Imagine knowing the busiest time of day at any store location within seconds. This is absolutely possible with modern foot traffic data analysis tools powered by geographic information systems (GIS).  

As a commercial real estate (CRE) professional, getting access to accurate footfall information is essential to helping your clients strategize and make informed decisions in a competitive market.

Perhaps you’ve already tried a few GIS tools to analyze foot traffic and they’ve fallen short of your expectations. Not to worry, we’ve got your back.  

In this guide, we’ll unpack the details of foot traffic data, highlighting its importance, origins, and methods to ensure you get reliable data. We’ll also introduce some of the benefits of using a top-quality GIS tool like AlphaMap.  

What is Foot Traffic Data?

Foot traffic data - sometimes called footfall data or mobility data - is a measure of the number of people passing through a place over a period of time.  

In CRE, foot traffic indicates the number of people that visit a property location during specific times, how often they visit, and how long they typically stay.  The resulting data helps to identify trends and patterns that inform important decision-making.

Foot traffic analytics are used by CRE brokers, investors, owners, developers, municipalities, property managers, retailers, retail analysts, and marketing agencies to better understand customer behavior.

Foot-Traffic Data: Example Use-Cases

• Leasing: Use foot-traffic signals to find prospective tenants with optimal customer-behavior profiles for your property

• Location Selection: Identify the best locations for retail businesses based on foot traffic patterns.

• Evaluating Store Performance: Gauge how well an existing retail location is doing.

• Timing Business Operations: Determine the optimal times to open or close a business.

• Strategic Store Placement: Make informed decisions about where and when to open new stores.

• Store Optimization: Enhance store layouts to improve the shopping experience.

• Trend Analysis: Spot and analyze retail trends.

• Marketing Planning: Develop effective marketing campaigns.

Where Does Foot Traffic Data Come From?

There are many ways to collect foot data, but the most common is through mobile GPS tracking devices like smartphones. The location data collected can then be combined with other information (such as demographic data) to produce insights into foot traffic patterns and trends.

Of course, collecting this amount of data with such ease would not have been possible before the smartphone era and retail researchers and analysts would have done most of their research manually. This might have involved sitting for hours at a store entrance counting people, using a counting mat to track the people walking in and out, and/or creating customer surveys to find out who visits the store and where they live. All very time-consuming and onerous.

Thanks to modern technology, this process is much easier (and quicker). Today, GIS analytics tools, like AlphaMap, allow CRE professionals to access detailed foot traffic counts for a specific location at the click of a button.

AlphaMap, for example, taps into over 130 million of those smartphones to estimate foot traffic trends and rankings.

This is an incredible potential resource of customer data provided the mobility data is accurate.

Common Problems Using Foot Traffic Data  

There are a few potential problems that can occur when using old-school methods of manually tracking foot data and also when collecting mobility data from mobile phone providers.

Low Foot-Traffic Data Volume

In order to analyze trends over time, you need a lot of data. It’s simply not enough to track footfall into your shop on only one day of the week, or for only one hour. You need a consistent amount of data from a large sample size. This is where mobile data by far outperforms anything you can collect yourself.

Estimating truth is hard & all providers have weaknesses

Until recently, a major unresolved problem with foot traffic data collected from smartphones was that businesses were relying on a single mobile service provider.

The problem is that data from one mobile provider can be wildly different to another because of key differences in their data-collection methodologies – differences which make one provider stronger in certain situations and weaker in others.  

To solve that problem, AlphaMap has partnered with three of the leading mobile data companies and uses the results from each provider to create a smarter ‘combined’ estimation, accounting for the unique strengths of each provider.

This means a much larger sample size, and much more accurate foot traffic analysis.

Manual Collection is inconsistent

Inaccurate data collection can be an issue when foot traffic data is collected using a sensor. For example, sometimes the foot traffic sensor is placed in the wrong location or it’s not working properly.  Or for example, if it rains, people may be less likely to go outside and walk around, which would lead to lower foot traffic numbers. This is a problem that can affect both manual and mobile data collection methods, highlighting the need for extensive data over time to account for such variables.

