Part of Lusha’s ongoing measurement of B2B data decay. Every finding in one place: How fast B2B contact data decays.
All data 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 at the time of the signal. No third party estimates or extrapolated figures are used. Source: B2B Contact Change Report Q2 2026.
Between January 1 and June 1, 2026, Lusha detected 1,470,414 contacts who changed companies and 194,165 contacts who were promoted, across every function, industry and seniority level globally. That is an average of 13,600 contacts changing jobs every working day.
This report takes those numbers further. It maps which functions carry the highest commercial risk when contacts move, where the geographic concentration sits, the promotion problem few teams are tracking, and what the opportunity window looks like for a contact who just changed roles.
The numbers at a glance
Ratio of company changes to promotions: 7.6:1. For every contact promoted in place, 7.6 contacts changed companies entirely. Departures are the more common data quality problem in absolute terms. Promotions are the more invisible one.
Part one: the function by function risk matrix
Not all contact changes carry equal commercial risk. A sales rep moving companies is a data quality problem. A CIO moving companies is a deal altering event.
Sales: the highest volume problem
121,238 sales contacts changed companies in the window, the highest single function total in the database for this period. The commercially significant concentration is at director and head of level: the people who run regions, manage AE teams and carry relationships that survive vendor renewals.
Every sales contact who changes companies creates three problems at once. The departed contact’s record stays active in the CRM. The coverage gap at the old account goes unnoticed. And the new company opportunity, the window where that contact is assessing an inherited stack, goes untouched.
IT: the highest impact per change
60,384 IT contacts changed companies. The significant detail is not the volume, it is what happens when an IT leader moves. The new employer’s evaluation process often restarts from nothing, and the prior employer’s vendor selection, which may have been months in, loses its internal champion.
The fastest growing IT mobility categories in this window are cybersecurity leadership and data and AI leadership: chief data officer, chief AI officer, chief digital officer. The newest senior roles in technology, and the ones with the fiercest competition for people.
C suite: the lowest volume, the highest consequence
23,301 C suite and VP level contacts changed companies, 1.6% of all changes and the slice with the largest consequences.
A C suite departure creates four problems at once: deal risk at the current account if the contact was the economic buyer; a new opportunity if they evaluate the new employer’s stack in their first 60 to 90 days; CRM contamination as their record stays active across multiple accounts; and relationship map disruption when that contact was the only entry point.
Part two: the promotion problem nobody is tracking
194,165 contacts were promoted in the window. A promoted contact is still reachable; the email address and phone number may be unchanged. The problem is personalization, routing and scoring.
The 7.6:1 ratio tells the full story. Departures are more common. But a departed contact eventually reveals itself through a bounce or a returned call, and a promoted contact does not. It may go months before anyone notices the title is wrong, and by then the personalization, the routing and the score are all wrong together.
Part three: where contact mobility is highest
The Q2 2026 data shows a consistent geographic pattern, and a separate cohort measurement puts numbers on it.
Share of dataset from the Q2 2026 signal window. Measured annual rates from the cohort study in How fast does B2B contact data decay?, rerun September 12, 2026.
The UK sits first on both measures. A fluid labor market, a dense concentration of technology and financial services employers and a strong executive recruitment market together produce more contact turnover than any comparable European economy. A UK sales list goes out of date about 1.7 times faster than an equivalent US one.
For teams running the same refresh cadence everywhere, that means the data is worst in the market where it is hardest to notice, and nothing in the process says so.
Part four: the 30 day opportunity window
Every contact change is a data quality problem at the old company and an opportunity at the new one. When a contact changes companies, a buying window opens immediately and closes within roughly 30 days.
1,470,414 company changes means 1,470,414 of these windows opened in the first half of the year. A large share closed with no vendor reaching out, because nobody was watching the signal. A team that catches a change within 14 days, while the contact is still assessing, is working a different opportunity from a team that finds out via a bounce four months later.
The phase timings above are directional guidance based on observed patterns in contact movement, not a measured conversion curve. Treat them as a planning frame, not a number.
Part five: the stale contact estimator, and which rate to use
The totals above are global. The number that is useful to you depends on your list, and there are two rates you could apply to it. They differ by roughly nine times, so it is worth knowing which is which before you multiply anything.
The two rates
Why they are so far apart
The denominator, not the data. Dividing detected changes by the full contact base includes every record in the database, the bulk of which are not senior, not in a tracked market and not observed as closely. That makes the resulting rate a floor on detection across the whole base, and it is the right number if you want to know how much movement a database of that size produces.
It is the wrong number for planning, because nobody’s CRM looks like the whole database. A working contact list is concentrated in a few functions, a few seniority bands and a few countries, and those are exactly the segments that move fastest. The cohort rate measures one of those segments directly, with the population counted rather than estimated.
Use the cohort rate for planning
Take your active contact list size. Multiply by 0.0105, the measured monthly rate for senior sales contacts. Multiply by the months since your last full verification pass.
Example. 50,000 contacts, six months since the last pass: 50,000 × 0.0105 × 6 = about 3,150 contacts likely to have changed companies. At eight outreach attempts before a contact is marked unreachable, that is roughly 25,000 wasted attempts.
The same list run against the detection floor returns about 344. The gap between those two numbers is the gap between a plan and a comfort blanket.
Three adjustments to make it yours. Multiply by 1.7 for a UK list and by 1.12 for a marketing list. And if your list skews junior, the cohort rate will overstate it, because this measurement covers VP and C suite only.
Every figure here counts employment changes that Lusha detected. Title changes within a company, phone reassignments and email migrations are not included, so the rate at which records become wrong in some way is higher than either number above.
What the data means for GTM teams
1. Monitor sales contacts on a rolling cadence, not a quarterly one. 121,238 sales contacts changed companies in five months, more than any other function. At 13,600 changes a working day, a list verified at the start of a quarter has drifted noticeably by the end of it. Monthly verification for anything on an active deal or in a live sequence is the minimum the rate justifies.
2. Treat C suite and IT changes as commercial events, not CRM tickets. 23,301 executive changes and 60,384 IT changes represent a continuous stream of deal risks and new opportunities. The CIO who just joined somewhere is evaluating an inherited stack within 60 to 90 days. The champion who just left an open deal needs replacing before it goes dark. Both are sales actions.
3. Build promotion detection into scoring and routing. 194,165 promotions, none of which broke an email address, and almost none of which anybody spotted. A contact who crossed from director to VP is a different routing target, a different score and a different opening line.
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: a company change is a detected move to a new employer during the window. A promotion is a detected title change at the same employer during the window. Both are verified through Lusha’s multi source verification before being recorded.
Department classification: Lusha’s internal contact classification, applied at the time of the signal.
Geography: the contact’s location as recorded at the time of the signal, not the employer’s headquarters.
Two rates, stated: this report’s volume figures and the detection floor derived from them cover the whole database. The cohort rate used in the estimator comes from a separate measurement of a defined population, rerun September 12, 2026, and is the rate to use for planning. Part five explains the difference.
What this cannot tell you: only detected changes are counted, so every figure is a floor rather than a ceiling. Title changes, phone reassignments and email migrations are not included. The opportunity window timings in part four are directional guidance, not a measured conversion curve.
Privacy and compliance: all data is aggregated and anonymized. No individual contact details are included or derivable from the figures presented. Lusha is GDPR compliant, certified by ePrivacyseal GmbH and validated by TrustArc. Full documentation at lusha.com/trust-center.
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 cohort rate, measured twice
- Monitor CRM contacts for promotions and company changes
- Write the outreach when a contact changes jobs
- Find people who just changed roles in your target persona
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