product-metrics

name

product-metrics

description

Helps founders and product teams select the right KPIs and product metrics using Jason Cohen's value-chain framework. Use this skill whenever the user mentions KPIs, product metrics, OKRs, north star metrics, measuring product success, leading/lagging indicators, choosing what to measure, product dashboards, metric selection, or asks "what should I track/measure?" — even if they don't explicitly say "KPI." Also trigger when users are setting up analytics, building a metrics board, preparing for a board meeting or investor update, or trying to align stakeholders around what matters. If someone is debating which metrics to use for their product, startup, or feature launch, this skill applies.

Product Metrics Selection Skill

Help the user select the right KPIs for their product using Jason Cohen's value-chain framework. This skill turns an abstract "what should we measure?" into a concrete, structured set of metrics that satisfies product teams, executives, and stakeholders alike.

Core Framework: Metrics as a Value-Chain

The framework organizes metrics along two axes:

Axis 1: Time, Immediacy, and Control (horizontal)

Metrics are placed on a timeline from left to right:

1. Leading Indicators — Signals that precede product team activity. Market trends, pipeline health, inbound demand, customer signals that predict future needs. The team doesn't control these but must monitor them.

2. Work (Immediate, In-Control) — What the team directly produces and controls sprint-by-sprint. Feature delivery, bug fixes, infrastructure improvements. These are measurable immediately and the team is fully accountable.

3. Lagging Indicators — Downstream outcomes that take weeks or months to materialize. Revenue, retention, NPS, market share. Multi-input and multi-actor — the product team contributes but doesn't solely determine these.

This resolves the classic tension: executives want revenue impact now, while teams insist they're doing important work. Both are right. Work metrics prove execution; lagging metrics prove impact. When work succeeds but outcomes don't follow, the conversation becomes diagnostic rather than blame-oriented.