B2B data decay calculator

How fast do B2B phone numbers go bad?

Plan on roughly 15 to 30% of a B2B contact list going out of date every year. That is the range most published estimates fall into, and it matches what labor statistics tell us about how often people change employers. But the phone numbers in that list do not decay as one block.

Two separate clocks are running. The first is the person: in the United States the median worker had been with their employer 4.1 years in January 2026, and 20.6% had been there a year or less (Bureau of Labor Statistics, Employee Tenure in 2026). The second is the line itself: a number gets disconnected, ported, reassigned or swapped during a phone system migration, whether or not the person stays.

Which clock matters depends on the type of number. A direct dial belongs to the desk: when the person leaves, it reaches a successor or nothing. A mobile belongs to the person: it survives the job change, so you still reach them, just at a different company. A switchboard belongs to the company: it outlives everyone, but the person you asked for may be long gone. That is why the calculator asks for your mix of number types, and why the same list ages very differently depending on it.

B2B data decay rate: what the published figures really say

Most “data decay statistics” pages copy each other. Here is where the usual numbers come from, and how much weight each deserves (checked November 2026).

• 4.1 years

  Primary measurement

  U.S. Bureau of Labor Statistics, Employee Tenure in 2026 (released September 24, 2026)

  Median tenure with the current employer in January 2026, up from 3.9 years in 2024. 20.6% of workers had been with their employer a year or less (22.2% in 2024). Public sector 5.6 years, private sector 3.9. By industry: financial activities 5.0, information 4.9, manufacturing 4.9, professional and business services 3.8, health care 3.8, retail 3.2, leisure and hospitality 2.4. By age: 3.0 years at 25 to 34, 9.6 at 55 to 64. A constant departure rate consistent with a 4.1-year median is about 17% a year: that is the calculator’s base rate, and the industry gaps set its industry multipliers.

• 22.5% a year

  Widely cited, original study not accessible

  HubSpot, Database Decay Simulation

  HubSpot states that “email marketing databases naturally degrade by about 22.5% every year.” Secondary sources trace it to a 2.1% monthly rate measured by MarketingSherpa; we could not find that study online. It is about email databases, not phone numbers.

• 25 to 30% a year

  Vendor claim, no method published

  Dropcontact, “Your prospect files become obsolete by 30% per year”

  The headline says 30%, the article gives 25 to 30% for full prospect records, with no method or source. In a separate article, Dun & Bradstreet describes the same 2.5% a month (about 30% a year) as something “collectively believed” in the data world, not as a measurement. Plausible as an order of magnitude for full records (title, email, phone together).

• 70.3% a year

  Could not be traced

  Attributed to Gartner in many vendor blogs, to the Forbes Business Council by others

  We found no Gartner publication with this figure. Most blogs cite it with no link; Landbase attributes it to a Forbes Business Council post on B2B data decay, others to ZeroBounce. Dun & Bradstreet only mentions “a Gartner report” putting decay at more than double 30%, with no reference. Treat it as a worst case for fast-moving segments at best, never as an average.

• 5 to 10% vs 15 to 20%

  Vendor estimate

  Prospeo, Customer Data Quality

  Prospeo puts annual decay at 5 to 10% for mobile numbers and 15 to 20% for direct phones, with work email faster still at 20 to 30%. No method is published, but the pattern (mobiles most stable, direct dials faster) is the one every data vendor describes, and the one the calculator reproduces with its two clocks.

• 42.9% a year

  Vendor compilation

  Landbase, Data Decay Rate Statistics (January 8, 2026)

  This compilation says 42.9% of contacts get a new phone number every year, attributed to IndustrySelect. A new number is not always a dead one: a person who changes jobs often keeps the same mobile. “Changed” and “dead” are not the same thing, which explains most of the spread between published figures.

The honest summary: there is no measured, public, phone-specific decay rate for B2B lists. What we do know is how often people change jobs, and that different number types react differently to it. The calculator is built on that, and lets you replace every assumption with your own measurement.

How the calculator models decay

A transparent model, not a black box. Every rate is shown in the “How the numbers are worked out” panel and can be overridden by calibrating with your own data.

