Contact data goes stale on its own, and a list that is not refreshed gets worse every month.
Key points
- HubSpot's database decay simulation, citing MarketingSherpa research, uses a rate of 2.1% per month, or about 22.5% per year, for B2B data [1].
- The main causes are job changes, promotions, company acquisitions and closures, rebrands and domain changes.
- Decayed email addresses cause hard bounces, which raise Bounce Rate and hurt Sender Reputation [4].
- Data quality frameworks treat timeliness as a core dimension of quality, alongside accuracy and completeness [2].
- Under GDPR, personal data must be accurate and, where necessary, kept up to date [3][5].
- The fixes are regular Email Verification, re-enrichment and suppressing addresses that bounce via a Suppression List.
How fast data decays
Estimates vary, but all agree the loss is significant. HubSpot's database decay simulation uses a figure of 2.1% per month from MarketingSherpa research, which compounds to about 22.5% per year [1]. Some studies that re-check the same records continuously report higher figures. The rate depends on the audience: startup employees and sales staff change roles more often than, say, long-tenured finance leaders, so a Lead List of fast-growing SaaS companies tends to decay faster than average. Firmographics also shift as companies hire, shrink or get acquired. The practical conclusion is that any list older than a few months should be treated as partly wrong until it has been re-verified.
Costs of stale data
Decay has direct and indirect costs. Directly, emails to departed employees bounce, and a high Bounce Rate tells mailbox providers you are not maintaining your list, which can reduce Inbox Placement for all your mail. Twilio SendGrid's documentation notes that bounces are tracked and suppressed because repeated sending to invalid addresses harms reputation [4]. Some abandoned addresses are eventually recycled as spam traps, which is worse. Indirectly, stale records waste rep time, distort Lead Scoring and reporting, and send messages to the wrong person at the right company. Wikipedia's treatment of data quality lists timeliness as one of the core dimensions for exactly this reason [2].
Keeping data fresh
Good practice is to verify email addresses shortly before sending rather than only at import, to re-enrich important accounts on a schedule, for example with Waterfall Enrichment across several providers, and to act on bounces immediately by adding them to a Suppression List. Replies such as out-of-office messages saying someone has left are useful updates and should change the record, and can prompt a search for the successor with an Email Finder. Privacy law points the same way: GDPR Article 5 requires personal data to be accurate and kept up to date, and to be erased or corrected without delay when inaccurate [5]. The UK regulator's guidance on the accuracy principle adds that you should take reasonable steps to check accuracy, especially where the data affects decisions [3]. Working from fresh, recently found prospects reduces the problem at the source.
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