Imagine: You’re the leader of a real estate team at a restaurant brand looking to open a new location in Manhattan. You have two options you’re evaluating: one site in SoHo, and another site in the Flatiron neighborhood. Which do you choose?
Companies that need to make these types of decisions leverage foot traffic patterns coupled with additional data sources to build a sound approach to real estate and investment decisions. Below, we take a closer look at points of interest and foot traffic patterns to demonstrate how location data can be leveraged to inform better site selection strategies.
Here’s how foot traffic data can impact site selection or real-estate decisions.
Look at your competitive set: Identify current venues in a neighborhood or area to determine where there might be white space and to quantify the competitive landscape. Analyze your overall competitive set (e.g., in this report we looked at all restaurants) as well as more specific, relevant categories of venues (e.g., in this report we looked at cafes). Know which places your prospective customers go now, and where you might have an opportunity to take market share or position yourself alongside businesses that provide synergies.
Know whether your consumer traffic would come from tourists, or locals: Classify tourists versus locals by looking at individuals with home ZIP codes more than 120 miles away in your analysis to better understand the catchment area (i.e., where consumers are coming from).
Know more about consumers in your neighborhood: Analyze the demographics of consumers in a particular neighborhood to understand the types of people a prospective site might draw so that you can select the optimal location based on your target audience.
Uncover changes in visit patterns over time, and within a typical week: Look at a particular neighborhood over time in order to capitalize on trends, selecting a site where traffic may be on the rise. Compare visitation patterns by neighborhood to understand the traffic you might expect to see throughout the week at a given site, informing and validating (or invalidating) your projections. Know what day of week experiences the most natural footfall traffic.
Understand the trends and what your consumers like: It’s critical to know what consumers are looking for, how they spend their time and what they like now and into the future.
Use data-visualization platforms and tools to make insights easy: Data-visualization platforms make complex information and insights easier to understand and ultimately react to. You’ll see companies that adopt data visualization are empowered and can spot emerging trends and speed reaction time.
We’ve demonstrated the benefits of using foot traffic data to in a use case that evaluates data to determine whether to build a restaurant in SoHo or the Flatiron neighborhood. For this analysis, we aggregated Census Block Groups into Census Tracts to define and analyze Manhattan’s SoHo and Flatiron neighborhoods. By leveraging the new home and work CBG attributes, we were able to provide a more granular understanding of where consumers live and work to inform business analysis and decisions. This new level of detail allows you to layer census data such as demographics onto your analysis in order to learn more about visitors to categories, chains and venues of interest. Foursquare analyzes consumer behavior based on foot traffic data from millions of Americans that make up our always-on panel. For the purpose of this report, all data is anonymized, aggregated and normalized against U.S. census data to remove any age, gender and geographical bias.
Key learnings:
Different target audiences with different needs
SoHo: Consumers visiting restaurants in SoHo are primarily locals (83%) ages 25-34 (44%). Restaurants in this area attract super shoppers, affluent socialites, health-conscious consumers and a cultured and artsy crowd.
Flatiron: People visiting restaurants in Flatiron are primarily locals (86%) ages 25-34 (46%). Restaurants in this area attract health-conscious consumers, corporate professionals, college students and people who crave unique experiences.
Visitation patterns and staffing/hours of operation vary
Soho: A restaurant in SoHo may struggle to draw consistent foot traffic throughout the earlier part of the day and week: Restaurants in SoHo rely heavily on weekend visits (38% of total weekly visits) in the late afternoons (60% of total daily visits occur after 3 p.m.).
Flatiron: A restaurant in Flatiron may struggle to draw consistent foot traffic throughout later day-parts and weekends: Restaurants in Flatiron rely heavily on weekday visits (70% of total weekly visits) in the earlier part of the day (45% of total daily visits occur before 3 p.m.).
Competitive differences
SoHo: A new restaurant in NYC's SoHo neighborhood will face tough competition with more than 435 restaurants in the area, including over 48 cafes. Top-visited restaurants in this area include Gitano, Prince Street Pizza and Thai Diner.
Flatiron: A new restaurant might face less competition in Manhattan's Flatiron neighborhood, with roughly 267 restaurants in the area, including only 25 cafes. Top-visited restaurants in this area include Eataly, Shake Shack and The Smith.
While a new restaurant in NYC's Flatiron neighborhood may face less competition compared to a new restaurant in SoHo, location data verifies what it takes to be successful in both neighborhoods.
Outcomes and next steps
In order to be successful in Flatiron, a restaurant will need to draw a weekday lunch crowd with healthy offerings and a work-friendly setting for professionals; to stand out among nearly double the restaurants in SoHo, a new restaurant should lean into arts and culture with a design-forward setting, and focus on evening and weekend offerings.
Read the full report to better understand the role of location data in uncovering trends in consumer behavior, assessing the competitive landscape and unlocking unique opportunities for venue expansion.