How to Access Foot Traffic Data and Trends

In today’s technologically driven world, brokers and retailers can throw away their clipboards and counting mats!  

There are several GIS tools on the market today which allow you to input a chosen location and get foot traffic data which relates to the site or the area surrounding it.  

You can view hourly and daily foot traffic statistics and analyze trends over time.  

Cross Visitation

A smart feature which today’s GIS tools provide is the ability to see where else the people who visit your business go in the same visit.

This is called ‘cross visitation’. You can access detailed demographic data for those people which makes the insights more meaningful.

For example, do they visit a few others stores in the same area, and what can these visits tell you about this specific buyer’s behavior? You can usually break down the other visits by venue, retail brand, and type of retail (such as gas stations, fitness centers, restaurants, and more).

Cross visitation helps you to find synergistic properties near your specific location, which can be particularly useful for CRE investors who are choosing an ideal location for a new store and are wondering about the viability of their specific brand in that area.

Customer Retention

Another useful feature, particularly for retailers, shows customer retention. That is, the number of recurring visits (from the same people) over a period. Variations in this information could yield many clues to store owners such as:

• Drop in customer visits – a new competitor opening down the road?

• Increase in customer visits – a highly successful marketing campaign which could be repeated?  

These insights, however, should be interpreted cautiously and within context.

GIS tools provide side-by-side comparisons for more accurate interpretations.

Ranking

CRE brokers find it useful to compare the performance of a specific location against key benchmarks such as:

• Other store locations of the same retail brand

• Other locations in the same business category

• Across levels of geography (city, metro, county, state, and national)  

• Trends over time

Foot traffic data forms the foundation for these comparisons, helping retail brands and locations rank against each other.  

There are many other retail decisions that a ranking tool can assist with, providing that much-needed competitive edge.  

How to Test the Quality of Foot Traffic Data

Since we know that footfall data can sometimes be inaccurate depending on the source and collection methods, it’s essential to have a way to test its quality. Some useful questions to ask are:

1. How Many Data Points are Available in the Sample?

The number of data points can give you an idea of how reliable the data is. A large sample size is more likely to be accurate than a small one. This is the primary reason mobile phone data is the most accurate. Some online GIS platforms track millions of mobile phones across the country.  

2. How Well Does the Data Match Up With Other Data Sources?

If you have access to other sources of foot data, you can compare them to see how well they match up. This can help you identify any potential issues with the data.

This is also why it’s important to have mobile phone data from more than one service provider.

Having access to data from three providers allows an aggregated sample to be created, which is more likely to be reflective of reality.

3. How Often is the Data Updated?

Foot traffic data changes over time, so it’s important to know how often the data is updated. This will help you know how current the data is. Most online GIS analytics tools update their data in real-time.

4. What Methods are Used to Collect the Data?

Mobile location data is by far the most accurate method used today, so make sure you’re accessing it from a tool that sources foot traffic statistics from mobile data (preferably from more than one provider).

Final Thoughts on Foot Traffic Data

Foot data is the cornerstone of retail analytics and well-informed decision making, but accessing good-quality, reliable data can be a challenge.

Today’s GIS analytics tools make it infinitely easier for CRE professionals to view and analyze up-to-date and accurate footfall statistics and trends.

In a competitive industry like retail, where one wrong decision can sometimes mean success or failure, it’s important to interrogate the source of foot data. A user-friendly analytics tool, such as AlphaMap, gives you access to some of the most accurate and reliable foot traffic data available. Why not book a demo with our team and see for yourself?

Find Foot Traffic Data Using AlphaMap

If you’re using AlphaMap as your analytics tool, you can follow these simple steps:

• Select your property location on the map

• Scroll down the list on the left-hand side of the screen

• Click ‘Location Visits’

• View foot traffic data and trends

• Utilize the filters to refine your search based on time duration