Key Takeaways
- Location-based audiences combine online behavior with real-world visits for more accurate targeting.
- Location-based targeting is the tactic, while a location-based audience is the reusable segment it creates.
- Location data varies in precision. IP, GPS, demographic, and POI polygon data each balance scale and accuracy differently.
- POI polygons confirm visits to a specific building, making targeting far more reliable than proximity alone.
- Privacy-first practices, including consent and aggregated location data, are now a competitive advantage.
- Combining mobility, POI, and demographic data delivers stronger personalization and better return on ad spend.
Location data can differentiate companies looking to stand out in a crowded advertising space. Consumer data use in advertising has grown enormously over the last decade, spanning demographics, online activity, purchase history, and geography. All of it has changed how companies get closer to their audiences and place smarter bets to improve advertising performance.
To stay competitive, brands are layering additional data into their audience analysis to make sure they’re reaching the right people at the right time. Places data is one of the most powerful additions to this mix. Enriching privacy-compliant consumer data with real-world place context gives marketers a new level of sophistication in planning, buying, optimizing, and measuring programmatic campaigns, especially for online-to-offline attribution.
We wrote previously about the rise of location-based marketing. Here, we go a level deeper and focus on what it takes to actually build location-based audiences: what data goes into them, how location-based targeting differs from the audiences it produces, and how to do all of this in a privacy-first way.
The Value of Location-Based Audiences
To stay competitive in today’s market, brands need to carefully curate the end-to-end customer journey. Many ingredients go into optimizing this experience: what the consumer prefers, how they behave, where they go, and other signals that enable personalization. But consumer data without offline behavior is limited in providing that insight.
Census block group (CBG) level data and generalized household demographics may describe who a target consumer is, but they fall short on the reasons, locations, and relevance needed to target them effectively in a way that feels useful rather than intrusive. Mobility data combined with place data blends well with digital signals to build a more complete picture of the target consumer and improve return on ad spend (ROAS).
For example, consider car enthusiasts who routinely browse websites without real purchase intent. They look at models they never plan to buy. Places data can be used to cross-reference online signals with physical movement, identifying whether they’ve recently toured an auto dealership (a much stronger intent signal) or, better yet, walked into the actual showroom (an even stronger one). By separating window-shoppers from real buyers, a whole audience segment can be excluded from targeting, saving budget and improving ROAS.
Benefits of Location-Based Marketing for Audience Building
Location-based marketing works because it replaces assumptions about a customer with evidence of what they actually do in the physical world. When that evidence feeds into audience building, the benefits of location-based marketing show up across the funnel:
- Sharper targeting
Instead of guessing who might be interested based on demographics alone, brands can build segments around confirmed behavior, like actual store visits. - Reduced wasted spend
Excluding low-intent users (the window-shoppers) means the budget goes toward people who are more likely to convert. - Stronger personalization
Knowing where someone actually goes, not just where they live, allows for more relevant messaging and offers. - Better cross-channel consistency
The same location-informed audience can be activated across display, social, and location based mobile marketing channels, including in-app and push notifications, rather than being rebuilt for each one. - Improved measurement
Because visits can be tied to a specific place, marketers get a clearer read on which campaigns actually drove foot traffic, not just impressions.
These benefits compound when the underlying location data is accurate and refreshed regularly. Stale or low-precision data undermines every benefit on this list, which is why the type of location data behind an audience matters as much as the audience strategy itself.
Types of Location-Based Audience Data
Before choosing a location-based targeting strategy, it helps to know that not all location data is built the same way, and each approach comes with a different tradeoff between reach and precision.
IP-based targeting is the broadest and cheapest method. It infers a general location from a device’s IP address, which works for city- or region-level targeting but says nothing about which specific places someone actually visits. It’s a useful starting point, not a precision tool.
GPS and mobility-based targeting relies on device location signals, often tied to a Mobile Advertising ID (MAID), to track where a device has physically been. This is far more precise than IP-based targeting, but on its own it only confirms that a ping occurred somewhere nearby, not which building it happened inside. A ping near a shopping center could belong to any of a dozen stores. This is also the layer that underpins most location based mobile marketing, since MAIDs are tied to smartphones and in-app activity.
Household and demographic-indexed targeting links geographic areas to aggregated demographic and lifestyle profiles, similar to the neighborhood segmentation used in tools like Experian’s Mosaic categories. It’s useful for hyper-localized campaigns based on area-level characteristics, but it targets the type of area, not a confirmed individual visit.
