Contents
TL;DR
- Your personal profile and company page serve different functions. Track them as separate systems, and check who is engaging before you trust any engagement rate percentage.
- LinkedIn's current ranking system rewards dwell time, saves, and comment depth over likes. A “good” number built on likes alone is measuring the wrong signal.
- The Visibility Ledger is a four-column spreadsheet that connects a LinkedIn post to a closed deal, with no CRM and no paid tool required.
- LinkedIn keeps raw post data for roughly two years, but who-engaged detail starts disappearing at 90 days and demographic data at 180 days. Capture it as you go, or lose it.
Why “Is This Good?” Is the Wrong Question to Ask About Your LinkedIn Analytics
There is a version of this guide to LinkedIn analytics that opens with a definition: impressions, reach, engagement rate. This is not that guide.
LinkedIn analytics are the data LinkedIn gives you on how your posts, profile, and company page perform. Not what looks impressive. What actually moved something. That's the difference between a vanity metric and a real one, and it's the only distinction that matters here.
You already know the moment this stops feeling abstract. A post clears a strong engagement rate. Comments roll in all afternoon. It feels like traction.
Then a month passes, and nothing downstream changed. No inbound DM, no investor intro, no candidate mentioning the post in an interview. The number was real. It just wasn't measuring the thing you needed measured.
Retire “is this good” as the question you're asking. Keep “does this tell me if it's working.” Everything below answers the second one.
Personal Profile vs. Company Page: Why the Analytics Split Is Structural
Here's the mechanic in one sentence: LinkedIn's algorithm trusts an individual voice more than it trusts a brand voice. A post from a founder reads as a person talking to people. A post from a company page reads as a brand talking at an audience. The distribution math follows from there, before content quality enters the picture at all.
This is why a personal profile outperforming the company page is not a company-page failure. It's the platform working as designed. Company pages exist for a different job: credibility backdrop, not growth engine. Someone who lands on the page after meeting the founder should find it professional and current, not a mirror of the founder's own voice.
Widely cited practitioner estimates put personal profiles at roughly 65% of LinkedIn's feed allocation, with company pages at around 5%. LinkedIn hasn't officially confirmed these exact figures, but the directional gap shows up consistently across independent analyses, and it matches what most founders already see comparing their own two dashboards.
Two tracks, two owners, two rhythms:
Personal-profile analytics live under your own profile's Analytics tab. Company-page analytics live in a separate Page admin dashboard entirely. That's the full orientation needed here. This isn't a tutorial on where to click. It's confirmation that you're looking at two different systems, not one system wearing two names.
If the analytics show your personal profile is the stronger track, see how to optimize your LinkedIn profile for thought leadership for the credential and signal structure that amplifies that advantage.
Which surfaces the real question underneath the split: where does your own time go, and what gets handed to a hire or an agency? The personal-profile track can't be delegated away. The voice has to stay yours. The company-page track can.
How to Check If the Right People Are Engaging With Your LinkedIn Posts
Most engagement rate guidance lands between 3% and 5%, with strong native-format content occasionally clearing 6% or higher, per Sprout Social's LinkedIn engagement benchmarks. Hold that range loosely. A good rate with the wrong audience is a worse outcome than a modest rate with the right one.
Here's the check, and it takes five minutes, not a dashboard subscription:
- Pull the list of people who engaged with your last three posts.
- Check title, seniority, and company against your actual ICP list.
- If fewer than half match, the number is decorative. If most match, it means something.
In practice, that match rate varies a lot depending on how narrow your ICP is. No tooling required to find out. Just a habit, run against a list you already have.
For a breakdown of which post formats produce the most saves and comments, see LinkedIn post formats that perform best for B2B.
Here's why raw likes matter less than they used to. LinkedIn's feed ranking now runs on 360Brew, a roughly 150-billion-parameter foundation model detailed in LinkedIn's own research and the company's March 2026 engineering blog post on rebuilding the feed. 360Brew evaluates topic relevance and demonstrated expertise rather than network size or reaction counts. LinkedIn hasn't confirmed that this exact research model, by name, is what's running the live public feed today, so treat 360Brew as the best-documented description of the mechanism, not a confirmed live spec.
Independent research points the same direction. Richard van der Blom's 2026 Algorithm Insights report, based on 1.3 million posts, found comment depth and dwell time are the dominant distribution signals, with likes carrying minimal weight on their own. The “comments count 15x more than likes” figure that circulates in LinkedIn content circles doesn't trace back to a confirmed source. Treat the direction as solid and the multiplier as unconfirmed.
The practical shift: dwell time, saves, and comment depth now carry more weight than a quick tap on a like button. This isn't an official score LinkedIn publishes anywhere, and it shouldn't be treated as one.
A post with 40 likes and three long, specific comments from the right people is outperforming a post with 400 likes from people who scrolled past.
Not sure if the people engaging with your posts are the people who can actually buy from you, hire you, or fund you? SuperStrat Labs runs a quick audience-quality check on your last 90 days of posts, no strings attached. Get the free check.
How to Track LinkedIn ROI Without a Paid Tool: The Visibility Ledger
Outbound sales solved this problem years ago: connection, reply, positive reply, meeting booked. A chain, dated at every step, that turns activity into a number a board believes. Inbound content never built its own version. Call this one the Visibility Ledger.
