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.

Role metrics on a profile: each one tracks against its target and trends continuously as the data updates.

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 metric editor dialog with a configured Avg Cycle Time metric: a sparkline preview, the query written in English, and a target of 5 or below. — A configured metric: the query (here written in English) produces the sparkline, and the target of ≤ 5 days sets what counts as on-track.

The query behind a metric

A metric is powered by a query over your artifact data. You can write it in plain English and let Admire generate the SQL, or write the SQL directly; both stay in sync, so pick whichever is easiest and toggle to the other at any time.

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.

A few examples to give a sense of the range (toggle each between English and SQL):

Average ticket cycle time in days, per week, for :staff_id

SELECT date_trunc('month', closed_at) AS month,
     SUM(amount) AS revenue
FROM deals
WHERE owner_id = :staff_id
GROUP BY month
ORDER BY month

Median first-response time in minutes for tickets handled by :staff_id, per week

SELECT date_trunc('week', created_at) AS week,
     PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY first_response_minutes) AS median_response
FROM tickets
WHERE assignee_id = :staff_id
GROUP BY week
ORDER BY week

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.