What it is
Data decay is the rate at which stored records in a CRM or ATS lose accuracy over time, as the people and companies behind them change without anyone updating the entry. A decision-maker moves to a new employer, a headcount figure shifts after a restructuring, a phone number is reassigned, a title changes after a promotion, and the record sitting in your system carries on stating what used to be true. Nobody edited it wrong. It simply stopped being current.
The rate is faster than most desks assume. Industry modelling puts overall B2B contact and company data decay at roughly 2.1% a month, compounding to around 22.5% a year, and individual fields decay faster still: job titles by 25-35% a year, phone numbers by 20-25%, email addresses by as much as 43%. A list built or bought in January is already meaningfully wrong by the summer, and by year end a large share of it is unusable, without anyone having done anything wrong along the way.
A contact list bought once starts decaying the moment it lands, whether or not anyone opens it again.
Why it matters
Data decay is the reason a one-off data purchase is a depreciating asset from the moment it lands. A list enriched once and left alone starts accurate and gets worse every week after, quietly, with no alert to say a field has gone stale. Outreach built on it inherits the rot: a message to a decision-maker who left the company months ago, a pitch pegged to a headcount figure that no longer matches the business's stage, a signal read against an org chart that is already out of date.
The damage runs deeper than a wasted send. Decayed data erodes the very things it is meant to support. Lead scoring ranks accounts on fields that may no longer be true. A buying signal loses its value if the contact record behind it points at the wrong person entirely. And a message that looks personalised lands worse, not better, when the personalisation turns out to be months out of date, because it signals that the account was never actually looked at recently.
How boilr handles it
boilr treats enrichment as continuous, not a one-off purchase. Your AI sales employee re-checks accounts and contacts on an ongoing basis rather than enriching once and filing the result away, and every buying signal it detects triggers a fresh look at the account it fired on, so a funding round or a leadership change prompts an update instead of sitting next to data that predates it. Every field carries a data lineage record of its source and last verification, so staleness is visible rather than hidden inside a number that still looks fine.
That continuously refreshed state lives in the Company Brain, shared across the whole desk rather than sitting in one consultant's export or spreadsheet. When any part of the agent touches a record, the correction benefits every future task built on that account, and a consultant leaving does not take the freshest version of the truth with them. A task that reaches your inbox is built on what is true now, not on what was true the last time someone happened to look.