Four columns, one row per lead:
- Signal: the post or profile-view spike that started it
- Engagement: who engaged, with title, company, and ICP match
- Conversation: the DM or call, dated
- Outcome: the dated result, win or loss
The Visibility Ledger works best when your personal-brand content is already calibrated to your ICP. For how to build that calibration, see building LinkedIn thought leadership as a founder.
Here's what that looks like, start to finish. A founder posts a breakdown of a pricing mistake made early on. A VP of Ops at a target account leaves a comment asking what the mistake actually cost in lost deals, two hours after the post goes up.
That's the Signal, and the first half of Engagement, logged the same day. The founder sends a follow-up DM referencing the comment. Four days later, a 20-minute call gets booked. That's Conversation, dated. Six weeks after that, a contract closes at $40,000 ARR. That's Outcome, dated, closing the row.
Most founders running this for the first month log a handful of rows, not hundreds. That's the point. The Ledger isn't tracking everything. It's tracking the signals that are actually ICP-matched, and over a few months that short list shows a pattern no engagement-rate chart will ever reveal: which topics generate real conversations, and which just generate applause from the wrong people.
If the instinct is to reach for Sales Navigator or an attribution platform before this exists, resist it. Paid tools organize data. They don't build the habit of writing a row down the day it happens. Build that habit on a spreadsheet first. Add a tool later, only if volume ever outgrows one tab.
How Often to Check Your LinkedIn Analytics
Check the numbers once a week, tied to your posting cycle, not a calendar date. Here is why the cadence matters as much as the check itself.
- Don't Check Your Analytics Every Day: A post that looks flat on day one often gains traction over the next few days as comments build and LinkedIn expands its reach. Daily checks encourage decisions based on incomplete data.
- Don't Wait Until the End of the Month: Monthly reviews make it harder to understand what actually worked. By then, detailed engagement insights, such as who interacted with your post, may no longer be available.
- Review Your Last 5 to 7 Posts Every Week: A weekly review gives you enough data to improve your next post without waiting too long. Spend 10 minutes looking at these three things:
- Who Engaged With Your Posts: Check whether the people engaging match your ideal audience. One relevant prospect is often more valuable than hundreds of irrelevant likes.
- Which Posts Kept People Interested: Look beyond likes. Saves, meaningful comments, and time spent engaging tell you which formats and topics performed best.
- What Should You Change in Your Next Post: Use the patterns from recent posts to adjust your topic, angle, or format before you repeat the same mistakes.
- Match Your Review to Your Posting Schedule: Review your analytics after your weekly posting cycle, not on a fixed weekday. For example, if you post on Tuesday and Thursday, review your analytics on Friday while the insights are still fresh.
How Long LinkedIn Actually Keeps Your Analytics Data
If you've read that LinkedIn only keeps post analytics for 60 days, that's out of date. Here's what LinkedIn's own Help documentation says is actually retained, and for how long:
The raw counts survive for roughly two years. The demographic detail that tells you who engaged by industry, seniority, and company size disappears after about 180 days. And the specific who-engaged data, the individual people, is only available for around 90 days. That's the real wall.
It's exactly why the Ledger gets a new row the week a signal happens, not a reconstruction from memory once someone asks for a quarterly update.
Running the dual-track split and the Ledger solo is realistic. Most founders running this for the first time misjudge which signals are actually ICP-matched, or let the log lapse past the 90-day window before the individual detail is captured. SuperStrat Labs offers a system check on exactly this: what's already being tracked, and what's about to slip past the window before you can act on it. Book a 20-minute audit.
This compounds over months, not weeks. The founders still checking a spreadsheet in month six are the ones who show up in a board deck with a number that means something.
This Is the GTM System SuperStrat Labs Builds for Founders
Everything above works because none of it treats LinkedIn as a content channel. The dual-track split, the audience-quality check, the Ledger, the retention discipline: these are all decisions about a GTM channel, not a posting schedule. That's the actual shift most founders need to make before any of these mechanics matter.
This is what SuperStrat Labs builds for founders and executives who already have real expertise and a strong product, but haven't turned LinkedIn into a system that proves it. Not more content. A structure that connects visibility to pipeline, investor conversations, or the next hire, with numbers behind it instead of a feeling.
If you're already running some version of this and want a second read on whether it's actually working, SuperStrat Labs offers a free audit of your current LinkedIn presence: what's tracked, what's missing, and where the next 90 days should focus. Get your free audit.
References
Frequently Asked Questions
Log a four-column row (Signal, Engagement, Conversation, Outcome) every time a post sparks a real conversation. Engagement rate shows volume; the Ledger shows whether it turned into anything.
The real cutoffs are: individual who-engaged detail disappears at around 90 days, demographic breakdown data at around 180 days, and aggregate post counts are retained for approximately two years. The 90-day window is the one to track actively.
LinkedIn's algorithm favors individual voices over brand voices. Personal profiles get an estimated 65% of feed allocation versus roughly 5% for company pages, so the gap is structural, not a failure of your company page.
Increasingly, yes. LinkedIn is the most-cited domain for professional queries in AI search, per a SEMrush analysis of 325,000 prompts, but visibility comes from topical authority, not follower count.
360Brew is LinkedIn's current feed-ranking model, which weights topic relevance, dwell time, and comment depth over likes. A post with strong saves but few likes may be reaching more people than your numbers show.
Native analytics covers everything in this guide. A paid tool is only worth it if you manage multiple pages or need data beyond the two-year retention window.
Not directly. LinkedIn limits you to public signals only: follower count, post frequency, and visible engagement. Individual competitor post-level data is not accessible natively.
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