1. Analysis Objective
This analysis presents various indicators (with varying levels of granularity) that allow you to understand footfall and its characteristics.
2. Methodology and Computation
💡More information about our methodology: Quantification algorithms
As the analyses are specific to a particular event, the data are not updated throughout the year.
3. How to use it
Total number of visitors to the event (1): Total number of visitors to the event.
Additional visitors (2): Indicates the total number of additional visitors per week, i.e. the number of visitors generated by the event compared to the rest of the year.
Footfall per week (3): This graph allows you to monitor the traffic per week (before, during and after your event), see the impact of your event and compare this traffic to a customisable average (represented by a dashed yellow line - by default we show the average of the previous year or the traffic of the event last year if the polygon already existed).
Example: average 2023 = average 2023 - event footfall
By hovering over the points on the lines, you can see the number of visitors per week.
Footfall per day during the event (4): Following the same principle as the previous graph, but with a daily granularity, you can observe the daily attendance during your event and see the evolution of this attendance throughout the event.
Average footfall per day during the event (5): This analysis allows you to see the hourly breakdown of visitors during your key moments. In other words, the hours when attendance is at its highest, broken down into weekdays (Monday to Friday) and weekends (Saturday and Sunday), and the percentage change compared to the annual average for the same day.
This graph shows trends in % (rather than absolute values).
Example: On average, at 3pm on a weekday, attendance is x% higher than the annual average. At the same time, attendance is higher on weekends, with x% more attendance compared to the annual average.
Comments
0 comments
Article is closed for comments.