Decoding the LinkedIn Algorithm: Alicia Teltz’s Experiment on Feed Personalization

In a recent LinkedIn post, Alicia Teltz is conducting an experiment to understand the evolving dynamics of the platform’s algorithm, particularly its shift towards a more personalized, “For You” page-like feed. Teltz, a prominent voice in the business community, is actively seeking user input to analyze how content is surfaced to individuals.

The core of Teltz’s inquiry revolves around the balance between network-driven content and interest-based discovery. She notes a significant change, stating:

“LinkedIn’s feed has changed from a pure network-first to more of a persona-first feed, similar to a ‘For You’ page on Instagram or TikTok.”

This observation suggests a departure from the traditional model where users primarily saw updates from their direct connections. Teltz proposes that the algorithm now prioritizes content that aligns with a user’s established persona, which is derived from their profile details, content engagement, and network interactions.

The Algorithm’s Persona-First Shift

Alicia Teltz argues that this shift means users are increasingly encountering content from individuals they do not know, provided that content resonates with their inferred interests. This is a departure from the past, where visibility was largely dependent on the strength of one’s network. As Teltz highlights:

“You no longer just see content from people you know. You increasingly see content from random strangers if it fits your interests based on your persona…”

This evolving landscape raises critical questions for content creators. Teltz frames the central query as:

Quantifying the “For You” Page Influence

Teltz is seeking to quantify this shift, asking her audience to help determine the ratio between interest-based content and network-driven content in their feeds. She poses the question directly: “HOW MUCH is LinkedIn moving towards a ‘For You’ page? And how much of your feed is still determined by your network?” This exploration aims to shed light on whether the platform is moving towards a 50/50 split or a more extreme leaning towards algorithmic curation.

Implications for Content Creators

The outcome of Teltz’s experiment has significant implications for creators. She outlines two potential scenarios:

• Scenario 1: Interest-Based Discovery Dominates. In this case, content competes based on relevance to a user’s interests rather than solely on the size of their network. Teltz suggests this scenario offers a greater opportunity for smaller creators and emphasizes the importance of niche expertise and profile optimization.
• Scenario 2: Network Remains Paramount. If the network continues to be the primary driver of feed visibility, then growth strategies should prioritize building and nurturing connections over content creation alone.

Teltz emphasizes the collective benefit of understanding these dynamics, stating her intention to share the experiment’s findings publicly. “I’ll share the findings publicly so we can all collectively stop pretending we understand LinkedIn’s algorithm when in reality we’re all just throwing content into the void and hoping for the best,” she writes.

By engaging her audience directly, Teltz aims to gather real-time data to demystify the LinkedIn algorithm, offering valuable insights for anyone looking to navigate and succeed on the platform.