POI polygon-based targeting is the most precise tier. Rather than relying on proximity or a rough centroid, it uses the actual building footprint, meaning SafeGraph Geometry data, to confirm whether a mobile ping fell inside a specific place rather than just near it. This distinction matters most in dense environments: a mall, a strip mall, or a downtown block where dozens of businesses sit within a few hundred feet of each other. Without polygon precision, “visited the coffee shop” and “walked past the coffee shop on the way to the bank next door” can look identical in the data.
Most effective audience strategies combine several of these layers: mobility data for visitation signals, POI polygons to confirm which specific place was visited, and demographic data to add context about who that visitor likely is.
Building Location-Based Audiences in a Cookieless, Privacy-First World
Third-party cookies were never a great fit for location-based audiences to begin with. They don’t work on mobile apps, they don’t capture real-world visits, and browsers have been phasing them out for years. As the broader ad industry moves away from cookie- and ID-based identifiers, marketers building location-based audiences need a strategy that is privacy-conscious from the start, not one that’s simply adapting to survive a deprecation deadline.
Consent has to be the foundation
Any workflow using device-level location signals, including MAIDs collected from mobile pings, depends on users having opted in to location sharing somewhere in that data’s chain of custody. Marketers should be able to explain plainly how location data was collected, how it will be used, and how someone can opt out.
Aggregated, privacy-compliant place data reduces exposure
Building audiences around POI and polygon data, rather than raw individual tracking, means the sensitive part of the equation (a specific device ID and its movement history) can stay with a compliant data provider, while the location intelligence layer, meaning what a building is, its boundaries, its category, and its hours, carries no personal information at all. This separation lets advertisers get precision without handling raw personal location data themselves. This is one reason accurate, well-documented Places and Geometry data matter: the audience gets built on the compliant layer, not the sensitive one.
Privacy concerns are already shaping adoption, not just theoretical.
Privacy concerns are already shaping behavior, not just sentiment. Recent industry analysis citing IAPP data found that 48 percent of consumers say they have already stopped buying from a brand over privacy concerns. Separately, 2025 data from Liquid Web found that 69 percent of US consumers have abandoned a transaction outright because of concerns about how their data was being used. That kind of drop-off at the point of purchase is exactly what a privacy-first location data strategy is meant to prevent.
The payoff for getting this right shows up on the business side too. Cisco’s 2026 Data Privacy Benchmark Study found that 96 percent of organizations say their privacy investments deliver returns that exceed the cost, with a median return of 1.6 times. Privacy-first design is a competitive advantage, not just a compliance checkbox. Brands that can clearly explain their data practices tend to face less resistance to location-based personalization, not more.
In practice, this means asking a location data or audience partner not just “how precise is this data?” but “where does the personal data live, and what happens if regulations tighten further?” Providers built around place-level data, meaning buildings, POIs, and boundaries, rather than persistent individual tracking, are generally better positioned for a future with fewer identifier-based options.
Suggested graphic: A short bar chart or stat callout box pulling 2 to 3 privacy stats (percentage of businesses holding back due to privacy concerns, percentage of consumers who’d stop shopping with a brand after a data incident). This directly supports the section’s argument and gives you a shareable “trust” visual to pair with the McKinsey stat used later in the guide.
Successful Examples of Location-Informed Audiences
Here are a few examples of companies using places data well to create advertising value:
- RainBarrel builds a proprietary audience graph based on commercially available geospatial data, allowing advertisers to target messages to the right audience.
- Mobsta’s visual ad planning platform uses POI data to help agencies pinpoint the right ad units in the right locations to reach target audiences.
- Media Storm uses polygon-based POI geofences to accurately attribute visits from MAID pings, resulting in stronger, more efficient campaign performance.
- InMarket’s location-enriched audience creation methodology helps programmatic advertisers reach consumers where they’re most likely to convert.
- Viant’s data platform links disparate data sources, including POIs and other geospatial datasets, to build a more accurate understanding of ROAS.
Each of these companies has a competitive edge that isn’t possible without clean, accurate, places-based insight.
Best Practices for Using Places Data to Build Audiences
1. Leverage a Mix of Data Sources
In today’s omnichannel world, consumers interact with brands both online and offline. Bridging the physical and digital world means using a mix of data sources for a more holistic view of audience profiles.
Combining signals such as visitation patterns, browsing behavior, and purchase history gives better insight into how an audience interacts with a brand across channels. These insights help explain why people are engaging with a brand in a certain way, which in turn helps derive consumer affinity for brands and products. This lets marketing teams build more effective campaigns that reach audiences wherever they are, including through location based mobile marketing channels like in-app messaging and push notifications.
