Measuring LinkedIn personal brand ROI without guessing

Most practitioners track impressions and follower count. Both can grow while pipeline contracts. Those numbers are not meaningless, they just lag revenue by four to eight weeks and say nothing about whether the right people are paying attention. Three leading indicators move first. This guide covers how to measure them before your CRM catches up.

LinkedIn Personal Brand ROI Starts with the Wrong Metrics

The measurement problem sits upstream of the measurement. LinkedIn's algorithm in 2026 is optimizing for relevance, expertise, and conversations rather than vanity metrics or broad reach. If your dashboard is built around impressions and follower growth, you have built a scoreboard for the thing the platform is deliberately de-emphasizing. The numbers will still move. They will just stop correlating with anything commercial, and you will not notice for a quarter.

We see the consequence of this constantly in activity logs, and it has a shape worth naming. Call it reaction-optimized drift. A post written to collect reactions, broad, emotionally resonant, widely relatable, will beat a post written to start a specific conversation on every metric LinkedIn shows you natively. More impressions. More reactions. Better engagement rate. And it will produce fewer qualified conversations, sometimes none. A practitioner looking only at the native dashboard draws the rational conclusion from that data: broad content wins, niche expert content underperforms. Then they optimize toward broad content for six months and wonder why the inbound dried up.

That is the failure mode. Not that impressions are shallow, but that impressions and reactions actively point strategy in the wrong direction when they are the only feedback you receive. The signal is not noisy. It is inverted.

There is already public evidence for the inversion if you look at conversion rather than volume. Posts generating 20 or more meaningful comments convert 4x better than posts with 100 or more likes but minimal discussion. Comment depth beats reaction volume as a revenue predictor, and comment depth is not surfaced as a headline number anywhere in the native creator view. You have to go looking for it, per post, and compare it against something. Most people never do, because the dashboard puts impressions at the top of the screen in a large font and buries the rest.

The same asymmetry runs through the reach mechanics. Posts that lead to direct messages or connection requests receive 5x more organic reach than posts receiving only likes and comments. So the content that generates conversation is also the content the algorithm distributes hardest, which means the reaction-optimized post is not just commercially weaker, it is also giving up distribution over time. The two failure modes compound in the same direction.

The fix is not to throw impressions away. Impressions and follower count are useful as lagging confirmation: they tell you something worked, after it worked. Keep them, demote them. Put profile-to-connection rate, DM reply rate from your target accounts, and inbound DM source at the top of the hierarchy instead, because those three tell you what is happening four to eight weeks before revenue does. For context on where the floor sits, average personal profile engagement rate on LinkedIn in 2026 runs 3.85%, up roughly 44% year over year from the 2024 baseline. That is a reasonable sanity check on whether your content is being seen. It is a terrible proxy for whether it is being bought.

The Tiered Framework for Measuring LinkedIn Personal Brand ROI

LinkedIn works as a trust amplifier, not a direct-response channel, and that single structural fact breaks standard conversion attribution. Last-click and first-touch models both assume a discrete acquisition event. What happens on LinkedIn is a prospect reading eleven posts over four months, checking your profile twice, never engaging publicly once, and then sending a DM. Last-click credits the DM. First-touch credits whatever they clicked first, if anything. Neither model can see the eleven posts, which is where the entire value was created. Because standard attribution fails here, a tiered framework is required to measure ROI accurately at all.

The framework has four tiers, and the discipline is keeping them separate rather than averaging them into a single health score. Awareness covers impressions, reach, and profile views broken out by seniority of the viewer. Engagement covers comment depth, DM-to-post correlation, and inbound connection requests from your ICP. Conversion covers DM reply rate, meeting-booked rate, and proposal requests. Revenue covers attributable pipeline, closed revenue, and the conversion rate differential between inbound and outbound leads. Each tier answers a different question, and collapsing them is how people end up reporting a strong quarter with no pipeline.

The tiers also fail in a specific order, which makes the framework diagnostic rather than just descriptive. When revenue drops, you walk back up the stack. Did conversion drop while engagement held? That is a messaging or offer problem. Did engagement drop while awareness held? You are reaching the wrong people. Did awareness drop first? Usually cadence. A single blended metric cannot answer any of those questions, which is why most dashboards are decorative.

