Using Foot Traffic Data to Discover Retail Success
What defines retail success? Brand? A great product? Or is it something else? Location and convenience? Retail managers will probably tell you it’s a combination of all those things. But to repeat that success daily is perhaps the biggest challenge that presents itself to franchise managers. The solution is to find a successful model that will yield success again and again.
For consumer brands, today’s omnichannel balance comes with its own set of challenges because while e-commerce offers convenience, there is still a distance between the brand and the customer. Getting potential customers to interact with the product at a brick-and-mortar establishment is often the best way to build trust.
So, the question becomes—how do you measure customers’ attraction to your best retail stores and keep them coming back? It’s more than a gut feel and takes analytics to understand store performance and develop a model for repeat success. This entails understanding local demographic characteristics, competitive impacts, and the introduction of a relatively new type of location data: retail foot traffic data.
How Is Foot Traffic Data Collected?
Today, we carry mobile devices on which we utilize apps for weather, map directions, and to check the local news. Apps collect location-based data, anonymize these data and sell them to advertisers. These “breadcrumbs” of data allow advertisers to understand how people move and interact with their surroundings.
One significant data provided by mobile devices is how many times we frequent a retail store and how long we spend at that particular location. The combination of very specific location information and dwell time provides retailers and brand managers with key insights as to the attractiveness of their goods.
Why Use Retail Foot Traffic Data?
Foot traffic data is referred to as “ground truth.” The data is an actual measurement of the frequency of visitor traffic. It is not an approximation. The data can reveal when and how many people travel past your brick-and-mortar establishment. While the “who” is anonymized, the personally identifiable information (PII) of the “who” such as their demographic, social status, and ethnic affiliation, can be deduced.
Use Foot Traffic Analytics to Understand Store Performance
The example in the following graphics illustrates how one coffee shop, Caffe Nero, in the Boston, Massachusetts area, has performed. In the first figure, footfall data can detect the preponderance of females aged 20–24 and males aged 15–19 as the predominant demographic in the store’s ZIP Code, 02124, in which it also holds the dominant market share among all coffee shops, including Starbucks. The demographics suggest a younger cohort than perhaps would have been expected but allows the retailer to ask other questions regarding both location and its product offering.
In the second graphic, the data can be sliced by time of day (by the hour) and by day of week. It shows both the moving average of daily traffic and the dwell time at the store. Taken together, these data illustrate the target market most likely to frequent the store, the time of day they arrive, and how long they spend at the store.
The result is optimized marketing efforts and a better understanding of how to target each segment of the market and when, for example through social media. Here also, footfall data will support merchandising since the time of day when most of the traffic is recorded can help define the amount and type of food service to deliver for improved customer experience.
At this point, the retailer can stitch together a more complete understanding of the pedestrian traffic that defines the clientele. From here, Caffe Nero could decide to expand into other areas and use its existing location as an analog model to predict success at other proposed locations. Or they can look to expand sales at the existing location by targeting other times of day or days of the week with a different product mix that is more appropriate to a different demographic audience.
Get Help for Your Foot Traffic Analysis
Mobility data such as vehicle traffic data and foot traffic data is becoming a vital tool in the retail industry. As a result, to remain competitive, retailers need to become more informed about what kind of data will best support their business objectives. This data provides a level of detail that is invaluable in trying to gain a competitive edge.
Korem’s expertise, whether it’s helping select the best vehicle and foot traffic data source—such as detailed historical daily traffic (DHT)—, preparing and delivering the data, or deploying visual dashboards for a common operating picture, is unsurpassed.
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