RevOps
Estimate how much of your database has decayed and gone stale.
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- Claude, via MCP
How much of my contact database has gone stale?
Apply an annual decay rate as compounding: stale share = 1 − (1 − annual decay)^(months since cleanse ÷ 12). At 22.5% a year, a 50,000-contact database untouched for 18 months has about 32% stale records, roughly 15,900 contacts.
About 15,887 records (32%) have likely drifted since your last cleanse. Mailing them risks bounces and spam traps that hurt deliverability for everyone. Suppress and re-verify before the next send.
Decay compounds over time, not linearly, job changes, domain moves and role turnover accelerate the longer a list sits unverified.
About this calculator
A contact database doesn't stay accurate just because nobody's touched it, job changes, company moves, and role turnover erode it steadily even while it sits untouched. This estimator applies a B2B data-decay rate to your database size and time since last cleanse to project how many records have likely gone stale, and the bounce risk that carries into your next send.
How to use it
- Enter total contacts in your database.
- Enter an annual data decay rate, 20-30% per year is a commonly cited range for B2B contact data.
- Enter months since your last cleanse or verification pass.
- Read estimated stale records, still-clean records, and likely hard bounces if you mailed the list as-is.
Methodology
The decay fraction is 1 − (1 − annual decay rate)^(months since cleanse ÷ 12), a compound decay formula rather than a simple linear one, reflecting that decay compounds continuously over time rather than accumulating at a flat monthly rate.
Stale records is total contacts × decay fraction. Clean records is the remainder, total contacts minus stale records.
Estimated hard bounces is stale records × 0.4, a rough proxy assuming that a bit under half of stale records will actively bounce if mailed, rather than merely being outdated but still deliverable.
The tool flags decay of 30%+ as serious, 15-30% as worth acting on, under 15% as relatively fresh. Because decay compounds rather than accumulates linearly, a list two years past its last cleanse can be substantially more degraded than one year past it would suggest by simple extrapolation.
Compounding data decay
Stale share = 1 − (1 − Annual decay rate)^(Months since cleanse ÷ 12) Stale records = Total contacts × Stale share Clean records = Total contacts − Stale records Likely hard bounces = Stale records × 0.4
- Annual decay rate
- Share of records that go out of date in a year: people change jobs, companies close or rename, emails stop working. 22.5% is the tool's default.
- Months since cleanse
- Time since the database was last verified or cleaned.
- 0.4
- Tool assumption that about 40% of stale records would hard-bounce if emailed.
The default 22.5% and the 40% bounce share are tool assumptions, not measured values. Replace the decay rate with your own: run a list through an email verification service, or compare bounce rates on a sample with records of known age. Compounding means each year decays the records that were still good, so two years is less than double one year's decay.
Worked example: 50,000 contacts, 18 months since cleaning
A B2B company has 50,000 contacts in its CRM and has not cleaned or verified them for 18 months. It uses the tool's default 22.5% annual decay rate.
- Stale share = 1 − (1 − 0.225)^(18 ÷ 12) = 1 − 0.775^1.5 = 1 − 0.682 = 31.8%.
- Stale records = 50,000 × 31.8% = 15,887.
- Clean records = 50,000 − 15,887 = 34,113.
- Likely hard bounces = 15,887 × 0.4 = 6,355.
- At 12 months, stale records would be 11,250; the extra six months add about 4,600.
Sending to the whole list could produce over 6,000 hard bounces, enough to harm sender reputation with mailbox providers. Verify the list before the next large send, suppress invalid addresses and re-confirm older contacts. The time cost of working stale records is sized by the CRM cleanup ROI calculator.
Why B2B contact data decays (US labour data)
| Measure | Figure |
|---|---|
| Median tenure with current employer | 4.1 years (January 2026) |
How often should a database be cleaned?
Keep the stale share low enough that sends stay safe and reports stay accurate. A practical rhythm:
- Verify before any large send to contacts not emailed in the last six months.
- Suppress hard bounces at once and remove repeated soft bounces.
- Re-check key accounts quarterly, because a buyer changing jobs can stall a deal.
- Run a full clean yearly.
The tool calls under 15% stale fresh and 30% or more serious. Those are its own thresholds.
Data decay in India vs the US
Indian B2B databases decay through job changes and through phone numbers: people change numbers or use several. Many Indian contacts are reached by phone and WhatsApp more than email, so verify mobile numbers as well as emails. India's Digital Personal Data Protection Act, 2023 also makes keeping inaccurate or unneeded personal data a compliance question.
US decay is driven mainly by job changes; BLS data put median tenure at 4.1 years in January 2026, so a meaningful share of contacts move every year. Email deliverability matters more in the US because email is the main channel. Pair cleaning with enrichment; see the data enrichment value predictor.
Common database decay mistakes
- Emailing an old list in full and damaging sender reputation.
- Deleting stale records that hold deal or account history instead of archiving them.
- Using a generic decay rate when your own bounce data is available.
- Cleaning emails but not phone numbers or company details.
- Cleaning once without rules to keep it clean.
Sources
- Employee Tenure in 2026, US Bureau of Labor Statistics. Accessed 2026-10-19.
Go deeper
FAQ
Because each year's decay applies to the records still considered "current" from the prior year, not to the original total, so the compounding formula mirrors how job changes and role turnover erode a list continuously rather than in one flat annual chunk. This is the same math behind compound interest, just working against you instead of for you.
20-30% annually is a commonly cited range for B2B contact databases, skew toward the higher end for lists concentrated in fast-turnover industries like tech and startups, and toward the lower end for more stable sectors like government or established manufacturing.
Yes, mailing to a list with significant projected staleness risks hard bounces and spam-trap hits that damage sender reputation for future sends to everyone on the list, not just the stale contacts. Re-verify or suppress the estimated stale segment before a broad send rather than risk deliverability for the whole database.
Data decay is the gradual loss of accuracy in a database as people change jobs, companies change and contact details stop working.
Suppress them from sends first. Delete or archive personal data you no longer need, in line with your privacy policy, but keep account and deal history.
Verify a random sample of records of a known age and see what share is invalid. Divide by the age in years for a rough annual rate, or use the compounding formula in reverse.
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