Email list decay calculator: how much of your list has gone stale?
Email list decay compounds: at a yearly rate r, the share of contacts gone stale after m months is 1 − (1 − r)^(m/12). Enter your list size and months since it was verified, then pick a rate: a model built on US job tenure data, or your own measured rate. No reliable industry-wide figure exists, so none is assumed.
Or since the list was built, if it never was.
Presets model the share of people leaving their employer each year from BLS median job tenure, January 2026.
Type your own to override the preset.
Measure your own rate from a re-verified sample
Re-verify a random sample of contacts that were valid on a known date and enter what you found. This is the only rate that describes your list.
- Estimated gone stale
- 1,560
- Share of the list
- 15.6%
- Estimated still current
- 8,440
- Yearly rate used
- 15.6%
At 15.6% a year (the BLS-tenure model for all us workers), about 1,560 of 10,000 contacts have probably moved on in 12 months: 1 − (1 − 0.156)^(12/12).
| After | Stale | Share |
|---|---|---|
| 3 months | 415 | 4.2% |
| 6 months | 813 | 8.1% |
| 12 months | 1,560 | 15.6% |
| 18 months | 2,246 | 22.5% |
| 24 months | 2,877 | 28.8% |
| 36 months | 3,988 | 39.9% |
Not every leaver bounces: companies keep deleted mailboxes for weeks or turn them into shared inboxes, so some stale addresses still accept mail. And some addresses die without anyone leaving, after a rebrand or domain change.
Next step
About 1,560 stale contacts is an estimate. A verification run gives you the real number.
The model can't tell you which contacts are gone. A mailbox check marks every address valid, invalid or risky, so you can remove the dead ones before they bounce; 10,000 contacts fit in the $19 one-time pack (10,000 emails).
- Drop your list as a CSV
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Ready to verify now? Verify the list
Sample result
About 1,560 of 10,000 contacts (15.6%) are estimated to go stale within 12 months.
- Estimated gone stale
- 1,560
- Share of the list
- 15.6%
- Estimated still current
- 8,440
What this means for you
People change jobs, so re-verify a list that has sat for months before you send to it.
- Six job-tenure presets from BLS data
- Or your own measured or typed rate
- Stale counts from 3 to 36 months
- Constant-rate model, labelled as one
- Data decay
- BLS tenure
- Re-verify
By SimpleVerifier Team · Last updated · Runs in your browser; nothing you paste is sent to us
How do I calculate email list decay?
- Enter how many contacts are on the list and how many months since it was last verified (or built, if it never was).
- Pick the role closest to your audience. Each preset is a modelled share of people leaving their employer per year, from BLS median job tenure.
- Better: open Measure your own rate, enter a re-verified sample's results, and use that rate instead. Or type any rate you trust.
- Read the estimated stale contacts now and at 3 to 36 months, then verify the list for the real number.
Example
- You enter
- 10,000 contacts, last verified 12 months ago, with a 15.6% yearly job-change rate.
- You get
- About 1,560 contacts (15.6%) estimated stale now and 8,440 still current, rising to 2,877 at 24 months and 3,988 at 36 months. It is an estimate from the rate, not a scan of the list.
- Limits
- No limit; runs in your browser.
What is the email list decay rate formula?
Stale contacts = list size × (1 − (1 − yearly rate)^(months ÷ 12)). Decay compounds because each year's rate applies to the contacts still left, so two years at 15.6% is 28.8% stale, not 31.2%. A 10,000-contact list at the all-workers rate has about 1,560 stale contacts after 12 months and 2,877 after 24.
In a spreadsheet with list size in A2, yearly rate as a percentage in B2 and months in C2, the formula is =ROUND(A2*(1-(1-B2)^(C2/12)),0). To turn a monthly rate into a yearly one, use =1-(1-D2)^12; that is how 2.1% a month becomes 22.5% a year.
How fast do B2B email lists decay?
A model built on US job tenure puts it at about 10.7% to 25.1% of people leaving their employer each year, depending on role, and most work email dies when someone changes employer. The US Bureau of Labor Statistics' Employee Tenure survey for January 2026 puts median tenure with the current employer at 4.1 years for all wage and salary workers, 6.1 years in management occupations, 3.3 in sales and 2.4 in leisure and hospitality (by occupation, by industry).
Assuming people leave at a steady rate, median tenure T converts to a yearly leave rate of 1 − 0.5^(1/T): 10.7% for management, 15.6% for all workers, 18.9% for sales, 25.1% for leisure and hospitality. That's a model, not a measurement. New hires leave faster than long-serving staff, BLS covers US employees only, and job changes aren't the only way addresses die. Our post on how fast B2B contact data decays walks through it.
Is the 22.5% a year email list decay rate real?
It can't be checked. The figure, 22.5% a year (2.1% a month), is attributed to MarketingSherpa on HubSpot's database decay page, but we couldn't find the original study, its year, sample or definition of decay. The arithmetic is right (1 − 0.979^12 ≈ 0.225); the source isn't checkable. If you want to use it anyway, type it into the rate field; the calculator won't present it as fact.
