Data accuracy and match rates: The truth about contact-level identification

Everyone’s suddenly talking about data accuracy. Usually right after they’ve claimed a 70 - 80% “match rate.”

That’s not a coincidence. As contact-level identification becomes more common, the numbers being thrown around are getting bigger, and less clear. What’s actually being identified? How accurate is it? And most importantly: does any of it drive pipeline?

Let’s separate reality from marketing math.

Why data accuracy is under fire

The skepticism around data accuracy didn’t appear out of nowhere.

Over the last few years, the industry has leaned heavily on headline match rates (often without explaining what those numbers include). The result has been:

• Inflated claims driven by blended metrics
• Confusion between account-level and contact-level identification
• Eroding trust when “identified” visitors don’t convert, respond, or show real buying intent

When data feels unreliable, teams stop using it. Sales loses confidence, marketing hesitates to act, and pipeline slows down. Why? Because clarity disappeared.

Account-level vs. contact-level identification

A lot of the confusion comes from treating account-level and contact-level identification as variations of the same thing. They’re not.

Account-level identification (IP → company) is easier to do and more consistent. It tells you which company visited your site, which can be useful for reporting and directional targeting.

But it stops there.

Contact-level identification (person-level) is harder. You know who engaged, what they looked at, and what context brought them there. Match rates are lower and the technical lift is real. What you gain is usefulness, which is what turns identification into action.

The two metrics that actually matter

If you’re evaluating contact-level identification, there are only two metrics worth asking for:

1. What percentage of traffic is identified at the contact level?
2. How accurate are those identifications?

Most inflated claims come from collapsing these into a single number.

Account-level matches, contact-level matches, cookies, and inferred guesses get bundled together and marketed as one “match rate.” It sounds impressive, but it hides what marketers actually need to know.

The reality of match rates (and why ~30% is best-in-class)

Here’s the part that often surprises people: True contact-level match rates typically top out around 25 - 35%.

Claims of 70 - 80% almost always rely on conflating multiple data types: IP-based identification, probabilistic inference, or low-quality traffic that inflates volume without improving usefulness.

But focusing only on the percentage misses the point. You don’t need to identify everyone. Identifying a small percentage of high-intent visitors, accurately and consistently, can unlock outsized value (if the data is reliable).

How contact-level accuracy is actually achieved

High-accuracy contact-level identification isn’t the result of a single technique. It comes from layering signals and being selective about what counts.

In practice, that means:

• Prioritizing known, first-party identifiers (cookies, form fills, logins)
• Using email and UTM signals where available
• Validating predictions against confirmed identities
• Filtering bots and low-quality traffic
• Refreshing datasets to avoid stale or misleading matches

This approach produces fewer matches, but it also produces matches teams can trust enough to act on.

Match rates only matter if you can act on them

Of course, you know that identification alone doesn’t move revenue – it’s activation that moves the needle.

Contact-level data only becomes valuable when it’s used immediately and in context through alerts, system syncs, or paid campaigns that actually change how teams operate day to day. If identified contacts just sit in a dashboard, the match rate doesn’t matter.

Ads are especially important here (and more overlooked than most teams admit). Paid is often the most expensive channel in the GTM mix, yet it’s rarely connected back to who actually engaged. Contact-level context changes that by showing which campaigns brought real people to your site, not just anonymous traffic or abstract lift.

That connection is what turns identification into leverage. Without it, match rates are just an interesting statistic that’s easy to report, but hard to use.

What “only” 20 - 30% can actually do

Match rate conversations often assume there’s some magic percentage where contact-level data suddenly becomes valuable. In reality, value shows up much earlier than that.

Even identifying 5% of your ICP visitors is meaningful, because that’s 5% more real buyers than you had visibility into before. If just one of those contacts turns into an opportunity, it can outweigh the cost of the entire effort. Doing something with imperfect data is almost always better than doing nothing with none.

The leverage comes from how the data is applied, not how big the number is.

Teams using contact-level identification, say at 5%, 20%, or 30%, have been able to:

• Focus paid spend on known buyers instead of broad guesswork
• Tighten audiences to improve CTR and reduce wasted impressions
• Lower CPC and CPL by cutting noise out of expensive channels
• Prove ROI faster by tying engagement back to specific campaigns

This is why teams see meaningful performance gains even with what the industry would call “modest” identification rates. The impact compounds when accurate contact-level data is fed directly into ads, alerts, and GTM workflows.

Forget about hitting a threshold and focus on turning any signal into action.

How Vector achieves industry-leading accuracy

High-accuracy contact-level identification comes from ordering signals correctly and being disciplined about what counts.

Vector prioritizes known, first-party signals before anything probabilistic. That means building identification from the strongest evidence first, then layering only when it improves confidence.

The signal waterfall starts with the layering approach shared above:

• HubSpot UTK cookies
• Form fills
• Authenticated logins
• Email + UTM signals
• Other first-party identifiers

Only after those are exhausted does Vector apply fingerprinting and enrichment, supported by seven data providers, waterfalled deliberately for quality, not volume.

Accuracy improves over time through continuous learning. Predictions are compared against known identities and real engagement, tightening confidence instead of inflating numbers.

Accuracy gains through filtering and focus 💪

One of the fastest ways to improve accuracy is removing the wrong data.

Vector filters aggressively:

• Bot and low-quality traffic filtering removes roughly 30% of noise upfront
• ICP filtering prioritizes visitors who actually matter to the business
• Regular dataset refreshes keep identities current instead of stale

This kind of focus lowers raw match volume, but dramatically increases usefulness. Fewer false positives, fewer dead ends, and more confidence acting on what’s identified.

And that confidence shows up where it matters: performance. Customers working with what the industry would call a “modest” 20 - 30% site de-anonymization rate have seen:

• Fingerprint: 3× lower CPC
• Goldcast: 17× ROI in three months
• OpenBrand: 7.8% CTR on LinkedIn
• DataGrail: 48% lower CPL on power campaigns

Same match-rate reality, but uh… very different outcomes.

The bottom line: quality beats hype

Vector is transparent about real-world limits on contact-level identification because the percentage isn’t the point. What happens after identification is.

Even a 20 - 30% accurate match rate delivers massive GTM leverage when the data is trustworthy, tied to paid campaigns, and activated immediately.

That’s how teams lower CPL, improve ROAS, and turn contact-level data into revenue impact instead of reporting noise.