B2B data decay: what we measured against 148,000 records

Every B2B data vendor cites the same statistic: contact data decays at roughly 30% a year. We have cited it ourselves. It appears in dozens of vendor blogs, in sales enablement decks, in pitch material across the category.

We went looking for the original study behind it. There isn’t one we could find. The figure appears without primary attribution wherever it is repeated, each source citing another source citing another.

So we measured it against our own database instead. Here is what came back.

The short version: among US Sales leaders we track, 12.6% changed roles over twelve months, and 25.7% over twenty-four. The widely-repeated 30% figure is close to a two-year rate, not an annual one. The monthly rate is stable at about 1%, and it varies more by country than by anything else we measured — UK Sales leaders changed roles at 1.73 times the US rate.

Every figure on this page was measured live against Lusha’s contact database on August 15, 2026. Methodology, sample sizes, and limitations are stated in full at the end. No individual contacts are named.

The curve: about 1% a month, and it holds

We took a single cohort — 140,964 US Sales leaders at VP and C-suite level — and asked the same question over three different time windows.

The twelve and twenty-four month figures agree almost exactly at just over 1% per month. That agreement is what makes the annual number trustworthy: it isn’t an artifact of where we drew the window.

And 25.7% over two years is close enough to the industry’s 30% that the likely explanation is simple. The number is roughly right. It is being quoted over the wrong period.

The three-month figure is lower, and that is a finding in itself

At three months the rate drops to 0.50% per month, half the longer-run figure. Job changes do not slow down in recent months, so this is a detection lag: a change takes time to surface in any dataset that observes employment rather than being told about it directly.

The practical consequence is worth sitting with. A record that looks current may already be wrong. Recency of a refresh is not the same as accuracy, because the newest changes are the ones least likely to have been captured yet. That is an argument for verifying at the point of use rather than trusting a refresh date.

Where you sell changes the answer more than what you sell

We ran the same twelve-month measurement on Sales leaders in four countries.

A UK sales list goes out of date 1.73 times faster than an equivalent US one. If you run the same quarterly refresh cadence across both territories, your UK data is meaningfully worse than your US data at every point in the cycle, and nothing in your process would tell you.

One detail here is worth flagging because it addresses the obvious objection. The UK and Germany cohorts are almost identical in size — 4,223 and 4,204 — and return different rates, 21.7% against 17.2%. If the regional variation were simply an artifact of how much data we hold per country, two cohorts of the same size would not diverge by four and a half points. The variation appears to be in the underlying labour markets rather than in the measurement.

By function: Marketing moves fastest, Engineering slowest

A 1.42x spread between the fastest and slowest function. If you sell to marketing leaders, your list decays noticeably faster than if you sell to engineering leaders, and a single blended refresh policy will over-serve one and under-serve the other.

What did not vary: seniority

We expected seniority to matter. It doesn’t.

Two tenths of a percentage point apart. We are publishing this because a null result is still a result, and because the assumption that senior people are more stable — or less stable — is one we have heard often enough to be worth testing. On this evidence, seniority is not a useful input to a refresh policy. Country and function are.

What to do with this

Stop budgeting refresh cadence off a single annual figure. On these numbers a US Sales list is about 6% wrong at six months, 12.6% at twelve, and 25.7% at twenty-four. A UK list reaches that same 12.6% in roughly seven months.

Set cadence by territory before you set it by anything else. Region was the strongest variable we measured, at 1.73x between the fastest and slowest markets. A uniform global refresh policy is systematically under-serving your fastest-moving territory.

Verify at the point of use for anything high-stakes. The three-month detection lag means a recent refresh date is weaker evidence than it looks. For a list going into a sequence, a check at send time catches what a scheduled refresh has not yet seen.

Ignore seniority when planning cadence. It does not predict anything useful here.

Methodology

Every figure comes from Lusha’s contact database, measured on August 15, 2026, through the same API our customers use.

