The LinkedIn Data Decay Report 2026: How Fast Scraped Data Goes Stale (and What It Costs)

Last updated: 2026-06-18

Everyone extracts LinkedIn data, and almost everyone treats the extract as if it stays true. It does not. A scrape is a snapshot: accurate at the instant you take it, decaying from the moment you save it. This report quantifies how fast an exported LinkedIn list goes stale, field by field, models the half-life of a scrape, and prices what a list extracted once and never refreshed actually costs a sales team. The point is not that scraping is wrong, it is that a one-time extract is a depreciating asset.

The thesis is about the export, not the platform. Extracting profiles is a perfectly good way to build a list; the mistake is stopping there. The value is not in extracting once, it is in being able to re-verify and re-enrich on demand, so the snapshot becomes a living record instead of a photograph of who your prospects used to be.

A scrape is a snapshot

The core property of any extract is that it freezes a moment. The instant you export a LinkedIn list, every field in it stops updating while the real people keep moving, changing jobs, titles, companies, and contact details. The platform behind the data keeps current; your copy does not. That gap between the live source and the frozen extract is the entire subject of this report, and it widens every single day after the export.

This is why extract quality at the moment of capture tells you little about list quality a quarter later. A scrape can be flawless on the day, every field correct, and still be substantially wrong by the time you work it, not because the extraction failed but because the world moved. Judging a scraped list by how good it looked on export day is judging a photograph by how accurate it was the moment it was taken.

The reframe, then, is to treat extraction as step one of a refresh loop, not as the finished product. The export gets you the right people; keeping their data current is a separate, ongoing job. The export-to-Sheets workflow that starts this is covered in the export to Google Sheets guide.

It helps to picture two identical lists scraped on the same day, one worked immediately and one shelved for a quarter. The first reaches real, current people; the second reaches a population that has partly dispersed, with no visible difference between the two files. The only variable that changed is time, and time is exactly the variable a static export cannot account for. That thought experiment is the whole case for treating freshness, not extraction quality, as the thing that decides outcomes.

Job-change velocity drives the decay

The engine of extract decay is job mobility. Roughly 15 to 20 percent of professionals change roles in a given year, median job tenure sits under four years and is falling, and LinkedIn's own Economic Graph and Workforce reporting track this churn directly. For a scraped list, that means a meaningful share of the contacts have moved company or title within months of the extract, and the list does not tell you which ones.

Velocity also varies by segment, which matters for who you extracted. Faster-moving functions and industries, sales and tech among them, churn well above the average, so a list scraped in a high-mobility segment decays faster than the headline rate suggests. A list of, say, growth-stage tech leaders is a faster-rotting asset than a list of long-tenured operators, even though both looked equally fresh on export day.

The practical consequence is that the same extract ages at different speeds depending on who is in it, and you cannot tell from the file. This is why a fixed refresh schedule is a blunt instrument and refreshing at the moment of use is precise: it accounts for the churn that already happened, whoever it happened to. The company-info extraction angle is in the extract company info guide.

There is a lag effect that makes the decay worse than the mobility rate alone implies. People do not update their LinkedIn the instant they move, so an export captures a hidden layer of recent movers whose profile still shows the old role. Your list therefore overstates its own accuracy on export day, because some of the contacts are already wrong in reality even though the profile, and thus your scrape, still looks right.

Decay by field for an export

Not every column in an export decays at the same rate. Company and job title are the most perishable, because a single job change invalidates both, and at 15 to 20 percent annual mobility a large slice of any list is wrong on these within a year. Contact fields, email and phone, follow the general B2B decay curve of roughly 2.1 percent per month, compounding to about 22 to 30 percent per year. Name and profile URL are essentially stable. So an export is not uniformly stale, it is stale exactly where it is most useful.

This uneven decay is what makes a scraped list deceptive. The stable fields keep looking fine, lending the whole list an air of reliability, while the volatile fields you actually act on, the current company, the title, the working email, quietly go wrong underneath. A list that passes a glance can fail a campaign, because the glance lands on the durable fields and the campaign depends on the perishable ones.

The takeaway is to refresh by field, not by file. You do not need to re-extract everything; you need to re-confirm the volatile fields, company, title, and contact details, at the moment you use a record. That targeted refresh removes most of the decay risk at a fraction of the cost of re-scraping the whole base, and it is exactly the kind of repetitive verification that should be automated.

