--- title: "B2B Data Decay Calculator: How Fast Phone Numbers Go Bad" description: "Free B2B data decay calculator: your list’s yearly decay rate, how many phone numbers are dead or moved, what wrong numbers cost, and when to refresh." url: https://skipcall.io/en/tools/b2b-data-decay-calculator language: en --- [Free tools](https://skipcall.io/en/tools) # B2B data decay calculator Enrichment date, source, roles, number types: how much of your calling list has gone bad, what the wrong numbers cost your reps, and the month to refresh it. Direct dials die first Your list Every change recalculates everything. The list When and where the numbers came from. Contacts in the list contacts Months since enrichment months Where the numbers came from Who is in it Seniority and industry change how fast people move. Seniority of the contacts Their industry Number types From your file, or a rough guess. Switchboards take the rest. Mobile numbers % Direct dials % Mobile 40% Direct dial 40% Switchboards: 20% Calling costs What a wrong number costs before a rep gives up on it. Dials per bad number dials Minutes per wasted dial min Most teams try twice before marking a number as wrong. Ringing, gatekeeper, “wrong person”, logging the outcome. Loaded cost of a rep hour $ / hr Currency Salary, commission, taxes and tools, divided by working hours. Cost to re-enrich one contact Editable assumption $ / contact Example value. Compare real prices in the phone enrichment pricing tool. Refresh when this share has gone bad points Points of the list gone stale since enrichment. 15 is a sensible default. **Calibrate with your own dataOptional. Your measured wrong-number rate beats any benchmark.** Take the last few hundred dials on one list, count the outcomes “wrong number”, “no longer works here” and “disconnected”, and divide by the contacts dialed. The model then rescales every decay rate to match you. Wrong numbers on your last calls % Age of the list at the time months Results updated: 605 wrong numbers, 24% of the list. ## Your list today Due for a refresh 605 wrong numbers your reps will dial (24%) Likely range: 488 to 771 · 757 records out of date (30%) Decay rate of this list: 18% of accurate records lost a year - Still right 1,743 70% Right person, number works. - Moved, still reachable 152 6% Changed company, mobile still rings. Wrong record, live lead. - Person gone 237 9% Line works but reaches someone else, or a switchboard that says they left. - Dead number 368 15% Disconnected, reassigned, or wrong from day one. The list crossed your threshold around 2 months ago. Re-verify before you dial it again. (range: 6 months ago to in 2 months) ## How each number type has aged Same people, same age, three very different outcomes. - Mobile 40% of the list 68% Still right 83% still reach the person, counting those who moved - Direct dial 40% of the list 70% Still right - Switchboard 20% of the list 72% Still right ## How your list decays over time Share of records in each state, month by month since enrichment. The band shows the low to high range. Months since enrichment Still right Moved, still reachable Person gone Dead number Range of “still right” Right now the list loses about 1.1% of its accuracy every month. Half-life of this list: 2 years 11 months. After that, most records are out of date. ## Dial it as is, or refresh first? One calling pass through the whole list. Rep time and enrichment credits only. - Dial it as isFixes nothing $1,211 1,211 wasted dials (30.3 hours), and 757 records stay wrong afterwards - Re-enrich everything, then dialCheaper fix $1,400 2,500 contacts re-enriched, plus $400 of dials on day-one errors that a re-run will not fix - Dial first, then re-enrich what bounced $1,514 The same wasted dials as dialing as is, plus 757 stale records re-enriched Re-enriching everything before you dial is $114 cheaper than dialing first, and your reps start the pass on clean data. Compared with dialing as is, each record you fix costs about $0.25. Worth it as soon as a fixed contact is worth more than that to you. Re-enriching everything first has been the cheaper fix since about 1 year after enrichment. Not counted: the meetings you never book with the people who moved, the caller ID reputation lost on repeated dials to dead lines, and the stale records a re-enrichment fails to match (no provider finds a new number for all of them). ## Your action plan 1. 1 Dial mobiles first: 83% of them still reach the person, including the ones who changed jobs. 2. 2 Re-verify direct dials before you call them: only 70% still reach your contact. 3. 3 Treat the 152 people who moved as leads, not junk. A job change is a buying signal: find their new company and re-qualify them. 4. 4 Re-enrich this list now, before your next calling session on it. 5. 5 For this kind of list, plan a refresh every 1 year. 6. 6 Before re-enriching, clean the file: delimiters, duplicates and number formats. 7. 7 Log “wrong number” as its own call outcome and calibrate this calculator with it every quarter. [Contact CSV Cleaner](https://skipcall.io/en/tools/csv-cleaner) [Compare enrichment prices](https://skipcall.io/en/tools/phone-enrichment-pricing) **How the numbers are worked outTo check every figure by hand** - People leaving their company: base 16.9% a year × seniority ×1 × industry ×0.84 = 14.2% a year. - Lines that stop working on their own: mobile 7%, direct dial 5%, switchboard 3% a year. - Effective age: 1 year 2 months since enrichment + 3 months the data was already old at the source = 1 year 5 months. - Wrong from day one for this source: 8%. - Still at the company = e^(−λt). Line still working = e^(−μt). Mobile: right = both, moved = left but line works. Direct dial and switchboard: right = both, gone = left but line works. Dead = line stopped, plus the day-one errors. - Wasted rep time: 2,500 contacts × 24.2% wrong × 2 dials × 1.5 min ÷ 60 × $40.00 = $1,211. - Re-enrich everything: 2,500 contacts × $0.40 + day-one errors (8%) still dialed 2 times = $400, total $1,400. - Dial first: $1,211 of wasted rep time + 757 stale records × $0.40 = $1,514. - Decay rate: share of records still right at enrichment that are no longer right 12 months later = 17.8%. ## Share or save The link reopens the calculator with your inputs, no account needed. **See your real wrong-number rate, list by list.