Part of Lusha’s ongoing measurement of B2B data decay. Every finding in one place: How fast B2B contact data decays.
All data in this report is drawn from Lusha’s live signal database. Signal counts reflect contacts with a detected company change or promotion event between January 1 and June 1, 2026. Department and seniority breakdowns use Lusha contact classifications. Geography is the contact’s location as recorded at the time of the signal. All data is aggregate; no individual names or employers are disclosed. Source: Lusha B2B Contact Change Report Q2 2026.
121,238 sales contacts changed companies between January and June 2026. That is the highest total of any function we report.
There is an irony in it that is worth sitting with. The lists B2B sales teams depend on more than any other are built from the function that moves more than any other. The data you use to sell is made of the people who keep changing jobs.
This report maps what those 121,238 changes mean in practice: which seniority levels carry which kind of risk, the three problems every departure creates at once, the window that opens at the new company, and the coverage gap that surfaces last and costs the highest.
What is counted and what is not. The totals here are counts from Lusha’s database. The 30 day window, the seniority risk descriptions and the tenure context are drawn from how this function behaves in practice, not values we measured. They are labelled separately throughout.
The numbers at a glance
Counts cover the functions we report. Other departments exist in the database and are not broken out here, so “first” describes this set rather than every function.
Part one: the highest volume data quality problem
Sales moves more than any other function we report, and the reasons are not mysterious.
Context, not measurement: commission structures tend to reward an external move over an internal promotion, equity concentrates at senior levels, and good operators are recruited away because the supply of proven sales leadership is thin. Industry reporting has average sales tenure falling for several years. We count moves rather than tenure, so we cannot confirm or size that trend from this data.
What the count does support is simpler. A CRM built on sales contacts needs verification more often than any other segment, and it usually gets it least, because departures surface through bounced emails, unreturned calls and a notification somebody happened to see.
Seniority: volume in one place, consequence in another
We have not published a seniority split of the 121,238 for this window. The descriptions above reflect where the concentration sits and what each level costs when it moves, not measured shares. For a measured seniority comparison, the decay study found US sales VPs and C suite within two tenths of a point of each other.
Director and head of level is where the volume sits. These are the people who run regions, manage AE teams and hold the relationships that survive a renewal cycle. When one moves, the accounts they owned personally often go quiet, and the AEs beneath them may have no direct relationship with those accounts at all.
VP and above is where the cost sits. A VP of Sales who moves does not only leave a stale record. They take the strategic vendor relationship with them, and the person who replaces them usually arrives with a view about what the stack should look like.
Part two: three problems, one departure
Every sales contact who changes companies creates three problems at the same moment, and they surface in the wrong order: the cheapest one first, the dearest one last.
Part three: the window at the new company
The problem at the old company is immediate. The opening at the new one has a clock on it.
The 30 days below is a planning frame, not a measurement. Our database counts moves; it does not track when a new joiner locked in a stack or which outreach arrived when. The phases describe how these first months tend to run and are offered on that basis.
Detection lag, and what “day 8” really means
Arriving in week two requires knowing in week one, and our own measurement says that is not guaranteed for any dataset, ours included.
Measuring job change rates across three windows, the three month window returned about half the monthly rate of the twelve and twenty four month windows. Job changes do not slow down in recent months, so the gap is detection lag: a change takes time to surface anywhere that observes employment rather than being told. The measurement is here.
So two things. Checking continuously still beats waiting for a bounce, because it is deliberate rather than accidental. And “detected on day 3” is not “started on day 3”, so use the start date where you have it and treat detection as a floor when you do not.
Part four: the coverage gap
The third problem surfaces last and costs the highest. When a sales contact leaves an account, the selling organisation often has nobody else there. The account is not flagged at risk. It is simply not worked, until a renewal falls due, a pipeline review turns it up, or an email bounces.
121,238 changes in five months is 121,238 chances for that to have happened somewhere. On any given day a team with a large contact base has some share of accounts where the primary contact has moved and nobody has noticed.
The expensive version. A renewal account where the primary contact changed months ago, the new one has never heard of the vendor, and the first thing they receive is a renewal reminder addressed to the person they replaced.
Part five: what to do with this
1. Check sales contacts at director level and above on a rolling cadence. Director and head of is where the volume sits; VP and above is where the cost sits. Both deserve checking on any active deal, open renewal or named account. On your own book the number of changes will be small, which is the point: a short list of accounts where something specific just happened. Quarterly verification does not keep up with a function moving at this rate.
2. Build the new company motion before the window closes. Every sales contact who joins somewhere new opens a window that closes fast. Detecting the move and reaching out in the second week is a different conversation from finding out through a notification three weeks later. That requires checking a live source rather than waiting to notice.
3. Audit coverage gaps when a contact departs, not at renewal. The gap is invisible in the CRM until somebody looks. Run a weekly check on named accounts where the primary contact has been flagged as departed, identify the replacement, verify them and assign them before the account goes quiet.
What we did not measure
Seniority split. We have not published director against VP counts within the 121,238 for this window. The descriptions above reflect where concentration sits, not measured shares.
Tenure. References to falling sales tenure are industry context, labelled as such. We count moves.
The 30 day window. A planning frame drawn from how first months run, not a measured conversion curve.
Undetected moves. Only detected changes are counted, and detection lags reality, so 121,238 is a floor.
Methodology and data notes
Data source: Lusha live signal database, 515M+ verified contacts, signals pulled June 1, 2026.
Signal window: January 1 to June 1, 2026, 108 working days.
Signal definitions: company change signals reflect contacts where a move to a new employer was detected during the window. Seniority and department classifications use Lusha’s internal taxonomy, applied at the time of the signal.
Privacy and compliance: all data is aggregated and anonymised. No individual contact details are included or derivable from the figures presented. Current certifications are listed on the trust center.
Source report: B2B Contact Change Report Q2 2026
Related resources
- How fast B2B contact data decays, and what it costs — every report on this, in one place
- How fast does B2B contact data decay? — the measured rate, by function and region
- Monitor CRM contacts for promotions and company changes
- Write the outreach when a contact changes jobs
- Find who owns an account after your champion leaves
