Shield analytics alternatives: export your history first, then replace it

Shield is gone, and the urgent job is not choosing a replacement. It is getting your history out. As of 2 August 2026 the shieldapp.ai homepage carries a wind-down notice reading “Shield is winding down” and “Both Google and LinkedIn made it clear that we could not continue operating Shield as it was built.” That page publishes no shutdown date, no data retention policy and no export instructions. Meanwhile LinkedIn's own data archive, which people assume is a fallback, contains no impressions, no reach and no post-level analytics of any kind. So the sequence is: export first, shop second. This piece covers both, in that order.

What happened to Shield analytics?

Shield was a LinkedIn analytics product that launched in 2018 and gave individual creators the post-level history LinkedIn's own interface does not retain: impressions per post over time, engagement rate trends, follower growth, best-performing formats, and comparisons across months rather than the recent window the platform shows you.

Its homepage is now a farewell letter. Two sentences carry the whole story, quoted verbatim as they appeared on 2 August 2026:

“Shield is winding down.” “Both Google and LinkedIn made it clear that we could not continue operating Shield as it was built.”

The page says the tool operated since 2018, thanks the creators, writers, founders and operators who used it, and is signed by co-founders Andreas and Alex. It gives a support address and nothing else. No end date. No note about what happens to stored data. No refund policy. No export walkthrough.

The announcement itself is dated to around 18 May 2026, which places the closure roughly ten weeks before this article. We are separating two confidence levels there deliberately: the wind-down is verified on Shield's own site, and the mid-May date comes from secondary sources rather than a dated statement on the page. If precision matters to you, treat the date as approximate and the closure as certain.

We are also not going to speculate past that quote. Shield named Google and LinkedIn and did not elaborate, and several pages in this niche have filled the gap with a story we could not verify. More on that below.

What should you export from Shield before it disappears?

We do not have an active Shield account and cannot tell you which buttons in it still work today. What we can tell you is which numbers are irreplaceable once any LinkedIn analytics account closes, because LinkedIn does not hold them on your behalf. Capture these, in this order, by whatever means the product still offers you: CSV export if it exists, screenshots if it does not.

1. Post-level impressions with dates.The single most valuable thing any third-party LinkedIn analytics tool holds, and the one thing LinkedIn's data archive definitively does not contain. Without it you cannot compute a historical engagement rate at all.
2. Follower count over time. Not your follower count today. The series. Your engagement rate is meaningless without the denominator that applied on the day, and LinkedIn shows you a current number rather than a history.
3. Per-post reactions, comments and reposts. These are partially recoverable from LinkedIn and from the posts themselves, but recovering them by hand across two years of posting is a weekend you will not spend.
4. Your own best-and-worst lists. Whatever ranking view the tool gave you, capture the top twenty and bottom twenty posts. That is your personal training set, and it is the input to every content decision you make next year.
5. Any timing or format breakdowns. Lower priority, because these are derived rather than raw, and because you can recompute them from the rows above if you kept the rows.
6. Drafts and saved posts. Easy to forget and annoying to lose. Copy them into a plain text file, not into another vendor.

Two practical notes. First, export to a format you own. A CSV in your own storage survives the next vendor closing; an import into another analytics tool does not. Second, do it in one sitting. Wind-downs do not usually end with a countdown timer, and a homepage that never published a shutdown date is not going to email you one.

Does LinkedIn's own data export replace Shield?

This is the assumption we most wanted to test, because if the answer were yes, the urgency would evaporate. It is not yes.

LinkedIn's Download your data page, read on 2 August 2026, describes a request flow (Me icon, Settings & Privacy, Data Privacy, How LinkedIn uses your data, Download your data, Request archive) and splits the available categories into two lists. Specific categories arrive “within minutes”; the larger download arrives “within 24 hours”; and the download link stays valid for 72 hours.

We read both category lists in full. Here is what they contain and, more importantly, what they do not.

The pattern is consistent and it explains the whole category. LinkedIn will give you your content and your own actions. It will not give you your performance. The archive is a privacy tool, built to satisfy data access rights, not an analytics export.

That gap is exactly the space Shield occupied, and it is why no free workaround fully replaces it. Request the archive anyway, today, because it is free and because the Shares file is the skeleton you will hang recovered numbers on. Just do not mistake it for your analytics history.

Why is there no like-for-like Shield replacement?

Because the read access this category needs is gated. LinkedIn's Posts API documentation marks r_member_social, the permission that lets an application read a member's own posts, comments and likes, as “restricted and is available to approved users only.” Write access is comparatively easy to obtain. Read access is not.

