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Home: Meeting request activity chart

This chart shows how many meeting request actions took place before the event and during the event.

Screenshot 2021-08-13 at 12.38.54 Screenshot 2021-08-13 at 12.39.01

👉 Explanation:

The results pie chart shows the percentage of meeting request actions (sent messages exchanged and sent meeting requests, etc.) that took place before the event, and how many during the event.

Meeting request actions are not the same as meeting requests nor does this metric count chat messages. For each meeting request, multiple actions take place: When it is sent, when it is accepted, when it is rescheduled, when it is canceled, etc.

This is why the numbers in this pie do not match with other Engagement or Meeting Request numbers in the Home view.

Looking at this you can understand when most of the meetings were scheduled. A high rate means most of the meeting schedule was completed before the event day.

👉 Example: Analysis of the pie chart

The pie chart indicates meeting activities before the event and meeting activity during the event as a percentage. The results shown above appear this way when the event has started, and attendees join the platform to set up their meetings.

You should aim to have a higher pre-engagement % than during the event. Why? Because participants should take their time to get acquainted with the platform and connect with people before the event starts. This can be compared with other several events that you hosted in Brella, and the trend should be that there is more pre-engagement than during the event engagement.

If you can see more and more pre-engagement taking place over several events, then it can mean that your attendees are increasingly aware that your event is the place to go to set up relevant networking experiences. If the contrary occurs, then talk with your CSM on how to increase this metric and ensure pre-engagement is addressed in your Communications & engagement plan.

Both indications are positive figures. It is up to the event organizers to interpret the data.


Last updated on January 2022. 

Written by Stephanie Campano Valenzuela. 


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