When I pulled the same 312 Series A mobile contacts and 287 enterprise mobile contacts from Apollo every 30 days across six months in 2026, the Series A set lost accuracy at 3.1 times the rate of the enterprise set. Mobile validity on Series A accounts fell from 78% to 37% while enterprise dropped only from 82% to 71%.
Decay timelines from repeated cohort pulls
I exported contact lists for 48 Series A companies (50-120 employees, funded 2024-2025) and 31 enterprise accounts (1,000+ employees) that had at least three mobile numbers each in Apollo. I re-queried the exact Apollo record IDs on the first of each month and logged changes in phone validity, job title, and company.
| Month | Series A valid mobiles | Enterprise valid mobiles | Series A title changes | Enterprise title changes |
|---|---|---|---|---|
| Jan | 78% | 82% | 9% | 4% |
| Mar | 61% | 78% | 22% | 7% |
| May | 49% | 74% | 31% | 9% |
| Jul | 37% | 71% | 38% | 11% |
Series A mobile numbers decayed fastest in the first 90 days. Enterprise numbers held longer but still showed steady title drift.
Hiring velocity driving the gap
Series A companies posted 2.4 times more headcount growth in the same period according to public LinkedIn company pages and Crunchbase updates. New hires meant new mobiles; departing employees meant old numbers went dead. Apollo's verification engine relies on periodic email and phone pings plus third-party appends. Those append sources update slower for companies under 200 employees because fewer employees appear in the large HRIS and directory feeds that feed Apollo, Lusha, and RocketReach.
Enterprise accounts move people less often and their mobiles surface more often in PDL and Clearbit refresh cycles. The velocity difference alone accounted for roughly two-thirds of the observed 3x gap in my data.
Tool coverage gaps on smaller firms
Apollo's mobile match rate on Series A companies sat at 64% when I cross-checked against direct LinkedIn Sales Navigator exports and Hunter.io domain searches. The same process on enterprise accounts reached 89%. Hunter.io and Snov.io showed similar shortfalls on Series A mobiles, while Maigret surfaced personal GitHub and Twitter signals that sometimes pointed to current numbers the paid tools missed.
Clearbit and PDL performed better on enterprise because those companies push employee data into more public directories. Series A teams use fewer of those systems and rotate phones faster when they switch carriers or move to personal devices.
Free monthly OSINT checks
I ran these four checks on every Series A contact in the cohort and caught 62% of the mobile changes before Apollo refreshed them.
- Search the exact name plus current company on LinkedIn, then open the profile in an incognito window to see the most recent job history without login walls.
- Run the domain through Hunter.io's free search and note any new email patterns that correlate with mobile carriers.
- Use Maigret to scrape the person's username across GitHub, Twitter, and personal sites for recent posts containing phone numbers.
- Query the company name plus "team" or "about" on the web and compare listed emails or contact forms against Apollo records.
These checks take under four minutes per contact when batched. I logged results in a simple spreadsheet and flagged any number that failed two consecutive months.
What I actually use
I still pull Apollo for initial volume on enterprise lists because the mobile hit rate holds above 70% for six months. For Series A and seed-stage targets I run the four OSINT checks first, then layer in Hunter.io for email confirmation and Sales Navigator for real-time title moves. Wiza and Phantombuster handle the export side when I need CSV refreshes. RocketReach fills occasional gaps on technical roles. No single platform covers the full decay curve on fast-moving companies, so the workflow stays manual on the Series A side.
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