20 CRM Data Decay Statistics for 2026

What the research says about how quickly a contact database stops being true, and what that costs the teams relying on it

Every CRM starts accurate and ends up wrong. People change jobs, companies rename, phone numbers get reassigned, and the record sitting in your system keeps saying what was true the day somebody typed it. The statistics below cover how fast that happens, what it costs, and why the usual fix, asking a person to keep it current, doesn’t hold. Contacts in this+that takes the other approach: the list assembles itself from the accounts a team already connects, so maintenance comes out of the communication rather than out of somebody’s afternoon.

Key Takeaways

• B2B data decays at about 2.1% a month, or 22.5% a year, on MarketingSherpa research that HubSpot uses as its benchmark. A database nobody maintains loses about a fifth of its accuracy annually.
• Poor data quality costs organizations an average of $12.9 million a year, according to Gartner.
• 76% of CRM users say less than half their organization’s CRM data is accurate and complete.
• Sales reps spend 70% of their time on nonselling tasks, with 9% of the week spent manually entering customer and sales information.
• 3.2 million Americans quit a job in a single month (BLS, June 2026). Every one of those is a contact record somewhere that just became wrong.
• Median employee tenure is 3.9 years, the lowest since 2002, so the churn driving decay is structural rather than temporary.

How Fast Contact Data Goes Stale

1. B2B contact data decays at about 2.1% per month

MarketingSherpa measured B2B data decaying at 2.1% a month, which HubSpot annualizes to about 22.5%. The monthly figure is the measurement; the annual one is derived from it. ZoomInfo’s own field-level breakdown runs higher for volatile fields: email addresses decay around 3.6% a month, job titles 2-3%. Compounded across a year that lands near 22.5%, which means roughly a fifth of a database stops being accurate every twelve months without anyone doing anything wrong.

2. Industry benchmarks put annual decay between 20% and 30%

Estimates vary by source and by how strictly “decayed” is defined, but the range converges on 20% to 30% per year, with ZoomInfo putting the spread wider still, at 22.5% aggregate up to 70% for email addresses alone. The spread matters less than the direction: every estimate describes a database that degrades continuously rather than occasionally.

3. Email fields decay faster than the record as a whole

Work email is tied to employment, so it breaks the moment somebody leaves. ZoomInfo puts email address decay as high as 70% a year against a 22.5% aggregate, well above the blended rate for a full contact record.

4. A list with 10% stale addresses crosses the bounce threshold

Mailbox providers start treating a sender as a problem at 2-3% bounce rates, and a database with 10% stale email addresses crosses that line quickly. Repeated hard bounces damage sender reputation, which then suppresses delivery of mail that would otherwise have arrived.

5. Healthy deliverability sits at 95% or better

Industry benchmarks put healthy email deliverability at 95% or better, with anything under 90% signaling real data problems. The gap between that and a list nobody maintains is the practical cost of letting one age.

What Decay Costs

6. Gartner puts the average cost of poor data quality at $12.9 million a year

Gartner’s estimate of $12.9 million annually per organization covers wasted resources, missed opportunities and operational drag. It is the single most cited number in this field, and it describes data quality broadly rather than contact records alone.

7. 37% of CRM users report losing revenue directly to poor data quality

Validity’s State of CRM Data Management in 2025 found 37% of respondents reporting direct revenue loss from bad data. The same study found 37% had delayed revenue-generating initiatives for the same reason.

8. 76% say less than half their CRM data is accurate and complete

The same Validity research reports 76% of CRM users saying under half their organization’s CRM data is accurate and complete. Worth pausing on: this is the majority view held by the people who use the system daily.

9. 44% estimate losses between 5% and more than 20% of revenue

Validity found 37% of CRM users losing revenue as a direct consequence of poor data quality. What the report doesn’t do is size that loss as a share of revenue, which is itself telling: the cost is hard to isolate, which is part of why it goes untreated.