1. 1

   People leave at a steady rate

   The base is 17% a year (from the 4.1-year median tenure), adjusted for industry and seniority. Industry multipliers are the ratio of 4.1 years to each industry’s median tenure in the BLS data (public sector ×0.73, finance ×0.82, information and manufacturing ×0.84, professional services and health care ×1.08, retail and hospitality ×1.5); only staffing (×1.35) is our assumption, since the BLS does not publish it separately. Seniority multipliers (C-level ×0.7 to entry level ×1.6) are our assumptions, in line with the BLS gap between workers aged 25 to 34 (3.0 years) and 55 to 64 (9.6 years). A steady 17% rate implies about 16% of people in their first year at a job, where the BLS measures 20.6%: real turnover is higher in the first months, so the base rate is on the cautious side for recently hired contacts.

2. 2

   Lines die on their own clock

   Independently of job changes, about 7% of mobiles, 5% of direct dials and 3% of switchboards stop working each year (central case; the range uses 4 to 12%, 3 to 8% and 2 to 5%). These are our assumptions, not measurements. Cross-check: with the two clocks combined, a direct dial stops reaching its contact at about 20% a year and a mobile goes dead at about 7%, in line with the 15 to 20% and 5 to 10% that vendors such as Prospeo publish.

3. 3

   The source adds hidden age

   A provider database or a bought list was already months old when you got it, and some numbers are wrong from day one (a namesake, a stale match). The calculator adds 0 to 12 months of hidden age and 3 to 12% of day-one errors depending on the source.

4. 4

   Each number type reacts differently

   A mobile whose owner changed jobs still rings (“moved”). A direct dial or a switchboard whose person left reaches someone else (“gone”). A dead line is dead whoever owned it.

5. 5

   The refresh point is measured from enrichment

   Re-running the same enrichment does not fix day-one errors, so the threshold counts only what went bad after enrichment: 15 points by default.

How often should you refresh enriched data?

There is no single right cadence: it depends on how fast your contacts move and how hard you work the list. Typical rhythms that the calculator usually lands on:

• Active sequences: before every pass

  If a list is about to go into a calling campaign and was enriched more than 3 to 6 months ago, re-verify it first, or dial first and re-enrich what bounced.

• Senior, stable contacts: about once a year

  C-level and public sector lists age slowly, especially when they are mostly mobiles. An annual refresh is usually enough.

• Junior or fast-moving segments: every quarter

  SDRs, assistants, startups, staffing and retail turn over fast. Direct dials there go stale in months.

• Bought lists: before the first dial

  A purchased list is often a year old on arrival. Verify a sample of 100 numbers before you load the whole file into a dialer.

Common mistakes with stale lists

• Deleting everyone who changed jobs

  A contact who moved is a wrong record, not a dead lead. People who just changed jobs often review their tools: re-qualify them at their new company.

• Using one decay rate for every number

  Direct dials and mobiles do not age alike. A list that is 80% direct dials can lose half its value in two years while a mobile-heavy list holds up.

• Trusting a “verified” label forever

  Verification is a snapshot. Ask your provider for the date each number was last verified, not just whether it was.

• Re-dialing dead numbers until they “work”

  Repeated calls to disconnected or wrong numbers waste rep time and can hurt how carriers rate your caller ID. Log the wrong number and move on.

• Confusing a stale list with a burned list

  A stale list has wrong numbers. A burned list has right numbers that you called too often. They need opposite fixes: re-enrichment versus rest.

Keep going

The other contact data tools, and guides for what comes after the refresh.

• Direct Dial vs Switchboard Checker Find out which of your numbers are mobiles, direct lines or switchboards, and the order to call them in.
• Phone Enrichment Pricing Real prices per credit for 15+ providers, turned into a cost per conversation.
• Waterfall Enrichment Merger Combine exports from several providers and keep the best number for each contact.
• Which data provider should you use? Eight questions, two or three unbiased recommendations.
• Phone data coverage by country Where each provider really finds mobiles, next to the local calling rules.
• Contact CSV Cleaner Fix delimiters, accents, duplicates and number formats before you re-enrich.
• Phone Number Validator Spot invalid and badly formatted numbers in seconds.
• Cost per Meeting Calculator What a booked meeting really costs you, data included.
• Burned call lists When the numbers are right but you have called them too often.
• B2B phone number enrichment Real match rates, method, and the compliance side of enrichment.
• Cold call connect rate benchmarks What a healthy connect rate looks like once the list is clean.
• Building B2B lead lists for cold calling How to build, buy and score a list worth dialing.