Hours worked and activity scores weren’t designed to be universal metrics of productivity. No matter the size or industry of the business, the most reliable way to use productivity metrics has always been with specific context.
Ask yourself: if you held your team members to the same standards as those of other roles whose responsibilities look nothing alike, would it sound fair to them?
A number can be true and still be the wrong number for the job. The goal behind productivity measurement isn’t to find an arbitrary number for every single person to meet. Instead, it’s to make sure each person is supported to do their best work, whatever that might look like in their specific role.
The most common productivity metrics are activity percentage, keyboard and mouse input, and hours logged.
Most productivity monitoring tools combine these metrics to calculate a single productivity score. Each tool has its own formula, but the core idea is the same across platforms: based on these inputs, this is the productivity number for this particular employee.
You can probably already see the problem this presents when applied to an organization. Activity percentage alone is not a reliable productivity signal; it just isn't built to measure every kind of work the same way.
When measurement is the same across team members, roles with high-activity input are favored and roles that don't generate constant clicks and keystrokes are penalized.
In practice, here is what that might look like across a few common roles:
| Role | Typical activity pattern | What's really happening |
|---|---|---|
Designer (focused work) Data entry specialist Sales representative Customer support agent Developer (debugging) | Low input for long stretches High, near-constant input Low input during calls and meetings Moderate, uneven input Low input during problem-solving | Deep thinking and concept work rarely involves constant clicking or typing The job is built around repetitive keyboard and mouse activity Time spent talking to prospects doesn't register as "activity" Activity spikes during tickets, drops during calls or research Reading code and thinking through issues looks identical to idle time |
As you can see, activity levels vary significantly between roles.
That's the core issue. Even when the metrics happen to favor a person's role, it isn't fair to measure everyone against the same single number. And when the metrics don't favor their role, that same number can make a hardworking employee look like they're falling behind, for reasons that have nothing to do with how well they're handling their responsibilities.
Tools handle this differently too, since each one has its own way of weighing these metrics using its own formula. For instance, Hubstaff and ActivTrak have different approaches to productivity measurement.
Fair productivity measurement starts with the leaders implementing the measurement, not the tool.
A productivity measurement tool can only report what happens. The rest (i.e., interpreting what those numbers mean and what the next move will be) is up to leadership.
The first and perhaps most important question you should be asking yourself is this: what is the output expected of this role?
Raw activity data is input. It represents how an employee is doing their work, not how good that work is. A high activity percentage doesn't mean great output. Conversely, a low one doesn't mean it was weak.
There's real value in understanding how your team works. Activity data can help you find ways to improve performance across the organization through metrics like:
However, that value only holds up if you start in the right place.
Start with "Is the job done well?" Using productivity formulas can help you answer this question.
Then, use activity data to answer "What can we do even better?"
Don’t do the inverse and lead with "Are you meeting this metric?" while ignoring the person’s output.
A designer can deliver exceptional work in a fraction of the time a metric expects. A rep can close the right deals without living inside the CRM every minute of the day. Both are examples of great work, but both look unimpressive from a purely activity metric perspective.
Prioritize output, then use activity data to support it.
Unless you're the one taking notes, no meeting looks productive on an activity tracker. Keyboard and mouse input stay low the entire time, no matter how much gets decided.
Picture a day with more meetings than hands-on execution work. Crucial decisions get made, and team-wide next steps get identified. Unsurprisingly, activity metrics say almost no work happened at all.
Does that mean the meetings were a waste of time?
No. It means the tool wasn't built to measure that kind of work in the first place.
Collaboration doesn't generate clicks, and getting aligned doesn't generate keyboard input. However, both are often the reason execution work goes smoothly afterward. A day that looks "inactive" on paper can very well be the day that made the rest of the week possible.
Meeting-heavy roles and meeting-heavy days need to be read differently than heads-down execution time. If not, you might misread the collaboration whole teams depend on to operate.
