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How can retailers be one step ahead of competitors with footfall data?

Written by Ángela ·

The planning stage to open any brick and mortar business is without a doubt the most critical, retailers across many sectors from salons to florists need to pay particular attention to their site selection.

What is the most critical stage for the retail, restaurants, salons, florists and other brick-and-mortar businesses? Site selection.

Planning analysts can review several indicators ranging from census population to the number of inhabitants per household. But topping it all is footfall data. Some companies have stated that analysing foot traffic is not only an indisputable criterion but also a pivotal indicator that impacts every pound spent on marketing and other business strategies.

Although all physical businesses benefit from footfall; restaurants, salons, and other customer-driven businesses have the possibility of elevating their performance to another level based on footfall figures. Hence, the aim of this blog post is to dive deep into the traditional methods of counting footfall and to highlight the role that Location Intelligence can play in gathering this data effortlessly and accurately.


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Traditional methods of analysing pedestrian data

To begin with, traditional methods include manually counting passers-by using simple devices such as a tally counter, however, this requires a human to monitor the activity throughout the entire day. Security cameras, on the other hand, are better than the tally counter although are also said to be time-consuming especially when the count has to be calculated for multiple days. Additionally, an automatic door counter helps to decipher the number of people entering a specific store, but this is to manage your in-store strategies so is not useful in the case of calculating footfall.

Evidently, traditional approaches are time-consuming and fail to provide an accurate pedestrian count. Thus, it is challenging to perform analyses due to constraints, that is where Location Intelligence plays a part in providing data with more granularity and accuracy.

Geoblink: The best alternative to count the footfall
With the shortfalls of traditional approaches in terms of accuracy, analysing this statistic while leveraging Big Data is undoubtedly more accurate and reliable. However, the work involved in this alternative is as equally daunting to that of traditional methods. The challenge lies in the process of analysing this huge amount of data and arriving at a decision without any chance of misinterpretation, leading to inaccurate conclusions.

Here is where Geoblink plays a part. In addition to providing socio-demographic and economic data, Geoblink provides footfall and vehicle traffic information, related to a specific section of street. But not only does it provide the details of footfall level, it also differentiates the traffic coming from different segments.

For example, let’s analyse the footfall data of an area in London.

Selecting a catchment area with a 15-minute walking distance, within a popular location in London, Regent Street, which is filled with the best fashion brands and restaurants.

As seen from the visual above and in the image from the Geoblink app, the footfall traffic is medium-high within Regent Street. Additionally, nearby streets such as Beak and Brewer also experience a medium level of footfall, most likely due to being located just a 2 minute walk away.

comparing Regent st and Brewer st

Through segmenting this data, it is clear that footfall from commercial activities is very high. This is not surprising as the street in question is saturated with fashion and retail stores attracting locals and tourists. On the contrary, traffic from educational institutions, health and transportation are high which does not necessarily reflect the high commercial volume.

Now, if we compare the footfall data of two streets in London, in order to find where would be a better location to run a business which relies on a high footfall level.

Chosen streets (with a 15-minute walking distance to the selected area): Piccadilly and Dover street. Two of London’s most popular streets.

Comparing their footfall segmentation, Piccadilly has a high level of foot traffic from commercial activities and Dover Street has a high level of foot traffic from Food and Drinks. However, comparing the data from all segments between the two streets, Piccadilly shows higher overall footfall.


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Apart from footfall, how can we use vehicle traffic data?

The driving metric is another indicator that adds a valuable layer to your business strategy. Geoblink enables you to view the live driving traffic status of your selected area. Let’s look at Oxford Street (with a 15-minute walking distance to the selected area) to spot the different driving metrics.

The catchment area within Oxford Street has a medium-low level of driving traffic along the selected street segment. This could be due to it being a highly commercial area making it difficult for vehicles to pass through the area.

Thus, we can conclude that:

  • Footfall and vehicle traffic increase a store’s visibility, driving more customers and sales
  • Analysing data along with other demographic indicators can help develop marketing and communication strategies. A great example of this could be perfume businesses where attracting consumers through scent marketing is more effective.
  • Finally, all businesses which target the general public should look to integrate this pivotal metric into their business practices.

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