The Outlook test simulates a project manager using email. The workload includes tasks such as creating emails, moving emails, searching for text within an email, saving attachments, making appointments, and backing up folders. This test is currently only available on Windows PC and requires Classic Outlook to be installed. This benchmark is not part of the Office Productivity MP Benchmark.
The Outlook data file is 154 MB in size. It has 128 emails in the Inbox and 8 emails in Sent Items. Some emails include attachments:
- 79 jpg images with sizes from 32 KB to 2.6 MB and a total size of 27.3 MB
- Excel document that is 7.3 MB
- PowerPoint document that is 4.4 MB
- Word document that is 8.5 MB
The Outlook test does not use the network.
Office Productivity Outlook score
The Outlook test produces an Office Productivity Outlook score. A higher score indicates better performance. The scaling constant in the score formula is used to bring the score in line with the traditional range for UL benchmarks.
[dop] tag - corresponds to dbg_office_productivity
Outlook score = 4300 / geometric mean of OfficeProductivityOutlookMoveMails OfficeProductivityOutlookNewAppointment OfficeProductivityOutlookSearchMails OfficeProductivityOutlookBackup OfficeProductivityOutlookWriteMail OfficeProductivityOutlookSaveAttachments
OfficeProductivityOutlookMoveMails
Measures moving 136 emails from imported datafile to empty profile. Repeated 7 times.
OfficeProductivityOutlookMoveMails result = geometric mean of [dop]_outlook_move_mails_1 [dop]_outlook_move_mails_2 [dop]_outlook_move_mails_3 [dop]_outlook_move_mails_4 [dop]_outlook_move_mails_5 [dop]_outlook_move_mails_6 [dop]_outlook_move_mails_7 where the maximum and minimum values are dropped from the calculation.
OfficeProductivityOutlookNewAppointment
Measures adding a new appointment with 3 attachments (Excel, PowerPoint and Word documents). Repeated 7 times.
OfficeProductivityOutlookNewAppointment result = geometric mean of [dop]_outlook_new_appointment_1 [dop]_outlook_new_appointment_2 [dop]_outlook_new_appointment_3 [dop]_outlook_new_appointment_4 [dop]_outlook_new_appointment_5 [dop]_outlook_new_appointment_6 [dop]_outlook_new_appointment_7 where the maximum and minimum values are dropped from the calculation.
OfficeProductivityOutlookSearchMails
Measures searching string from emails. Repeated 7 times.
OfficeProductivityOutlookSearchMails result = geometric mean of [dop]_outlook_search_mails_1 [dop]_outlook_search_mails_2 [dop]_outlook_search_mails_3 [dop]_outlook_search_mails_4 [dop]_outlook_search_mails_5 [dop]_outlook_search_mails_6 [dop]_outlook_search_mails_7 where the maximum and minimum values are dropped from the calculation.
OfficeProductivityOutlookBackup
Measures data archiving. Copies 128 mails and results in a 150MB data file. Repeated 7 times.
OfficeProductivityOutlookBackup result = geometric mean of [dop]_outlook_backup_1 [dop]_outlook_backup_2 [dop]_outlook_backup_3 [dop]_outlook_backup_4 [dop]_outlook_backup_5 [dop]_outlook_backup_6 [dop]_outlook_backup_7 where the maximum and minimum values are dropped from the calculation.
OfficeProductivityOutlookWriteMail
Measures creating an email with 3 attachments (Excel, PowerPoint and Word documents). Repeated 7 times.
OfficeProductivityOutlookWriteMail result = geometric mean of [dop]_outlook_write_mail_1 [dop]_outlook_write_mail_2 [dop]_outlook_write_mail_3 [dop]_outlook_write_mail_4 [dop]_outlook_write_mail_5 [dop]_outlook_write_mail_6 [dop]_outlook_write_mail_7 where the maximum and minimum values are dropped from the calculation.
OfficeProductivityOutlookSaveAttachments
Measures saving attachments from all emails. Writes out 82 files with a total size of 45MB. Repeated 7 times.
OfficeProductivityOutlookSaveAttachments result = geometric mean of [dop]_outlook_save_attachments_1 [dop]_outlook_save_attachments_2 [dop]_outlook_save_attachments_3 [dop]_outlook_save_attachments_4 [dop]_outlook_save_attachments_5 [dop]_outlook_save_attachments_6 [dop]_outlook_save_attachments_7 Measurements often contain variable number of high outliers. We eliminate them by applying k-means clustering to 3 clusters and dropping the highest cluster.