GPS Mobility Data and Site Selection: Opportunities, Limitations, and Strategic Use

Site selection has experienced an evolution over the past two decades. Traditional analytics relied mostly on basic demographics, traffic counts, and drive-time trade areas to evaluate potential locations. While these metrics remain foundational, the introduction of GPS mobility data has transformed how retailers, restaurants, and developers understand customer behavior and site performance.

When used correctly, this dataset can significantly improve site selection decisions and market planning strategies. However, mobility data is not a perfect dataset and should be used strategically alongside traditional analytics rather than as a replacement for them.

The Benefits of GPS Data

Defining True Trade Areas

One of the most valuable applications of GPS mobility data is defining true trade areas. While traditional drive-time software considers road networks, traffic patterns, and geographical barriers, mobility data provides an even more accurate representation of how consumers travel day-to-day. Mobility data allows analysts to identify where visitors live and work, build trade area polygons, and compare trade areas across multiple locations. This information can help identify underserved markets and white space opportunities, which leads to more accurate market planning and site selection decisions.

Store Performance Benchmarking and Competitive Analysis

Mobility data allows for store benchmarking across different retail areas, shopping centers, and even individual locations. By comparing metrics such as visit volumes/frequency, dwell times, and daypart traffic patterns, analysts can determine which types of centers generate the most consumer traffic and how a potential site compares to existing locations.

Other metrics, such as seasonal patterns and competitor locations’ mobility data, can help answer questions like “which competitor locations are outperforming” and “what level of traffic is required to support a new location.”

Customer Origin and Demographic Profiling

Mobility data can identify visitors' home locations, allowing analysts to overlay demographic and consumer segmentation data. This helps companies understand not just how many people visit a location, but who those customers are. Key demographics like household income, age, and education, as well as psychographics, such as consumer segmentation and spending patterns, allow companies to align site selection decisions with their target customer profile.

The benefits of this information are two-fold: It helps find retail areas/shopping centers where more of a company’s look-a-like customers are visiting, while also enhancing marketing efforts by identifying geographic areas where those look-a-like customers are coming from.

Cross-Shopping and Retail Adjacency Insights

Another major advantage of mobility data is the ability to understand cross-shopping behavior. Analysts can identify where visitors go before and after visiting a location, which provides insight into potential complementary retail locations, optimal tenant mix, and competitive overlap. Cross-shopping patterns also help identify shopping behaviors and trip planning, which allows companies to further develop their customer profile.

This not only helps individual brands. Shopping center developers and landlords can benefit from this information when it comes to leasing strategy and merchandising plans.

Limitations and Considerations When Using GPS Data

Mobility Data Is a Sample, Not a Census

GPS mobility datasets represent a sample of mobile devices, not the entire population. The data is modeled and scaled to estimate visits and trade areas, which means it should be used directionally rather than as an exact number of visitors. Sample size can vary by market size, density, demographics, and different time periods. Therefore, a solid understanding of sample size and methodology is critical when interpreting mobility data.

Location Accuracy and Tenant Attribution

GPS accuracy can vary depending on device signal strength and environment. In dense retail environments, it can sometimes be difficult to pinpoint exactly where a GPS device is located. Inline shopping centers and multitenant/multistory buildings can create “noise” in the data, making it difficult, or even impossible, to geofence individual locations in these areas.

These issues also arise with adjacent retailers or in areas such as food courts and entertainment districts. While the majority of mobility providers use dwell time filters and specific geofenced polygons to improve accuracy, some margin of error still exists.

Mobility Data Does Not Equal Sales

Foot traffic and visits do not automatically translate to sales performance. Store performance is influenced by many factors beyond traffic, including visibility and access, tenant mix, and parking. Competition and brand strength, as well as local demographics, also heavily contribute to the performance of a particular location. Finally, site-specific characteristics such as store size/format, operations, and real estate cost play a factor that mobility data does not account for. Thus, this data should be viewed as a behavioral indicator, not a direct measure of store revenue.

Final Thoughts

GPS mobility data has become an important tool in modern site selection and retail real estate analytics. It provides insights into real consumer behavior, trade areas, cross-shopping patterns, and location performance that were not possible with traditional demographic and drive-time models alone.

However, the most effective site selection strategies do not rely on mobility data alone. The best decisions come from combining mobility data with demographics, traffic patterns, retail supply, and real estate fundamentals to create a comprehensive view of a market and site opportunity.