product-metrics-analysis

Product Metrics Analysis

When To Use

• ## Success Metrics is empty, vague, or output-based
• A metric has no baseline, target, leading indicator, or counter-metric
• Product work needs a clearer outcome model before validation, PRD, or prioritization
• A metric tree is needed to connect user behavior to team-owned actions
• When analyzing growth strategy, market entry KPIs, or freemium economics — load references/growth-strategy-economics.md

Key Concepts

• North Star Metric: best single expression of value delivered
• Metric tree: decomposes a top-level metric into controllable leaves
• Leading / lagging indicators: leading metrics move earlier and help decision-making sooner
• Counter-metrics: protect against harmful local optimization
• HEART framework: WHY — business metrics alone (North Star, AARRR, retention) can be gamed by coercive mechanics that produce numbers without genuine user value; HEART adds a UX quality layer that exposes this. WHAT — five categories: Happiness (attitudinal satisfaction), Engagement (interaction depth), Adoption (new user uptake), Retention (continued use), Task success (completion rate, error rate). HOW — apply the Goals-Signals-Metrics (GSM) process: state the goal per category, identify observable signals, then define trackable metrics. Combine with North Star and counter-metrics so teams can see both business outcomes and whether users are achieving real value.

Rules

• Every primary success metric needs a baseline BEFORE a target. Do not set a target without first establishing the current baseline — a target without a baseline cannot be evaluated as ambitious or realistic. Sequence: measure baseline → analyze baseline → set target.
• Every initiative needs at least one leading indicator and one counter-metric
• When several measurement approaches are possible, present 2-3 options and recommend the smallest metric set that can still change a product decision — tracking metrics nobody acts on wastes instrumentation effort and dilutes team focus.
• Impact must be defined as behavior change, not feature shipment
• Use distributions instead of averages when tail performance matters
• Before defaulting to revenue as the primary KPI, check whether the market stage warrants a user-acquisition-first framing. Conditions: net-new market + low marginal cost + measurable activation/retention + sufficient runway. If conditions are not met, default to revenue or contribution-margin framing. See references/growth-strategy-economics.md.
• Apply all output quality gates from references/product-quality-gates.md.
• Every primary metric must have a decision trigger: "If [metric] drops below [threshold] for [duration], we will [action]." Decision triggers prevent teams from watching metrics decline without acting. Define triggers before launch, not after.
• If the pack manifest declares a teamContext file, use its North Star metric hierarchy as the default. Prioritize metrics that connect to product depth over mere presence.