5 Ways POI Data Improves Site Selection Strategy

Every retail or franchise brand knows the pain of a bad location decision. You invest capital, sign a lease, build out the space and then the foot traffic never materializes. This is the hidden cost of poor site selection strategy: it’s not just a missed opportunity. It’s a capital loss that takes years to recover from.

Traditional methods – broker recommendations, intuition, site visits, or basic census data are still widely used. However, they lack the scale and precision that modern expansion demands. They answer “does this location look good?” but rarely answer “why is this location better than 400 others?”

This is where POI data for site selection becomes transformative. At LocationsCloud, we work with retailers, QSR brands, real estate firms, and analytics teams who use structured point of interest data to evaluate locations systematically and not subjectively. In this post, we walk through five specific ways location intelligence for site selection changes outcomes.

What Is POI Data?

POI data (Point of Interest data) refers to structured datasets of physical places – stores, restaurants, hospitals, malls, gyms, offices, transit stations, and any other venue that people interact with. Each record in a POI dataset typically includes:

• Location coordinates: latitude and longitude
• Place name and brand: e.g., Starbucks, CVS Pharmacy
• Category and subcategory: e.g., Food & Beverage > Coffee Shop
• Operating status: open, closed, temporarily closed
• Address and postal information for regional filtering

Therefore, point of interest data forms the foundational layer of any serious location intelligence for site selection workflow. When aggregated across a city, region, or country, POI data reveals patterns that no broker walk-through ever could.

1. How Does POI Data Help Identify Demand Zones?

Before committing to a lease, every brand needs to answer a deceptively simple question: Is there enough demand here? POI data answers this by mapping commercial activity density across a market.

LocationsCloud’s retail site selection analytics datasets allow teams to:

• Map commercial, residential, and mixed-use clusters – so you see where activity concentrates
• Identify areas with strong POI density – a reliable proxy for high consumer footfall
• Highlight underserved neighborhoods – markets with spending potential but few competitors

Meanwhile, location data for expansion also helps brands avoid common pitfalls. A zone that looks “busy” on a map might actually be dominated by office parks that generate only weekday lunchtime traffic, not evening or weekend demand. POI category breakdowns reveal this nuance.

Business Impact	Detail
Faster shortlisting	Teams evaluate fewer, better candidates
Lower risk	Significant reduction in low-demand site selection errors
Smarter targeting	Demand zones identified before competitor awareness kicks in

2. How Do You Analyze Competitor Presence and Market Saturation with POI Data?

POI data can provide valuable competitive intelligence to inform site selection decisions. Understanding your competitors’ locations (and where they do not exist) fundamentally impacts your site selection strategy.

POI data can help brands:

1. Track the location of competitors’ locations, both by brand and category, at the city, metro area, or national level.
2. Assess the density and proximity of competitors – How many direct competitors are within a distance of 1 km?
3. Identify “white space” – areas that have demand for offerings but do not have direct competition.
4. Identify areas that are over served by competitors – locations where additional business locations could result in margin cannibalisation.

For example, a restaurant brand entering a new city may obtain point-of-interest data for existing fast-food restaurants in that city, filter the data for its marketplace segment, and map the results. With this information, they will be better able to cluster areas of consumer density that currently lack direct competitors, rather than simply entering markets quickly.

Business Impact	Detail
Smarter positioning	Enter markets where you win, not just where space exists
Avoid saturation	Data-backed decision to skip over-competed zones
Better timing	Identify underpenetrated markets before they close

3. How Does Co-Tenancy and Ecosystem Fit Affect Site Selection Strategy?

Location isn’t just about where you are. It’s about who your neighbors are. Co-tenancy, the presence of complementary businesses nearby, significantly influences foot traffic patterns and customer behavior.

Using location intelligence for site selection, LocationsCloud customers analyze:

• Complementary business types – gyms near cafes, pharmacies near clinics, QSRs near office clusters
• Co-location patterns – which brand combinations create traffic synergies
• Ecosystem compatibility – whether nearby POIs attract the same customer profile as your brand

Therefore, a fitness brand looking to open a new studio will benefit from proximity to healthy food options, corporate parks, and public transit. Retail site selection analytics that include full POI category breakdowns make this analysis systematic rather than anecdotal.

Business Impact	Detail
Higher footfall	Ecosystem synergy drives natural traffic to your door
Customer alignment	Neighbors validate your target demographic
Lower CAC	Less marketing spend needed when demand is already nearby

4. Can You Compare Multiple Locations at Scale Using POI Data?

In fact, this is why location intelligence, or POI data for site selection, differs from traditional approaches. For example, if you were evaluating 5 sites, it would be manageable to evaluate them all manually. However, if you are evaluating 500 sites within 20 cities, it is much less manageable without having structured location data for site selection.

With locationscloud, teams can:

• Create consistency in their criteria for evaluating sites regardless of the city, region, or country
• Objectively score potential locations based on the density of POIs, proximity to competitors, and the category mix
• Reduce decision timelines from months to a few days
• Establish a repeatable scoring approach that teams apply consistently at every stage of the expansion of their operation.

For example, if a Franchise Brand is looking at 300 prospective sites in 10 cities, they can utilize retail site selection analytics using POI data to automate their scoring based on established parameters (e.g. minimum density of POIs, maximum proximity to competitors/appropriate for the category) and identify the best 30 candidate sites to have greater scrutiny placed on them for a decision. It represents scalable intelligence.

