Writing North Star Metrics

Scope

Covers

• Defining or refreshing a product/company North Star and North Star Metric

• Translating a qualitative value model into measurable, decision-useful metrics

• Creating a simple driver tree: leading input/proxy metrics + guardrails

• Producing a “North Star Metric Pack” teams can use as a decision tie-breaker

When to use

• “We need one metric that defines success.”

• “Teams are optimizing different KPIs.”

• “We’re setting quarterly OKRs and need leading indicators.”

• “We’re launching a new strategy and need a metric that aligns decisions.”

When NOT to use

• You only need OKRs for an already-agreed North Star

• You need a full analytics taxonomy/event tracking plan from scratch

• Stakeholders haven’t aligned on the customer value model / mission at all (do product vision/strategy first)

• You’re choosing a single experiment metric for a one-off test

Inputs

Minimum required

• Product/company + primary customer segment

• The “value moment” (what the customer gets when things go well)

• Business model + strategic goal (growth, activation, retention, margin, trust, etc.)

• Time horizon (next quarter vs next year)

• Measurement constraints (what you can measure today; data latency; known gaps)

Missing-info strategy

• Ask up to 5 questions from references/INTAKE.md.

• If still missing, proceed with clearly labeled assumptions and provide 2–3 options.

Outputs (deliverables)

Produce a North Star Metric Pack in Markdown (in-chat; or as files if the user requests):

• North Star Narrative (value model, tie-breaker, scope)

• Candidate metrics (3–5) + selection rationale (evaluation table)

• Chosen North Star Metric spec (definition, formula, window, segmentation, owner, data source)

• Driver tree (leading input/proxy metrics + guardrails)

• Validation & rollout plan (instrumentation checks, dashboard cadence, decision rules)

• Risks / Open questions / Next steps (always included)

Templates: references/TEMPLATES.md

Workflow (8 steps)

1. Intake + constraints

• Inputs: User context; use references/INTAKE.md.

• Actions: Confirm product, customer, value moment, horizon, constraints, stakeholders.

• Outputs: 5–10 bullet “Context snapshot”.

• Checks: You can explain the customer value in one sentence.

2. Define the qualitative North Star (before numbers)

• Inputs: Context snapshot.

• Actions: Write a North Star statement and value model from the customer’s perspective.

• Outputs: Draft North Star Narrative (template in references/TEMPLATES.md).

• Checks: Narrative can act as a decision tie-breaker (“if we do X, does it move the North Star?”).

3. Generate 3–5 candidate North Star metrics (customer POV)

• Inputs: North Star Narrative + value moment.

• Actions: Propose metrics that measure delivered customer value (not internal activity). Include at least one “friction/absence of pain” option when relevant.

• Outputs: Candidate list with definitions.

• Checks: Each candidate is measurable, understandable, and not trivially gameable.

4. Stress-test and pick the North Star metric

• Inputs: Candidate metrics.

• Actions: Evaluate with references/CHECKLISTS.md and references/RUBRIC.md. Explicitly test:

• Leading vs lagging (avoid “retention as the only goal”; pair lagging outcomes with controllable inputs)

• Controllability within a quarter (proxy/input metrics you can move)

• Ecosystem impact (what breaks if you optimize this?)

• Outputs: Selection table + chosen metric + why others lost.

• Checks: A cross-functional leader could agree/disagree based on definitions and evidence.

5. Write the metric spec (make it unambiguous)

• Inputs: Chosen metric.

• Actions: Define formula, unit, window, inclusion rules, segmentation, owner, source, latency, and example calculation.

• Outputs: North Star Metric Spec.

• Checks: Two analysts would compute the same number.

6. Build the driver tree (inputs + guardrails)

• Inputs: Metric spec + product levers.

• Actions: Decompose into 3–7 drivers; identify leading input/proxy metrics you can move in weeks/months; add guardrails to prevent gaming/harm.

• Outputs: Driver tree table + guardrails list.

• Checks: Every driver has at least 1 realistic lever (initiative/experiment) and 1 measurement.

7. Define validation + rollout

• Inputs: Driver tree + constraints.

• Actions: Plan validation (sanity checks, correlation to outcomes) and operationalization (dashboards, cadence, owners, decision rules).

• Outputs: Validation & Rollout Plan.

• Checks: Plan includes “who does what, when” and works with current instrumentation.

8. Quality gate + finalize pack

• Inputs: All drafts.

• Actions: Run references/CHECKLISTS.md and score with references/RUBRIC.md. Add Risks/Open questions/Next steps.

• Outputs: Final North Star Metric Pack.

• Checks: Pack is shareable as-is; key decisions and caveats are explicit.

Quality gate (required)

• Use references/CHECKLISTS.md and references/RUBRIC.md.

• Always include: Risks, Open questions, Next steps.

Examples

Example 1 (B2B SaaS): “Define a North Star metric for a team collaboration tool.”

Expected: a pack that chooses a customer-value metric (e.g., weekly active teams completing the core value moment), plus a driver tree (activation → collaboration depth) and guardrails.

Example 2 (Marketplace): “Refresh North Star metric for a local services marketplace.”

Expected: a pack that measures delivered value (e.g., successful jobs completed with quality), plus input metrics for supply/demand balance and quality guardrails.

Boundary example: “Our North Star should be retention.”

Response: keep retention as an outcome/validation metric, and propose controllable input/proxy metrics (time-to-first-value, weekly value moments, repeat value delivery) as the operating focus.