Visits evolution

This first tab gives you an overview of trends in footfall in your store/centre (compared with your competitors) and its catchment area.

The analyses are complementary, and some are grouped by theme in this article to give you a better understanding of how to combine them.

💡You can export all analyses via the Export button (top right). Each analysis can be exported individually via the ‘...’ at the top right of each one.

DIAGNOSTIC - EVOLUTION OF THE TOTAL NUMBER OF VISITS

     1. Analysis Objective

These first 3 analyses show changes in the number of visits to your centre or store over the period of your choice. They enable you to understand the history and trends and measure the impact of internal and external changes compared to your competitors.

• Monthly visits: track changes in the total number of visits per month to your store or centre and across the whole catchment area, based on the filters selected.
• Total visits during the periods: see how your footfall has changed between the two periods selected, and compare your store or centre with your competitors.
• Weekly visits: track the total number of visits per week to your store or centre, over the whole catchment area, based on the selected filters.

Here are some use cases and tips to use these analyses effectively.

For retailers and shopping centres:

• Track changes in footfall in your store (monthly or weekly granularity).
• Compare your store's footfall performance with that of your competitors.
• Compare your competitors' footfall over time.
• Study the percentage change in footfall over a comparative period.
• Monitor your competitors by tracking weekly footfall during their high points (openings, store name changes, operations)

     2. Methodology and computation 

• Total number of unique visitors per day: a person is counted once a day if he/she stays more than 10 minutes.
• We exclude employees for shopping centres and stores.
• The data is updated once per month, on the 8th, for the last month.
• 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.

💡More information about our methodology: Quantification algorithms

     3. How to read and interpret the analyses?

💡Find information on how to configure analyses here: Tabs, filters and competitors.

Weekly and monthly visits

• Each asset is identified by a colour. Your centre is always blue.
• The dotted line represents the average number of visits to your store or centre over the past year.
• By hovering your mouse over the dots, you can observe and compare the total volume of visits (per month or week, depending on the analysis).
• The period analysed and comparison appear in blue on the monthly analysis.

Example (chart by month): Between March 2023 and June 2024, the total number of visits increased from 34,000 to 62,100 per month, with a peak in May 2024.

Example (graph by week): There has been a steady increase in visits since March 2024, with a peak of 18,900 visits from 18 to 24 March and a second peak of 20,800 visits from 29 April to 5 May 2024.

Total visits during the periods

💡When selecting filters: indicate the previous period in period A (the period to be compared) and the most recent period in time in period B.

• Observe the percentage change in the number of visits between the 2 periods compared for each asset.
• The redder the box, the greater the loss of visitors. Conversely, the greener the box, the more the total number of visitors has increased between the 2 periods.
• Arrange the data in each column in ascending or descending order.

Example: Between the 2 periods, footfall increased by 107%, from 126,900 to 263,200. In comparison, visits to the Castorama fell by 4% and visits to the Brico Dépôt store increased by 6%.

     4. Frequently asked questions

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.

WHERE IS THE EVOLUTION OF VISITS COMING FROM?

     1. Analysis Objective

Origin analysis shows where visitors to your store or centre come from. You can :

• Determine the real catchment area of your location
• Measure changes in your catchment area between two given periods
• Understand your visitors better
• Optimise your catchment area and measure the impact of your action plan on it.

Here are some use cases and advice on how to use this analysis effectively for retailers and shopping centres:

Catchment area maps:

• Measure your store's catchment area by identifying the cities/neighbourhoods from which your visitors come.
• Visualise the cities and districts that attract the most footfall from your visitors.
• Depending on the period analysed, see which towns are gaining and losing visitors to improve your targeting.

Top zones by increase or decrease in visits: 

• Track gains and losses in the number of your visitors by comparing 2 periods.
• Create your targeting lists from ‘Visits evolution’ to ‘Area targeting’ for future campaigns (only available at the neighbourhood level).
• Define the communication actions to be implemented: prioritise or not communication actions thanks to increased qualification, then analyse the impact of your actions.

     2. Methodology and computation 

• The origin corresponds to the number of visits made to the area analysed from each city or neighborhood over a month, in absolute value.
• The indicator takes into account the recurrence of unique daily visits during the week. If a person comes 3 times during the month, they will be counted 3 times (if he or she comes several times during the day, he or she will be counted only once).
• The analysis shows 80% of the cumulative origin of visitors. For greater precision, we exclude: residual origin, minimum rates, dispersed, exceptional and inconsistent origins.
• The data is updated once per month, on the 8th, for the last month.

💡More information about our methodology: Origin and penetration algorithm.

     3. How to read and interpret the analyses?

💡Find information on how to configure analyses here: Tabs, filters and competitors.

💡Tip: You can export the data from the 3 maps in excel format.

Catchment area of your visits (maps)

The first 2 maps show the catchment area over the initial and comparison periods.

• The darker the blue areas, the greater the number of visits to these residents.
• Conversely, the lighter the blue areas, the lower the volume compared with the other areas.
• Move your mouse over the zones to see the total number of visits.

The 3rd map shows the changes in catchment areas between the 2 periods analysed.

• Look at the areas where you have gained or lost visits.
• The greener the area, the more visitors you have gained, the redder the area, the more visitors you have lost.

Top zones by increase or decrease in visits

The 2 tables give you a quick overview of the cities where you have gained or lost visitors.

• Look at the number of inhabitants in each zone.
• The 2 middle columns show the number of visitors for each period analysed.
• The 4th ‘Evolution’ column allows you to compare the 2.
• You can see the cities where you have gained or lost the most visitors.

Example (maps and tables): Between March and June 2023, 5,700 visits were made by Rivières inhabitants. Over the same period in 2024 (March to June), 12,600 visits were made by its inhabitants. In total, this represents a gain of 6,900 visits between the 2 periods (+ 121%).

     4. Frequently asked questions

What is the difference between penetration and origin?

To calculate origin: we take the total number of visits made to your store/centre. Then, for each visit, we look at the visitor's geographical origin. This gives you the total number of visits made by residents of each zone.
To calculate penetration: out of 100 residents in an area, we look at how many have visited your store/centre (and your competitors' stores). This will tell you out of 100 visitors to your centre, how many come from each zone.