Where's my 99% match rate!? Well, right here, but it’s complicated…

If you’ve spent five minutes in the identity resolution world, you’ve probably heard someone throw out a number like “we match 99% of records, guaranteed!”

Sounds great, right? Until you make campaign projections for your CMO that … don’t entirely pan out.

The thing is, flashy numbers might close deals, but do they drive value? Only if they’re accurate.

So sure, Faraday can hit 99% matching in certain conditions (we’ll get to that later). But does that mean it’s the number you should plan your campaign around? Well, that depends: on what identifiers you’re looking for, and what you put in to find them.

Match rates aren’t one-size-fits-all, and they’re not a magic number

We’ve written before about why match rates matter (because they’re the foundation of enrichment and predictions) and why transparency and accuracy matter even more (because vanity numbers break downstream models). But here’s the thing: when it comes to match rates, there’s no universal “magic number.” Anyone selling a consistent 90%+ is probably doing one of three things:

• Counting duplicates (two bad emails for the price of one!)
• Leaning on easy identifiers like household address and presenting that rate as always true
• Or—a personal favorite—straight-up inflating numbers.

In reality, match rates depend almost entirely on what identifiers you hand us. Think of it like cooking: bring fresh ingredients, and you’ll get a great meal. Show up with a half-empty pantry and a can of expired beans, and we can only do so much.

Moreover, it’s not just about whether something matches—it’s about at what level they match. Faraday can resolve identities at the person or household level, and in some cases at a broader level like ZIP code or metro area. Which one matters depends entirely on your use case. Sending direct mail? Household-level matches are usually perfect. Calling or emailing individuals? Then person-level accuracy is what you need.

So what can you actually expect?

With all of that context in mind, here’s what you can actually expect with Faraday:

• Email-only datasets → ~60%+ individual match rate. Not bad, considering how many emails are throwaways, duplicates, or churned.
• Add phone numbers → things stabilize. Old numbers still get recycled, but you’re closer to ~65%+.
• Bring addresses into the mix → now we’re cooking. Household match rates can climb into the 80–99%+ range depending on data quality, with 70–80% at the individual level.

And here’s an important point: yes, on a clean dataset with addresses, we can technically match 99% of residences. But do we advertise a 99% match rate?

No. Because that would be misleading. Of course we can recognize residence level matches on that dataset—we literally have the addresses—but what we’re actually proud of in that example is that we can match ~80% of individuals on the same dataset. Is that less flashy? Sure. But is it accurate, actionable, and potentially value driving? Absolutely.

And that means you won’t be backtracking later when projects built on inflated numbers don’t pan out.

Why this matters for CMOs

If you’re a CMO you already know where this is headed. Inflated numbers might look good in a pitch deck, but they don’t hold up when you’re presenting results to the board. Every padded “match” that isn’t real will:

• Waste spend in acquisition
• Poison your enrichment
• Undermine predictive models you rely on

That’s why Faraday takes the harder road: we dedupe, we verify, and we only count matches that are actually usable in production. For you, that means you can confidently plan campaigns and allocate budget knowing the foundation is solid—no backtracking when the numbers don’t pan out.

So what’s the punchline?

At the end of the day, match rate isn’t about chasing the biggest number—it’s about trusting the one that will actually power your campaigns. Faraday’s approach gives you the clarity and reliability you need to move forward with confidence.

Stop planning campaigns on vanity numbers. Let's see what actual match rates look like with your data.