One constraint on this is unforgiving: measurement infrastructure has to exist before the program launches, not after. Baseline discovery rates, opportunity source tracking, and consistent cost accounting cannot be reconstructed from historical data once a program is already running. You cannot go back and ask the eleven inbound leads from last spring which post they read. You cannot retroactively establish what your profile view rate was before you started posting three times a week. If the baseline was never captured, every number you produce afterward is absolute rather than comparative, and absolute numbers on LinkedIn are close to meaningless because the denominators differ so wildly between accounts.

Inside that framework, three leading indicators do most of the predictive work. First, the ratio of profile visits to inbound connection requests, which is a proxy for profile credibility and content-driven curiosity. Second, the DM reply rate from target-ICP connections, which measures brand warmth rather than reach. Third, inbound DM volume from people who reference a specific post before asking to connect, which is the closest thing to direct evidence that content is generating pipeline. When those three rise together, revenue typically follows within one sales cycle. We watch this pattern repeat often enough to plan around it.

The practical difference between tooling and manual tracking shows up here rather than in the dashboards. SocialNexis surfaces all three of those indicators automatically as part of its activity log, because the agent already records profile visit events, connection events, and message events with timestamps. A practitioner tracking manually sees the follower count, which lags those three by weeks, and a spreadsheet they stopped updating in week five.

Leading Indicators that Predict LinkedIn ROI Before Deals Close

Start with DM reply rate, because it is the cleanest available measure of what your personal brand is worth in isolation. Cold email averages a 5.1% reply rate. Average LinkedIn DMs run 10.3%, roughly double. LinkedIn Messenger campaigns targeting first-degree connections reach 16.86%, while single-action DMs with no prior relationship land at 4.88%. Line those last two up. Same platform, same message format, and the response rate more than triples based entirely on whether a relationship existed first. That gap is your personal brand, expressed as a number, before any outreach sequence begins. It is the most defensible ROI figure most practitioners have access to and almost nobody tracks it.

The second leading indicator is a funnel stage most measurement frameworks skip entirely: profile-visit-to-connection-request conversion. It sits between content performance and DM pipeline, and it is the only metric that tells you whether your content made someone curious enough to investigate you. A post that generates 500 impressions but zero profile visits has failed as a credibility signal regardless of its like count. People saw it, felt something, scrolled on. Nothing entered the funnel.

The calculation is simple enough to run weekly on a napkin: inbound connection requests in a period divided by profile views in that same period. A post that generates 80 profile visits and 12 inbound connection requests from ICP members is running a 15% profile-to-connection conversion rate. That rate is a direct measure of content-triggered interest, and no engagement metric captures it. Two posts with identical impression counts can differ by an order of magnitude here, and the one with fewer reactions is frequently the one converting.

Segment it by post type and it becomes a content strategy instrument rather than a health metric. If document posts consistently lift the rate while text-only posts leave it flat, you have learned something with revenue implications, not an engagement curiosity. We track profile visit spikes in the 24 to 72 hour window after each post and correlate them against inbound connection events, which is what makes the per-post version of this calculation possible without manual reconciliation. Doing it by hand means logging into analytics daily and remembering what you posted.

The third indicator is connection request acceptance rate on outbound, which functions as a diagnostic rather than a growth number. B2B outbound contexts average 30 to 45% acceptance, based on a dataset of more than 50,000 requests. Anything below 20% is a targeting or profile quality problem, and it should be fixed before you touch messaging, cadence, or content. This is the earliest correction signal in the entire stack. It moves within days, it predates any revenue outcome by months, and it costs nothing to watch.

What makes these three worth the setup effort is timing. They move four to eight weeks before pipeline does, which means they are the only metrics that give you room to change course. By the time closed revenue tells you something is wrong, the quarter it describes is already spent. Acceptance rate dropping from the healthy range to the teens tells you the same thing in week two, while it is still cheap to fix.

What Most LinkedIn Personal Brand Analytics Miss

Strong engagement with weak revenue is the most commonly misdiagnosed pattern in personal branding, and it is almost never a content quality problem. It points to a structural issue: the wrong audience reached, a weak conversion path from content to conversation, or an offering that is not clear enough to act on. Engagement data cannot distinguish between those three, which is why people respond to the pattern by writing more and better posts. More good content delivered to the wrong audience produces more engagement and the same zero pipeline.

