Foot Traffic Data

What Is Foot Traffic Data?

Foot traffic data, or footfall, refers to the number of consumers passing through and visiting certain areas by foot, such as malls, street vendors, or businesses. The data shows the number of people in an area during specific times, and how long they stayed. The data also focuses on the most popular times and days people walk past or stay in an area.

Where Does Foot Traffic Data Come From?

There are many tools to collect footfall data for a certain store, mall, or area. Laser beams are a simple tool that is limited to a basic count and can often be misleading. Thermal imaging sensors are also a relatively basic tool for footfall analytics, though they present a problem as their bulbs burn out quickly and are expensive to replace.

The most common and reliable tool for the micro level (store or mall) is WiFi, which doesn’t even require a phone to connect to. Alternately, you could use Bluetooth.

On the macro-level (large areas like shopping areas), GPS mobile location is useful, but it is not always updated in real-time. It is also not accurate enough for the micro-level.

What Types of Columns/Attributes Should I Expect When Working With This Data?

Attributes of footfall data can include the number of visitors in a certain POI over a certain amount of time, mobile devices located at a POI, exact location, certainty and accuracy of location, and demographic data about the location of your POI.

What Is This Data Used For?

Footfall data directly helps improve store performance. By measuring local foot traffic data against your sales numbers, you can confirm the effect of foot traffic on sales volume, the percentage of shoppers who actually make purchases, which stores in a franchise are underperforming, and whether recent advertisements have been effective. You can also set the best staff schedules by analyzing your lowest-performing hours, and your busiest times.

How Should I Test the Quality of Foot Traffic Data?

To assess the quality of a dataset, consider location accuracy and detail, the scale and volume of the dataset, and the recency of the database updates. How the data was obtained is also important in determining the accuracy.

What Are the Most Important Factors I Should Vet When Selecting This Data?

You should use the same factors used to vet data providers in order to assess the quality of your data. For example, a dataset with accurate locations that updates infrequently is not completely accurate, and vice versa.

Interesting Case Studies and Blogs to Look Into

Compilantia: The Evolution Of Foot Traffic Analysis And How To Use It In 2020
DataBricks: Building Foot-Traffic Insights Dataset

Tangible Examples of Impact

In 2018, the coffee brand recorded an average monthly foot traffic gain of 6.6% that expanded to 13.8% in 2019.

Foot traffic at Dunkin’ stores continued at an “exceptionally strong” growth rate of 9.2% in the first two months of 2020, he said.

Yahoo!Finance: Is Foot Traffic Growth Key To Inspire Brands’ Interest In Dunkin’?