B2B data accuracy: how job changes make contact data go stale

Why B2B contact data goes stale

Contact data goes stale because people change jobs, and the records do not change with them. A contact who moves takes their title, email, phone and buying authority with them. The record in your CRM keeps all four, now wrong.

How fast that happens depends on how long people stay. The US Bureau of Labor Statistics put the median tenure with a current employer at 4.1 years in January 2026, up from 3.9 years in 2024, and at 4.9 years for management and professional occupations, the group most B2B lists target. As a rough rule, if a typical contact stays four to five years, about 1.7 to 2 percent of a list changes employer every month. (Median tenure is not the same as the average turnover rate, so treat this as an estimate. Younger staff, startups and high-turnover industries move faster.)

Job changes inside the same company add to it: a promotion, a new team, a new territory. The person still works there, but the role you were selling to is gone.

The corrected math

The first version of this post, published in June 2024, said people change jobs every 2.73 years and that this works out to about 4 percent of contacts a month. It does not: 2.73 years is 32.8 months, and 1 divided by 32.8 is about 3 percent a month. The 2.73 figure also had no source. This version uses the BLS figures above and keeps the rest of the model, with the arithmetic fixed.

There are two separate gaps, and they add up differently.

Gap 1: people who moved since your last refresh. This compounds with time. The share of a list that has changed employer since it was last verified is 1 minus (1 minus the monthly rate) raised to the number of months:

Gap 2: people who moved but have not updated their public profile yet. This one does not grow without limit, because people do update eventually. The original model assumed half of movers update in the first month and 30 percent of the rest update each month after. At those assumptions, the share of a list in this gap settles at about 1.7 times the monthly rate: about 3.3 percent at 2 percent a month, 5 percent at 3 percent. That is what a fresh pull from public profiles misses even on the day it is made.

The original post also added up its month-by-month figures (2 percent, 3.4 percent and 4.38 percent) into a 9.78 percent "calculated" stale rate. Those figures are running totals, not separate amounts, so adding them counts the same people more than once. On the post's own assumptions, the stale share after three months was 4.38 percent, not 9.78.

What graph8 measured

When this post was first published, graph8's data showed that about 12 percent of contacts in a typical customer list were no longer at the company on file. On the model above, that is roughly a list that had gone four to five months without verification at about 2 percent a month (7.8 to 9.6 percent), plus about 3.3 percent of profile lag: about 11 to 13 percent.

A real list can drift above the model over time, for reasons the model leaves out:

• People who never update their profile. The lag assumptions treat everyone as updating eventually. Some never do.
• Internal role changes. The person is still there, in a role you no longer sell to.
• Turnover above the median. Some industries and roles turn over much faster than the overall median suggests.

How often to refresh B2B data

The table above is the case for refreshing on a schedule rather than once a year.

• Before every campaign. Re-verify the emails and phone numbers on any list right before it goes out. Bounces cost the sending domain, not just the contact.
• The whole database, at least quarterly. A quarterly cycle keeps job-change decay near 6 percent between refreshes at 2 percent a month. An annual cycle lets it reach about 21 percent.
• Company data on a longer cycle. Headcount, technology and funding change more slowly than people do.
• Faster for high-turnover segments. Startups, younger roles and high-churn industries decay faster than the median.
• Log every signal back to the record. Hard bounces, auto-replies that name a replacement, and wrong-number calls are free evidence of a job change. A contact who moved is a lead at the new company, not a record to delete.

For a wider list of causes and fixes, including duplicate records and form data, CIENCE's guide to data decay in B2B sales goes further.

How graph8 keeps records current

graph8 keeps one record per contact and per company, with duplicate checks. Firmographics, technographics and intent refresh on their own, and waterfall enrichment asks several data providers in turn until one returns a match. Each change to a record is logged with what changed, who or which agent changed it, and when, and enrichment results go to staging for review before they are written to the records. See graph8 data.