Your CRM Data Is Decaying at 30% Per Year

You verified those email addresses two years ago. They were good then. Your marketing automation was humming along, deliverability was solid, and sales had accurate contact info for their accounts.

Now your bounce rates are climbing. Reps are calling disconnected numbers. Half the contacts in your key accounts have moved on to other companies. And you're not sure which half.

This is data decay. It's not a bug or a failure of process. It's physics. People change jobs. Companies get acquired. Email addresses stop working. And it happens faster than most companies realize.

That 30% number comes from multiple industry sources, including Gartner, Validity, and ZoomInfo. It's been consistent for years. And it means that every year, nearly a third of your contact data becomes unreliable.

What Decay Actually Looks Like

Data decay isn't abstract. It's specific, measurable, and expensive.

Job Changes

The average tenure in a job is now about 2.5 years, down from over 4 years in the 1980s. In tech, it's even shorter. And every job change means:

• The email address you have stops working
• The phone number (if it was a work number) no longer reaches them
• The job title is wrong
• The company association is wrong
• The buyer persona mapping is wrong

A CMO who leaves their company isn't just an invalid email. They're a completely different opportunity. They might be at a better-fit company now. Or a worse one. You won't know unless you update the data.

Company Changes

Companies don't stay static either. Every year, thousands of businesses:

• Get acquired (new parent company, often new domain)
• Merge (which company survives? which domain?)
• Rebrand (domain changes, company name changes)
• Go out of business (everyone's email stops working)
• Get restructured (divisions spin off, get absorbed)

When Salesforce acquired Slack, every @slack.com email eventually became @salesforce.com. Your contacts didn't tell you that. The data just silently became wrong.

Technical Changes

Even when people stay at their jobs and companies stay stable, technical infrastructure changes:

• Email domains change (company rebrands, consolidates)
• Phone systems change (new provider, new numbers)
• Office locations close (addresses become invalid)
• LinkedIn URLs change (people update their profile slugs)

The Compound Problem

The real issue with 30% annual decay isn't the first year. It's the compounding.

Data Age	Remaining Valid (est.)	Invalid Data
1 year old	70%	30%
2 years old	49%	51%
3 years old	34%	66%
4 years old	24%	76%
5 years old	17%	83%

If your CRM has been running for five years without serious data maintenance, roughly 80% of your oldest contacts are wrong. Not slightly wrong. Completely unusable.

Most CRMs have a mix of ages. Some records are recent and probably fine. Some are ancient and probably garbage. But you can't tell which is which by looking at them.

The Hidden Costs

Bad data doesn't announce itself. It just quietly makes everything worse.

Marketing Waste

Email to invalid addresses bounces. That's obvious. But the less obvious cost is deliverability. High bounce rates damage your sender reputation. Your emails start landing in spam, even for valid addresses. You're paying for email marketing that nobody sees.

Marketing automation sequences run against people who left the company years ago. Lead scoring gives points for engagement that will never happen. Campaign metrics are inflated by unreachable contacts.

Sales Waste

Reps call numbers that don't connect. They send emails that bounce. They research accounts based on outdated information. According to a Validity study, sales reps spend an average of 13 hours per week dealing with data quality issues.

Worse, they lose trust in the CRM. When data is wrong often enough, reps stop trusting any of it. They maintain their own spreadsheets. They don't log activities. The system becomes a compliance exercise instead of a useful tool.

Strategic Waste

Your ICP analysis is based on CRM data. Your market sizing uses it. Your account scoring relies on it. If the underlying data is 30-50% wrong, those analyses are unreliable.

You might be targeting the wrong segments because the data told you those segments converted well, when really it was just data quality issues masking the truth.

How to Measure Your Decay Rate

Before you can fix the problem, you need to understand how bad it is.

Run an Email Validation

Take your email list and run it through a validation service like NeverBounce, ZeroBounce, or Kickbox. They'll tell you what percentage of your emails are:

• Valid: Deliverable, working addresses
• Invalid: Hard bounces, dead addresses
• Risky: Catch-all domains, temporary addresses
• Unknown: Servers that didn't respond

Industry average is about 10-15% invalid for a reasonably maintained list. If you're seeing 25% or higher, you have a serious decay problem.

Check Last Activity Dates

In Salesforce, report on contacts grouped by their last activity date:

• No activity in 12 months
• No activity in 24 months
• No activity in 36 months
• Never any activity

Contacts with no recent activity are more likely to have decayed. If 40% of your database has no activity in 2+ years, you should assume a significant portion of that is dead data.

Sample and Verify

Take a random sample of 100-200 contacts that are 2+ years old. Manually verify them. Check if the email domain still exists. Check LinkedIn to see if they're still at the company. Call the phone numbers.

This is tedious, but it gives you ground truth. If 40 out of 100 sampled contacts are invalid, you have a 40% decay rate for that cohort.

What to Do About It

Data decay is inevitable. But letting it destroy your database is optional.

Establish a Baseline

Run the assessments above. Know your current state: email validity rate, percentage of stale records, estimated decay by age cohort. You can't improve what you don't measure.

Clean the Backlog

If you haven't done a serious data cleaning in years, start there. Validate emails. Remove or archive obviously dead records. Enrich gaps in firmographic data. This is a one-time project to get back to a usable baseline.

Implement Ongoing Maintenance

Quarterly data quality audits at minimum. Check email validity before major campaigns. Re-enrich key accounts annually. Set up processes to catch decay before it compounds.

Consider Automated Enrichment

Some tools can continuously monitor your CRM and update records when they detect changes (job changes on LinkedIn, company acquisitions, etc.). If you have budget for it, automated enrichment can significantly slow decay.

Accept What You Can't Control

You will never have 100% accurate data. The goal isn't perfection. The goal is keeping data fresh enough to be useful. If you can maintain 85-90% accuracy on core fields, you're ahead of most companies.

Common Questions

Related: How to Clean Salesforce Data | Data Enrichment Services | Email Enrichment