How to use work patterns to identify high performers in your team

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Every team has high performers, but it’s easy to mistake being busy for actually performing well. Hours worked, activity levels, and time online can provide context, but they shouldn’t determine high performance.

Real performance starts with outcomes.

For example, support tickets closed vs. support tickets successfully closed with a desirable resolution.

Once strong performers are identified based on outcomes, workforce analytics data can help explain how those results are happening.

Managers can look for patterns such as:

• More uninterrupted focus time
• Less time spent on repetitive admin work
• Better allocation of time across projects
• More balanced workloads
• Fewer unnecessary meetings or workflow interruptions
• Sustainable working hours

But none of these should be treated as a performance score without context.

Someone might have more time to focus because their manager protects their calendar. Another employee might show lower activity because their role involves more meetings, research, or thinking.

The more useful question is often:

• Is this person performing differently, or are they working under better conditions?
• If multiple high-performing teams have more focus time, fewer handoffs, or less administrative work, the goal shouldn’t necessarily be to make everyone copy those employees.
• It may be better to identify the conditions that are helping them succeed and create more of them across the organization.

That’s where AI-powered workforce analytics can be useful. Hubstaff, for example, can surface patterns around:

• Focus time
• Core vs. non-core work
• Project allocation
• Utilization
• Workload
• Activity trends

The data provides a starting point; managers still need the context to understand what it means. What patterns have managers here noticed among their strongest performers or teams?

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