Explore AI Summary

B2B data decay: how fast contact data goes stale, and how to measure yours

B2B data decay is the share of your contact records that stop being true over time: people change jobs, companies close or merge, and emails and phone lines stop working. No verified study gives one annual rate for it.

The best official signals: about 1 in 5 US workers had been with their employer a year or less in January 2026 (Bureau of Labor Statistics), and ZeroBounce found at least 23% of email lists degrade each year, based on 11 billion addresses it checked in 2025.

Below: what the verified data says, why the famous “70% a year” figure does not hold up, how to measure decay on your own list, and how often to re-verify.

What is B2B data decay?

B2B data decay is the gradual loss of accuracy in business contact records, as the people and companies they describe change. A record decays when any field you rely on stops being true: the job title, the employer, the work email, the direct dial or the mobile number.

Data decay is not a bug in your CRM; it is the world moving while your records stand still. Every list starts decaying the day it is built, so the useful questions are how fast your own list decays and how often you need to check it.

Why B2B contact data goes stale

B2B contact data goes stale for five reasons, and only some of them show up as a bounce.

Why B2B data decays: job changes, promotions, company closures, mergers and domain changes, and closed mailboxes or reassigned phone lines — Job changes drive most B2B data decay, but promotions and mergers go stale silently because nothing bounces.

Job changes. US employers recorded 62.8 million separations in 2025, and 38.0 million of them (60.6%) were quits (BLS JOLTS). These are events, not people: one person can leave several jobs in a year.
Promotions and title changes. Same person, same company, new title. The email still works, but your segment is wrong.
Company closures. About 700,000 US business locations closed in 2023, 9.4% of all employer establishments (Census Business Dynamics Statistics).
Mergers, rebrands and domain changes. Census does not count a firm that disappears through a merger or acquisition as a firm death, so real company churn in a B2B database runs higher than the closure numbers suggest.
Mailbox and line changes. A work mailbox is usually closed when its owner leaves, and office lines get reassigned.

How fast does B2B data decay?

B2B data decay has no single measured rate, but four verified numbers show its scale.

B2B data decay statistics: 20.6% of US workers in their job a year or less, 23% of email lists degrade a year, 9.4% of establishments closed, 48% of CRM admins — Each number measures a different thing, which is why no honest source can give you one decay rate for your list.

The BLS tenure figure is the one to anchor on: 20.6% of US wage and salary workers had been with their current employer 12 months or less in January 2026, down from 22.2% in January 2024. It includes first-time workers and people who lost a job and found another, so it is an upper signal for job changes, not a pure rate.

ZeroBounce’s 23% is useful for email, with a caveat: it measures the addresses customers chose to submit for checking, which skews toward dirty lists, and it mixes consumer and business email. Nearly half of CRM admins (48%) told Validity in 2024 that customer data was decaying faster than a year earlier.

Why the popular decay numbers are hard to source

Most decay statistics quoted in sales blogs trace back to an opinion, a dead link or another vendor’s blog.

The claim you will see	Where it traces	The problem
“B2B data decays 70% a year”	A 2011 MarketingSherpa article quoting a Salesforce marketing executive	One person’s upper bound (“as high as 70 percent”), not a measurement
“70.3% a year, per Gartner”	Vendor blogs citing each other	No Gartner source found
“30% of people change jobs every year”	No primary source found	BLS puts short tenure at 20.6%, first jobs included
“22.5% a year” or “2.1% a month”	HubSpot, citing MarketingSherpa	The original study was never found; the figure is about a decade old
“43% a year”	One month of one vendor’s email data, compounded	A single month is not an annual rate

Data decay claims traced: 70% a year, 70.3% Gartner, 30% job changes and 22.5% have no primary data; BLS, Census and ZeroBounce figures are sourced — The biggest decay numbers are the least sourced; the sourced ones are smaller and each measures only one part of decay.

Which contact data decays fastest?

Job title, employer and work email decay fastest, because all three change the moment someone leaves. Mobile numbers decay slowest, because they usually belong to the person.

