Metric

In depth

A good metric is more than a number. It is a definition - a contract between business users and data professionals. That definition includes the measure to calculate, the aggregation to apply, the time grain to use, and any filters that keep the calculation consistent.

• Measure and aggregation: For example, sum of order_amount or average of resolution_time.
• Time grain: The consistent period for comparison, like day, week, or month.
• Filters and scope: Rules that decide which rows are in or out, such as status equals active or channel equals paid.
• Dimensions: Fields you can group by to compare slices, like product, plan, or region.

Clear definitions let different teams read the same chart the same way. You reduce double counting, mixed formulas, and shifting targets. This creates trust, which is the foundation for self‑serve analytics.
Metrics differ from KPIs. A KPI is a prioritized target tied to an outcome. A metric is the calculation you track. You might have dozens of metrics, but only a few are KPIs at any given time. Metrics also differ from dimensions. Dimensions are labels you group or filter by, not values you compute.

Pro tip

Write metric definitions like a recipe. Name, purpose, formula, filters, time grain, and allowed dimensions. Publish that recipe with the metric, where teams can find it. Small changes to filters or time grain can change a story, so make them explicit.

Why it matters

When you define metrics once and use them everywhere, everyone speaks the same language. Decisions get faster. Forecasts get clearer. New teammates get up to speed without a long handoff. Data teams spend less time policing definitions and more time improving them.

Metrics - In practice

Here are common business metrics and how they are typically defined:

• Revenue: sum(order_amount), filtered to completed orders, monthly time grain, with dimensions like product, plan, or country.
• MRR: sum(recurring_amount) for active subscriptions on month end date.
• Conversion rate: count(signups) divided by count(website_sessions), over the same period, by channel or campaign.
• Customer churn rate: count(churned_customers) divided by count(active_customers_start), monthly.
• Average resolution time: average(hours_to_close) for tickets with status closed, by team.

Use a small set of base metrics to compose more advanced ones. For example, ARPA can come from Revenue divided by Active Customers for the same time grain.

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Product‑specific notes

PowerMetrics treats metrics as governed, reusable building blocks.

• Metric catalog: A central place where you define and discover trusted metrics with names, descriptions, tags, owners, and certification.
• Consistent math: Define formula, time grain, and default filters once, then reuse on any dashboard or chart.
• Dimensions and comparisons: Slice by common dimensions and compare periods, segments, or cohorts with a click.
• Stored history and refresh: Maintain metric history at the chosen grain, with refresh options that match your source cadence.
• Goals and alerts: Set goals, get notifications when thresholds are met, and track progress visually.
• Explorer and views: Explore a metric ad hoc, then publish saved views for teams to use.
• Access control and lineage: Control who can view or change a metrics, and see where a metric is used.

These features help you standardize definitions, reduce debate, and share clear performance signals across your organization.