That asymmetry has a direct consequence for what you can buy. A tool can publish for you through a sanctioned interface with a normal OAuth grant. A tool that wants deep historical analytics on your personal profile either gets approved for restricted read access, or gets the data some other way, usually by running inside your browser and reading the page you are already looking at. Those two architectures carry very different durability, and that difference is the reason this article exists.

We wrote up the mechanics of the sanctioned interface and where its limits sit in our guide to LinkedIn scheduling tools and how they connect. The short version is that the platform is generous about letting software write on your behalf and stingy about letting it read.

Was Shield shut down by an April 2025 LinkedIn crackdown?

No, and this is worth a section because the claim is everywhere.

A large number of pages in this niche assert that LinkedIn ran an “April 2025 crackdown on cookie-based authentication and Chrome extension overlays.” We could not verify that this event exists.The pages asserting it cite nothing: no LinkedIn statement, no help-page change, no press coverage, no primary source of any kind. LinkedIn's own prohibited software and extensions page self-reports “Last updated: 2 years ago” and contains no April 2025 change and no mention of cookies anywhere on it. The phrasing repeats near-identically across several vendor blogs, which is the signature of copying rather than reporting.

Absence of evidence is not proof of absence, and we are stating the honest version: we could not trace the claim to any source, not that it definitely did not happen. But it is not an explanation for Shield, and Shield did not offer it. The only first-party explanation is the sentence on Shield's homepage naming Google and LinkedIn without elaborating.

There is a real enforcement timeline in this space, with dates you can check, and we laid it out with the evidence in our review of whether LinkedIn automation tools are safe. Shield's closure is the most recent entry in it. That article is the one to read if the question behind your search is really “is my next tool going to disappear too.”

What are the actual Shield analytics alternatives?

Every option below is a trade rather than a replacement. Prices were read from each vendor's own pricing page on 2 August 2026 and prices in this category drift, so check before you buy.

A note on what each vendor says versus what we verified. Buffer's free tier is frequently misquoted in this niche as “one profile, fifteen messages.” Read on 2 August 2026, Buffer's own pricing page says “Connect up to 3 channels,” “10 scheduled posts per channel,” and “1 user account.” If a comparison page gets a competitor's free tier wrong, treat the rest of its table with the same suspicion.

Two of these are covered in depth elsewhere on this blog, because the switching questions are different from the analytics question: what AuthoredUp does and does not do and how Taplio's tiers actually work.

How do you rebuild a LinkedIn analytics baseline from scratch?

If you take one thing from this page, take this section. It costs nothing, it works, and it is the only version of LinkedIn analytics that no company can take away from you.

Open a spreadsheet. One row per post. Fill it in within 72 hours of publishing, because LinkedIn's numbers keep moving and a consistent capture window is what makes rows comparable to each other.

• Date and time posted. Local time, and note the day of the week separately. You will want to sort by it later.
• Format. Text, image, video, document, poll, link. Six values, one column. This turns out to be the column that explains the most variance.
• First 200 characters. The visible hook. Paste it. This is the field you will study when you want to know why something worked.
• Impressions.From LinkedIn's own post analytics, captured at your fixed 72-hour mark.
• Reactions, comments, reposts. Three columns, same moment.
• Follower count that day. The one nobody records and everybody later wishes they had.

Then one derived column: engagement rate. Which formula you use matters less than using the same one every week, because the value of this exercise is the trend, not the absolute number. If you want ours, our LinkedIn engagement rate calculator computes it and shows where you land against a real cohort, and our benchmarks pieceexplains why every vendor's definition differs.

That last point is not pedantry. LinkedIn's own help documentation defines engagement rate as interactions divided by impressions and counts clicks as interactions, which is a perfectly reasonable internal definition and is not comparable to the follower-based rates most third-party tools report. When you moved from Shield's number to your next tool's number, you were probably going to see a discontinuity anyway. A spreadsheet removes the problem by making you the only definition-setter.

Which LinkedIn metrics actually mattered in Shield?

Analytics tools show a lot of numbers and only some of them change what you do next. Our analysis of 12,988 English posts from 65 creators, ranked by engagement rate, points at a clear hierarchy.

Comments are the scarce signal. The gap between tiers is far wider on comments than on reactions. Top-decile posts earned a median 72 comments against 5 for the bottom half, while reactions moved only from 80 to 235. If your replacement tool tracks one thing well, make it comments. We went deeper on why in our piece on comment mechanics.

Impressions without a denominator are decoration. A post with 40,000 impressions is excellent at 2,000 followers and unremarkable at 200,000. This is the reason the follower-count column above matters more than it looks, and it is why our cohort benchmarks are expressed per 1,000 followers: a median engagement rate of 0.40, a 75th percentile of 1.27, and a 90th percentile of 5.95. If you want the difference between impressions and reach spelled out, we did that separately.

