This project was a collaboration with our partner company makepath, an Austin-based spatial data science company that specializes in visualizations and working with Open Source GIS libraries.
As the economy reopens, foot traffic data has never been more valuable. Brands that rely on in-store visits are faced with the challenge of recovering after months of reduced revenue while also identifying how their customer base has changed. Despite increasing lifts of shelter in place orders, many individual routines remain altered. Those who are able are often opting to work from home, and some people have even moved their place of residence altogether. Foot traffic trends are not the same as they once were.
Understanding the competition - and how your brand stacks up - is crucial for success in the retail industry with or without a pandemic. Part of understanding the competition is analyzing market penetration by geography. Data like SafeGraph Patterns, which provides mobile device activity associated with brand locations, helps paint a geospatial picture of consumer engagement, brand loyalty, and market penetration. With a detailed and up-to-date understanding of foot traffic for both their own locations and those of competitors, brands can accurately plan marketing campaigns and site selection strategies to help them bounce back.
One of the most popular examples of regional brand loyalty is in the Dunkin’ vs Starbucks debate. Most coffee consumers have a strong opinion one way or the other. To analyze the spatial component of this debate, we can use aggregated foot traffic data from the SafeGraph Patterns dataset to see which brand has the strongest market penetration in a region.
In this sample of data from New York, Dunkin’ is leading in overall regional foot traffic and market penetration. This is aggregated at the regional scale, giving high-level insight into how the brands compete across this large geographic space. Analysis at this level can inform marketing campaigns with a relatively large reach, such as commercials during local news programs or ads on commuter trains.
For a more detailed picture of market penetration in this area, we can zoom into specific block groups or places. Although Dunkin’ is leading market penetration for the aggregated New York area, Starbucks is more popular in some locations, like this area of the Upper West Side. This could be an indicator to Dunkin’ to ramp up advertising on local digital billboards, or to deploy enticing mobile offers to consumers in this area. Similarly, Starbucks could identify what advertising techniques are working well on the Upper West Side and implement them at other locations where they would like to increase their market share.
An analysis of another well-known brand rivalry - McDonald’s vs Burger King - reveals how foot traffic data can be applied to site selection workflows. Again, SafeGraph Patterns data is used to visualize and measure the market penetration of each brand.
At the regional level, this time focusing on the Philadelphia area, McDonald’s has a much larger share of both foot traffic and market penetration. Both brands can leverage this information to decide where to open or close locations throughout the region. For example, Burger King could choose to open more stores in the area in an effort to gain more market share than McDonald’s, or to consolidate stores and focus on a few key locations.
To pinpoint specific areas to focus site selection efforts, we can zoom in for greater detail. The suburb of King of Prussia shows a very different trend for both brands than the larger region. While both McDonald’s and Burger King have a strong presence here, Burger King has more market penetration. This analysis could inspire either brand to open another location here to move the needle, or serve as a model for which markets to either open in or avoid when selecting future sites.
Foot traffic data is always helpful for advertising and site selection, but is particularly relevant when a pandemic has shifted where people spend their time. Locations that were once bustling with corporate activity may now be deserted, while residential enclaves are now populated throughout the day.
With these shifts in the demographic landscape come changes to market penetration for brands across industries. Measuring how foot traffic has changed over time can help brands identify the relationship between brand loyalty and geographic proximity or convenience. The insights derived from SafeGraph Patterns help brands develop strong, data-driven strategies for success in the re-opening economy.
Academic researchers, non-profits, and government organizations: Join SafeGraph’s COVID-19 Data Consortium to get SafeGraph Patterns data at no-cost.
If you are with a for-profit business, please contact our team today to evaluate a data sample.
