Businesses often make strategic decisions based on where they believe their customers are. Whether it’s tailoring campaigns, triggering localized experiences, segmenting audiences, or attributing conversions, location is treated as a solid data point. Yet for both B2B and B2C marketers, the majority of location data in analytics tools, advertising platforms, and CRM enrichment is built on fragile assumptions.
Most devices do not report where a user actually is—they report where the connection appears to originate, or where a network node happens to be. The result is location signals that can be off by blocks, miles, or entire regions, even when the user explicitly grants permission.
I have a fiber connection at home that I’ve had for about a decade. I looked up my IP address and its registered location. That location is 11 minutes away… 4.2 miles and crosses a major highway, and is in a different ZIP code. If I were a business that segmented traffic based on visitors in my ZIP or a 5-minute drive, I wouldn’t show up in their targeting. And I’m an exception. Most businesses will find their visitors in other cities or even states.
Browser-Based Location Data Is Inherently Unreliable
IP locations come from the registries where ISPs report their network blocks: When an internet provider acquires a range of IP addresses, they register that block with a regional internet registry (such as ARIN, RIPE, APNIC), listing the business address of the provider or the routing facility—not the end user’s real location—which third-party geolocation databases then use as the physical point for that entire range.
Unless a user is on a smartphone with GPS enabled inside a mobile application, location almost always defaults to IP-based geolocation. This method associates an IP address with a physical location, but that location is typically tied to an internet provider’s infrastructure rather than an individual.
As a result, residential fiber and cable networks may be located at the nearest major routing facility, which may be in a different city altogether. In office networks, shared corporate gateways can place hundreds or thousands of employees in a single incorrect location. In rural or satellite connections, the mismatch can grow to hundreds of miles. Marketers who read analytics dashboards often assume they are seeing genuine geographic patterns, unaware that the signals come from routing equipment rather than real customers.
How Different Connection Types Distort Location
Every access method alters accuracy differently, and understanding these distinctions is critical for interpreting geographic data:
Mobile GPS: The gold standard, accurate within meters when permission is granted. This is rare outside mobile apps.
Cell tower triangulation: Falls back when GPS is unavailable; accuracy ranges from reasonably close in urban areas to many miles off in suburban or rural regions.
Wi-Fi geolocation: Matches access points to known databases but quickly becomes inaccurate when networks move, change names, or lack mapping data.
Cable and fiber broadband: Typically maps to provider hubs, not residences or offices. Users may appear to be in the next town or several cities away.
Enterprise networks: Corporate routing can collapse vast physical footprints into a single misleading location, confusing B2B segmentation and territory-based reporting.
Satellite and rural wireless: Often among the least accurate due to centralized uplink points that can place users in entirely incorrect geographic regions.
VPNs and privacy tools: Routinely place users in different cities or countries, even when they are nearby, making location-based assumptions nearly meaningless.
These variations affect both individual consumers browsing at home and entire B2B organizations accessing platforms from centralized networks.
How Virtual Devices and Spoofing Manipulate Location
A growing and under-examined challenge is the use of virtual mobile devices—software-defined smartphones that emulate GPS, rotate IP addresses, and even simulate physical movement. Initially built for testing apps, these tools can be repurposed to fake engagement patterns. They can walk through neighborhoods, visit storefronts, or check in around a city.
This enables manipulation of location-based systems such as Google Business Profiles (GBP), foot-traffic analytics tools, proximity-based ad targets, and SEO signals. Because many platforms trust any GPS-like coordinates without verifying real-world proximity, fabricated activity can pollute datasets and distort competitive insights.
Your Customer’s Location Are Likely Miles Away From Reality
Even without spoofing, most users’ physical locations, their geographic latitude and longitude, rarely match their reported ones. A person may be working from home on the edge of town while their data appears to originate downtown. A rural customer may appear to be in a completely different county. A B2B buyer working from a branch office may be tagged as being at corporate headquarters. For privacy-focused VPN users, their supposed location might be in a different state or even country. Every analytic system that displays user location reflects these distortions.
The Missing $5 Chip That Could Solve All of This
Modern GPS chips are inexpensive—single-digit dollars in bulk—but most laptops, desktops, and connected home devices still rely on antiquated location methods. Contrary to privacy advocates, including GPS in all devices isn’t about enabling surveillance. It’s about allowing consumers to receive the most relevant experiences when they choose to give permission. Accurate location could improve everything from fraud protection and delivery accuracy to retail personalization, while providing businesses with confidence that their geographic insights are meaningful. Yet the industry continues to rely on proxies and inference instead of direct measurement.
How Google Analytics Determines Location
Google Analytics uses a multilayered approach to detect user location, and understanding these mechanics explains why its geographic reports often appear surprisingly imprecise.
Unknown users (not logged into Google)
Google Analytics relies almost entirely on IP-based geolocation. It matches the visitor’s IP address against a commercial geolocation database that points to ISP facilities rather than actual user addresses. Browser-granted location permissions do not flow into Google Analytics; they are used only by the website itself during that session. Because Analytics does not receive GPS data from browsers, its reports show routing hubs rather than users’ actual positions.
Known users logged into a Google account:
If a user is logged into Google across devices, the company may associate activity with a broader understanding of that person’s identity. However, Google Analytics does not receive this richer location data. Even for logged-in users, the Analytics interface still displays only IP-based location. Google may internally resolve a more accurate location for advertising systems like Google Ads. Still, the analytics product does not expose cross-device GPS coordinates, mobile app data, or the user’s known location history.
Cross-device tracking
While Google can identify a user across devices when they are logged into their account, it still does not merge GPS-level accuracy into Analytics reporting. Instead, it merges sessions but still assigns a location at the point of each device’s network entry. A mobile session with GPS enabled in an app may be precise, while a laptop session on a VPN or office network may appear to be in a distant city. Analytics treats each as-is, without reconciliation.
The result is deceptively confident-looking location reports that often reflect infrastructure more than real-world geography.
The Impact on Geographic Segmentation and Strategy
Inaccurate geographic signals cascade across marketing operations. Segments based on city or region may actually be grouping users by their internet provider’s routing center. Local campaign performance may appear underwhelming simply because users are misattributed to neighboring cities. B2B territory analysis becomes muddled when entire companies route through a single gateway. Retail foot-traffic insights become unreliable when spoofed devices or triangulation errors pollute datasets. For both B2B and B2C marketers, location should be treated as directional guidance rather than a precise metric.
Moving Forward With Smarter Expectations
Digital location will remain essential for analytics and targeting, but only when interpreted with appropriate skepticism. By understanding the limitations of IP geolocation, network-based inaccuracies, spoofing tools, and platform-level constraints, marketers can avoid overestimating the precision of their geographic data. Until devices adopt universal location hardware—and until analytics systems can validate authenticity—location signals should be viewed as approximations rather than truths.
©2025 DK New Media, LLC, All rights reserved | DisclosureOriginally Published on Martech Zone: How Accurate is Digital Geographic Location Data?