Key Takeaways
- POI data is the location layer behind mapping, real estate, retail site selection, CPG, finance, healthcare, geofencing, logistics, and EV infrastructure planning.
- It only works if it's accurate and updated frequently, especially when paired with mobility or demographic data.
- Points of interest are dynamic: businesses open, close, and relocate constantly, so stale data leads to wrong conclusions.
- Site selection, geofencing, and logistics are the fastest-growing, highest-intent use cases, and are often overlooked in generic overviews.
- Update cadence should match the use case: daily for logistics, monthly for most site selection, near-real-time for geofencing.
Most teams already know they need location data. What’s less clear is what “point of interest data” actually does once you have it, and which of your teams should be asking for it. This article breaks down the nine most common ways organizations put POI data to work today: mapping, real estate, retail site selection, CPG, financial services, healthcare, geofencing and advertising, logistics, and EV infrastructure planning, so you can see exactly where it fits into your own stack.
Point of interest (POI) data is structured information about physical locations, business name, category, address, geometry, and open or closed status, that anchors every other location dataset to a real place. Without it, mobility, demographic, and economic data have no context to attach to. Our full guide to POI data covers the fundamentals in depth; this piece focuses on where it gets used and why the data quality behind it matters so much.
Why This Matters Now
The market for POI data solutions is projected to grow from roughly 3.5 billion dollars in 2026 to 7.2 billion dollars by 2033. The broader location intelligence and geospatial AI category is expected to expand even faster, from about 60 billion dollars in 2025 to 472 billion dollars by 2034, a 25.75 percent annual growth rate.
Three trends are driving that growth right now. First, AI agents are becoming direct consumers of POI data. By 2026, agent platforms increasingly expect live access to points of interest, addresses, and spatial layers, not static extracts refreshed once a quarter. Second, retail media and geofenced advertising have turned physical proximity into a targeting variable worth real money: global retail media ad spend hit 184 billion dollars in 2025 and is on pace to cross 312 billion dollars by 2030. Third, new physical infrastructure, EV charging stations chief among them, is expanding fast enough that site planners can no longer work from data that’s a year old.
This is also why update cadence matters as much as coverage. SafeGraph’s Places dataset, for example, tracks more than 80 million points of interest worldwide and refreshes monthly for exactly this reason: a business that closed last month is worse than no data at all if your model still treats it as open.
Places Are Dynamic, Not Static
It’s tempting to treat points of interest as fixed reference points, the same way a street name rarely changes. But commercial locations are anything but static. Storefronts open, close, relocate, and rebrand constantly, and the pace has only picked up as retail media, delivery-only kitchens, and pop-up formats become more common.
Once you accept that POIs change constantly, the real question becomes: at what cadence is your data catching up? Mobility data tracking daily movement across a city is close to useless if the POI file it’s matched against is a year out of date. That’s the gap between organizations that treat POI data as a one-time reference table and those that treat it as a living dataset.
The 9 Most Common POI Data Use Cases
1. Mapping and Navigation
Mapping and navigation is the most familiar POI use case: knowing where things are and keeping that information current when a store closes or a name changes. What’s changed is who’s asking. Voice assistants, ride-hailing apps, and now AI agents all rely on POI data as grounding truth before they can answer a question or plan a route. 57.3 percent of organizations had AI agents running in production in 2026, up from 51 percent the year before, and a growing share of those agents operate in the physical world, meaning they need live, structured POI data rather than a static map layer.
The practical stakes are small errors with real consequences. A coffee shop that moves half a block, or a mall entrance that closes for renovation, doesn’t just misplace a pin. It sends a delivery driver to the wrong door or gives an AI assistant an answer that sounds confident and is wrong. Mapping companies solve this by treating “knowing where things are” as a continuously updated process, not a one-time data load, which is also why coverage and refresh frequency matter more here than almost any other use case on this list.
2. Real Estate and Market Analysis
Real estate teams use POI data to spot growth before it’s obvious. The classic pattern: track where a popular brand is physically expanding, since its target demographic tends to follow. If Starbucks locations in a Chicago neighborhood skew toward a particular lifestyle segment, real estate analysts read that as a signal about who’s likely to move into surrounding blocks next, and may use it to decide where to invest or where to hold off.
This works in both directions. A cluster of closures in a category, say, a wave of bank branch shutdowns in a given trade area, is just as informative as a cluster of openings. What makes this analysis useful rather than anecdotal is doing it at scale across thousands of POIs and categories at once, which is only possible with structured, current location data.
3. Retail Site Selection and Trade Area Analysis
Site selection is arguably the highest commercial-intent use case for POI data, and it’s where the return on good data is easiest to measure. Trade area analysis uses POI density, category mix, and competitor proximity to answer a concrete question: will this specific address perform, and where will its customers actually come from?
Retailers using data-driven site selection platforms are already seeing measurable results. Cavender’s Western Wear opened 27 new stores in 2026, compared with 9 in 2024, after moving to a location-analytics-driven process, and analysts at Books-A-Million report saving 25 hours a week that used to go into manual site research. The underlying logic is straightforward: POI data shows which businesses already anchor a trade area, competitor data shows saturation, and demographic overlays confirm whether the local population matches the target customer. None of that works without an accurate, current base layer of places.
4. CPG and Consumer Goods Expansion
Consumer packaged goods companies, from snack brands to household goods, use POI data to size and prioritize their total addressable market: the full set of stores that could plausibly carry a given product. Turning that into a spatial map lets a CPG brand focus expansion effort where it will actually move volume, rather than spreading sales teams thin across a whole metro area.
The same logic works at a much more granular level. A candy brand trying to get into convenience stores in a new city can start by mapping clusters of independent corner stores neighborhood by neighborhood, using category codes and location density to prioritize which blocks to approach first, instead of working from a generic list with no sense of concentration or reachability.