What we measured. For a defined cohort — a department, a seniority band, a country — we counted the total population, then counted how many of those contacts have an employment change recorded within a given window. The ratio is the job change rate.

Cohort definitions. Seniority is Vice President and C-suite unless stated otherwise. Function is the department classification on the contact record. Country is the contact’s location, not the company’s headquarters.

Sample sizes are stated in every table. The largest cohort is 148,011 contacts and the smallest is 4,204. Nothing here is extrapolated from a sample; these are counts of the full population within each cohort.

Total measurement cost: 15 API calls. The method is reproducible by any Lusha customer against their own filters.

What this data cannot tell you

Four limitations, stated plainly, because a benchmark that hides them is not worth citing.

This is a job change rate, not a full decay rate. We measured employment changes. A record can also go stale through a title change within the same company, a phone number changing, or an email format migration. Those are not counted here, which means the true rate at which records become wrong is higher than the figures above.

It measures detected changes. A change we have not yet observed is not in the count. The three-month figure demonstrates this directly. Every number here is a floor rather than a ceiling.

The denominator is the present-day population, not a cohort fixed twelve months ago. A true longitudinal measurement would define the group at the start of the window and follow it forward. We measured the group as it stands today and looked backward. These are close but not identical, and the difference is worth knowing before anyone cites the figure as a decay rate.

Four countries and three functions is a narrow slice. The regional variation we found is striking enough that we would want a wider set before treating 1.73x as the outer bound. It may not be.

Why we published a lower number than the one we had been using

The figure we measured is well below the one this category repeats, including on our own pages, which we are now correcting.

A smaller number is less useful for creating urgency. It is more useful for planning, which is what the number is actually for. And a figure with a stated methodology, stated sample sizes, and stated limitations is worth more than a larger one that nobody can trace to a source.

If you find an error in this, we would like to know. The method is in the section above and the cohorts are reproducible.

Go deeper

• Refresh stale CRM records before you reach out — the prompt that applies this to a list you already hold
• Enrich CRM records with verified B2B data — how enrichment and refresh differ, and which you need
• Buying signals — job changes as a trigger rather than a problem

Want to run this measurement against your own territory and function? See what Lusha’s connector covers

FAQ

How fast does B2B contact data actually decay?

We measured 12.6% of US Sales leaders changing roles over twelve months, and 25.7% over twenty-four. That works out to roughly 1% per month, and the rate holds steady across both windows. The figure varies by market — UK Sales leaders changed roles at 21.7%, against 12.6% in the US. Note this counts employment changes only, so the rate at which records become wrong in some way is higher.

Where does the 30% data decay statistic come from?

We could not find a primary source for it. It appears across vendor blogs, sales decks, and pitch material throughout this category, each citing another secondary source. What our measurement suggests is that the number is roughly right but attached to the wrong period: 25.7% over twenty-four months is close to 30%, while the annual figure is about half that.

How often should I refresh my CRM contact data?

Set the cadence by territory before anything else, since region varied more than any other factor we tested. On our numbers a US Sales list is around 6% wrong at six months and 12.6% at twelve; a UK list reaches that same 12.6% in roughly seven months. For lists going into a sequence, a check at send time catches what a scheduled refresh has not yet seen.

Does a recent refresh date mean the data is accurate?

Less than you would expect. Our three-month measurement returned half the monthly rate of the longer windows, which is a detection lag rather than a real slowdown — the newest job changes are the ones least likely to have surfaced in any dataset that observes employment rather than being told about it. A record that looks current can already be wrong.

Which job functions have the highest turnover?

Among US VP and C-suite contacts: Marketing at 14.1%, Sales at 12.6%, Engineering and Technical at 9.9%. A 1.42x spread between fastest and slowest, which means a single blended refresh policy over-serves one function and under-serves another.

Do senior people change jobs less often?

No meaningful difference in our data. US Sales VPs came in at 12.6% and C-suite at 12.4% — two tenths of a point apart. Seniority is not a useful input to a refresh policy; country and function are.