What Is B2B Data? How to Choose a Provider

B2B data is not a list you buy once. It is a record that decays the moment it lands in your CRM. Most teams treat provider selection like a one-time purchase: pick the database with the biggest contact count, sign the annual contract, and move on.

The number that matters is not the size of a provider's database. It is the share of records it gets right for your exact target market, and how fast those records go stale after you load them. The industry average for accuracy sits around 50 percent. Top providers reach 90 to 95 percent on verified emails, but only on the segments they cover well. This guide explains what B2B data actually is, the four types you will buy, and how to choose a provider on evidence instead of marketing claims.

What is B2B data?

B2B data is verified information about businesses and the people who work inside them. It splits into two layers: the company (firmographics, technographics, revenue, headcount) and the person (name, title, work email, direct phone). A complete record connects both, so you know the account is a fit and you know exactly who to reach.

The reason B2B data is hard is that it does not sit still. People change jobs. Companies get acquired, rebrand, and swap their tech stack. A contact record verified six months ago has already lost a chunk of its accuracy, because B2B contact data decays somewhere between 22 and 70 percent per year depending on the field. Email is the fastest-moving piece, decaying around 3.6 percent every month. That is why "how big is your database" is the wrong opening question. The better one is "how often do you re-verify them."

The four types of B2B data, and what each one does

You are not buying one kind of data; you are buying four, and most providers are strong at one or two. Firmographics tell you whether an account fits. Technographics and intent tell you whether it is in-market. Contact data tells you who to reach. A provider that nails contact data can be useless for technographics, which is why the "which provider" question only makes sense once you know which type you actually need.

The takeaway from clicking through those four: a single vendor almost never leads all four categories. Contact data is the most mature. Firmographics and revenue are the least accurate, because they are inferred more often than verified. So the buying decision is never "who is best." It is "who is best at the specific layer this campaign depends on."

Why no single B2B data provider wins

The provider with the highest accuracy almost never has the highest coverage, and that tradeoff is the entire reason a single source leaves money on the table. Accuracy measures whether a record is correct. Coverage measures what share of your target list the provider can find at all. A provider can be 97 percent accurate on the half of your list it knows, and silent on the other half. Another can find almost everyone but get more of them wrong.

Clay benchmarks providers against ground-truth datasets and publishes the numbers. Across the work-email category, here is how five widely used providers actually performed, with cost per lookup attached.

Look at Hunter and Findymail in that view. Hunter is the most accurate at 97.15 percent but finds only 52.87 percent of records. Findymail gives up a single point of accuracy and nearly doubles the coverage at 90.26 percent. Neither is "the best." If you buy only Hunter, you reach half your list with great data. If you buy only Findymail, you reach most of your list with slightly worse data. The teams that win run both, ordered cheapest-first, so a record only escalates to a pricier source when the cheap one comes up empty.

This is the mechanic behind every strong enrichment setup, and it is what moved OpenAI off a single-provider gap.

A single provider left OpenAI's inbound enrichment with holes on roughly six of every ten records. Stacking sources closed most of that gap without forcing them to commit to one vendor's blind spots.

How to evaluate a B2B data provider on five criteria

Provider marketing is useless as evidence, because every vendor claims 95 percent accuracy and the field average is 50. The only way to know how a provider performs is to test it on your own ICP and grade it on criteria that predict downstream results. Five hold up.

Two of those criteria do almost all the gating work. Accuracy decides whether the data is safe to send, since every bad email costs you sender reputation as well as a rep's time. Freshness decides how long that accuracy lasts. A 275-million-contact database refreshed quarterly is worse than a 150-million-contact database refreshed weekly, because the bigger one is decaying faster than your reps can use it. Coverage, compliance, and depth matter, but only once a provider clears the accuracy and freshness floors. The single most revealing question to ask any vendor is not "how big is your database." It is "how often do you re-verify a record, and what triggers it."

Run a paid pilot before you sign

A sample list is a marketing artifact; a paid pilot on your own workflow is the only test that predicts production. Vendors hand-pick samples from their strongest segments. The provider that looks flawless on a generic demo can collapse on EMEA mid-market manufacturing or US healthcare, because coverage is wildly uneven by geography and vertical. Test on the exact slice of the market you sell into, not on whatever the vendor wants to show you.

The pilot itself is short and mechanical. Pull 200 to 500 accounts you already know are in your ICP. Ask the provider to return contacts matching your buyer persona. Then grade the output yourself: run the emails through an independent verifier, spot-check 50 titles against the companies' own sites, and calculate the real match rate and bounce rate. If the sample bounces above 5 percent, the verification is not rigorous enough for outbound. If the provider finds contacts at fewer than 60 percent of accounts you know exist, it will have the same gaps across your full TAM. Run that same test on a second provider and you have a real comparison instead of two sets of brochure claims.

How to stack providers into a waterfall in Clay

The fix for the accuracy-versus-coverage tradeoff is not a better single provider; it is an ordered sequence of providers where a record only escalates when the cheaper source misses. This is what a data waterfall is. Clay's data marketplace holds 150-plus providers behind one interface, billed by consumption, so you are not buying five annual seat licenses to assemble one. You add providers as columns, order them, and a record falls through to the next source only when the prior one returns empty.

Order matters more than which providers you pick. Put your cheapest acceptable-quality source first, since it will resolve the bulk of records at the lowest cost. Reserve the expensive, high-accuracy source for last, where it only runs on the records nobody else could find. That ordering is why a waterfall lifts coverage past any single provider without multiplying your spend: most records never reach the costly source.

For the fields no provider sells cleanly, like a one-line read on whether an account fits a niche thesis, you fill the gap with AI research instead of another database. Claygent runs a research prompt against each account and returns a structured answer in a column. Here is a prompt you can paste to verify and standardize a firmographic field that off-the-shelf databases get wrong:

The evidence field forces the model to ground its answer in something on the page, so you can audit the classification instead of trusting it blind.

Where to start

Start with the layer your next campaign depends on, test two providers on your own ICP, then stack them; do not buy a database and hope. Name whether the campaign needs firmographics, technographics, contact data, or intent. Pull a 200-account ICP sample and run a paid pilot on the two strongest candidates for that layer. Keep the cheaper one as your first waterfall step and the more accurate one as the fallback. Layer Claygent on top for the custom fields no provider sells. That sequence turns provider selection from an annual gamble into a measured, ordered system that gets coverage and accuracy without overpaying for either.