North Star Metric

North Star Metric (NSM) Expert

Overview

A North Star Metric (NSM) is the single number that best represents the value your product delivers to its customers. Sean Ellis popularized the framing; Amplitude codified the playbook; Lean Analytics calls a related concept the "One Metric That Matters" (OMTM). The NSM is one number, not a dashboard. Its job is to align the entire team -- engineering, marketing, sales, support -- on a shared definition of "we won this quarter."

This skill produces a complete NSM specification: the NSM itself, 3-5 input metrics the team can directly influence, the leading indicators that move days or weeks before the inputs, the anti-metrics (things that must NOT move in the wrong direction), and counter-metrics that guard against gaming. The Python tool (metric_tree_builder.py) emits the spec as JSON, Markdown, or a Mermaid tree diagram for a README or Confluence page.

This is the first artifact a team should produce after defining strategy and before writing OKRs. Once the NSM is set, OKRs map directly to moving the input metrics, and roadmaps justify themselves by which input metric they target.

Core Capabilities

• NSM selection — score candidates against the five tests (customer value, strategic alignment, leading, single number, movable) and the five Amplitude archetypes.
• Metric-tree decomposition — break the NSM into 3-5 input metrics with an explicit formula (multiplicative / additive / funnel / ratio).
• Leading indicators — assign 2-3 per input that move before the input does (the daily/weekly dashboard).
• Guardrails — anti-metrics (protect the customer) and counter-metrics (protect the business), each with explicit thresholds.
• Rendering — Mermaid tree, JSON dashboard config, or Markdown via the Python tool.

When to Use

• New product or strategic direction -- before OKRs and roadmap, define the NSM and inputs.
• Strategic re-alignment -- the dashboard has 47 metrics and no one knows which to optimize.
• Cross-functional friction -- marketing, product, and growth disagree on what "success" means.
• Investor / board reporting -- the NSM becomes the headline metric.
• A/B experimentation guardrails -- every test reports NSM impact plus counter-metric impact.

When NOT to use: very early-stage discovery (use discovery/ first — you don't yet know what value you deliver); pure infrastructure work with an indirect user-value chain; before the org has aligned on strategy (the NSM exposes disagreement but does not resolve it).

Clarify First

Before defining the NSM, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• Core customer value the product delivers — drives NSM candidate selection; the NSM must be a proxy for this, not a revenue lagging metric
• Business archetype — attention / transaction / productivity / marketplace / engagement sets the Amplitude archetype and the input-metric formula (multiplicative/additive/funnel/ratio)
• Input metrics the team can directly influence — the 3-5 nodes of the tree; if the team can't move them, the tree is decoration
• Anti-/counter-metrics to guard — the thresholds that protect customer and business against a gamed NSM

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Quick Start

python scripts/metric_tree_builder.py --input nsm_spec.json --format mermaid   # render the metric tree
python scripts/metric_tree_builder.py --demo --format markdown                 # worked SaaS productivity NSM