Audience demographic alignment has to sit next to engagement volume in your reporting, not in a separate tab you open quarterly. LinkedIn's post analytics show viewer breakdowns by job title, seniority, company size, and industry, and the demographic view is where the diagnosis usually lives. If your best-performing posts reach marketing managers while your ICP is VP of Sales, a high engagement rate is not evidence of success. It is masking a distribution problem, and it will keep masking it for as long as you report the two numbers separately.

The largest gap, though, is attribution on inbound DMs, and it is a gap of missing data rather than misread data. When a prospect messages you cold, they rarely volunteer which post triggered the outreach. Asking directly, some variant of how did you find me, returns accurate answers less than 30% of the time. People genuinely do not remember. They read something six weeks ago, thought about you intermittently, and the message they send you names none of it. Most practitioners are therefore systematically undercounting what LinkedIn contributes to pipeline, not because their tracking is sloppy but because the source data does not exist in any panel LinkedIn offers.

This is the single biggest divergence between what vanity-metric tools report and what we see operationally. A local automation agent running on the user's home IP can correlate post publication timestamps against DM arrival timestamps and connection request timestamps, which produces a probabilistic attribution log. It will not tell you with certainty that post seven generated a given deal. It will tell you that post seven was followed by a cluster of profile visits, four ICP connection requests, and two inbound DMs, and that post eight was followed by nothing. Across a quarter, that is more than enough to identify which content is producing pipeline. No native LinkedIn analytics panel provides it.

The cost of getting attribution wrong is larger than a misreported channel number, because inbound and outbound leads do not convert anything like each other. Inbound leads convert at 14.6% versus 1.7% for cold outreach leads, an 8.6x difference. Filing an inbound lead as an outbound conversion does two things at once: it understates LinkedIn's ROI, and it inflates outbound's apparent productivity with a lead that outbound did not create. Do that consistently for a few quarters and the go-to-market analysis built on top of it recommends hiring SDRs when what actually worked was the content.

That distortion is self-reinforcing, which is what makes it worth flagging as a named failure mode. Under-attributed content gets less investment, which reduces inbound volume, which makes outbound look like a larger share of what is working, which justifies the next reallocation away from content. The measurement error does not just misreport the system. It reshapes it.

Measure Profile-to-Connection Conversion Rate, Not Just Follower Growth

The structural case for personal brand over company page is settled and not especially interesting. Personal profiles generate 561% more reach than company pages on identical content, and employee-generated leads convert 7x more frequently than company-generated leads. The useful question is not whether to invest in personal brand. It is how to quantify what the personal brand is producing in terms someone outside marketing will accept as pipeline evidence.

Follower count is the wrong candidate for that job, and the reason is worth stating precisely. Follower count is a cumulative record of people who opted in at some point, under some prior version of your positioning, for reasons you cannot recover. It says nothing about whether they are in your ICP, whether they see your posts now, or whether they are moving through any funnel stage. It only goes up, which is exactly why it feels good to report and why it tells you nothing. Profile-to-connection conversion rate is the better single number because it measures content-triggered curiosity in the current period rather than accumulated audience from the past.

It is also worth being honest about what scale means here. The 1% of LinkedIn users who post weekly generate 9 billion impressions per week out of 1.3 billion total members. Consistent posting alone places you in the visible tier, which means visibility is not the scarce resource for anyone reading this. Conversion from visibility into conversation is the scarce resource, and that is the thing profile-to-connection rate measures.

Track the rate by post type, weekly, and treat divergences as format signals. If document posts reliably lift inbound connection requests and text-only posts leave the rate flat, that is a content decision with direct revenue consequences. It is not a nice observation about what the audience enjoys. Run this for a month and you typically find one or two formats doing most of the funnel entry work, which changes how you allocate writing time more than any engagement report will.

There is an infrastructure constraint behind whether you can measure any of this reliably, and it gets ignored because it sounds like a vendor talking point. Tools operating from data-center IPs get rate-limited or flagged before they accumulate enough events to produce statistically reliable DM conversion data. The tracking does not fail because the methodology is wrong, it fails because the sample size never arrives and the data that does arrive is distorted by throttling. A real-browser local agent running on the user's home IP can sustain multi-step sequences, connection request, wait, message, follow-up, at safe cadence, which is what builds a personal DM conversion benchmark within 60 to 90 days.

