Dynamic footfall

     1. Analysis Objective 

The dynamic footfall analysis shows the evolution of pedestrian traffic in front of an address or the footfall in a specific location (store, shopping centre and shopping area) over a certain period of your choice. 

This analysis allows you to understand the history and trend of a specific location, measure the impact of internal and external changes on your asset, and compare it with up to 3 other locations.

     2. Methodology and Computation

• Based on mobile application GPS data: 200bn+ of GPS points per year in all our countries 
• We work with more than 100 partners mobile apps
• 65,000 field counts in Europe to adjust raw data

💡The data is updated once per month, on the 8th, for the last month.

When the last calendar week of the month overlaps with the following month, it will be visible on the platform when the following month is updated (e.g.: in October 2024, the last calendar week was from 27 October to 2 November. The first 3 weeks were available in the 8 November update, while the last week was available on 8 December).

Counting methods
Address	Total number of visits per person per day within a radius of 16 metres of the GPS point: a person is counted as many times as he or she passes in front of the address.
Store and shopping centre	Total number of unique visitors per day: a person is counted if he or she stays in the zone for more than 10 minutes. Weekly and monthly view: A person is counted only once per day (pedestrian and vehicle) even if he or she enters and leaves several times. We exclude employees for shopping centres and stores.
Shopping area	Total number of unique visitors per day: a person is counted if he or she stays in the zone for more than 10 minutes. Weekly and monthly view: A person is counted only once per day (pedestrians only) even if he or she enters and leaves several times.

💡More information about our methodology: Quantification algorithms

     3. How to use it

Features: 

• Compare up to 3 competitors on top of your asset
• Add events markers
• Filter on a weekly or monthly view
• Export in PDF or Excel

To start an analysis: 

• Select your asset (you can analyse a unique location).
• For comparison mode (1): add a second asset by clicking on the box below the main one.
• Define a period (2): choose a start and end time.
• Dwell time (3): you can display it for polygons on a monthly basis or compare it to a previous year by checking the box.
• Compare to a previous year (4): display the comparison by checking the box.
• Time period (5): define the granularity on a weekly or monthly basis.
• Events markers (6): create some markers.

Showcase your analysis on a graph or a table (7):

• By clicking on the icon in the upper right corner. 
• In the table version, you will see the footfall evolution compared to the previous week in percentage.

Important: Comparing different types of assets (address vs shopping centre) is not always relevant. Because of the lack of relevancy and the different methodologies behind the footfall computation., comparisons are available only between assets that can be compared:

• An address is comparable only to another address. 
• Stores or shopping centres can be compared between them.
• A shopping area is comparable only to another shopping area

     4. How to read it

Example: If we observe footfall in Paris Rivoli Centre (red line), we see a drop in traffic in August 2023 (summer period), then an increase from September, corresponding to the start of the school year, followed by a peak in December (end-of-year festivities).

Here are some use cases and advice on how to interpret this analysis effectively.

For retailers:

• Diagnose your highlights and sales operations over time.
• Monitor peaks in footfall linked to your competitors' events.
• Compare your shop with your competitors over time to obtain an overall trend in footfall.
• Track changes in your competitors' footfall in the event of a store name change or reopening.
• Identify the internal or external impact (works in the store, roadworks, etc.) on your store footfall.

     5. Frequently Asked Questions

Why can I not access the analysis?

You may not have access to the analysis because the average pedestrian traffic of your address may be below the minimum thresholds that guarantee data reliability.

Why is there no traffic history for my competitors?

You may not have data for one of the asset analysed if it was created after the period you wish to analyse. When a request is made to create a polygon, we start collecting data from the month following its creation. For example, if the polygon is drawn in February, data collection begins in March and the data will be available in April. This process ensures that we have accurate and up-to-date information for the new polygon.

Why are there discrepancies between my store/shopping centre sensors and MyTraffic data?

It is normal to observe differences between your sensors and our data. As the counting methods are specific to each system, the data analysed is therefore different.
First of all, look at the trends: if the traffic figures are similar, there's no problem. It's simply a difference in methodology, as explained below.

Sensor technologies are very varied and differ from one supplier to another. As a general rule:
- they count a person each time they pass in front of the sensor during the day.
- their coverage depends on the number and location of devices, which can leave areas uncovered.
- they are mainly used for real-time tracking.

MyTraffic data is based on our methodology:
- we count unique visitors per day, a person will only be counted once per day.
- we use geolocation data processed in a uniform way across Europe, which guarantees consistent and comparable data.
- our data is designed for long-term analysis, focusing on weekly and monthly trends, and offering a methodology suitable for comparative analysis.

In addition, it's important to check that the polygon tracked on the platform actually corresponds to the perimeter covered by your sensors.