Use this when helping a client define what to measure and how to structure their metrics for a product or initiative.

How it works

1. You provide the client name, product or initiative, objectives, and relevant context
2. The skill designs a metrics framework with North Star, input metrics, health guardrails, and business metrics
3. It returns a client-ready metrics dashboard specification with definitions, targets, alert thresholds, and review cadence

Prompt

You are designing a metrics framework as part of Kate's strategic-comms-roadmapping engagement. Before writing, read knowledge/voice-tone-guide.md -- use the client-facing voice (clear, structured, professional -- this will be shared with the client's team).

Inputs I will provide:

• Client: {{CLIENT}} (company name and engagement context)
• Product: {{PRODUCT}} (the product, feature, or initiative being measured)
• Objectives: {{OBJECTIVES}} (business goals, OKRs, or strategic priorities the metrics should track against)
• Context: {{CONTEXT}} (current analytics setup, team maturity, tools in use, any constraints)

Step 1: Understand the measurement landscape

Before defining metrics, clarify:

• What decisions will these metrics inform?
• Who will be looking at this dashboard (executives, PMs, engineers, the whole company)?
• What is the team's current data maturity (no analytics, basic events, mature pipeline)?
• What tools do they have or are willing to adopt?

If any of these are unclear from the inputs, note them as [CLARIFY WITH KATE: ...].

Step 2: Pull relevant frameworks

Read knowledge/pm-execution-templates.md for metrics framework structures Kate uses with clients.

Read knowledge/pm-growth-frameworks.md, specifically:

• North Star Metric methodology -- how to identify the single metric that captures core value delivery
• AARRR / Pirate Metrics for acquisition-through-revenue coverage
• Input metrics concept -- the levers that drive the North Star

Step 3: Design the metrics framework

North Star Metric

• Metric: [name]
• Definition: [exact calculation -- numerator, denominator, time window]
• Why this metric: [how it captures core value delivery for {{PRODUCT}}]
• Current baseline: [if known, or [NEEDS DATA]]
• Target: [goal value with timeframe]

Input Metrics (3-5)

The levers that drive the North Star. For each:

Health Metrics

Guardrails that ensure the team is not optimizing the North Star at the expense of product health:

Business Metrics

Revenue, cost, and unit economics the leadership team cares about:

Step 4: Capture baseline snapshot

Before designing the dashboard layout, document current-state measurements for each proposed metric. This establishes the "before" so the team can track improvement.

If the team has no baseline for a metric, that's critical to surface now -- you can't measure improvement without a starting point. Flag each gap with [NEEDS INSTRUMENTATION]. If more than half the metrics have no baseline, recommend an instrumentation sprint before dashboard design.

Step 5: Specify the dashboard layout

Provide a visual layout recommendation:

• Top row: North Star with trend indicator
• Second row: Input metrics with sparklines
• Third row: Health guardrails
• Bottom row: Business metrics

Include which visualization type suits each metric (line chart, bar, single number, funnel).

Step 6: Define the operating rhythm

Review Cadence

• Daily: which metrics to check and who checks them
• Weekly: which metrics to review in team standups
• Monthly: which metrics to present in leadership reviews
• Quarterly: strategic recalibration -- are these still the right metrics?

Alert Rules

For each alertable metric:

• What threshold triggers investigation
• Who gets notified and through what channel
• Expected response time

Revenue Attribution Layer

For each input metric, estimate the financial impact so the dashboard connects operational movement to business outcomes:

• Sensitivity analysis: For each input metric, estimate: "If this metric improves by 10%, the expected revenue impact is approximately $X/month." Ground estimates in the client's unit economics where available, or flag as [NEEDS DATA: revenue sensitivity estimate requires baseline conversion and ARPU figures].
• Unit economics integration: Map where CAC, LTV, and payback period sit in the metric hierarchy. Input metrics should ladder up: acquisition inputs drive CAC, retention inputs drive LTV, and the ratio between them determines payback period. If the dashboard does not make this connection visible, flag it.
• Financial leading indicators: Identify which input metrics predict revenue 30, 60, and 90 days out. Activation and engagement metrics typically lead by 30-60 days; retention and expansion metrics lead by 60-90 days. Label each input metric with its revenue lead time.
• Threshold annotations: For each alertable metric, add a financial annotation: "If [metric] drops below [threshold], expected revenue impact is $X/month based on [assumption]." This turns operational alerts into business urgency.

Step 7: Validate metric quality

For each metric in the framework, check against these criteria:

1. Understandable -- does it create a common language across the team?
2. Comparative -- can it be tracked over time, not just as a snapshot?
3. Ratio or rate -- is it a ratio (more revealing) or a raw number (less useful)?
4. Behavior-changing -- if this metric moves, will the team actually do something different?

Flag any metric that fails these checks:

For defining the North Star Metric that anchors this dashboard, use the north-star-metric skill. For setting OKRs around these metrics, use okr-workshop.