Business Impact	Detail
Scalable expansion	Evaluate hundreds of sites without adding headcount
Consistent framework	Same scoring criteria applied across every market
Faster time-to-market	Decision cycles shrink from months to days

5. How Does POI Data Support Long-Term Expansion and Network Planning?

Site selection is rarely a one-time decision. Brands build networks. They plan phased rollouts across regions. They need to understand how markets evolve – which areas are growing, which are saturating, and where the next wave of opportunity lies.

LocationsCloud’s point of interest data supports long-term planning by enabling teams to:

• Enable city- and country-level rollout planning – with consistent data across all markets
• Track market evolution over time – new openings, closures, and category shifts
• Support phased expansion strategies – prioritize Tier 1 cities first, then expand outward
• Allocate capital more accurately – invest in markets with validated demand signals

Furthermore, as location data for expansion is refreshed regularly, teams can monitor whether their initial analysis still holds. Markets change. A neighborhood that was underserved 18 months ago may now be over-saturated. Updated site selection strategy requires updated data.

Business Impact	Detail
Sustainable growth	Phased expansion built on live market intelligence
Better capital allocation	Invest where data says yes, not where instinct guesses
Board-level visibility	Network planning backed by verifiable location data

POI Data vs. Traditional Site Selection Methods

The table below illustrates why brands are moving from intuition-based processes to data-driven site evaluation:

Aspect	Traditional Methods	POI Data Approach
Scale	Limited (Local/Regional)	City → Global
Speed	Slow (Weeks/Months)	Fast (Hours/Days)
Objectivity	Subjective & Biased	Data-Driven
Repeatability	Low	High
Cost of Error	Very High	Significantly Reduced
Multi-Market Expansion	Difficult	Scalable

Using POI data for site selection is not only faster but also more accurate and reliable. While traditional methods can be useful during final site visits, the shortlisting and scoring process should always rely on data.

What Happens When You Combine POI Data with Other Location Signals?

POI data is most effective when paired with additional location intelligence for site selection. LocationsCloud datasets work well with:

Signal Combination What It Reveals

• POI and Foot Traffic Validates actual demand at a site, not just theoretical activity
• POI and Mobility Data Reveals how people move through an area at different times of day
• POI and Demographics Confirms whether the local population matches your brand’s target customer profile

This multi-signal approach to retail site selection analytics is how leading brands build conviction before committing to a location, not after.

How LocationsCloud Enables POI-Driven Site Selection

LocationsCloud is the best way to get reliable POI data. Here are the reasons why our data sets are superior to others:

Accuracy and validation

All POI entries pass through multiple validations and are cross-referenced against a variety of data sources in order to ensure they are accurate and no duplicates exist.

Extensive global coverage and local coverage

Our databases cover some of the biggest towns in the world and the smallest towns. Both global and local strategies benefit from our data.

Industry-specific categories

Our POI data sets are available by category to assist you with focused analysis on competition and the overall market of your location. Some examples would include Retail, QSR (quick service restaurants), Healthcare, Financial Services.

Multiple formats to choose from

We provide our data in API; bulk; and CSV, JSON, & GeoJSON formats that are simple to use and analyze. You can easily integrate the data into your existing tools.

Frequent updates

We frequently refresh our POI data in order to provide you with the most accurate information possible regarding the current market environment.

Better Site Selection Starts with Better Data

Site selection is no longer a discipline that rewards instinct. Today, the brands winning the best locations are those building their decisions on validated, structured point of interest data, not broker opinions or spreadsheet guesswork.

Across all five dimensions covered in this post, demand zone identification, competitor analysis, co-tenancy fit, multi-site comparison, and long-term expansion planning, POI data for site selection consistently delivers what traditional methods cannot: objectivity, scale, and speed.

Furthermore, businesses that adopt location intelligence for site selection don’t just make better individual decisions. They build repeatable, scalable systems that compound over time, improving every expansion cycle.

LocationsCloud helps analytics teams, real estate strategists, and expansion managers build exactly these systems. Our retail site selection analytics datasets are accurate, current, and designed for real-world decision-making.

Ready to Improve Your Site Selection Strategy with POI Data?

LocationsCloud delivers the validated point of interest data your team needs to make faster, smarter expansion decisions.

FAQ

How does POI data help in site selection?

POI data shows where businesses operate, where competitors are located, and how different factors interact around any potential site. This allows for objective, data-driven scoring instead of relying on personal opinions.

What types of POIs are most important for site analysis?

The most important types of points of interest (POIs) are direct competitors, businesses that go well together, transit systems, markers for where people live, and anchor stores that attract visitors to an area.

Can POI data be used for multi-city expansion planning?

Point of interest data provides a standard way to score locations in any city. This makes it great for brands that want to evaluate between 50 and 5,000 potential locations at the same time across different regions.

Is POI data suitable for real estate and retail decisions?

Real estate investors, store owners, and fast-food brands use point-of-interest (POI) data to choose the best locations for their businesses. They analyze this data to check if a site is a good choice, look at the competition, and find areas with high potential before they invest money.

How often should POI data be updated?

For active expansion plans, it’s best to provide updates every quarter. At the very least, do this once a year. Markets change, and using outdated information can lead to poor decisions when choosing sites.

Does LocationsCloud provide POI data via API?

LocationsCloud provides access to its API, offers bulk data delivery, and delivers files in formats ready for analysis. These features are designed to work well with GIS tools, business intelligence platforms, and expansion dashboards.