Skip Tracing Accuracy and Match Rates, Explained
Updated June 17, 2026
Match rate is the percentage of records a skip trace returns at least one contact for; accuracy is whether that contact is actually current and belongs to the right person. They're different numbers, and providers love to quote the first while staying quiet on the second. A high match rate with low accuracy just means you're paying to call wrong numbers — both metrics matter.
Every skip-trace provider advertises a match rate, and almost none advertise accuracy — which tells you which number is easier to make look good. A service can hit a 95% match rate by returning any phone it can attach to a record, including stale and mismatched ones. That number looks great on a sales page and means little when you start dialing.
The metric that actually governs your economics is the rate of usable, correct contacts per dollar spent. Understanding the difference between match rate and accuracy — and how to test it on your own list — is what separates investors who quietly burn money on bad data from those who don't.
Match rate vs accuracy: not the same thing
Match rate answers: of the records I submitted, how many came back with at least one contact? Accuracy answers a harder question: of the contacts that came back, how many are current and actually belong to the owner? A provider can have a high match rate and poor accuracy — it returns numbers, they're just often wrong.
The gap between the two is where money leaks. If a service matches 90% of your list but a third of those numbers are disconnected or wrong-person, your effective accurate-contact rate is closer to 60%. That's the number that determines how many real conversations the list can produce, and it's the one no sales page leads with.
What's realistic to expect
Match rates in the 70–95% range are common depending on list quality and provider. Accuracy is fuzzier and harder to pin down, because phone numbers go stale constantly — people change carriers, drop landlines, and move. Even a genuinely good trace is a snapshot of best-available data, not a guarantee the number rings the right person today.
List type matters as much as provider. A clean absentee-owner list with real human names traces far better than a list full of LLCs, trusts, and vacant parcels. When you compare providers, hold the list constant — the same input to two services is the only fair test.
| Metric | What it measures | How providers report it | Why it matters |
|---|---|---|---|
| Match rate | Records returning any contact | Loudly, on the sales page | Caps how many rows are workable |
| Hit count | Phones returned per record | Sometimes | More numbers, more chances to connect |
| Accuracy | Contacts that are current and correct | Rarely, if ever | Decides real conversations per dollar |
| Cost per accurate lead | Spend divided by usable contacts | Almost never | The only number that governs ROI |
What match rate and accuracy actually measure
How to test a provider honestly
Don't trust the advertised number — run a controlled test. Take a sample of 100–500 records from your real list, run it through two providers, and compare not the match rates they report but the results you get when you actually call and email. Track connects, wrong numbers, and disconnects. The winner is the one with the lowest cost per real conversation, not the highest headline match rate.
Once you've found data you trust, the leverage shifts entirely to follow-up. Accurate contacts only pay off if every one gets worked persistently — which is the job BILT is built for. You source and verify the data; BILT runs the email, SMS, and AI follow-up that converts accurate contacts into deals.
Frequently asked
What is a good skip tracing match rate?
Match rates of 70–95% are common, but the headline number is the wrong thing to optimize. A high match rate full of stale or wrong numbers is worse than a lower one full of current contacts. Judge providers on accurate-contacts-per-dollar, not the advertised match percentage.
Why are some skip-traced numbers wrong?
Phone data goes stale fast — people change carriers, drop landlines, and move. A trace returns the best-available number from licensed databases, but those files lag reality. Even an accurate provider can return a number that was correct last year and isn't today.
How do I compare two skip-trace providers fairly?
Hold the list constant. Run the same 100–500 record sample through both, then measure real outcomes — connects, wrong numbers, disconnects — when you actually call and email. Compare cost per real conversation, not the match rates each provider advertises.
Does a higher match rate mean better data?
Not necessarily. A provider can inflate match rate by returning any number it can attach to a record, including wrong ones. Match rate tells you how many rows are workable; accuracy tells you how many are correct. You need both to judge data quality.
The takeaway
Match rate is how many records return a contact; accuracy is how many of those contacts are right. Providers quote the first and bury the second, so the gap is where money leaks. Test on your own list, measure cost per real conversation, and once you trust the data, let a system like BILT work every accurate contact relentlessly.