Site Selection

Our site selection data solutions help you make more data driven decisions, leading to higher-performing business locations under any market conditions.

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How to Use Site Selection Data

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.

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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

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Identify which areas have gaps in the local marketplace

Evaluate the foot traffic/performance of a potential site

Forecast capture rates for new sites

Site Selection Example

Work with Unacast's Jupyter notebook to see an in depth example of how clients can use our data to evaluate new retail sites. This example will show you how to examine area foot traffic, competing venue foot traffic, and respective catchment areas.

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Explore Sample Data

See all data tables in .csv format, together with schemas, used in our Jupyter notebook for our retail site selection solution.

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Check out Unacast’s Site Selection Solution in Action

Built using our Site Selection dataset, you can explore the sample data yourself and get started with Unacast Now today!

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Featured Resources

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How to work with the data

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.

Market Assessment

Market Assessments are conducted to identify and prioritize markets to enter. In addition to traditional static metrics such as consumer spending,in-store transactions, and general migration patterns, Unacast insights augment your analyses by:

Monitoring actual foot count to project store potential and possible cannibalization of stores

Accounting for population movements and changes in demographic profiles

Understanding consumer behavior with competitors’ sites

Discovering gaps in trade areas based on how far people travel to a location

Trade Area Analysis

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
gravity model

Our Site Selection Data

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.

The Site Selection Process

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

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.

Data sets in
this solution

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.

Download our Data Dictonary
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Emerging areas and migration patterns

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Macro-level location data such as foot traffic at the county level

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Micro-level location data such as foot traffic to a particular venue

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Catchment area data such as origin of foot traffic or distance traveled

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Cross-visitation traffic between venues

Additional Resources

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Methodology & FAQs

You can learn more about our methodologies and view our frequently asked questions

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