The alternative is what most people experience. They start a spreadsheet, log DMs and outcomes conscientiously for three weeks, hit a fortnight with four data points, cannot compute anything meaningful from four data points, and quit. Measuring DM-to-meeting conversion manually almost always ends this way. The difference between practitioners who have a personal benchmark and practitioners who do not is rarely discipline. It is whether the infrastructure kept producing consistent, unthrottled events long enough for a benchmark to exist.

What ROI Should You Expect from LinkedIn Personal Branding?

First Page Sage reports a 3-year average ROI of 229% on LinkedIn organic content, with meaningful spread by vertical: Staffing and Recruiting at 529%, Financial Services at 390%, B2B SaaS at 388%, Medical Device at 316%. Note the units before you use those numbers in a deck. They are three-year figures. A single-quarter ROI calculation on a new personal brand program will read as negative or inconclusive for most of the first year, and that result is correct rather than a sign of failure. Reporting it as failure is how programs get cancelled in month five.

The timeline underneath those returns is reasonably consistent across sources. First measurable signals, meaning relevant inbound connection requests and profile views from decision-makers, typically appear within 3 to 6 months of consistent effort. Tangible business results such as unsolicited client inquiries generally require 6 to 12 months. First Page Sage puts time to first measurable results at 6 to 8 months, which lands in the same window. Plan the evaluation date before you start, and put it far enough out that you are not measuring noise.

Faster outcomes do happen, and the mechanism when they do is instructive. One documented case ran from £12k to £47k MRR in 4 months, with conversion rate improving from 2.3% to 4.1% over 6 weeks. The revenue number is the headline, but the conversion rate is the part worth studying: the leading indicator moved first, over weeks, and the lagging revenue number followed. That ordering is the whole argument for tracking leading indicators. The conversion improvement was visible and measurable while the MRR still looked flat.

Then there is a structural bias in the arithmetic that nobody corrects for. Attribution windows under 90 days undervalue creator influence on pipeline by 40 to 70% for enterprise deals with ACV above $50K. If you run a quarterly window on enterprise deals, your ROI figure is not conservative, it is wrong, potentially by more than half. Extending the window well beyond 90 days for enterprise programs is not optimistic accounting. It is matching the measurement period to a buying cycle that was always longer than a quarter.

For cost comparison, LinkedIn paid ads average $125 cost per lead in the software and IT sector, within a $100 to $150 range. Organic personal brand lead generation, once established, runs at close to zero marginal cost for each additional lead, which is the entire structural advantage and also the reason the ROI multiples look large over three years. The fixed cost is front-loaded and it is time.

Cost that gets left out of the calculation makes the whole exercise indefensible, so price the time properly. Use your effective hourly rate, annual revenue divided by billable hours, rather than a salary-derived number, and count the engagement time as well as the writing time. Stakeholders discount ROI figures that omit the founder's hours, and they are right to. A defensible calculation that shows a smaller return survives scrutiny. An inflated one does not survive the first finance review.

Build Your LinkedIn Personal Brand Measurement Infrastructure First

Set up the opportunity log before your first post goes live. For every inbound inquiry, record four fields: discovery source, first-contact date, any content piece the person referenced, and current pipeline stage. That is it. A spreadsheet is fine. Without this log started on day one, revenue arriving in month seven is unattributable, and you will spend a week trying to reconstruct it from memory and DM history before giving up and reporting impressions instead.

Capture baselines in week one, before content activity moves the numbers. Four suffice: profile view rate per week, inbound connection request rate per week, DM reply rate from first-degree connections, and connection acceptance rate on outbound requests. Each takes minutes to pull. What they buy you is the ability to make every future measurement comparative rather than absolute, which matters because there is no useful cross-account benchmark for profile views. A hundred a week is excellent for some accounts and a collapse for others. Only your own prior number tells you which.

The attribution layer is the part manual tracking cannot replicate. A local automation agent running on your home IP can correlate post publication timestamps with DM arrival timestamps and connection request timestamps inside a 48 to 72 hour attribution window. What comes out is a probabilistic attribution log: not proof that a specific post closed a specific deal, but a reliable map of which content preceded funnel entry events and which content preceded nothing. Given that direct self-reports are accurate less than 30% of the time, probabilistic timestamp correlation is the better dataset, not the fallback.

