Why B2B Lead Data Goes Stale (And How Fast)

A lead list is a photograph, not a map. It records what was true on the day it was collected, and from that moment it starts drifting away from reality.

Most people know this abstractly and act as though it isn't true — which is how a list bought eighteen months ago ends up in a campaign, and how a bounce rate of 14% becomes a mystery.

The Published Numbers

There's no single authoritative figure, and the range in the literature is wide enough to be worth knowing about.

The most commonly cited benchmark comes from HubSpot's database decay work, which puts contact data decay at around 2.1% per month, compounding to roughly 22.5% a year. ZoomInfo has published figures in the 25-30% per year range. Some more recent vendor studies using more frequent re-verification report considerably higher rates, though it's worth noting those studies are generally published by companies selling data refresh services, and more frequent sampling will always catch churn that an annual snapshot misses.

The honest summary: somewhere around a fifth to a third of B2B contact data goes wrong within a year, and the exact figure depends on your market and how you define "wrong".

What Actually Decays

The headline number hides the fact that different fields fail in different ways and at different speeds.

Named contacts decay fastest, because people change jobs. This is the primary driver in most published estimates, and it's why enterprise B2B data ages so badly — you're tracking individuals, and individuals move.

Email addresses decay with the people attached to them, and also independently when a company changes domain or email provider.

Phone numbers are stickier. A business landline can outlast several owners.

Physical addresses and business names decay slowly but catastrophically — slowly because businesses don't move often, catastrophically because when they do, everything else changes too.

Local Business Data Decays Differently

Most published decay research is about enterprise contact databases, where the dominant failure is job changes. Local business data behaves differently, and mostly better.

You're usually holding a generic business contact rather than a named individual, so the job-change problem largely disappears. A restaurant's info@ address survives the head chef leaving.

What you get instead is business closure, which is more absolute. There's no updated record to find — the business is gone. UK small business closure rates run at several percent a year across most sectors, with hospitality and retail well above average. That's a smaller decay rate than enterprise contact churn, but a harsher one, because a closed business isn't a stale record you can refresh.

The second local-specific failure is rebranding and ownership change. Salons, cafés, and gyms change hands and change names frequently, keeping the same premises and phone number. Your record isn't dead, it's wrong in a way that's embarrassing rather than undeliverable.

Why This Argues for Scraping Over Buying

If data decays at 20-30% a year, the age of a list matters more than its size.

A purchased list has been collected, packaged, and resold, and you generally have no reliable way to know when the underlying data was gathered. A list assembled from a directory today reflects that directory today. That gap is the entire practical case for building your own list rather than buying one, and it's covered properly in buying lead lists vs scraping your own.

It also argues against hoarding. A lead list is not an asset that appreciates. Holding twenty thousand contacts you scraped last year is worse than holding two thousand you scraped last week, because the large list carries an unknown error rate you'll discover through bounces.

Under UK GDPR there's a data minimisation angle too — keeping personal data indefinitely because you might use it one day is difficult to justify.

What To Actually Do

Date-stamp every list at collection. Without that field you can't reason about any of this.

Re-scrape rather than refresh for local business data. Since you're not tracking individuals, re-running the search is usually cheaper and more accurate than trying to verify records one by one.

Treat anything older than six months as suspect and anything older than a year as needing requalification before use. Spot-check twenty rows before committing to a campaign — the lead list quality checklist covers what to look at.

And clean at the point of use rather than at the point of collection, because the cleaning that mattered when you scraped it may not be the problem a year later. The scraped data cleaning guide covers that pass.

Working From Current Data

LeadSnipe re-runs the search rather than serving a stored list, so what you get reflects the directory now rather than whenever a database was last refreshed. Results are cached for a week to avoid re-paying for identical searches, then re-fetched.