The rate that actually describes your list is one you measure. Take contacts that were valid on a known date, re-verify a random sample of a few hundred, and enter how many are now invalid and how many months passed. The calculator converts that to a yearly rate, which you can then apply to the whole list.
Does a stale contact always bounce?
No. Not every leaver's address bounces right away. Google Workspace lets admins restore a deleted user for 20 days and Exchange Online keeps deleted mailboxes for 30, and some companies turn a leaver's mailbox into a shared inbox indefinitely. Those addresses still verify as valid, though nobody reads them. Others die with nobody leaving, when a company rebrands or changes domain.
So treat the result as a sizing estimate: roughly how much of the list you're about to waste sends on. A verification run marks each address valid, invalid or risky and removes the hard bounces before they cost you reputation; how often to re-verify turns your rate into a cadence.
Which decay rate should I use?
| BLS tenure model (presets) | Your measured rate | The quoted 22.5% a year | |
|---|---|---|---|
| What it measures | People leaving their employer | Addresses on your list that became invalid | Not stated |
| Can you check the source? | Yes, BLS tables | Yes, it's your own sample | No original study found |
| Fits your audience? | Roughly, by role | Yes | Unknown |
| Effort | None | Re-verify a few hundred contacts | None |
Spreadsheet version of the calculator: =ROUND(A2*(1-(1-B2)^(C2/12)),0) with list size in A2, yearly rate in B2 and months in C2.
Who uses this tool, and for what?
- Marketers
- Before mailing a list that has sat unused, estimate how many sends would hit dead or unread addresses, then clean the list and verify it.
- Sales ops
- Set a re-verification cadence for the CRM from a rate you measured on your own contacts, instead of an industry figure nobody can source.
- Cold emailers
- Size the damage on an old lead export, then price the clean-up with the verification cost calculator before it goes into a sequence.
- Agencies
- Show a client why their two-year-old list needs checking before a campaign, with the model and its BLS source on the page.
What can’t this tool tell you?
- The presets are a model of job changes from US median tenure, not a measured email decay rate. New hires leave faster than long-serving staff, which a steady-rate model ignores.
- BLS data covers US wage and salary workers only. Lists of founders, contractors or non-US contacts may decay at different rates.
- A stale contact isn't always a bounce. Leavers' mailboxes are often kept for weeks or forwarded, so they verify as valid for a while.
- It estimates how many contacts are stale, never which ones. Only checking each address answers that.
- Job changes aren't the only cause. Domain changes, rebrands and abandoned personal accounts add decay the model doesn't count.
Common questions
- What is email list decay?
- The share of a list's addresses that stop working, or stop reaching the right person, over time. In B2B lists most of it comes from people changing jobs; companies changing domains and abandoned personal accounts add to it.
- How fast do email lists decay?
- It depends on the audience. A model built on US job tenure data suggests about 10% to 25% of people leave their employer each year, depending on role: around 10.7% for managers and 18.9% for sales roles. Your own rate, measured by re-verifying a sample, is the only reliable figure for your list.
- Is the 22.5% a year email decay statistic real?
- It's widely quoted and attributed to MarketingSherpa, but the original study isn't available to check. The maths (2.1% a month compounds to 22.5% a year) is correct. This calculator lets you enter it, but doesn't use it as a default.
- How do I calculate my own list decay rate?
- Re-verify a random sample of contacts that were valid on a known date. Divide the number now invalid by the sample size, then annualise for the months elapsed: yearly rate = 1 − (1 − invalid ÷ sample)^(12 ÷ months). The calculator does this under Measure your own rate.
- How much of a two-year-old email list is still good?
- At the all-workers model rate of 15.6% a year, about 71% of contacts would still be at the same employer after 24 months, so roughly 29% have gone stale from job changes alone. Your audience's rate may be higher or lower; verifying the list gives the real number.
- How old is too old for an email list?
- There's no cut-off date, but at the modelled rates a list not verified for a year has likely lost one contact in six or seven to job changes alone. Verify anything you haven't checked in the last few months before sending.
- Does this tell me which contacts are stale?
- No. It estimates how many, from a rate. Only checking each address tells you which ones are gone.
Sources
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<script>(function(){if(window.__svEmbed)return;window.__svEmbed=1;window.addEventListener("message",function(e){if(e.origin!=="https://simpleverifier.com")return;var d=e.data;if(!d||d.type!=="simpleverifier:embed-height"||typeof d.slug!=="string"||typeof d.height!=="number"||!isFinite(d.height))return;var f=document.querySelectorAll("iframe[data-sv-embed]");for(var i=0;i<f.length;i++){if(f[i].contentWindow===e.source){f[i].style.height=Math.min(Math.max(Math.ceil(d.height),200),4000)+"px";}}});})();</script>A tidy list can still be full of dead addresses.
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