How can businesses walk forward with the pedestrian traffic data?
Writen by Ángela ·
What is the most critical stage for the retail, restaurants, salons, perfumeries, florists and other brick and mortar businesses? Site selection. The executives analyse several indicators ranging from census population to number of inhabitants per household. But to top it all, is the walking or the pedestrian data. Some companies have stated that analyzing the foot traffic is not only an indisputable criteria but also a vitalizing indicator that impacts every pound spent on marketing and other business strategies.
Although all brick and mortar businesses benefit from this indicator; restaurants, salons, and other customer-driven businesses have the possibility of elevating their performance to a higher magnitude with this indicator. Hence, the aim of this blog post is to dig into the traditional methods of counting foot falls and to spotlight the role of Location Intelligence in determining this data effortlessly and accurately.
Location Intelligence can reshape your business for the better.
Traditional methods of analysing the pedestrian data
To begin with, the traditional methods involved are manual counting of passers by and using simple devices such as Tally counter and camera to do a similar work. Tally counter uses a clicker to manually count the passersby. This however requires a human to monitor the activity all throughout the day. The security camera on the other hand is better than the tally counter. But it is also said to be time consuming especially when the count has to be calculated for multiple days. There is an automatic door counter to decipher the number of people entering a specific store, but this is to manage your in store strategies. In some cases, expansion managers also make a note of what brand bags passersby carry to identify where they make purchases.
That said, the traditional approaches are time consuming and fail to provide accurate pedestrian count. Thus, it is not advisable to conduct or perform analysis with this traditional method but with more granular and solid analysis.
What could be an alternative?
With the demerits of traditional approaches in context, analysing this statistic with leverage on Big Data is pronounced accurate and solid. The work involved in this alternative is equally daunting to that involved in the traditional methods. The process of visualizing the huge amount of data and arriving at a decision is exhaustive and chances are data misinterpretation leading to conclusions with inaccurate details.
Here is where Geoblink, in addition to providing socio and economic data, offers walking and driving details along the stretch of a specific street. Not only does it provide the details of the footfall intensity but also differentiates the traffic coming from different sectors.
For example, let us analyse the pedestrian details of an area in London.
A catchment area on Regent Street, one of the popular streets in London, touting with the best fashion brands and restaurants, is selected within a 15-minute walking distance.
It can be seen from the application and also from the visual above, the pedestrian traffic is very high along the Regent Street. It is not only high on this street but also is dense on the nearby streets such as Beak and Brewer streets which can be traversed within a 2-minute walk.
Breaking down the walking data, the foot fall from commercial activities is very high. This is not a surprise, when the street in subject has intensive fashion and retail stores attracting the locals and tourists. On the contrary, traffic from educational institutions, health and transportation are high but does not match the very high commercial volume. Also, medium traffic is reported in South Audley street, which is at a 12-minute walking distance from Regent.
Now, let’s compare the pedestrian data of two streets in London and choose the better of the two to run a business where foot accessibility is the key.
Streets chosen (15-minute walking distance from the respective area centres): Bond street and Savile row. Both are London’s two of the most prominent streets.
The two streets have a considerable amount of footfalls, however, the one on the Bond street is higher than in Savile Row. Comparing the two areas based on different origins, Savile Row shows an enhanced foot traffic coming from the office domain which in Bond Street seems a little weaker. But comparing the data from all domains between the two streets, the Bond Street shows a better pedestrian strength.
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Besides the walking, what is the driving data?
Driving metric is yet another indicator that adds a layer of lucidity to your business strategy. Geoblink allows you to explore live the areas that have severe to low driving statistics. Let’s navigate the Oxford Street (an area selected within a 15-minute walking distance) in London to spot the different driving metrics.
The area under study in the Oxford Street, although with a combination of all driving metrics, has more of low driving traffic along the street. This could also be due to high commercial and shopping scenes in the street making it difficult for much driving.
Thus, we can conclude the following:
- It increases the store’s visibility driving more customers and sales
- Data analysed along with other demographic indicators impact marketing and communication strategies. For example, this could be a foremost data to perfumery businesses where driving consumers through scent marketing wins over driving by other strategies.
- Similarly, all the public targeted businesses need the integration of this vital metric into their business practises.