Overview
In Teramind any productivity metric that starts with “Avg …” (e.g. Avg active time, Avg productive time) answers:
“How much of this metric does a typical working day have per employee in the selected period?”
We always use employee working days, not raw rows and not calendar days.
Formula:
Average = Sum of the metric over all work days ÷ Number of work days
Key points:
A work days = where an employee worked, i.e. Total work time > 0.
We first sum all sessions for a user on a date → one total per user per day.
Then we average those per-user-per-day totals across the relevant days (and users, for group views).
Days where a user has no work are excluded, not treated as zero.
All “Avg …” time metrics (e.g. Avg active time, Avg productive time, Avg idle time) follow this same pattern.
Why We Calculate It This Way
This approach:
Prevents weekends, holidays, and days off from skewing the average down by counting them as zero.
Ensures each (user, working day) contributes equally to the average, regardless of how many raw activity rows or sessions they generated.
Gives a value that actually reflects a typical working day per employee, which is more realistic for productivity assessment.
Example
Table 1: Sample Data
Here’s an example dataset showing an employee’s daily work metrics for 4 days:
Date | Employee | Productive time: Apps & Web | Avg productive time | Total work time |
2026-02-14 | Aymon Cousteau | 02:00 | 2h 00m | 6h 00m |
2026-02-13 | Aymon Cousteau | 00:00 | 0m | 0h 30m |
2026-02-12 | Aymon Cousteau | 01:00 | 1h 00m | 8h 28m |
2026-02-11 | Aymon Cousteau | 00:00 | 0m | 0h 00m |
For each date, the metrics (e.g., Productive time, Avg productive time, Total work time) are the sums of all sessions for that day. For example, if Aymon had several productive sessions on Feb 14 (for example, three 40‑minute sessions and one 20‑minute session), Teramind adds them up to show a total productive time of 02h for that day.
Note: When you view a daily report like this table, any Avg time value is basically the per-day total value for that metric. That’s why Productive time: Apps & Web and Avg productive time values are the same.
Table 2: Productivity Detail
The table below shows how the above data will look in the Productivity detail widget on the Productivity dashboard:
Employee | Productive time: Apps & Web | Avg productive time | Total work time |
Aymon Cousteau | 3h 00m | 1h 00m | 14h 58m |
Here:
The Productive time: Apps & Web and Total work time are simply the sums of the 4 days’ values from Table 1.
The Avg productive time is 1h. This is calculated as 2h (Feb 14) + 0h (Feb 13) + 1h (Feb 12), divided by 3 days*.
Feb 13 is included in the calculation because Aymon did work that day even though his Productive time: Apps & Web was 0.
*Feb 11 is not included in the calculation because it’s not treated as a working day (Total work time was 0).
All other “Avg …” time metrics (like Avg active time, Avg idle time, etc.) behave the same way.
FAQ
Q1. Why don’t the dashboard averages match “total hours ÷ number of days”?
Because we show a typical working day per employee, not a value spread across all calendar days. Only days where the employee actually worked (Total work time > 0) are counted. Days with no activity are ignored, not treated as zero.
Q2. Why can "Avg productive time" look high over a long date range?
Avg productive time is averaged over working days only. If an employee has a few very active days and many non-working days, the average reflects the intensity of the days they actually worked, instead of being diluted by days with no work.
Q3. What exactly is a “working day” in these calculations?
A working day for an employee is any day where that employee has any recorded work (Total work time > 0). On that day:
All sessions are summed into per-day totals.
Those per-day totals are included in the average.
Days with Total work time = 0 are not included in the denominator for averages.