** In Skipcall, “wrong number” is its own call outcome and the dashboard counts it separately, so you know exactly when a list has gone stale instead of guessing. Enrichment runs inside the contact table, on credits, only on the records you pick. [Create an account](https://app.skipcall.io/signup) [See enrichment in Skipcall](https://skipcall.io/en/features/enrichissement) Your inputs never leave your browser. The tool sends nothing and stores nothing. How to check ## 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. Direct dial Dies with the job Reaches a successor, a voicemail box nobody checks, or nothing. Mobile Follows the person Still rings, but at a different company. A wrong record, and a fresh lead. Switchboard Outlives everyone Almost never dies. The person you ask for may be long gone. ## 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)](https://www.bls.gov/news.release/archives/tenure_09242026.htm) 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](https://www.hubspot.com/database-decay) 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”](https://dropcontact.com/blog/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](https://prospeo.io/s/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)](https://www.landbase.com/blog/data-decay-rate-statistics) 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.](https://skipcall.io/en/tools/direct-dial-checker) - [Phone Enrichment Pricing Real prices per credit for 15+ providers, turned into a cost per conversation.](https://skipcall.io/en/tools/phone-enrichment-pricing) - [Waterfall Enrichment Merger Combine exports from several providers and keep the best number for each contact.](https://skipcall.io/en/tools/waterfall-enrichment-merger) - [Which data provider should you use? Eight questions, two or three unbiased recommendations.](https://skipcall.io/en/tools/which-data-provider-quiz) - [Phone data coverage by country Where each provider really finds mobiles, next to the local calling rules.](https://skipcall.io/en/tools/b2b-phone-data-coverage-by-country) - [Contact CSV Cleaner Fix delimiters, accents, duplicates and number formats before you re-enrich.](https://skipcall.io/en/tools/csv-cleaner) - [Phone Number Validator Spot invalid and badly formatted numbers in seconds.](https://skipcall.io/en/tools/phone-number-validator) - [Cost per Meeting Calculator What a booked meeting really costs you, data included.](https://skipcall.io/en/tools/cost-per-meeting) - [Burned call lists When the numbers are right but you have called them too often.](https://skipcall.io/en/blog/burned-prospect-list) - [B2B phone number enrichment Real match rates, method, and the compliance side of enrichment.](https://skipcall.io/en/blog/b2b-phone-number-enrichment) - [Cold call connect rate benchmarks What a healthy connect rate looks like once the list is clean.](https://skipcall.io/en/blog/cold-call-connect-rate-benchmarks) - [Building B2B lead lists for cold calling How to build, buy and score a list worth dialing.](https://skipcall.io/en/blog/b2b-lead-lists-for-cold-calling) ## Your inputs never leave your browser The calculation runs entirely in your browser. Nothing you type is sent to Skipcall or anyone else, and nothing is stored. The page address stays clean while you type: the share link is only built when you click “copy link.” To check: once the page has loaded, turn off your connection and change a value. The results keep updating. ## A calendar full of meetings starts with a call. [Get started now](https://app.skipcall.io/signup) [Book a demo](https://skipcall.io/en/demo) **No commitment** · Number live in 2 minutes ## Frequently asked questions **What is B2B data decay?** It is the share of your contact records that stop being accurate over time: people change jobs, get promoted, companies move or switch phone systems, numbers get disconnected. For a calling team, it shows up as wrong numbers, “she no longer works here” and dead lines. **What is the B2B data decay rate per year?** Published estimates mostly fall between 15 and 30% a year for full contact records. The most cited figure, 22.5%, comes from HubSpot and is about email databases. The 70.3% figure often attributed to Gartner could not be traced to any Gartner publication. For phone numbers alone, vendors put mobiles at 5 to 10% a year and direct dials at 15 to 20%. Your own rate depends on seniority, industry and number types: the calculator shows it as “decay rate of this list”. **How fast do B2B phone numbers go bad?** Direct dials go bad fastest, because they die with the job: expect roughly a fifth of them to stop reaching your contact within a year. Mobiles survive job changes, so fewer go dead, but the people behind them move to new companies. Switchboards almost never die, but the person you ask for may have left. **How often should I refresh my enriched data?** Before every major calling campaign if the list is more than 3 to 6 months old, every quarter for junior or fast-moving segments, and about once a year for senior contacts reached on their mobiles. The calculator gives a refresh date for your list based on the share that has gone bad since enrichment. **Is it cheaper to re-enrich everything or only the numbers that bounce?** It depends on the age of the list. On a young list, dialing first and re-enriching only what bounced is cheaper, because most credits would go to records that are still right. Past a break-even age, re-enriching everything first wins: the rep time burned on wrong numbers costs more than those extra credits. The calculator gives that break-even age for your own costs. **Are mobile numbers more reliable than direct dials?** For reaching the person, yes: a mobile follows its owner from job to job, while a direct dial stays with the desk. The trade-off is that a mobile can reach someone who has moved to another company, so the record still needs updating. **How do I measure my own data decay rate?** Log “wrong number”, “no longer works here” and “disconnected” as call outcomes. On a list you know the enrichment date of, divide those outcomes by the contacts dialed. Enter that rate and the list age in the calibration panel: the calculator rescales every assumption to match your data. **Is my data sent anywhere?** No. Everything is calculated in your browser and nothing is stored. The share link carries your inputs in the address, and it is only created if you click “copy link.”