Everything on this page is measured against Lusha’s own database, with sample sizes and limitations stated. Nothing is estimated or borrowed from another vendor. Last updated September 2026.
TL;DR
- What we measure: how often B2B contacts change jobs or get promoted, counted directly in Lusha’s database rather than estimated. Two methods, described below, because they answer different questions.
- The rate: 12.25% of US sales leaders changed roles over twelve months and 25.67% over twenty four. That is about 1% a month, and it held when we re-ran it a month later.
- The volume: 1,470,414 contacts changed companies and 194,165 were promoted between January and June 2026. An average of 13,600 job changes every working day.
- Who moves fastest: by function, marketing then sales then engineering. By region, UK sales leaders changed roles 1.75 times as often as US ones. By seniority, no meaningful difference at all.
- What to do: set refresh cadence by territory before anything else, and verify at the point of use for anything going into a sequence, because the newest changes are the ones least likely to have surfaced yet.
The number everyone repeats, and where it comes from
Contact data decays at roughly 30% a year. It appears in vendor blogs, enablement decks and pitch material across this category. It has appeared on our own pages.
We went looking for the study behind it and could not find one. Every source cites another source citing another.
So we measured it instead, against 148,000 records, and published the method. The annual rate for US sales leaders came back at 12.25%. Over twenty four months it is 25.67%, which is close enough to 30% that the likely explanation is simple: the number is roughly right and attached to the wrong period. It is closer to a two year rate being quoted as an annual one.
We are correcting it wherever it appears on our own site, including on reports published before we measured it. A smaller number is worse for urgency and better for planning, which is what the number is actually for.
→ How fast does B2B contact data decay?
Two methods, two different questions
Two kinds of measurement run across these reports, and they produce numbers about nine times apart. Knowing which is which is the difference between a plan and a comfort blanket.
The gap is the denominator, not the data. Dividing by the whole database includes every record in it, and a working contact list looks nothing like that: it concentrates in a few functions, seniority bands and countries, which are exactly the segments that move fastest.
For planning, use the rate. Both numbers are floors rather than ceilings, because a change nobody has detected is in neither.
It held when we re-ran it
A measurement published once is a claim. A measurement that survives a re-run is a rate.
Four weeks after the first pull we ran the same four cohorts again, same filters, six API calls. The twenty four month rate moved from 25.7% to 25.67%: three hundredths of a percentage point across 140,000 people. The regional gap moved from 1.73x to 1.75x. The function gap did not move at all.
The twelve month figures each drifted down about a third of a point, and we do not know why. Summer seasonality, a window effect and detection lag all fit the data equally well, and two data points cannot separate them. We said so rather than picking one, and we will publish the next reading rather than forecasting it.
Sales moves fastest
121,238 sales contacts changed companies in five months, the highest total of any function we report. There is an irony in that: the lists B2B sales teams depend on more than anyone are built from the function that moves more than anyone.
Every departure creates three problems at once, and they surface in the wrong order. The stale record shows up first, when something bounces. The opening at the new company never shows up unless somebody is watching. And the coverage gap at the old account surfaces last, usually once a renewal is already at risk.
→ How often do sales contacts change jobs?
IT moves with the largest consequence per change
60,384 IT contacts changed companies in the same window. When a sales contact leaves, a record goes stale. When an IT leader leaves, an evaluation loses the person running it, and a second one starts somewhere else.
The expensive version is not a bounce. It is a selection process that resets quietly while every vendor in it still believes it is running.
→
C suite is 1.6% of moves and the bulk of the exposure
23,301 contacts at C suite and VP level changed companies. A rounding error by volume, and the largest exposure on the list, because those are the people who approve the contract and carry the relationship.
Across four named roles, the CIO moved more than any other at 869 and the CISO least at 287. But the tightest window belongs to the CRO, not because CROs move more often than the others, but because a new CRO runs a stack review inside the first 30 to 60 days.
→ Which C suite change opens the tightest window?
→ How often do CROs change companies?
→ Which contact changes actually put a deal at risk?
Promotions are the invisible half
194,165 contacts were promoted in the same window. The ratio is 7.6 company changes for every promotion, so departures are the larger problem by volume and promotions are the harder one to see.
A departed contact eventually announces itself with a bounce. A promoted one does not. The email still works, so nothing breaks, and the sequence that opens “I know you’re running the sales team” lands with someone who now runs the whole revenue org.
→ How often do B2B contacts change jobs?