Field	What makes it stale	Survives a job change?	How to check it
Job title	Promotion, new role, new employer	No	Re-find the person
Employer	Job change, closure, merger	No	Re-find the person
Work email	Mailbox closed, domain changed	No	Verify before each send
Office line or direct dial	Desk or company phone reassigned	Usually no	Connect test
Mobile number	Rarely changes	Usually yes, now at a new company	Call, then confirm the employer

A mobile that still rings after a job change is not a dead record; it is a warm one. Confirm the new employer on the call, then update the CRM. Our guide to direct dial vs mobile numbers covers why the line type matters.

Decay by role, age and industry

Some lists decay much faster than others. Salespeople, service workers and younger contacts change employers far more often than managers and senior staff.

B2B data decay by role: median tenure is 6.1 years in management, 4.6 in business and computer roles, 3.3 in sales and 2.9 in service (BLS 2026) — A list of salespeople goes stale almost twice as fast as a list of managers, so size your re-verification to the roles you sell to.

By age: median tenure is 3.0 years at 25 to 34, 4.7 at 35 to 44, 7.0 at 45 to 54 and 9.6 at 55 to 64 (BLS, 2026). A list of SDRs and coordinators ages faster than a list of CFOs.
By industry: the average monthly separations rate in 2025 was 4.6% in professional and business services, 2.8% in information, 2.4% in manufacturing and 2.3% in financial activities (BLS JOLTS).

How to measure your own data decay

You measure data decay by re-checking a sample of records verified on a known date and counting what changed. It takes one afternoon per quarter.

How to measure data decay: pick 200 verified records, re-verify emails, re-find each person, call 50 numbers, count stale records and annualize — Counting a record as stale when any field changed gives you the number that matters: how many records you can no longer use as they are.

Pick 200 records last verified on a known date, ideally 3 to 6 months ago. Use your real segments, not your cleanest accounts.
Re-verify every email and count those no longer Valid.
Re-find each person and count those at a new employer or in a new role.
Call 50 of the phone numbers and count wrong people and dead numbers.
Count records with at least one stale field, divide by 200, and convert to an annual rate.

To annualize, use compounding, not multiplication: annual decay = 1 – (1 – d)^(12 / months), where d is the share that went stale over the period.

Example (illustrative numbers): Priya Nair, RevOps manager at Harborline Payments in Charlotte, North Carolina, re-checks 200 contacts verified in March 2026. In September, 14 emails are no longer Valid and 22 people changed employer; 26 records have at least one stale field. That is 13% in 6 months, or 1 – 0.87^2 = about 24% a year.

Decay log columns (one row per record)

Run the same sample method when you compare vendors: our guide to testing a B2B data provider uses it to score found, verified and correct contacts.

How often should you re-verify contact data?

Re-verify emails right before they enter a sequence, and re-check your whole active CRM once a quarter. The schedule below is our recommendation, built for a team that emails and calls every week.

Data re-verification cadence: re-verify emails before a send, log wrong-person calls, remove bounces weekly, re-verify the CRM and rerun the decay sample quarterly — The check right before a send does the most work, because it catches decay at the only moment it can still hurt you.

Before every send: re-verify any email not checked in the last 30 days. Keep Risky addresses out of your main sending domain.
Every week: remove hard bounces and flag wrong-person calls in the CRM the same day.
Every quarter: re-verify active contacts and rerun the 200-record decay sample.
When a champion leaves: re-find them at the new company. A past buyer in a new job is one of the warmest leads you have.

Contact monitoring (beta) re-checks the contacts you upload, if you would rather not rerun the sample by hand; see Scalelist monitoring for what it covers.

Common data decay problems and fixes

Decay rarely announces itself. These are the four places it hides.

Catch-all domains hide decay

A catch-all domain accepts every address, so a mailbox check cannot tell you whether a person still works there. Fix: send Risky addresses separately and in small batches; see our guide to catch-all emails.

