Location-Based Metrics: Hyper-Local Traffic Measurement & Analysis
Intro
Location-based social networks and services present the next frontier in the interaction between consumers, brands, and technology. Services that allow to “check in” at bars, restaurants, grocery stores, sports stadiums and movie theaters offer people a personal connection with their direct environment. Combined with the power of one’s social network, services such as Foursquare, Loopt, Gowalla and, most recently, Facebook’s Places are quickly becoming a mainstream phenomenon.
So what?
Traditional market research focuses on a relatively large designated market area, such as New York City or Chicago. But for businesses that do not serve such a large area, this type of research is unhelpful. Location-based data allows us to observe traffic within a small geographical area, and answer questions such as:
- Who are my biggest competitors on this street/within a five-block radius?
- What are the peak times for our competitors, and how do we compare?
- How could we pull more traffic to our store/restaurant/bar?
- Where should we open our next store?
What do we offer?
SuperData Research is a leading research outfit that actively collects, stores, cleans and analyzes location-based data. Our proprietary dataset enables us to identify key characteristics for market areas of any size and advice you on how to improve your day-to-day traffic.
SuperData Research has both the experience and the necessary data to help you better understand your local competitive environment. For more information, shoot us a line.
Case Study
To find out if check-in data presents any actual value to retailers, SuperData Research ran a study that compared Whole Foods and Trader Joe’s in New York City.
- Data: 179,129 unique Foursquare check-ins for Grocery store segment in New York Tri-State area
- Time Period: July 19 to August 26, 2010
- Venues: top 15 most popular outlets for both Whole Foods and Trader Joe’s, totaling 30 stores
- Analysis: compare branches to each other and to overall grocery store traffic across different variables
Our findings show that this type of data enables a hyper-local comparison between competing branches, and offers a unique method to assess local market share for individual outlets.
