How can insights gleaned from foot traffic and based on distance improve catchment area analysis?
By using location data to measure things like store visits, travel time and origin mapping to understand where your customers come from. This areas based analysis is critical for local governments, retail stores, and large employers. Why?
By studying foot traffic in defined catchment areas (sometimes called trade areas), you can assess market potential, identify potential customers, and even calculate their walk time or drive time to make store visits. This is important for trade area analysis and planning new store locations, public sites, and business districts, for example.
Below, we'll ask and answer some FAQs about catchment areas and foot traffic, and provide an example or two of how catchment area analysis can be applied in a retail site selection scenario.
What influences foot traffic in catchment areas?
Catchment areas are dynamic and they have several qualities that influence the effect of foot traffic. Beyond noting changes in ‘feet on the street’, retailers and others are reliant on understanding traffic connected to local transit options, the influence on foot traffic of other nearby venues and brands, and the demographic characteristics of those who visit the studied trade area or other defined location.
- Transit options and accessibility - Where and when people can catch transit and how long it takes them to ride a given route to reach a trade area or location is one consideration. The trade area or retailer is also beholden to disruptions in transit service, including downtime from traffic, maintenance, accidents, and weather events.
- Competitive influence – Does a competitive brand nearby make it easier or more difficult to attract target customers? What other brands are people cross-visiting and what does this indicate in terms of local partnership opportunities or missing products?
- Demographic insight – Knowing more about the visitor/customer and how to serve them is key for every trade area, location and business. That deeper knowledge is triggered by augmenting aggregated location data with demographic information. This provides actionable insights down to a custom defined geographic area. That’s possible with Unacast’s location data.
3 steps to use foot traffic for catchment area analysis
Our datasets can help you understand foot traffic to any defined catchment area, Point of Interest, or venue, and your potential customers' shopping journeys. 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 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.
For instance, measuring market share over time, understanding competitor performance by location or trade area, or probing foot traffic patterns at competitor locations to help inform how to place and operate your own.
Five Below: Catchment area analysis using foot traffic
We’ll use the good news of Five Below’s expansion to demonstrate how our products can help retailers grow. To do that, we’ll play the role of a real estate group within Five Below seeking to do three things:
- Identify areas with depleting population or growing population;
- Benchmark competitors’ visitation and foot traffic and identify unknown competitors; and
- Find out which areas have opportunistic gaps in the local marketplace.
Through this exercise, we’ll explore population indicators, migration and income patterns and neighborhood recovery indicators, all of which have a lot to do with creating stores in new locations.
To penetrate new markets, Five Below plans to open 170 to 180 new stores in 2021; that’s up from 120 in 2020. The brand will also enter 2 new state markets: Utah and New Mexico.
To expand distribution, Five Below is adding an additional facility in Arizona. To be an effective part of their network that facility must be optimally located to support future growth
To expand assortment, Five Below is committing 30% of the store fleet to in-store concepts by the end of 2021, and introducing assortments above the brands’ signature $5 threshold.
To meet their goals, Five Below should follow four simple steps:
Identify areas of growth
Our migration patterns dataset measures census tract growth by month, letting you measure gains or losses in both population and income down to a pretty fine area. Since we know Five Below is interested in expansion in Utah, let’s look for growing areas there.
Benchmark visitation and foot traffic
Using retail data analytics and mobile location data to study the peak visitation and foot traffic patterns around their stores will help inform our decision-making around where we locate and how we position ours. What we observe in the data analytics is that the location farthest to the south, captures the highest number of visitors.
Turning to the mobile location data captured by our neighborhood insights dataset in the free portal, we observe over-arching foot traffic patterns in the Salt Lake City area. The further away you get from SLC’s core, the greater the propensity for higher recovery. But where, exactly, are the gaps in the marketplace?
While almost two-thirds of the visitors came from within 6 miles of the store, we also see about one-third travel greater than 6 miles. That’s one way retailers can use mobile location data and retail data analytics together.