Part of Lusha’s ongoing measurement of B2B data decay. Every finding in one place: How fast B2B contact data decays.
We enriched 197 real US contacts across four industries: SaaS, financial services, healthcare and manufacturing.
Work email resolved for every one of them. All 197. Mobile phone came back for at least 93.9 percent in every industry, and for all of them in SaaS and financial services.The two lower numbers look like an industry split. Tested, they are not one yet: the entire difference is five phone numbers, and that is too few to claim. This page shows the working rather than the headline.
What we tested
Real contacts pulled through Lusha’s own prospecting and enrichment tools, nationwide across the US, manager through director seniority. Four industries: Software Development, Financial Services, Healthcare and Manufacturing.
For each, we searched, then ran a real enrichment call on every result, and counted a hit only when a usable value actually came back. Not when a field was listed as available. That is the same standard used in our metro and company size test.
Total sample: 197 contacts, every reveal run in real time on July 28, 2026.
Work email resolved in all four industries
SaaS, financial services, healthcare, manufacturing. All four came back complete on work email: fifty for fifty, forty eight for forty eight, fifty for fifty, forty nine for forty nine.
Industry did not move this number once across 197 contacts.
Mobile phone: a visible difference that does not clear the bar
The whole difference is five phone numbers
Two missed contacts in healthcare and three in manufacturing. That is the entire gap.
We tested whether it holds. Comparing SaaS with manufacturing gives p = 0.12. SaaS with healthcare gives p = 0.49. Pooling the two tiers, 98 contacts against 99, gives p = 0.059. None of those clears the conventional threshold, which means the difference you can see in the table is not one we can claim.
Look at the confidence ranges and the same thing shows up from another angle. SaaS runs 93 to 100 percent and manufacturing runs 83 to 98 percent. Those ranges overlap substantially.
Why we are saying this rather than the headline. A vendor reporting on its own data has an obvious reason to present a clean story. We already published one case where a 15 contact sample swung twelve points and told a story that a 47 contact sample dissolved. The same discipline applies when the direction happens to be interesting: at 197 contacts we can say phone coverage is high across all four industries, and we cannot yet say it differs between them.
A pattern in the misses, offered as a hypothesis
The five missed contacts were not spread randomly across roles. They clustered in department and program management positions at large industrial and pharmaceutical organisations, rather than in customer facing or field roles.
That is consistent with a workforce explanation: some roles have historically not been assigned a corporate mobile line, so there was never a number to capture. It fits what you would expect from how these industries staff, and it is exactly the kind of story that sounds right and can still be a coincidence at n = 5.
Treat it as a hypothesis worth testing at a larger sample, not as a finding.
What this means for a GTM team
Work email is dependable across all four industries tested. 197 for 197. If your outreach is email led, industry is not a variable to plan around in these sectors.
Phone coverage is high everywhere here, and slightly lower in two industries. Between 93.9 and 100 percent, with the lower figures in healthcare and manufacturing. Plan for roughly 1 in 20 to 1 in 16 contacts being email only in those two, and treat that as a prudent buffer rather than a measured industry rate.
Do not build a targeting rule on this. A four point difference that does not reach significance is not a reason to rank a sector down. If your ICP is manufacturing, this data says phone coverage there is high.
Methodology notes
Sample composition. 197 contacts, manager through director seniority, US wide, not limited to specific metros or company sizes.
Hit definition. A hit required an actual revealed, non empty value from a real enrichment call, not a preview flag indicating a field might be available. An availability rate measured from search results will produce different numbers on the same contacts.
Industry definitions. SaaS and software uses Lusha’s Software Development sub industry. Financial Services uses the sub industry of the same name. Healthcare and Manufacturing use the full main industry category rather than a narrower cut, so those two samples are broader in composition than the first two.
Statistical testing. Comparisons use Fisher’s exact test; the 95 percent ranges are Wilson score intervals. p values are reported in the body rather than summarised, so the reader can judge them.
What this does not cover. Four industries, one seniority band, nationwide rather than segmented by metro or company size. Other industries and other seniority levels were not tested and may behave differently. The healthcare and manufacturing samples use broader industry categories than the other two, which is one plausible source of variation we did not control for.
Privacy. The misses described are real contacts surfaced through Lusha’s verified data layer. No individual contacts or employers are identified, and no contact details are included or derivable from the figures presented. Current certifications are on the trust center.
FAQ
Does B2B contact data quality vary by industry?
Not on work email, in this test: 197 contacts across four industries all resolved. On mobile phone, healthcare and manufacturing came in a few points below SaaS and financial services, but the difference did not reach statistical significance at this sample size, so we are not claiming an industry split.
Which industries had the lowest phone hit rate?
Manufacturing at 93.9 percent and healthcare at 96 percent, against 100 percent in SaaS and financial services. In absolute terms that is three missed phone numbers and two missed phone numbers respectively, out of 197 contacts.
Is the industry gap statistically significant?
No. SaaS against manufacturing gives p = 0.12, SaaS against healthcare gives p = 0.49, and pooling the two tiers gives p = 0.059. The confidence ranges overlap substantially. It is a difference we can see and cannot yet claim.
Why did some contacts have no mobile number?
The five misses clustered in department and program management roles at large industrial and pharmaceutical organisations rather than in customer facing roles. Some roles have historically not been assigned a corporate mobile line, so there was no number to capture. That is a plausible explanation at this sample size, not a demonstrated one.
Should I rank healthcare or manufacturing accounts lower?
No. Phone coverage in both was above 93 percent and work email was complete. If anything this test argues the opposite: the sectors people assume will be harder were not meaningfully harder here.
What counts as a hit in this test?
A real enrichment call returning a usable value. Not a flag saying a field might be available. That distinction is why a hit rate and an availability rate on the same list will disagree.
Can I run this on my own industry?
Yes. Take a list you already hold, run real enrichment calls rather than checking availability, and count only values that came back. Your own segment result is more useful than any vendor average, including this one.
Related resources
- How fast B2B contact data decays, and what it costs — every report on this, in one place
- How accurate is B2B contact data in the US? — the same test across metros and company sizes
- How fast does B2B contact data decay? — what goes stale, and how fast
- Validate direct dials before a calling block
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