Historical Data Drift Estimator
Estimate how much of your database has decayed and gone stale.
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a rough proxy assuming that a bit under half of stale records will actively bounce if mailed, rather than merely being outdated but still deliverable.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.
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.