B2B contact data decays faster than you think:
here is the evidence

Imagine paying for a list of 10,000 B2B contacts today. By the time your team finishes enriching, sequencing, and actually reaching out, three to six months later, roughly 2,250 of those contacts have already changed something material about themselves. A new job title. A new company. A phone number that no longer connects.

This is not a hypothetical. According to HubSpot's Database Decay Simulation, based on MarketingSherpa research, B2B contact databases decay at a rate of 2.1% per month, compounding to 22.5% per year.^[1] Most outbound teams either do not know this, or choose not to think about it.

22.5% per year is the figure you will encounter most consistently across the B2B data industry. It originates from MarketingSherpa research, has been validated by HubSpot's own simulation tool, and is referenced by Cognism in their analysis of data decay causes.^[1][2]

To be precise about what this means: roughly 1 in 4 records in a typical B2B database becomes materially inaccurate within twelve months. Not slightly off, wrong enough to cause a bounce, a failed call, or a message that reaches the wrong person entirely.

The compounding effect is what makes data decay so damaging. After two years, barely 60% of your database is reliable. After three years, you are working with less than half of what you originally paid for, yet your team operates as if the data is current.

Data does not decay randomly. There are structural reasons why B2B contact information becomes stale at this rate, and understanding them helps predict which segments of your database are at highest risk.

Assume a mid-sized B2B company buying 5,000 contacts per month, at a blended cost of $0.04 per contact, totalling $2,000/month or $24,000/year. Applying a 22.5% annual decay rate:

The $22,000 figure does not include one of the most expensive downstream consequences of bad data: sender domain damage. Google's 2024 Sender Guidelines require bulk senders to maintain a spam complaint rate below 0.30%, with a recommended operating target below 0.10%.^[7]

Industries with high employee mobility, technology, professional services, financial services, and early-stage startups, see significantly faster decay than stable sectors like government, healthcare, or established manufacturing. Tech employees average around 2 years of tenure versus the 4.1-year US average across all sectors.^[4]

Seniority level creates a counterintuitive dynamic. Junior roles change frequently but are easier to re-find. Senior roles change less frequently, but are much harder to re-enrich when they do. Middle management sits in the worst position: mobile enough to decay quickly, senior enough to cause damage when data is wrong.

Treat data as perishable. Contacts older than six months should be flagged for re-verification before entering a sequence, not after a bounce.

Separate coverage from accuracy when evaluating data providers. A tool with 90% coverage but 70% accuracy delivers fewer usable contacts than one with 75% coverage and 95% accuracy.

Monitor deliverability as a leading indicator. Google's 0.10% spam complaint target is your operational ceiling. Teams that watch this metric per list and per list age can catch decay before it damages their domain.

Re-enrich rather than re-buy. Buying new lists every quarter while the existing database rots is an expensive habit. Re-enriching existing records is almost always more cost-efficient than starting from scratch.

Data decay is not a dramatic problem. It does not announce itself. It works quietly, record by record, until a meaningful share of your outbound investment is directed at people who are no longer where you think they are.