Format mix is the highest-yield report nobody runs. In our cohort, native video made up 26.3% of top-decile posts against 10.1% of the bottom half, while shared article and link posts ran 22.1% against 32.7% the other way. Two columns in a spreadsheet surface that. No subscription required.

Optimal-time dashboards are the lowest-yield report everybody runs. Timing shifts who sees a post in its first hour, which is real, and it does not rescue a weak opening line. Our analysis of 34,000 posts found the separation lives in the visible hook, not in the clock.

What should you look for in a Shield replacement?

Shield's closure is a lesson about vendor selection, not just a gap in your dashboard. Six questions, ordered by how much each one would have helped a Shield user in May.

1. Can you export everything, on demand, in a format you can read without the product?CSV or JSON. If the answer is “you can view it in the app,” you are renting your own history.
2. How does it get its data?Official API with an OAuth grant you approve on LinkedIn's own screen, or a browser extension reading the page. Both exist. They fail in different ways and on different timescales.
3. Does it keep history, and for how long? Ask the retention question before you need the answer. Some tools only hold a rolling window, which is fine until you want a year-over-year comparison.
4. Whose engagement rate is it? Ask for the formula. Denominators in this space differ enormously and vendors rarely publish theirs.
5. Does it cover personal profiles or only company pages? The restricted read permission described above is why several suites are rich on pages and thin on personal profiles. Check which one you are buying.
6. What happens on the day it shuts down? An uncomfortable question that any vendor worth using can answer. The correct answer involves the word export.

Question one is the whole lesson. A tool you can export from is a tool whose closure is an inconvenience. A tool you cannot export from is a tool whose closure erases two years of evidence about your own audience.

Should you replace Shield at all?

For a lot of people, honestly, no. Worth saying out loud on a page that could easily just sell you the next subscription.

Analytics earn their price when they change a decision. If you look at a dashboard weekly and it has never once made you write a different post, you were buying reassurance rather than information, and a spreadsheet supplies reassurance at a lower price. The people who genuinely need a paid replacement are the ones running content for several accounts, reporting to somebody else, or testing formats deliberately enough that a two-week comparison is a real experiment rather than a vibe.

Everyone else is better served by fixing the input. In our cohort the separation between the top decile and the bottom half sat almost entirely in the visible hook and the format choice: first-person openers appeared in 19.6% of top-decile posts against 10.1% of bottom-half posts, direct address to the reader in 47.5% against 32.7%, and four or more hashtags in 25.9% of top posts against 41.9% of weak ones. None of those are things a dashboard tells you. They are things the hook patterns piece tells you, and you can test a line for free in our LinkedIn hook analyzer.

These are correlations across a cohort that skews toward established creators, not causal laws, and the counts are lifetime-cumulative at scrape time across posts of different ages. We say that every time because the alternative is pretending a dataset is a rulebook.

A migration checklist for the next two weeks

Concrete and in order. This is the whole article compressed into things you can tick off.

1. Today. Log into Shield if you still can and export or screenshot the six things listed earlier, starting with post-level impressions and follower history.
2. Today. Request your LinkedIn data archive. It is free, the specific categories arrive within minutes, the full download arrives within 24 hours, and the link expires after 72 hours, so plan to be at a computer when it lands.
3. This week.Build the spreadsheet. Backfill it for your last twenty posts from whatever LinkedIn's in-product analytics still shows you. Twenty rows is enough to see a format pattern.
4. This week. Decide whether you actually need a paid tool, using the test in the previous section: has a dashboard ever changed a post you wrote?
5. Next week. If yes, trial one option rather than three. Run the six questions above at the vendor before you enter a card, and start with the export question.
6. Ongoing. Ten minutes every Friday. The spreadsheet only works if it is boring and consistent, which is also the reason it outlasts every product in the table above.

Shield analytics alternatives: the short version

Shield is winding down and said so in its own words, with no end date attached, which makes exporting your history the only genuinely time-sensitive task on this page. LinkedIn's data archive will not save you: it holds Shares, Comments and Reactions and has no category for impressions, reach or follower history. Once you have your numbers out, the replacement decision is easier than the SERP suggests. LinkedIn's own analytics plus a six-column spreadsheet covers most individual creators for free. AuthoredUp at $19.95 a month, Taplio from $39 and Supergrow from $19 cover the people who want it inside a product, at prices observed on 2 August 2026 that will not stay still. And whatever you pick, ask the export question first, because the one thing this closure proved is that the tool is temporary and the data should not be.