10. Each stale record carries roughly $100 of downstream cost

Salesmotion puts 20-30% of a rep’s time into working around data quality problems. Put a stale record at roughly $100 in wasted rep time, failed outreach and deliverability damage, and a 10,000-record database decaying at 22.5% runs to about $225,000 a year before any pipeline effect. The $100 is our assumption rather than a figure anybody publishes.

Why It Happens: People Move

11. 3.2 million Americans quit a job in June 2026

The Bureau of Labor Statistics recorded 3.2 million quits in a single month, a rate of 2.0%. Contact decay is not a data problem in origin. It is a labor-market fact arriving in your database on a delay.

12. Median employee tenure has fallen to 3.9 years

BLS puts median tenure at 3.9 years, down from 4.1 in 2022 and the lowest reading since 2002. Shorter tenure means faster decay, structurally, regardless of how carefully anyone maintains a list.

13. Annualized separations mean a large share of the workforce changes employer each year

Rolling BLS separations forward across twelve months implies that a substantial share of the workforce, on the order of a third or more, changes employers annually once involuntary separations are included alongside quits. Each move invalidates a work email, a direct line, a title, or all three.

14. Decay is uneven across industries

Separation rates vary sharply by sector, from under 2% monthly in government and finance to far higher in hospitality. A CRM covering high-churn industries decays faster than any blended benchmark predicts, which is why an aggregate number is a starting point rather than an answer.

Duplicates and Completeness

15. The average company carries 10% to 30% duplicate records

Duplicates accumulate whenever the same person arrives through a second channel. Estimates put the typical rate at 10% to 30% across contacts, leads and accounts.

16. Well-run data operations hold duplicates under 5%

The benchmark for well-run data operations is a duplicate rate below 5%. Reaching it generally requires dedicated tooling and someone whose job includes owning it.

17. Poor data quality is estimated to cost US businesses trillions annually

Salesforce found only 35% of sales professionals completely trust their organization’s data

What It Does to the People Using It

18. Reps spend 70% of their time not selling

Salesforce’s State of Sales splits the rep week into 30% selling and 70% non-selling. Admin, internal meetings and manual data entry absorb the rest.

19. Manual data entry takes 9% of the rep week

The same report breaks that non-selling time down. Manually entering customer and sales information takes 9% of the week, the same share it gives to administrative tasks. That’s measurable work, and it falls to the most expensive people on the team.

20. Data entry and chasing bad records consume hundreds of hours a year

Salesforce puts manual data entry at 9% of the rep week, and administrative tasks at another 9%

The Pattern Underneath the Numbers

Read together, the statistics describe a loop rather than a list of problems. Data decays because people move, which is outside anyone’s control. The company hands the fix to a person, usually one person, usually with another job. That person is slower than the decay, so the database drifts. And because a drifted record looks identical to a current one, nothing signals the drift until an email bounces or a call reaches somebody who left two years ago.

Buying a bigger CRM doesn’t change the loop, because the loop is about maintenance rather than storage. What changes it is maintenance that comes from a source already moving at the speed of the decay, which is the communication itself. When somebody emails from a new address, or signs off with a new title, or introduces a colleague who has taken over an account, the correction has already arrived. It just needs reading.

That is the bet behind Contacts in this+that: the list builds and maintains itself from the accounts a team already connects, so nobody is asked to keep it current by hand. We wrote about the reasoning in contacts should build themselves.

How We Sourced This

Where primary research exists we cite it: the Bureau of Labor Statistics for job mobility, Gartner for the cost of poor data quality, Validity’s State of CRM Data Management for practitioner survey data, and Salesforce’s State of Sales for how reps spend their time.

Several widely circulated figures in this field, including the 2.1% monthly decay rate and the 546-hour data entry estimate, are repeated across vendor publications without an accessible primary study behind them. We have linked the best available source and flagged them as estimates rather than presenting them as measurements. Ranges are reported as ranges.