In the IP geolocation market, there’s one figure that shows up over and over again as if it were an unquestionable seal of quality: “99.8% accurate.” It sounds definitive, it feels scientific, and for anyone who needs to make a quick purchasing decision, it’s reassuring. The problem is that, once you look closely, that promise often rests on shaky ground: there is no shared definition of “accuracy,” no global reference dataset, and no standard methodology.

The uncomfortable consequence for any product, security, fraud, or analytics leader is this: two providers can both claim “99%” and be talking about completely different things. One may count success as getting the country right; another may require the correct city; a third may accept a broad radius (for example, tens of kilometers) and still present it as a win.

An Internet Too Large (and Too Dynamic) for a Magic Number

If the goal were to validate a global percentage like “99.8%” in a rigorous way, the baseline requirement would be verified ground truth at massive scale, with a representative sample. And that’s the first wall you hit: the internet isn’t a closed lab.

IPv4 alone has over 4.29 billion possible addresses. IPv6 operates at a scale so vast that “complete coverage” is not even a practical concept. But size isn’t the only issue. IPs move, they get reassigned, ASNs change, mobile pools rotate, NAT and CGNAT obscure users, and traffic flows through VPNs, proxies, corporate networks, and public clouds. Everything behaves differently.

Trying to compress all of that into a single global percentage is, at best, an aggressive oversimplification.

The Real Problem: Not All IPs Are the Same “Type”

IP geolocation doesn’t fail at random. It fails because large parts of the internet are designed to be dynamic, or to conceal origin.

Residential broadband: relatively stable, though reassignment and CGNAT exist in many markets.
Mobile networks: often rotate; a single IP can serve many users and shift locations.
CGNAT: thousands of users may sit behind one public IP.
VPNs and proxies: their explicit purpose is to hide or relocate origin.
Cloud infrastructure: reassigned constantly; blocks change usage frequently.

So when a provider says “99.8%,” the critical question is: 99.8% of what mix of IPs? Because a dataset dominated by stable residential broadband is not comparable to one with heavy mobile, VPN, and cloud representation.

“Accurate” According to Whom: The Trick Is in the Success Criteria

Another core issue is the definition of “correct.” There’s no universal standard in this industry, which makes comparisons slippery. Common success criteria include:

Correct at the country level (much easier).
Correct at the region/state level (harder).
Correct at the city level (where expectations are highest).
Correct within a distance radius (e.g., “within 50 miles” or “within 50 km”), sometimes without clearly disclosing the threshold.

That means two companies can show similar-looking percentages while one measures “country right” and the other measures “city right with a wide tolerance.” On paper, they compete. In reality, they’re not measuring the same thing.

When vendors publish tests at all, they often suffer from familiar weaknesses: small or non-representative samples, geographic concentration, self-selected environments, or internal comparisons rather than independent validation.

Why the Industry Stays Stuck in “Accuracy Theater”

This persistence isn’t mainly technical — it’s commercial. The market buys numbers.

RFPs and procurement processes ask for a percentage because it fits neatly into a spreadsheet. A vendor that refuses to provide a number may get eliminated early, even if their approach is more honest or verifiable. That creates an arms race: if one vendor claims “99%,” competitors feel pressure to say “99.5%.” Over time, the numbers rise while the meaning erodes.

Information asymmetry makes it worse. Most buyers can’t audit methodology, sample size, geographic distribution, or the mix of IP types. “99.8%” becomes a cognitive shortcut.

A More Useful Alternative: Continuous Verification and Inspectable Evidence

Instead of obsessing over one global metric, a more practical approach is to change the frame: fewer universal promises, more evidence that can be inspected. Treat accuracy as a living system, grounded in real network signals (routing, latency, movement, service behavior), multiple data sources, anomaly detection, and frequent refresh cycles.

Rather than “trusting” a headline number, buyers should be able to answer practical questions:

How often is the dataset refreshed?
What signals detect movement and reassignment?
How does it handle mobile, CGNAT, cloud, and VPN traffic?
Are there tools to validate real cases and inspect disagreements?
Does the provider publish methodology, sample size, and success criteria?

In short: accuracy shouldn’t be a claim — it should be an auditable process.

What Organizations Should Demand Before Buying IP Data

For any team that depends on IP geolocation (fraud, compliance, personalization, ad tech, cybersecurity, access control), a minimum evaluation checklist should include:

Definition of success (country, city, radius, tolerance).
Methodology (how it was measured, with what sample, and what biases exist).
Segmentation by network type (mobile vs residential vs hosting vs VPN).
Update cadence (daily, weekly, monthly) plus mechanisms to detect change.
Transparency (validation tools, real examples, explanations of discrepancies).

If a provider can’t clearly answer these, a marketing percentage won’t reliably predict real-world performance in production.

FAQs

Why does IP geolocation fail more often on mobile networks and CGNAT?
Because IPs are shared among many users and rotate frequently; the “one IP = one location” assumption breaks down.

What does “city-level accuracy” actually mean in IP geolocation?
It depends on the vendor: it can mean the exact city, or “near the city,” sometimes within a distance threshold that isn’t always disclosed.

How often should an IP geolocation database be updated to stay reliable?
The more dynamic the IP mix (cloud, mobile, proxies), the more critical frequent updates become. In practice, continuous signals plus frequent refresh matter more than a static monthly update.

How can a company validate an IP data provider without falling for marketing claims?
Test with real traffic, segment by network type (mobile/residential/cloud/VPN), measure error by country/city/radius, and require methodology, sample size, and success criteria in writing.

source: hackernoon