[GTM glossary](/glossary.txt) Data decay

Data & Enrichment

# Data decay

In short

Data decay is the steady process by which stored records become inaccurate because the real world changes while the record does not. People switch jobs, change titles, and drop email addresses. Companies move, merge, rebrand, and change size.

In a B2B CRM, decay is constant and quiet. No alert fires when a champion leaves. The record just keeps looking valid until an email bounces or a rep calls the wrong person.

## What causes data decay

* **Job changes:** Contacts move companies or roles, so titles and emails stop being valid.
* **Company changes:** Mergers, rebrands, relocations, and layoffs change firmographic fields.
* **Domain and email changes:** Addresses are deactivated or switched after acquisitions.
* **Human entry:** Typos and half-filled forms add errors from day one.

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## Why it matters for GTM teams

Decay hurts every motion. Outbound sequences bounce, which damages sender reputation. Territory and scoring models use outdated size or industry values. Marketing sends to people who left. Reps lose time on dead leads and miss the new role of a former champion.

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## How to slow data decay

### Re-verify on a schedule

Set a refresh cadence based on how fast a field changes. Contact emails and titles change faster than industry or country, so refresh them more often.

### Refresh before use

Verify a list right before a campaign instead of trusting last year's export.

### Track job-change events

Watching for role changes lets you update records and reach champions at their new company.

### Monitor bounces

Feed bounce and invalid-number results back into the CRM so bad records are flagged automatically.

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## A simple way to estimate your own decay

Take a random sample of records, say 100 contacts, and verify them today. If 15 have a changed job or a dead email, your sample shows a 15% decay rate for that period. Repeat the check each quarter to see the trend. The number is only valid for your own data, so avoid applying industry averages.

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## Common mistakes

* **One-time cleanup:** A single big clean without a recurring process decays again.
* **Treating all fields equally:** Refresh frequency should match how quickly each field changes.

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## Data decay FAQs

**What is data decay in B2B?**

Data decay is the gradual loss of accuracy in stored contact and company records. It happens because people change jobs and companies change details, while the CRM record stays the same until someone updates it.

**What causes data decay in a CRM?**

The main causes are job changes, company mergers and rebrands, deactivated email addresses, and manual entry errors. Records also decay when nobody owns refreshing them, so small changes pile up unnoticed.

**How do you prevent data decay?**

You cannot stop it, but you can slow it. Re-verify records on a schedule matched to each field, refresh lists before campaigns, track job changes, and feed bounces back into the CRM so invalid records are flagged.

**How fast does B2B data decay?**

It depends on the field and your market, so no single rate fits everyone. Sample your own records, verify them, and measure how many changed. Repeating that quarterly gives a rate you can plan refresh cycles around.

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## More related phrases

* [CRM hygiene](/glossary/crm-hygiene.txt)
* [Batch enrichment](/glossary/batch-enrichment.txt)
* [Contact data](/glossary/contact-data.txt)

## Similar phrases

* [B2B data erosion](/glossary/b2b-data-erosion.txt)  
B2B data erosion is the gradual loss of accuracy in company and contact records as people change jobs and companies change, which hurts outreach and reporting.
* [Data freshness](/glossary/data-freshness.txt)  
Data freshness describes how recently the values in a record were collected or verified, and whether they still reflect the current reality of a company or contact.
* [Data quality](/glossary/data-quality.txt)  
Data quality describes how well a dataset is fit for its intended use, measured by traits such as accuracy, completeness, consistency, timeliness, and uniqueness.
* [Data hygiene](/glossary/data-hygiene.txt)  
Data hygiene is the ongoing practice of keeping CRM and marketing data clean, accurate, consistent, and usable through regular checks, standards, and ownership.
* [Data accuracy](/glossary/data-accuracy.txt)  
Data accuracy is how closely the information in your records matches reality, such as whether an email, job title, or company size is correct, and it determines whether outreach reaches real buyers.