Cadence discipline is the difference between a measurement system and a pile of charts. Review leading indicators weekly, engagement quality monthly, and lagging revenue outcomes quarterly. Weekly means profile views, DM reply rate, and inbound connection requests from ICP. Monthly means comment depth and the ICP demographic mix of your viewers. Quarterly means attributable pipeline, closed revenue, and the inbound versus outbound conversion rate comparison. Mixing the cadences produces noise dressed as signal: checking pipeline weekly gives you six weeks of random walk and a panic, while checking profile view rate quarterly means you miss the correction window entirely.

Cadence on the publishing side feeds the measurement side more directly than most people expect. Gaps in posting cadence produce measurable drops in profile view rate that precede pipeline drops by four to eight weeks. A structured mix posted roughly four times a week holds the leading indicators steadier than daily unstructured posting, and the reason is mechanical: consistency stabilizes connection acceptance rate and keeps the early engagement window, the first hour or two after publishing, populated with people who expect your posts. Erratic cadence destabilizes both, and you see it in profile views before you see it anywhere else.

One thing to resist: do not instrument everything at once. The four opportunity log fields, the four baselines, and the weekly-monthly-quarterly split are enough to run a program for a year. Every additional metric you add in week one is a metric you will stop updating in week six, and a half-maintained measurement system is worse than a small complete one because it produces gaps you will not notice until you need the data.

DM Pipeline Velocity: the ROI Signal Native Analytics Won't Show You

DM pipeline velocity is a composite of three measurements that native analytics omit entirely: DM reply rate from target accounts, reply-to-meeting conversion rate, and time-to-reply as a warmth signal. The third is the one nobody tracks and the one that carries the most information. A prospect who replies within hours of a connection request is in a different pipeline state than one who surfaces weeks later, even if both eventually take the meeting. Reply latency is a warmth reading, it predicts close rate in a way impression data structurally cannot, and it is sitting in your inbox timestamps right now unused.

The reach mechanics reward the same behavior the pipeline does, which is rare enough to be worth exploiting deliberately. Posts that lead to direct messages or connection requests receive 5x more organic reach than posts receiving only likes and comments. So DM generation is simultaneously a conversion metric and a compounding distribution signal: the post that starts conversations gets shown to more people, who then start more conversations. Reaction-optimized content gets none of this compounding, which is part of why broad content plateaus while narrow content tends to build.

Here is the observation that inverts most people's optimization logic. A lower-impression niche post aimed at a specific ICP segment frequently generates 3 to 5x more qualified DM conversations than a higher-impression broad post. Read that against the native dashboard and the broad post wins on every visible number. Read it against downstream DM volume and the niche post wins on the only number that pays. Practitioners optimizing for impressions are optimizing against revenue, and they cannot see it because the evidence of the inversion lives outside the analytics panel.

Seeing it requires holding two datasets next to each other: impression count per post, and DM plus connection-request volume per post inside the attribution window. Neither alone reveals anything. Together they produce a ratio, conversations per thousand impressions, that ranks your content completely differently than the engagement view does. The posts at the top of that ranking are usually ones you assumed underperformed. We see this reordering in nearly every account that starts tracking both.

The DM benchmarks give you something concrete to measure the brand contribution against. Messenger campaigns to first-degree connections achieve 16.86% response rates, and multi-step sequences reach 20 to 30% or more. Cold DMs sit at 10.3%. A personal brand that has warmed contacts through consistent content raises the starting point of every sequence you run afterward, and that lift is directly measurable in campaign performance terms rather than inferred from sentiment. If your first-degree ICP reply rate sits below the cold DM average, the brand is not warming anyone and the problem is upstream of your messaging.

Run this for a quarter and the composite becomes a forecast rather than a report. Reply rate rising while latency shortens means the next sales cycle is going to be easier, and you know that before a single deal registers in the CRM. Reply rate holding while latency stretches out is the early warning that content relevance is slipping, usually before profile views move and well before pipeline does. Neither pattern appears anywhere in LinkedIn's native analytics, which is precisely why it is worth building the log to capture it.

Frequently asked questions

How do I calculate the ROI of my LinkedIn personal brand in dollars?

Add up total cost: your time at your effective hourly rate plus any tool subscriptions. Then track attributable revenue from inbound leads tagged to LinkedIn activity at first contact. Divide net attributable revenue minus total cost by total cost, then multiply by 100. First Page Sage reports a 3-year average of 229% on LinkedIn organic. The calculation only works if you start logging opportunity source before posting begins, not after.

