Trade area analysis is an essential component of determining the location and configuration of new public venues, workplaces and retail locations. Foot traffic greatly aids trade area analysis by providing dimension to people’s movements within a given area. Further, when combined with retail analytics, transit information, or other alpha data, foot traffic data can be used to measure competitors' performance and even inform demand forecasting, providing insight to the local supply chain.
But what are the common use cases for foot traffic data in trade area analysis and what are the necessary steps to conduct a trade area analysis using foot traffic? Below, we will address these questions and provide the example of an enterprise retailer using foot traffic to conduct trade area analysis as part of their site selection process for placing a new store. We'll explore:
- What is trade area analysis?
- Common use cases for foot traffic in trade area analysis
- How to identify trade areas of growth
- How to benchmark competitors' performance
- How to identify gaps in the marketplace
- A summary of trade area analysis
To get started, let’s define trade area analysis and ask why foot traffic plays such a key role.
What is Trade Area Analysis?
A trade area is defined as the geographic distance where retail customers come from. Foot traffic can help measure what is happening in a trade area, a.k.a. the area surrounding a place (venue, neighborhood, etc.) composed of all the home areas of visitors to the place.
Trade area analysis means examining where visitors live and how far they are likely to travel to a particular place. This is valuable for measuring customer loyalty with insights into ever-evolving visitor patterns. Why?
Foot traffic at a specific site or area – how many people visit, the type of visitor, and traits of the visit (when people visit, how long they stay, and how often they return) - is of particular interest when assessing commercial real estate value and retail performance.
Common use cases for Trade Area Analysis
Foot traffic is often applied in one of three trade area analysis use cases: Site Selection, Competitive Intelligence and Demand Forecasting.
1. Site Selection: A critical process for many organizations in evaluating what markets to enter or divest from, where to build or close stores, and which new or additional locations to prioritize. Leading organizations utilize foot traffic data in order to:
- 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
2. Competitive Intelligence: To uncover new trends about their competitive landscape, leading organizations leverage trade area analysis to build better products and make smarter decisions with real-world data.
- Measure market share and regional dominance over time
- Benchmark locations against competitors’ sites or total addressable market
- Understand consumer behavior with competitors’ sites
- Learn where, when, and how often customers visit your location vs. your competitors’
- Compare your visitor demographics to your competitors’
- Discover common characteristics of your competitors’ locations
3. Demand Forecasting: To gain a deeper understanding of supply chain, logistics, distribution, and demand forecasting, leading organizations rely on trade area analysis to build better products and make smarter decisions.
- Measure consumer activity at precise locations to optimize supply chain planning
- Explore changes in area demographic profiles that impact sales
- Understand site potential using venue-specific foot traffic as a proxy
- Plan for opening, closing, or growing of sites and market areas
- Enhance site analysis with capture rate, visit duration, customer origin
- Unlock trends for any area using custom-defined locations
How to identify areas of growth
Unacast supplies the example of an enterprise retailer looking to expand with multiple locations. The retailer uses foot traffic to conduct trade area analysis in the Utah area. The first step in the process is to identify areas with depleting populations or growing populations.
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. Knowing this retailer is interested in expansion in Utah, we look for growing areas there and get nice indicators right away, with Census Tract 1020 in Salt Lake County jumping out as a representative example.
Both population (+200 annually) and income are trending positive here, with each new person coming-in averaging about $14,000 more than the local median income. That is very useful information when considering assortment for a store in this area.
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
- Aiding in a demand gap analysis to examine areas of saturation or shortage
- Providing granularity about where visitors come from and how far they traveled
- Simulating retail sales potential in a gravity model
But how is the local competition doing?
How to benchmark competitors' foot traffic
The retailer in this example has a number of competitors. Using retail data analytics and foot traffic 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. Fusing foot traffic with retail data analytics is the key.
With this analysis, you can expand outwards to look at adjacent brands and venues and see if their performance is consistent with your competitor’s. In short, there’s all kinds of opportunities to explore the actual populations and communities that those stores are serving.
Visitation is one thing, but how have competitive store locations in our target area fared in terms of their foot traffic recovery lost in 2020 and throughout the pandemic? Resilience is the key attribute we are seeking in the data analytics, particularly among Locals who visit store locations and Workers who are employed there.
Turning to the mobile location data captured by our neighborhood insights dataset in the Unacast Now free data 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?
How to identify gaps in the marketplace
According to foot traffic and trade area analysis, where is the ideal location for expansion around SLC? And how can that inform the retailer’s in-store concepts and assortment?
While almost two-thirds of visitors came from within 6 miles of the store, we also see about one-third travel greater than 6 miles. This could present an opportunity to locate a store a little closer to the visitors coming from more than 6 miles away.
Digging a little deeper, the retailer could augment their knowledge of the current market-leader’s concepts and assortment, and create an optimally-suited in-store experience for the new location’s local shoppers. That’s one way retailers can use foot traffic and trade area analysis together.
While there are many factors and data sets to use when conducting trade area analysis, foot traffic is invaluable when determining where to construct new stores, offices, and public venues.
In isolation, foot traffic might not be able to provide clear-cut answers, but it can be used to enhance other tools and datasets, and help supplant assumptions with facts.
While there are many different approaches and methods to aid in trade area analysis, our foot traffic data sets can be broadly applied in market assessments. To learn more, schedule a meeting with one of our data experts.