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In this episode of the Growth Llama podcast, Barry Hurd shares his expertise on influencer mapping for market intelligence. He discusses the methodology behind his research, including the collection and analysis of data from LinkedIn posts, to identify influential voices in the AI conversation beyond the usual suspects. 

Gain insights into the mechanics of influencer mapping, the importance of looking beyond surface-level metrics like follower counts and engagement rates, and the potential applications of such mapping in their own industries. Barry's discussion highlights the challenges of content visibility on LinkedIn and the strategic use of data to uncover valuable but often overlooked insights within professional networks. 

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As a case study for influencer mapping, Barry walks us through his process by focusing on previous research he conducted. He focused on AI discussions within the Seattle area on LinkedIn. Barry's research involved visualizing thousands of posts from around 800 to 900 individuals to highlight topics from lesser-known experts. This allowed Barry to bring to the surface other voices with valuable insights from experts in AI who might be drowned out by major tech companies like Microsoft and Amazon.

By excluding the most prominent influencers from big tech companies, Barry's work reveals richer, more nuanced results of discourse around AI, showcasing how experts in fields like health, retail, and transportation are utilizing AI technologies. 

This is just one example of how influencer marketing can help understand and leverage the voices of influence within different markets and industries. 

Key Takeaways

• Exposing industry experts and subject matter experts on LinkedIn is crucial to provide a more diverse and valuable conversation.

• Engagement rates of big influencers on LinkedIn are often low compared to industry experts, making it important to look beyond follower counts.

• LinkedIn's algorithm and search features have limitations, making it necessary to develop your own algorithm to filter through the feed.

• Understanding the context and relevance of content on LinkedIn can be challenging due to the platform's walled garden approach.

Timecodes

00:38 Explaining the Influencer and Content Map

01:57 The Problem with Influencers

03:22 Engagement Rates of Influencers

04:33 Benefits of Excluding Big Voices

05:51 Understanding the Data in the Map

06:09 Collecting and Analyzing the Data

07:32 Discoveries from the Data Analysis

09:30 Value of Gaining Insight into Industry Conversations

10:19 Challenges of Understanding Context on LinkedIn

12:07 The Problem with LinkedIn Hashtags

13:17 Subscribing to Hashtags on LinkedIn

15:09 Creating a Graph and Filtering Data

18:20 Excluding Big Players in an Industry

20:09 Dealing with Spammy and Fake Engagement

21:08 The Limitations of LinkedIn's Algorithm

23:09 Developing Your Own Algorithm

25:35 The Impact of Filtering on the Graph

28:30 The Need for Better Algorithms on LinkedIn

30:47 The Lack of Visibility into Employee Activity on LinkedIn

32:34 The Challenges and Legal Issues of Scraping LinkedIn Data

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