B2B Contact Database Statistics

The B2B Database Network dataset spans 100M+ verified B2B and consumer contacts. Every record carries a layered email verdict (valid, catch-all, risky, or invalid) and a verified_at freshness date. Suppressed records are never counted or served. The figures below are recomputed live from the contact store on each page load, and the published breakdowns are refreshed each quarter (Q3 2026).

Data as of July 1, 2026 · refreshed quarterly.

What this dataset covers

These pages are a live window into the B2B Database Network contact dataset — a de-duplicated set of business contacts, each tied to a company and described by job title, seniority, department, industry, company size, and location. The breakdowns answer the questions a go-to-market team actually asks before a campaign: how many contacts exist in a given country, how the data distributes across industries, and where the decision-makers are concentrated by seniority and function.

Coverage is presented honestly. The numbers reflect genuine depth in the markets and segments where the dataset is strong, rather than a flat claim of completeness. That makes them useful for sizing a market: if a segment shows thin volume here, it will show thin volume in your campaign too, and it's better to know that before you build the list than after.

The distinction matters because most data marketing leads with a single, impressive headline count and stops there. A total record count tells you almost nothing about whether the dataset can support your specific campaign, because you will only ever email a narrow, specific slice of it. What you actually need to know is the depth of that slice — how many RevOps leaders in US fintech, how many engineering directors at mid-market firms in Germany. These breakdowns exist to answer the second kind of question, which is the one that predicts results.

Why each dimension matters

The five breakdowns aren't arbitrary slices; each maps to a real targeting decision. Read together, they let you build an ideal customer profile from both the company side and the contact side and confirm there's volume behind it before you spend anything.

• Country scopes a campaign to where you can sell, support, and comply. It also flags where local e-marketing law (GDPR-style notice in the EU/UK, opt-out under CAN-SPAM in the US) shapes how you reach out.
• Industry is often the strongest predictor of fit, because it shapes a buyer's vocabulary, constraints, and budget. For most products, leading an ICP with industry produces tighter lists than leading with size.
• Seniority decides who can say yes and how you should talk to them — outcomes and risk for senior buyers, day-to-day workflow for practitioners.
• Department tells you which function owns the problem you solve, so outreach reaches the team that feels the pain rather than a title keyword that happens to match.
• Company size shapes the entire buying process — who decides, how long it takes, and what they'll pay — so it dictates whether a self-serve or an enterprise motion fits.

How these numbers are produced

The methodology is deliberately simple, because simple is auditable. Each count is a live aggregate, computed the moment you load the page, with a few firm rules applied consistently across every breakdown.

• Live, not cached numbers. Totals and facet counts are read from the contact store at request time. Nothing is hardcoded, so the figures track the dataset as it grows and is re-verified.
• De-duplicated. Each contact is counted once. A person who appears in more than one source is merged into a single record before counting.
• Suppression-aware. Opted-out and Do-Not-Sell records are excluded from every count, not just hidden from search. The aggregates reflect only data we would actually serve.
• Normalised dimensions. Countries, industries, departments, and seniority levels are mapped onto consistent taxonomies, so near-synonyms roll into one comparable bucket instead of fragmenting the count.
• Verdict-aware. Every email carries a verdict; catch-alls are labelled, never counted as verified, and invalids are refunded at export — so the counts reflect usable data.

Reading the breakdowns correctly

A few habits keep you from drawing the wrong conclusion from a count. The numbers are honest, but like any aggregate they reward careful reading.

How to use these statistics

The breakdowns are built to be acted on, not just read. Each table is clickable: selecting a value opens a filtered search for that slice, so you can move from "how big is this segment?" to "show me these contacts" in one step.

• Size a market first. Read the count for a country, industry, or size band before committing to a campaign, so you know there's volume to justify the effort.
• Stack dimensions. The real targeting power comes from combining filters — industry plus department plus seniority — and watching the live count narrow to your exact ideal customer profile.
• Preview before you spend. Masked results let you sanity-check a list for free; you only spend a credit to reveal a verified contact.
• Verify at export. When you take the data, it's re-verified, the verdict mix is shown up front, and invalids are refunded — so the count you saw here turns into contacts you can actually reach.

Explore the live breakdowns

Each dimension has its own page with a full, live distribution and a clickable table. Start with the one that matches how you segment:

• B2B Contacts by Country — live counts by country.
• B2B Contacts by Industry — live counts by industry.
• B2B Contacts by Seniority — live counts by seniority level.
• B2B Contacts by Department — live counts by department.
• B2B Contacts by Company Size — live counts by company-size band.

Freshness, refresh cadence, and citation

There are two layers of freshness at work here, and it's worth separating them. The aggregate counts on these pages are computed live on every load, so they always reflect the current state of the dataset. The published narrative breakdowns are reviewed and refreshed on a quarterly cadence. And beneath both, every individual email carries its own verified_at date, so a specific contact's freshness is never hidden behind a dataset-wide average.

This matters because B2B contact data decays at roughly 20–30% a year as people change jobs and companies restructure. A static snapshot would quietly drift out of date; a live aggregate re-verified over time stays honest. If you cite a figure from these pages, note the data-as-of date shown above, since the live count moves as the dataset grows and records are re-verified. The dataset is declared with schema.org Dataset markup precisely so that assistants and researchers can reference it with that context attached.

Privacy on these pages

These pages are intentionally aggregate-only. We never publish an individual's real contact details on a public page — only counts and masked samples that show the shape of the data, not the people in it. An individual's details are revealed solely inside the app, to a signed-in user spending a credit, and anyone can remove their information through our self-serve Privacy Center. Suppressed records then disappear from these counts, from search, from exports, and from the API. Methodology and provenance are documented in full on our data and privacy pages.