What is data decay rate?
Data decay rate is the percentage of your CRM records that become inaccurate over a given period, as people change jobs, companies change, and contact details go stale.
Formula
Data decay rate = Records that became inaccurate ÷ Total records checked
Example: If you check 1,000 contacts and 25 are no longer accurate after one month, your monthly data decay rate is 2.5%.
Data decays whether anyone is watching it or not. A commonly cited range for B2B contact and account data is around 2 to 3% a month, which compounds into a noticeably less accurate database within a single year if nobody corrects it.
Decay is the quiet mechanism behind a lot of CRM pain. Teams that spend hours each week fixing records by hand are fighting decay in real time, usually without anyone officially owning the job.
AI does not slow decay down. An agent or scoring model reading a decayed record acts on it with full confidence, so the bad outcome arrives faster than it would have with a person in the loop.
The link to CAC is direct. Rep time spent correcting records is acquisition cost with no new pipeline. Outreach sent to someone who changed jobs months ago is spend that never had a chance to convert.
What to do about it
- Sample 100 records a month and measure how many are wrong.
- Give one person ownership of your highest-value fields.
- Review decay before letting AI act on CRM data.
Frequently asked questions
What is a normal data decay rate?
For B2B contact data, around 2 to 3% a month is a commonly cited range. Fast-moving industries and job roles often decay faster.
How does data decay affect CAC?
It adds cost without adding customers. Time spent fixing records and outreach sent to stale contacts both count as acquisition spend that produced nothing.