Personalization targets specific audience needs and tends to lift conversion and engagement. McKinsey’s more recent work on personalization-driven customer value, built on its CustomerOne approach, points to revenue uplifts in the range of 10 to 20 percent for companies that apply advanced analytics and AI-driven personalization across the customer journey.
2. Use Accurate POI Data
Points of interest (POI), or Places data, matter because they give marketers a geospatial view of where their audience actually goes. Bad data leads directly to audience inaccuracies and misleading signals.
The physical world is constantly changing, with places opening, closing, and shifting operating hours. It’s important not to settle for POI data that updates annually or quarterly. Investing in a POI dataset that refreshes monthly, like SafeGraph Places, keeps audience data grounded in what’s actually true on the ground.
3. Better Yet, Use Accurate POI Polygons
POI polygons add an extra layer of detail that sharpens an audience profile. Polygon data takes POI data a step further by providing building footprints, estimated sizes, and, where possible, tenant splits, so you can build more specific location-informed audiences.
Polygons help distinguish whether someone actually visited a POI, and should therefore be part of a target audience, or simply walked past it. This level of specificity lets you confirm who in an audience actually visited a place, which is a far more reliable measure of campaign performance than estimates or projections.
4. Layer Location and Behavior Context Into Your Analysis
Context matters when using mobility data for audience profiling. Simply clustering people by proximity to a POI isn’t enough. Getting deeper insight means understanding how a place relates to behavior. A consumer might act very differently at two types of cafes (one serving strictly espresso, the other pouring wine late into the evening). Accounting for that difference produces stronger, more targeted audiences. It also matters to factor in typical operating hours, so a late-night ping gets correctly attributed to the bar that’s still open rather than the bagel shop next door that closed at noon.
Cross-referencing visits to other POIs can also surface patterns and preferences. For example, an individual who frequents both Target and Chipotle on the same trip reveals something about their routine that neither visit shows on its own. Without this kind of context, mobility data alone can produce incomplete or misleading conclusions about audience behavior.
Get Started With SafeGraph Data to Build Location-Informed Audiences
Accurate POI data plays a central role in building precise, strategic audience profiles, and it’s a major factor in improving return on ad spend by understanding audiences at a deeper level.
At SafeGraph, we track our accuracy efforts publicly, because data quality is the foundation everything else in this guide depends on. Our team can help you figure out how accurate POI and polygon data can strengthen your audience-building strategy and improve your return on ad spend. Schedule a demo or download a free sample of our Places data to see the level of detail firsthand.
FAQ’s
1. What are location-based audiences?
They’re audience segments built by combining geographic signals with real-world behavior data, like confirmed visits to a place. This produces more accurate targeting than demographic or online data alone.
2. What is the difference between location-based marketing and location-based targeting?
Location-based marketing is the broader strategy of using location data to reach and engage customers. Location-based targeting is the specific tactic within that strategy, such as geofencing or polygon matching, used to identify who qualifies for an ad, while the resulting audience is what gets reused across campaigns.
3. What's the difference between location-based audiences and geofencing?
Geofencing draws a virtual boundary around a location to trigger an ad the moment someone enters it. A location-based audience is the segment built from that signal and others, which can be reused across multiple campaigns and channels rather than triggered only at one boundary.
4. Why is POI data important for marketing?
POI data shows where consumers actually go in the physical world, not just where they live or browse online. That visibility helps marketers identify real intent and sharpen targeting accuracy.
5. How does location data improve ROI?
Location data cuts wasted ad spend by excluding low-intent audiences and targeting people who’ve shown confirmed real-world behavior. This shifts budget toward audiences more likely to convert, rather than ones selected on assumptions.
6. What role does mobility data play in audience building?
Mobility data tracks movement patterns over time, revealing behavior and intent that digital signals alone can’t capture. On its own it shows that a visit happened nearby, which is why it’s usually paired with POI polygons to confirm exactly where.
7. What are POI polygons and why do they matter?
POI polygons define the exact building footprint of a place, rather than just a rough point or radius. That precision confirms an actual visit occurred inside a specific location, instead of somewhere nearby.
8. Is location-based audience targeting legal and privacy-compliant?
Yes, when it’s built on properly consented data collected and used transparently. It must comply with regulations like GDPR and CCPA, which require clear consent for location tracking and the right to opt out, and using aggregated place data instead of raw personal tracking further lowers regulatory exposure.
