How to Evaluate a B2B Contact Data Provider (How We Test One)

The way to evaluate a B2B contact data provider is to pull the same real list through it and through the tool you already pay for, both natively, compare the results line by line, and ask one question: does this earn a layer?

That's how we test any new data source, and it's how we tested MoltSets when we got an early beta invite to Adam Robinson's new contact-data engine. Below is the method, the numbers it produced, and the part of the result I didn't expect to like as much as I did.

How should you set up a contact data test?

Use a real list of people you'd actually sell to, and pull it through both tools the way each one is meant to be used. We stress-tested MoltSets late one week on 110 real GTM leaders. We pulled the same list two ways, both native: once through MoltSets, once through a leading platform we already pay for. Then we compared it line by line.

Each piece of that setup is doing a job.

Real people from your ICP. A data vendor's demo list is the list they look best on. Your list is the one that matters. If you sell to GTM leaders, test on GTM leaders.

Same list, both tools. If the two tools see different inputs, the comparison only measures your inputs.

Native pulls. Run each tool the way it was designed to run, so you're not grading your own workaround.

Line by line. A summary percentage hides which records each tool got right. The record-level view is where you find out whether a new source covers your existing gaps or just duplicates what you already have.

Why ask "does it earn a layer" instead of "which one is better"?

Because we weren't shopping for a replacement. We already run a waterfall: multiple sources, each one filling in what the last one missed. So the useful question was whether this source adds something the stack doesn't already have.

It did. The leading platform got a work email for 96 of 110 and ran cleaner on verified company domains. MoltSets got 102, and recovered 10 of the 14 the other one had written off.

Read those numbers as a head-to-head and you'd pick a winner and move on. Read them as a waterfall and the story is better: the incumbent was stronger on domain quality, the new source was stronger on coverage, and the new source specifically recovered records the incumbent had given up on. Dropped into the stack, it backfills the gaps the rest leave. That's what "earning a layer" means in practice.

What should email validation actually look like?

It should come with a grade based on real behavior and a date. This was the part we weren't used to, and really liked. MoltSets grades every email A to F on real behavior, replies and opens, and stamps the date it last checked. Often same day.

A score and a timestamp, not a shrug labeled "verified."

That matters because "verified" with no date and no basis is a claim you can't check, and the list decays whether anyone checks it or not. A grade tells you how much to trust a record. A timestamp tells you how stale that trust is. Together they let you decide what to send to, what to warm slowly, and what to drop, instead of finding out from your bounce rate. That is what validation should look like, and we hope the whole data space makes it standard.

What about the bad records?

Look for whether the tool tells on itself. MoltSets wasn't perfect. A few came back stale, but MoltSets graded those D itself, so the junk flagged itself before it shipped.

That's a better test of a data provider than its hit rate. Every source has stale records. The question is whether you find out from the provider or from a bounce. A provider that grades its own misses honestly is one you can build a waterfall around, because you can route on its grades instead of second-guessing every row.

How do you run this test yourself on Monday?

Here's the version we use:

1. Pick a real list. People you'd actually sell to, in your actual ICP. Ours was 110 GTM leaders.
2. Pull it natively through your incumbent and the candidate. Same list, no doctoring, each tool used the way it's built.
3. Compare line by line. Count work emails found by each. Then look at the records one tool found that the other wrote off. That overlap, or lack of it, is the whole decision.
4. Check the quality dimension separately from coverage. In our test the incumbent was cleaner on verified company domains even though it found fewer emails. Both facts matter.
5. Look at how each tool grades and dates its records. If a record says "verified," ask: based on what, and as of when?
6. Inspect the misses. Did the bad records come back flagged, or did they come back looking exactly like the good ones?
7. Decide whether it earns a layer. If the candidate recovers what your stack writes off, it earns a spot in the waterfall, even if it doesn't beat your incumbent everywhere.

If you want the side-by-side on the tool we tested, we wrote up how we compare with MoltSets.

What does good contact data actually get you?

Good data gets you to the right person. Reading how they decide is on us.

That's the line I'd keep in front of any team running this test. A cleaner email list is table stakes for outbound, and it's worth testing hard because bad data burns sender reputation and rep time. But a valid address only gets you in front of the human. It doesn't tell you whether that human needs the risk math in writing first or tunes out the moment you slow down. That second part is the job GTM Heroes was built for, and no amount of enrichment does it for you.