User statistics
Overview
The user statistics are created using personal related information. The calculations are made with knowing which user booked what in the past. Please consult internally with your data processing officer to clarify, if you are allowed to collect the user statistics.
However, you may have another valid reason to store the data longer, or you are not under the GDPR jurisdiction, in this case you can store the bookings longer and user the user based statistics.
Configuration
To enable the user based statistics, navigate as an administrator in Flexopus to Dashboard > Settings > Analytics settings
and turn on the user level statistics. Additionally, you need to turn off the booking anonymization settings or set the anonymization interval
to at least 90 days
, for a shorter time frame a statistics makes not too much sense. After enabling the user statistics, please wait 24 hours. The data is being calculated once a day.
Statistics
To see the user statistics, navigate in Flexopus as an administrator to Dashboard > Analytics > User statistics
and select a user profile.
To calculate the statistics, you need to select a time frame. Please note that the displayed time options like the months, quarters, last 30 days and last 90 days are precalculated statistics. The custom time selection can be maximum 90 days. The custom time frame selection will always be calculated real time, the calculation of custom time frames takes longer than the precalculated statistics. The precalculation runs every night based on the latest data, including the reservation from yesterday.
Average days
On this first KPIs, you can see how many office, home office or parking spot bookings made the user in average per week in the selected time frame.
Avg. office days
: Sum of days with at least one working station reservation in the selected time frame divided by the number of weeks in the time frame. Avg. HO days
: Sum of days with at least one home office reservation in the selected time frame divided by the number of weeks in the time frame.Avg park. days
:Sum of days with at least one parking spot reservation in the selected time frame divided by the number of weeks in the time frame.
Average daily hours
See how many hours were booked per day during the week in average. Sum of the working station or home office reservations in hours for each day of the week divided by the number of days in the selected time frame.
Using this statistics, you can see which day of the week is preferred the most by this user.
Weekly office / home office days
See, on the weekly basis, how often user decided to go to the office or stayed in home office. CW 28
= Calendar Week 28
. A day count as office day, if there was at least one work station booking.
Time Map
Number of reservations during the week with a 60 minutes accuracy to see when the most of the reservations were made. Flexible reservations and permanent fix reservations are counted as 1
.
Calculation example for Monday 08:00 - 09:00:value = sum bookings / sum days
: Number of bookings in the selected time frame for the Mondays.
sum bookingssum days
: Number of Mondays in the selected time frame.
You can scope the statistic based on object types.
Daily office and home office hours
You can track the user reservations for each day in the selected time frame to have a better overview of the made reservations.
Favorite objects
List of objects that the user booked in the selected time frame. Sort the objects by the SUM
to see which object was occupied the most, or by the NO. BOOKINGS
to see which object was booked the most often. You can also export the list as CSV
or XLSX
.
You can scope the statistic based on object types.
Missed check-ins
The statistic is only active, if you have activated the Check In module in your Flexopus global settings. Learn more about the check in module here:
The statistic shows how frequently the user forget to check in for the reservations he created. The first chart summarizes the missed check ins on a weekly basis.
The second chart show how many missed check-ins were made compared to valid check-ins.