5. Financial Services and Investment Analysis
“Financial institutions” covers a wide range of players: investment banks, retail banks, private equity firms, hedge funds, and the consultancies that serve them. What they share is an interest in how changes in physical locations over time translate into investment signals. Tracking openings and closures within a business category across a region or country can inform a call on whether to expand exposure to that sector, and the same analysis can be run down to the individual block level for more targeted decisions.
This kind of alternative data analysis, treating foot traffic and location change as an early signal ahead of quarterly earnings, has become a standard part of the toolkit for funds looking for an edge, and it depends entirely on having an accurate, current picture of which POIs exist and which have closed.
6. Healthcare and Public Health
In healthcare, POI data is often discussed as a companion to mobility data, but a large share of its value doesn’t require mobility data at all. Measuring access to primary care in a given region starts with the places themselves: where clinics and pharmacies are located, layered with demographic data on who lives within a given radius or drive-time band, and survey data on what transportation options that population actually has.
This is the basis of catchment area analysis, a core method for identifying underserved populations and healthcare deserts. Our guide on using POI data for catchment area analysis walks through the method in more detail, but the short version is that accurate facility locations plus accurate population data is what turns a map of clinics into an actual planning tool for where care is missing.
7. Geofencing and Location-Based Advertising
Geofencing has become one of the largest commercial applications of POI data. The geofencing market alone grew from an estimated 3.39 billion dollars in 2025 to 4.21 billion dollars in 2026, a 24.1 percent annual growth rate, and geofencing now accounts for more than 37 percent of the entire location-based advertising market. The consumer behavior backs up the spend: over 72 percent of American mobile users have location services enabled, 61 percent say they prefer ads relevant to their current location, and geofenced campaigns average a 7.5 percent click-through rate, compared with roughly 0.9 percent for standard Facebook ads.
SafeGraph customer Clear Channel Europe is a direct example of this in practice. Its RADAR platform uses SafeGraph POI data to help advertisers understand the demographics, competing businesses, and trade area around each of its 280,000 out-of-home ad sites across 17 European markets, turning static billboard inventory into a targeted, audience-based buy. For more on how marketers build these campaigns, see our guide to geofencing marketing.
8. Logistics and Last-Mile Delivery
Last-mile delivery is both a fast-growing market and a use case where POI accuracy has an outsized effect on cost. The global last-mile delivery market is on track to grow from around 184 billion dollars in 2025 to roughly 200 billion dollars in 2026, and toward 349 billion dollars by 2033 at a 9.8 percent annual growth rate.
Delivery and routing platforms rely on POI geometry, not just an address point, to identify the correct building entrance, loading dock, or drive-through lane. A single wrong pin can turn into a failed delivery, a re-route, or a frustrated customer, and at the scale most logistics companies operate, even a small error rate compounds into real cost. This is one of the clearest cases where POI data and mobility or routing data have to work together: mobility data shows the path, POI data confirms the destination is correct.
9. EV Charging and Infrastructure Site Planning
EV charging is one of the newest and fastest-growing POI use cases, and it depends on location intelligence in a way that didn’t exist a decade ago. The global EV charging station market was valued at 50.7 billion dollars in 2025 and is projected to reach 65.7 billion dollars in 2026, on its way to 405.6 billion dollars by 2035, a 22.4 percent annual growth rate. In Europe alone, the public charging network is expected to grow from about 1 million to more than 2 million charging points by 2030.
Site planners choosing where to put a new charging station combine POI data (what’s nearby: retail, employment hubs, existing chargers) with demographics like EV adoption rates and dwell-time compatibility. It’s not a purely technical decision either: 46 percent of charging network operators cite energy and grid constraints as their top challenge, and more than 90 percent expect grid capacity to limit growth over the next year, which makes getting the site selection right the first time even more important.
Closing Thoughts
What ties these nine use cases together is that location context has stopped being a niche, industry-specific need and become a horizontal requirement. A mapping company, a hedge fund, and an EV charging network are solving different problems, but all three fail in the same way if their underlying POI data is stale, mislabeled, or too sparse to trust. As AI agents, retail media, and new physical infrastructure keep expanding what’s possible with this data, the gap between organizations working from accurate, frequently updated POI data and those working from a snapshot from last year is only going to widen.
Frequently Asked Questions
1. What is points of interest (POI) data?
POI data is structured information about physical locations, such as businesses, public facilities, landmarks, and services, including attributes like category, address, geometry, and open or closed status.
2. Why is POI data important for geospatial analysis?
It provides the context that other spatial data needs to be meaningful. Mobility, demographic, or economic analysis loses its value without an accurate underlying map of where places actually are.
3. How is POI data different from mobility data?
Mobility data shows how people move. POI data explains where they’re going and what’s actually located there. The two are most powerful combined, but POI data has plenty of standalone value, in healthcare catchment analysis, for example.
4. Which industries use POI data most?
Mapping and navigation, real estate, retail site selection, CPG, financial services, healthcare, adtech and geofencing, logistics, and EV infrastructure planning are the most common users today, though new applications are added constantly.
5. How often should POI data be updated?
It depends on the use case. Logistics and geofencing benefit from near-real-time or daily updates, while most site selection and market analysis work is well served by monthly refreshes. What matters most is that the cadence matches how quickly your specific market changes.
6. Are AI agents actually using POI data?
Increasingly, yes. As AI agents take on tasks in the physical world, planning a route, answering a location-based question, they need live access to structured POI data rather than a static, outdated file.
7. What attributes should I look for in a POI dataset?
At minimum: category code, precise geometry (not just a point), open or closed status, brand affiliation, and a clear, documented update cadence. These are the fields that determine whether a dataset is usable for serious analysis or just a rough reference.