Bounces arrive after the damage

A hard bounce tells you a record decayed only after it has hurt your sender reputation. Fix: verify before the send, and watch your email bounce rate per list, not per campaign.

Duplicates make decay look random

When one person exists three times in the CRM, one record gets updated and two keep decaying. Fix: remove duplicate contacts before you measure anything.

Enrichment overwrites good data with old data

A bulk refresh can replace a correct field with a stale one from an older source. Fix: only overwrite a field when the new value is verified and newer; our CRM enrichment guide shows the field rules.

Data decay best practices

Store a last-verified date on every contact, and sort by it before every campaign.
Keep work email and mobile in separate fields with separate verified dates.
Treat wrong-person calls as data, not bad luck: log them the same day.
Buy or refresh data only for the segment you will contact this month. Stored data decays whether you use it or not.
Re-measure decay each quarter on the same segments so the trend means something.
Refresh from a provider that verifies at the moment you ask, not from a stored export; our list of the best B2B data providers compares them, and our provider comparison by region shows found rates in 4 regions.
Know what a found rate proves before you trust it: the 2026 B2B data accuracy benchmark separates found, verified and correct.

How Scalelist helps with data decay

Scalelist, an AI lead finder, finds and verifies contact details on demand instead of serving a pre-compiled list, so an email is checked at the moment you ask for it. Every email comes back labelled Valid or Risky, and Not found is free.

Refresh a stale list: upload names and companies and get current emails (1 credit each) and mobiles (20 credits each), charged only when found.
Check what you already have: the Email Verifier is included with every plan.
Re-find people who moved: describe them in one sentence in Lead Finder and pull only the new records you need.

Because you pay only when something is found, refreshing a list costs nothing for the people Scalelist cannot find. Plans start at 29 USD a month for 500 credits on the pricing page. To weigh refresh costs across vendors, see what B2B data really costs and how credits compare with seats.

Re-verify the 200 records in your decay sample and see what is still Valid.

Get 20 credits to test it

FAQ

What is the B2B data decay rate?

There is no single verified B2B data decay rate. The closest official signal is that 20.6% of US workers had been with their employer 12 months or less in January 2026 (BLS). For email, ZeroBounce found at least 23% of lists degrade each year, based on 11 billion addresses checked in 2025. Your own rate depends on your roles, industries and regions.

Is it true that B2B data decays 70% a year?

No verified source supports it. The figure traces to a 2011 MarketingSherpa article quoting one Salesforce executive who said decay could be “as high as 70 percent”. That is an upper-bound opinion, not a measurement. A related “70.3%, per Gartner” figure has no Gartner source we could find. Measure decay on your own list instead.

How fast do email lists decay?

About a quarter a year, as a rough guide. ZeroBounce reported that at least 23% of email lists degrade annually, based on more than 11 billion addresses it verified in 2025, down from 28% in 2024. The data comes from lists customers chose to check, so clean, recently verified B2B lists may decay more slowly.

What causes data decay in a CRM?

Job changes cause most of it, followed by promotions, company closures, mergers and domain changes. US employers recorded 62.8 million separations in 2025, and about 9.4% of employer establishments close each year. Duplicate records and enrichment tools that overwrite newer data with older data make CRM data decay worse.

How often should you clean your CRM data?

Re-verify emails right before they enter a sequence and re-check your active CRM contacts every quarter. Remove hard bounces and flag wrong-person calls every week. Measure decay on a 200-record sample each quarter, so you know whether your list ages faster or slower than you assumed.

Do mobile numbers decay slower than emails?

Usually, yes. A work email is tied to the employer and is normally closed when the person leaves. A mobile number usually belongs to the person, so it keeps working after a job change, but it now reaches someone at a different company. Confirm the employer on the call and update the record.

How do you measure data decay?

Take 200 records verified on a known date, re-verify their emails, re-find each person, and call 50 of the numbers. Count records with at least one stale field and divide by 200. Annualize with compounding: 1 – (1 – d)^(12 / months). For example, 13% over 6 months is about 24% a year.

Sources

Arnaud Renoux

Co-Founder at Scalelist