Role metrics

Role metrics track key performance indicators for a role — the numbers that show whether skill development is translating into results. Unlike a skill observation, which is a discrete, expert assessment captured in a Meeting, a role metric is assessed automatically and continuously from data.

That makes metrics the right tool whenever a performance characteristic can simply be known from a number rather than judged in a session. They’re complementary to skill assessments: the metric gives staff faster feedback (it updates as the data does) with no overhead for coaches (nobody has to sit down and score it), freeing coaching time for the things that genuinely need a human eye.

Use cases

• KPIs — give staff continuous feedback on the numbers that define their role: average sales deal size, tickets delivered, cycle time, response time, revenue. As the underlying data updates, so does each person’s sparkline — no one has to stop and tally it.
• Continuous AI insights — use Admire’s AI to turn unstructured work into measurable signals, then track them as metrics. For example, run a preliminary pass over call or meeting transcripts to gauge sentiment or surface the key topics discussed, and watch those trend per person over time. (See artifact processing for computing these signals from your data.)

How it works

Attach a metric to a role and give it:

• a target value, and a direction — whether higher, lower, or exactly on target is good.
• the query that produces its value (see below).

On each person’s profile, the metric shows a sparkline of the value over time, colored by status (on-track vs. off-track) based on how they’re tracking against the target — so you can scan a team and see at a glance who needs attention.

The query behind a metric

A metric is powered by a query over your artifact data. The query must:

• include a :staff_id variable, so Admire can run it per person, and
• return a time series — a date column and a numeric value column.

Metrics vs. skill assessments

• Role metrics — continuous, automatic, and data-driven. Best for outcomes you can measure (tickets closed, response time, revenue).
• Skill assessments — discrete, expert judgments of observed behavior. Best for capabilities that need a human to evaluate.

Used together, they connect what people do to the results it produces.