What does not decay, and what we got wrong looking for it
Decay is one half of the question. The other is whether the record was right to begin with.
We enriched 125 real US contacts across two metros and two company sizes, then 197 more across four industries, using a strict definition: a hit required an actual revealed value, not a field flagged as available. Work email resolved for every contact in both tests. Mobile phone held between 93.9 and 100 percent in every segment.
Two findings came out of that, and the second one is the useful one.
Geography, company size and industry barely moved the numbers in the ranges we tested. An early 15 contact pilot suggested a real regional gap at 86.7 percent; expanding the same segment to 47 contacts moved it to 97.9 percent. Two missed contacts had swung a small sample by twelve points. We published both numbers rather than replacing one with the other.
What actually went stale was the job title and the employer, not the contact details. In every case where search and enrichment disagreed, the email and phone were correct and the role was out of date. That is the opposite of what teams usually verify, and it follows directly from the decay rate: contact details survive a job change, titles do not.
→ How accurate is B2B contact data in the US?
→ Does B2B contact data quality vary by industry?
What to do about it
Three tiers, set by what the record is worth, and by territory before anything else.
Active deal contacts: check monthly. At about 1% a month, a team holding 1,000 contacts on live deals should expect roughly 31 of them to go wrong each quarter. Finding a departed champion during the call is the expensive version. → Find every account where the main contact has gone dark or left
Territory contacts: check quarterly, adjusted by region. A quarterly refresh catches roughly 3% of a US list. A UK list moves 1.75 times faster, so the same cadence leaves it in worse shape at every point in the cycle, and nothing in the process tells you. → Refresh stale CRM records before you reach out
Campaign lists: check at send time, not on a schedule. Our three month measurement returned half the monthly rate of the longer windows, which is detection lag rather than a real slowdown. A recent refresh date is weaker evidence than it looks. → Validate emails before a campaign
And verify the title, not only the address. → Verify a contact’s title before you reach out
The other half of the ledger: every one of those 1,470,414 moves is also somebody arriving somewhere new with a stack to evaluate. → Find people who just changed roles in your target persona
FAQ
How fast does B2B contact data decay?
Measured on our own database: 12.25% of US sales leaders changed roles over twelve months and 25.67% over twenty four, which works out to about 1% a month. It varies by market, from 12.25% in the US to 21.40% in the UK. This counts employment changes only, so the rate at which records become wrong in some way is higher.
Where does the 30% data decay figure come from?
We could not find a primary source. It appears across vendor material with each source citing another secondary source. Our measurement suggests it is roughly right for a two year period and about double the real annual rate.
Which function moves fastest?
Marketing, at 13.73% a year among US VP and C suite contacts, against sales at 12.25% and engineering at 9.9%. By raw volume of moves, sales is the largest at 121,238 in five months.
How often should I clean my CRM?
Set the cadence by territory first, since region varied more than any other factor we tested and held steady when the absolute rates moved. Monthly for contacts on active deals, quarterly for territory lists, and at send time for anything going into a campaign.
What does a stale record cost?
Three things that are easy to miss. Bounces accumulate as domain damage and reduce deliverability for everything sent afterwards. A deal whose champion left mid cycle is far less likely to close, and few CRMs flag the departure. And every person who moved and was not detected is a warm opportunity at their new employer that nobody worked.
Is the contact data itself unreliable?
Not in what we tested. Across 322 real US contacts, work email resolved every time and mobile phone held between 93.9 and 100 percent. What went out of date was the job title and the current employer, which is a different failure mode and needs a different check.
How do I check whether a contact is still there?
Run a job change check against the list before you use it. It costs one credit per detected event, and the plays linked above run a free usage check before they start.
Every report in this series
- How fast does B2B contact data decay? — the rate, by function, region and seniority, measured twice
- How often do B2B contacts change jobs? — the volume: 1,470,414 changes and 194,165 promotions
- Which contact changes actually put a deal at risk? — the same moves mapped by function and commercial risk
- How often do sales contacts change jobs? — the busiest function
- — the largest consequence per move
- Which C suite change opens the tightest window? — CIO, CMO, CRO and CISO compared
- How often do CROs change companies? — the role with the shortest evaluation clock
- How accurate is B2B contact data in the US? — 125 contacts across metros and company sizes
- Does B2B contact data quality vary by industry? — 197 contacts across four industries
Want to run this measurement against your own territory and function? It is six API calls. Add the Lusha plugin to Claude · Browse the plays
