Hey everyone, I’m Yash. After spending 8+ years in data analytics, I realized something interesting : Most people look at data and only see numbers.I naturally started seeing the humans, emotions, and behavior patterns behind those numbers. That became my ikigai. So recently, I decided to step away from my job path (and even a strong opportunity in front of me) to build something around this obsession. What I’m building:I help LinkedIn creators — especially coaches and people offering 1:1 services — understand what their audience behavior is actually telling them. Not just: - views - likes - impressions But deeper questions like: - Why do certain posts emotionally connect? - Who is truly engaging deeply? - Which audience silently resonates but never converts? - What patterns are attracting the wrong audience? - What does their “algorithm” behavior reveal about positioning? I combine: - data analytics - audience behavior - psychology - content pattern analysis …into a diagnostic clarity-style report. I’ve created one sample analysis so far and would genuinely love feedback from people here. Link : https://canva.link/hfdi0s0ye8g1i67 Looking forward to learning from all of you and adding value however I can 🙌

Hi all, I want to comment more on LinkedIn to drive impressions/profile clicks. However when I open my feed, it's usually either people with virtually no engagement (friends/coworkers) or major influencers flooded with AI comments. Should I just comment on everything anyways (thoughtfully of course) or should I pick which posts I comment on?

I recently reanalyzed my LinkedIn performance using Shield’s post level data and wanted to share a perspective that may be useful for anyone experimenting with different growth models. I previously used MagicPost (https://www.skool.com/innovator/from-engagement-anxiety-to-authority-compounding-what-my-linkedin-data-actually-showed?p=eb51b20c) which showed trends. Shield revealed the mechanics. Many in this community optimize for volume, reach, and conversion, and that absolutely works when the goal is lead flow, offers, or audience monetization. My goals are a bit different, which led me to look at the data through a different lens. Shield showed something interesting in my case: - Impressions continued to rise even during periods of lower engagement - Follower growth stayed consistent - Posts that interpreted industry or leadership signals were distributed despite modest likes That pattern reflects the audience I am trying to reach. Senior operators, investors, and board level leaders tend to engage quietly. They read, save, and follow more than they like or comment. The takeaway for me was not that one approach is better than another, but that metrics have to match intent. When the goal is credibility, trust, and long term positioning, engagement can lag relevance for quite some time. For Q1, I am simplifying rather than scaling. Fewer posts, clearer themes, and more narrative continuity. That may not be optimal for selling, but it is aligned with my objectives. Sharing this here because it helped me avoid misreading my own data. Curious how others in the group adjust their scorecards when running multiple goals across different audiences.