How to Access and Employ Foot Traffic Data for Optimal Results

Envision having the capability to instantly identify the peak hours at any store location. This level of precision is achievable with contemporary foot traffic data analysis tools that utilize Geographic Information Systems (GIS).

For professionals in commercial real estate (CRE), accessing precise foot traffic data is crucial for aiding clients in crafting strategies and making well-informed decisions amidst a competitive landscape. If previous attempts with GIS tools for foot traffic analysis have not met your expectations, rest assured that support is available.

This guide will delve into the specifics of foot traffic data, emphasizing its significance, sources, and methodologies to ensure the acquisition of reliable information.

What is Foot Traffic Data?

Foot traffic data, also known as footfall data, quantifies the number of individuals passing through a location over a specified period. In CRE, this data reveals the frequency of visits, duration of stay, and the volume of people frequenting a property at different times.

Applications of Foot Traffic Data

Foot traffic analytics are employed by retailers, CRE firms, retail analysts, investors, and marketing agencies to gain insights into consumer behavior. This data is instrumental in identifying trends and patterns that inform critical decision-making.

Potential Applications in CRE

Trend Analysis: Identifying and analyzing retail trends.

Location Selection: Determining optimal locations for retail establishments based on foot traffic data.

Strategic Store Placement: Making informed decisions regarding new store openings.

Evaluating Store Performance: Assessing the success of existing retail locations.

Timing Business Operations: Identifying optimal operating hours.

Store Optimization: Enhancing store layouts to improve the customer experience.

Marketing Planning: Crafting effective marketing strategies.

Employee Scheduling: Aligning staff schedules with peak traffic times.

Sources of Foot Traffic Data

Foot traffic data is typically collected through mobile GPS tracking devices such as smartphones. This location data can be integrated with other information, such as demographic data, to derive insights into traffic patterns and trends.

Before the smartphone era, such data collection was labor-intensive, involving manual counting or customer surveys.

Challenges in Using Foot Traffic Data

Inaccuracy of Manual Data Collection: Manual methods can lead to inaccurate data if sensors are improperly placed or malfunctioning.

Insufficient Data Volume: Adequate analysis requires a substantial volume of data, which mobile data sources can provide more reliably than manual methods.

Inaccuracy Due to Small Sample Sizes: Relying on data from a single mobile service provider can lead to discrepancies.

Weather-Related Variations: Weather conditions can affect foot traffic, making extensive data collection over time essential to account for such variables.

Accessing and Utilizing Foot Traffic Data

In the modern digital age, brokers and retailers can forgo traditional methods such as clipboards and counting mats. Numerous GIS tools now allow users to input a location and obtain detailed foot traffic data for that site or its surroundings. These tools provide hourly and daily statistics and enable trend analysis.

Cross Visitation

A valuable feature in current GIS tools is 'cross visitation,' which reveals other locations visited by individuals who frequent your business. This data offers insights into consumer behavior and can identify synergistic properties nearby.

Customer Retention

Another useful feature for retailers is tracking customer retention, which measures the frequency of repeat visits. Variations in retention rates can indicate factors such as increased competition or successful marketing efforts.

Ranking

CRE brokers often use foot traffic data to compare the performance of locations against benchmarks, including other stores of the same brand, similar business categories, and various geographic levels. This comparison aids in evaluating store performance and making strategic decisions.

Evaluating Foot Traffic Data Quality

To ensure the reliability of foot traffic data, consider the following:

Sample Size: Larger sample sizes generally offer more reliable data. Mobile data platforms often track millions of devices, providing robust data accuracy.

Consistency with Other Data Sources: Compare data from multiple sources to identify discrepancies and ensure accuracy.

Data Update Frequency: Regular updates are crucial for maintaining current data relevance.

Data Collection Methods: Mobile location data is typically the most accurate, especially when sourced from multiple providers.

Foot Traffic in Small Towns

While foot traffic may be lower in small towns compared to urban areas, exclusivity can enhance your business's appeal. Small-town businesses often benefit from lower operating costs and stronger community engagement, which can offset lower foot traffic numbers.

Conclusion

Foot traffic data is vital for retail analytics and decision-making. Advanced GIS tools make accessing