a more in-depth understanding of these dynamics.

“This post is massively oversimplified. There’s much more nuance.”

Teltz’s detailed breakdown provides valuable guidance for professionals seeking to navigate the intricacies of content distribution on LinkedIn.