What a stale extract costs

The cost of working a decayed list is both direct and hidden. Directly, reps spend time on contacts who have moved, sending to bounced emails and chasing dead numbers, which is non-selling work; Salesforce's State of Sales research already shows reps selling only around 28 percent of their time, and a stale list deepens that drain. Hidden, the bounces damage sender reputation, which suppresses deliverability for the valid contacts too, so a partly stale list underperforms by more than its stale fraction.

At the macro level this is the same bad-data cost everyone underestimates: Gartner puts the average cost of poor data quality at 12.9 million dollars per organization per year, and a scrape-once-never-refresh habit is a direct contributor. The list felt free to build and quietly taxes every campaign run against it thereafter. The economics favor refreshing precisely because the cost of a bad send, in reputation and wasted effort, dwarfs the cost of re-verifying a record before using it.

This is the same decay our companion LinkedIn profile report analyzes from the source-of-truth angle; here the lens is the export you hold. Either way, a one-time capture depreciates, and the only durable fix is to keep the volatile fields live. The best-tools landscape for extraction is surveyed in the best LinkedIn scraping tools guide.

This also reframes how to compare extraction tools. Teams often choose a scraper on speed, volume, or field coverage, all properties of the moment of capture. None of those address the property that actually governs outcomes, how current the data is when you use it. A faster scraper that produces a list you then leave to decay is not better than a slower one whose output you keep refreshed; the differentiator has moved downstream of the extraction entirely.

The volume instinct makes this worse. Because extraction is cheap and fast, the temptation is to scrape large and scrape often, building ever-bigger lists. But a bigger stale list is not more valuable, it is more decay to manage: every extra contact is another record aging in the background. Past a point, the constraint on results is not how many people you extracted but how many of them you can still reach, which is a freshness question, not a volume one.

This is why the smart pattern is lean-and-live rather than large-and-frozen: extract the segment you will actually work, then keep that segment current, rather than hoarding a huge export you cannot maintain. A smaller list you refresh on use outperforms a giant one you scraped once, because results come from reachable contacts, not stored rows.

Scrape-once versus re-enrich on demand

The decision is not whether to extract, it is what to do after. Scrape-once treats the export as the deliverable and accepts steady decay; re-enrich on demand treats the export as a starting list and confirms the volatile fields at the moment of use. The second approach costs a little per record at the point of action and removes most of the decay tax, which is a far better trade than periodically re-scraping an entire base to fix a handful of fields, or worse, running campaigns on a list you know is months old.

This is exactly where Derrick fits, as a freshness-and-verification layer rather than a scraper. After you extract a list, Derrick re-enriches it on demand directly inside Google Sheets, confirming the current company and title, verifying the email, finding a direct phone, and refreshing profile data, at the moment you act. The snapshot becomes a live record. Nothing here says extraction is bad, it says an extract is only as valuable as how fresh you keep it, and freshness is a process you run in the sheet, not a property of the original scrape.

Keep your extracted LinkedIn lists fresh with Derrick, free for 100 credits per month, directly in Google Sheets. Extract however you like, then re-verify the volatile fields before each use so the list works as well on day ninety as it did on day zero. Group-level extraction is covered in the export group members guide.

A simple way to size your own decay is to take a list you scraped a few months ago and re-verify a sample of it today. The share that now bounces, has changed title, or has moved companies is your real decay rate for that segment, and it is usually higher than people expect. The same check, run continuously rather than once, is exactly what re-enrichment at the point of use automates.

Methodology and sources

This report draws on non-vendor, primary sources: Gartner for the cost of poor data quality; Salesforce State of Sales for how reps spend their time; LinkedIn's Economic Graph and Workforce reporting for professional mobility and how job changes surface through profile updates; and US labour statistics for median job tenure. Per-field decay rates are presented as a market consensus and recalculated for illustration rather than attributed to any data vendor; where a statistic could only be traced to a scraping or enrichment vendor's marketing, we did not use it.

A closing thought, with no drama about scraping. Extraction is a legitimate, useful way to build a B2B list, and this report is not an argument against it. It is an argument against treating the extract as finished. A scrape captures the right people at a moment; keeping the volatile fields current is what turns that moment into ongoing value. Extract freely, refresh at the point of use, and the list that would otherwise decay into a record of who your prospects used to be stays a record of who they are. The teams that get the most from extraction are not the ones that scrape the most, they are the ones whose extracted data is still true at the moment they act on it, which is a discipline anyone can adopt without changing how they scrape at all. Extraction gets you the list; freshness is what makes the list pay.