What is the difference between vanity metrics and real LinkedIn personal brand ROI?

Vanity metrics (impressions, follower count, reactions) measure distribution and attention. ROI metrics measure commercial outcomes: inbound lead rate, DM reply rate from target accounts, profile-to-connection conversion rate, and attributable pipeline. The critical distinction is that vanity metrics can grow while pipeline contracts. Broad emotional content routinely outperforms niche expert content on every native LinkedIn metric while generating fewer qualified conversations.

How long does it take to see measurable ROI from a LinkedIn personal brand?

First measurable signals, meaning relevant inbound connection requests and profile views from decision-makers, typically appear within 3 to 6 months of consistent posting. Tangible business results such as unsolicited client inquiries generally require 6 to 12 months. First Page Sage puts time to first measurable results at 6 to 8 months. Attribution windows under 90 days undervalue creator influence by 40 to 70% on enterprise deals, so do not evaluate too early.

What are the best leading indicators that predict LinkedIn personal brand revenue before deals close?

Three leading indicators predict revenue 4 to 8 weeks in advance: (1) profile-visit-to-inbound-connection rate, a proxy for content-driven profile credibility; (2) DM reply rate from target-ICP accounts, which measures brand warmth before any outreach sequence; and (3) inbound DM volume from contacts who reference a specific post before asking to connect. When all three rise together, revenue typically follows within one sales cycle.

How do I track where an inbound lead actually came from on LinkedIn?

The most reliable method is an opportunity log that captures post title, publish date, and any content reference a prospect makes at first contact. Asking 'how did you find me?' returns accurate answers less than 30% of the time. A local-browser automation agent can correlate post publication timestamps with DM arrival and connection request timestamps within a 48 to 72 hour window, creating probabilistic attribution that manual tracking cannot replicate.

What LinkedIn DM conversion rate should I be targeting to know my outreach is working?

For cold DMs to second or third-degree connections, 10.3% reply rate is the 2026 average. For Messenger campaigns targeting first-degree connections, the benchmark is 16.86%. Multi-step sequences with a prior touchpoint before the ask reach 20 to 30% or more. If your DM reply rate from target-ICP first-degree connections is below 10%, the issue is typically profile credibility or ICP mismatch rather than messaging quality.

How do I measure whether my LinkedIn profile is converting visitors into connection requests?

Divide inbound connection requests received in a period by profile views in that same period. This gives your profile-to-connection conversion rate. A healthy rate for an active personal brand is 10 to 20% inbound conversion on profile visits. Track this weekly segmented by post type: if a specific content format consistently raises this rate, that format is driving genuine ICP curiosity rather than just broad distribution.

Which LinkedIn personal brand ROI metrics should I review weekly versus quarterly?

Weekly: profile views, inbound connection requests from ICP, DM reply rate, and post-level comment depth versus reaction volume. Monthly: audience demographic mix (job title, seniority, company size), content format performance trend, and profile-to-connection conversion rate. Quarterly: attributable pipeline, closed revenue from LinkedIn-sourced contacts, inbound versus outbound conversion rate comparison, and full program ROI calculation. Mixing these cadences generates noise rather than useful signal.

What tools can I use to track LinkedIn personal brand ROI beyond native analytics?

Native LinkedIn analytics cover impressions, reach, reactions, comments, saves, reposts, link visits, and demographic breakdowns by job title and seniority. Beyond that: a manual CRM log (opportunity source, post reference, first-contact date), UTM-tagged links in posts for website attribution, and local-browser automation tools that correlate post activity with inbound DM and connection-request timing without triggering LinkedIn safety flags that data-center-IP tools routinely trigger.

How do I value my own time when calculating LinkedIn personal branding ROI?

Use your effective hourly rate (annual revenue divided by billable hours) as the cost input. At $200 per hour and 5 hours per week on LinkedIn content and engagement, monthly cost input is roughly $4,000 before tool costs. Compare this against attributable inbound pipeline value. A single mid-market deal sourced from LinkedIn can recover a full year of time investment, making the return visible and the ROI calculation defensible to stakeholders.

Sources and further reading

• LinkedIn post analytics for your content
• LinkedIn creator analytics overview
• LinkedIn attribution model metrics