Site selection teams, real estate brokers, and regional economic development groups all use our data to assess potential sites, advise investments, and sharpen their location strategies.
Read below to learn how location based site selection data can improve the retail and commercial real estate site selection process.
Discover areas that residents and/or visitors already congregate or travel to
Discover which site characteristics help create a lasting visitor base
Pinpoint which sites meet your ideal visitor mix
Identify which areas have gaps in the local marketplace
Evaluate the foot traffic/performance of a potential site
Forecast capture rates for new sites
See all data tables in .csv format, together with schemas, used in our Jupyter notebook for our retail site selection solution.Download Now
By using mobility data, brands can find high-potential areas for new store locations within markets of interest. In this guide, we explore the Nashville metro area to walk through a store selection process retailers can use in their businesses.
Built using our Site Selection dataset, you can explore the sample data yourself and get started with Unacast Now today!
While there are many different approaches and methods to aid in site selection, our data sets can be broadly applied in market assessments and trade area analyses.
Once the most suitable market has been identified, the optimal site can be selected using the location's trade area as the unit of analysis. Unacast’s foot traffic data provides additional dimensions to this analysis by:
Clustering GPS pings to identify trade areas and points of interest
Providing granularity about where visitors come from and how far they traveled
Aiding in a demand gap analysis to examine areas of saturation or shortage
Simulating retail sales potential in a
Our datasets can help you understand foot traffic to any defined urban area, Point of Interest, or venue. You can make more informed decisions for site selection or deselection, and learn how to boost your competitive advantage. To do these things, we help you follow three key steps:
Step 1: Gather raw data
Unacast uses GPS location data to measure foot traffic because it is the most reliable. It works by sending signals, or “pings,” from mobile devices to a constellation of satellites. GPS uses triangulation to determine where on the planet your device is, and describes that position using latitude and longitude. Each ping from a device also has a timestamp.
Step 2: Contextualize the data
The raw pings are clustered by our algorithms into events that indicate activity, such as dwelling at a location, or traveling and assigning retail venues and brands. An array of latitudes and longitudes are of no use. That's why Unacast's data engine translates the raw data feed into something understandable by adding context.
We make sure that private information, such as people’s exact home location, are obfuscated and not discernible to an address. We can add areas together to measure neighborhoods, cities, counties, states, and even an entire country.
Step 3: Create curated foot traffic datasets
We have datasets depending on your data maturity and needs: Foot Traffic Data, Dynamic Trade Areas Data, and Cross-Visitation Data. It’s tempting to think that once you add some context to the GPS pings on maps you’re done but there is more to it than data visualization. But there are literally an uncountable number of ways to make these location datasets say different things via catchment analysis, some of which are unhelpful.This is where the team of data scientists and business strategists at Unacast helps you “ask” the data just the right questions.
Unacast provides a selection of specific data sets in our Site Selection solution, many of which can be segmented by area: census tract, county, or state; frequency: day, week, and month; and venue: home and work. For a list of our complete data schema and additional details and descriptions of these data sets, check out our data dictionary here.
Emerging areas and migration patterns
Macro-level location data such as foot traffic at the county level
Micro-level location data such as foot traffic to a particular venue
Catchment area data such as origin of foot traffic or distance traveled
Cross-visitation traffic between venues