North Star Metric Advisor

Purpose

Define a single north star metric with input metrics, guardrails, and measurement plan that reflects core user value and ladders to business outcomes.

Input: Product vision or value proposition, business model, user journey, current analytics (optional)
Output: North Star Metric document with definition, input metrics, anti-metrics, and OKR alignment notes Examples: See examples.md for worked input/output.

Workflow

Step 1: Clarify value delivery

Establish:

• Primary user segment and core job-to-be-done
• Moment value is delivered (activation, habit, outcome)
• Business model link (subscription, transaction, ads, marketplace)
• Current growth stage (pre-PMF, growth, scale)

If value delivery is unclear, stop and request vision or value proposition context.

Step 2: Generate metric candidates

Brainstorm 3–5 candidate metrics. Each must be:

• Value-reflective — increases when users get more core value
• Measurable — trackable with existing or feasible instrumentation
• Actionable — teams can influence it through product changes
• Leading — predicts retention or revenue, not a lagging finance metric alone

Document trade-offs per candidate:

Step 3: Select north star metric

Apply selection criteria:

Select exactly one primary north star. Demote others to input metrics or discard.

Write precise definition:

• Name: [metric]
• Formula: [numerator / denominator / window]
• Unit: [count, rate, index]
• Scope: [all users, active users, paying users]

Step 4: Define input metrics

Identify 3–5 leading indicators that drive the north star:

Each input metric must have a hypothesized causal link, not just correlation.

Step 5: Set guardrails and measurement plan

Document anti-metrics that must NOT degrade while optimizing north star:

Define baseline, data source, event definitions, and review cadence (weekly/monthly).

Step 6: Validate

Run Validation checklist.

Decision Rules

Validation

• Exactly one north star metric selected with formula and scope
• 3–5 input metrics with causal hypotheses
• Anti-metrics / guardrails documented with thresholds
• Measurement plan includes baseline and data source
• Metric reflects user value, not internal output
• Business model connection explained
• Review cadence defined

Anti-patterns

• Revenue as north star — optimizing MRR without user value linkage.
• Vanity metrics — page views, downloads without retention signal.
• Composite kitchen-sink metric — unexplainable index nobody can act on.
• Unmeasurable aspiration — "customer delight score" with no survey or proxy.
• Gaming without guardrails — NSM rises while churn spikes.

Best Practices

• Use the "would users notice if this dropped?" test for NSM candidates.
• Anchor NSM to the aha moment in the user journey.
• Pair NSM with 2–3 hard guardrails, not dozens of metrics.
• Revisit NSM at stage transitions (pre-PMF → growth), not monthly.
• Connect NSM explicitly to OKR objectives in next planning cycle.

Output Structure

# North Star Metric: [Product Name]

## Context
- **Segment:** [primary user]
- **Value moment:** [when value is delivered]
- **Growth stage:** [pre-PMF / growth / scale]
- **Business model:** [how value converts to revenue]

## North Star Metric
- **Metric:** [name]
- **Definition:** [plain language]
- **Formula:** [calculation]
- **Scope:** [population and time window]
- **Baseline:** [current or TBD]
- **Target:** [optional 90-day goal]

## Input Metrics
| Metric | Drives NSM by... | Owner | Baseline |
|--------|------------------|-------|----------|

## Guardrails / Anti-metrics
| Metric | Threshold | Action if breached |
|--------|-----------|-------------------|

## Measurement Plan
- **Data source:** [analytics tool / warehouse]
- **Events required:** [list]
- **Review cadence:** [weekly/monthly]
- **Instrumentation gaps:** [TBD items]

## OKR Alignment Notes
[How objectives should ladder to this NSM]

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