Not all tracked time carries the same weight. Core work is what a role exists to produce, while non-core work is everything else that supports that production. The latter may not be the primary output, but it remains necessary.
For example, here’s what core and non-core tasks typically look like for a developer:
In contrast, here’s what a sales representative often looks at:
Be careful not to mistake non-core work for unproductive work, as it’s a different category entirely. A sales rep updating the CRM after a call isn't slacking. They're doing the supporting work that makes the core work possible.
The mix between the two also depends on context, not just role. A client strategy meeting can be core work for an account manager and non-core work for an engineer sitting in that same meeting.
Productivity isn't binary. There’s no single number that separates people doing well from people falling behind.
Measuring it well doesn't mean watching each person's performance move day to day, relative to yesterday. You shouldn't do that even if you had the means to. A better starting point is measuring productivity relative to the role itself, inside your organization.
Here's what that looks like across common roles:
| Role | What productive looks like | Metrics that matter | Metrics to de-emphasize |
|---|---|---|---|
Creative designer Software developer Sales representative Customer support Data entry specialist | Strong creative output delivered on time Working code and problems solved Prospects moving toward closed deals Fast customer issue resolution Accurate, consistent high-volume input | Completed work and revision speed Code shipped, bugs fixed, sprint completion Calls booked, deals closed, pipeline growth Resolution speed, customer satisfaction Volume completed, error rate, turnaround time | Keyboard/mouse activity, hours logged Idle time while debugging, activity % Active time in tools, hours at desk Time between tickets, idle time on calls Activity %, time away from keyboard |
Measuring productivity in remote teams can be more complex, though. In an office, you notice when someone seems overwhelmed or off their usual pace just by being in the room, but remote work removes that subtext. If you’re managing both types of teams, understanding how your remote team’s productivity compares to your in-office’s will help a lot.
The answer to this isn’t watching activity data even more closely in a distributed team. In fact, it’s the opposite: fewer but more purposeful check-ins based on the specific elements that are currently making work challenging for the specific person. The goal is knowing how someone's work is going, not watching their every move to make sure the work is done a specific way.
Everything up to this point has been about how to think about productivity data. The next step is to implement a framework that takes each role's unique responsibilities into account, so that no one gets measured against a standard not built for them.
Follow these five steps:
A good framework is one that doesn’t need to be overhauled every time a new person joins, teams are reorganized, or tools are changed. More importantly, from a team member's perspective, the experience should be easy and predictable. The framework has to be implemented exactly as it was communicated to them so as to protect trust.
Implementing a fair productivity framework starts with a leadership decision, but it requires a tool built to support those principles. Hubstaff gives teams the visibility and context they need to make fair measurement simple:
Fairness in productivity measurement comes from context: understanding the work a role involves and reading the data with that understanding in mind. A single number can't account for how different roles function, no matter how consistently it's applied.
Role-aware measurement is what turns activity data into something you can trust, and Hubstaff equips teams to achieve this. That way, fewer blind spots in performance slip through the cracks.
The result is valuable workforce analytics that enables leaders to understand their team’s work and make decisions with that understanding, one that employees can get behind. The next step is getting your team started on measuring productivity.
If you want to implement a productivity measurement framework that's transparent, effective, and fair, start with evaluating how you're currently measuring performance across your team's roles.
Look at the roles inside your organization, and identify the output that tells you whether someone is doing a good job. Then, identify the metrics that determine how they're doing that job, based on the unique role.
Capacity planning plays a crucial role here, as it helps you determine what each role can reasonably carry and if the metrics you’re seeing reflect the work itself or are the result of an unrealistic workload.
Once those metrics are defined, you have the foundation for a framework that you can count on for different roles.
That foundation is what you'll bring into a productivity measurement tool like Hubstaff.
If you want to see how Hubstaff can help you understand your team's productivity with more visibility and context, start a 14-day free trial. Or, if you want a clearer picture of how role-aware measurement would work for your specific team, book a demo for a role-based walkthrough.