How Fast B2B Contact Data Decays, and What to Do About It

There is no reliable single figure for B2B contact data decay, and the widely quoted 22.5% a year cannot be traced to a published study. A simple model on US job tenure data suggests 10% to 25% of people leave their employer each year, depending on role, the main reason work addresses die. Measure your own list.

Almost every article on data decay opens with a confident percentage. We tried to trace the most common ones back to their sources, and then looked at what the primary data can and cannot tell you.

The 22.5% figure, traced

The most quoted number, 2.1% a month or 22.5% a year, is attributed to MarketingSherpa, but no page we found links to the original research.

HubSpot's database decay page states that "email marketing databases naturally degrade by about 22.5% every year" and, under its sources, attributes this to "Marketing Sherpas research" showing B2B data decays at 2.1% per month. Vendor blogs repeat the figure, usually citing HubSpot, MarketingSherpa, or each other.

Two things are true about it:

• The arithmetic is right. 2.1% a month compounded over 12 months is 22.5%: 1 - (1 - 0.021)^12 = 0.225.
• The underlying study is not available. None of the pages we checked on 1 October 2026 linked to a MarketingSherpa report, gave its year, sample or method, or explained whether "decay" meant email bounces, job changes or any field changing.

That does not make the number wrong. It makes it unverifiable, and it is often presented with more precision than it deserves. We have not used it as a fact in this post.

What the primary data says

The best public evidence on work email decay is job tenure and turnover data, because most work addresses die when people change employer. The US Bureau of Labor Statistics publishes both.

From the Employee Tenure survey for January 2026, released 24 September 2026:

• Median tenure with the current employer was 4.1 years for all wage and salary workers (3.9 in January 2024).
• Private sector median tenure was 3.9 years; public sector 5.6 years.
• 20.6% of workers had been with their employer for a year or less.
• Median tenure was 3.0 years for ages 25 to 34 and 9.6 years for ages 55 to 64.

By occupation (Table 6) and industry (Table 5), January 2026:

Group	Median tenure
Management occupations	6.1 years
Business and financial operations	4.6 years
Computer and mathematical	4.6 years
Sales and related	3.3 years
Professional and business services (industry)	3.8 years
Retail trade (industry)	3.2 years
Leisure and hospitality (industry)	2.4 years

Turnover data points the same way. The JOLTS survey counted 62.8 million separations in 2025, with an annual average monthly separations rate of 3.3%.

Why a monthly separations rate is not a decay rate

You cannot multiply 3.3% by 12 and call it data decay. Separations count jobs ended, not people, and the same high-turnover roles are refilled and vacated several times a year. A retail position that turns over three times in a year adds three separations but only ever had one email address, if it had one at all. Managers and professionals, who make up much of a typical B2B contact database, have longer median tenure than the workforce as a whole.

A simple model by role

If you assume people leave their employer at a steady rate, median tenure converts into an annual leave rate. This is an illustrative model, not a measurement. Under a constant rate, the share leaving each year is 1 - 0.5^(1 / median tenure).

Group (BLS median tenure)	Modelled share leaving per year	After 2 years
Management (6.1 years)	10.7%	20.3%
Business and financial operations (4.6)	14.0%	26.0%
All workers (4.1)	15.6%	28.7%
Professional and business services (3.8)	16.7%	30.6%
Sales and related (3.3)	18.9%	34.3%
Leisure and hospitality (2.4)	25.1%	43.9%

Our calculation and inputs are saved with the research for this post. The limits are important:

• Real turnover is not constant. New hires leave faster than long-serving staff, so the model understates churn among recent hires and overstates it among veterans.
• Leaving is not the only change. Promotions and role changes keep the same email but change the job title. Rebrands, mergers and domain migrations kill addresses without anyone leaving.
• BLS covers US wage and salary workers. Founders, the self-employed and other countries are outside it.

So treat the table as a rough guide to which segments go stale first, not as a forecast for your list.

Why the email often outlives the job

A job change does not always produce a bounce, and when it does, it is often weeks later. That gap is why decay estimates and bounce rates rarely match.

• Google Workspace: an admin can restore a deleted user for up to 20 days, and Google suggests suspending accounts or using an Archived User licence instead of deleting in some situations (Google Workspace admin help).
• Microsoft 365 / Exchange Online: a deleted mailbox is kept in a soft-deleted state for 30 days (Microsoft Learn). Admins can also convert a departed employee's mailbox to a shared mailbox, which needs no licence under 50 GB (Microsoft Learn).

That last option creates the hardest kind of decay to detect. The address keeps accepting mail, so it verifies as valid, but the person has gone. No verifier can see that. Only replies, job change data, or the silence of a whole sequence will tell you.

Three kinds of decay, three different fixes

Contact data goes stale in three ways, and verification only catches one of them.

Type	Example	Caught by verification?	Fix
Hard decay	Mailbox deleted after the person left	Yes, as invalid	Verify before sending
Soft decay	Mailbox kept as a shared inbox; person gone	No, still valid	Job change signals, non-response
Field decay	Same person, new title or department	No, email still works	Enrichment and CRM updates

Only the first row hurts deliverability directly, because hard bounces count against your sender reputation. The second and third rows cost time instead: emails that reach nobody, and messages pitched at a role the person no longer holds.

How to measure your own decay rate

The only decay rate that matters is your own, and you can measure it with data you already have. The method:

1. Pick a cohort of records that were verified as valid on a known date, for example contacts imported and verified six months ago. Keep the verification date on each record.
2. Take a random sample of a few hundred.
3. Re-verify them today using the same tool.
4. Count how many are now invalid. Report risky results separately rather than counting them as decay, since risky means the server would not confirm either way.
5. Divide by the interval. If 6% of a six-month-old cohort is now invalid, your hard decay is running at about 1% a month for that source.

Repeat it by source (data provider, inbound, events) and by role. If one source decays much faster than the others, that tells you where to spend effort. Our post on how often to re-verify turns that rate into a cadence.

What to do about it

Match your re-verification cadence to how fast each segment decays, and do not rely on verification alone for the decay it cannot see.

• Verify before every send to records not checked recently. Store the verification date on the record so you know what "recently" means. See email verification for SDR teams for a workflow.
• Re-verify fast-moving segments more often. On the model above, a list of sales roles goes stale noticeably faster than a list of executives.
• Watch job change signals for the decay verification misses.
• Keep the person when the email dies. A contact who changed jobs is a lead at their new company.

You can check a single address with the free verifier, or estimate what regular re-verification will cost with the cost calculator.

The takeaway: treat any precise industry-wide decay percentage with suspicion, including the famous one. Job tenure data says a meaningful share of your B2B contacts will change employer every year, more in sales than in management. Measure your own rate by re-verifying a dated sample, and set your cadence from that.