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Business performance metric identifier

Identifies the right performance metrics and KPIs for a specific business function, team, and goal — organized into primary KPIs, supporting metrics, leading indicators, and guardrail metrics, each with definition…

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Purpose

Turn a business function, team, and goal into a prioritized, decision-grade metric set — separating the one or two KPIs that define success from the supporting, leading, and guardrail metrics around them — so analysts stop tracking vanity numbers and start measuring what moves the goal. Solves the "what should this team even be measuring?" problem with a structured, benchmarked answer.

When to Use

Invoke this skill when the user asks to:

  • Decide what metrics or KPIs a function or team should track
  • Build a KPI dashboard or scorecard for a specific team
  • Replace vanity metrics with metrics that drive decisions
  • Set the primary success metric (North Star) for a goal or initiative
  • Diagnose why a current metric set isn't surfacing the right decisions
  • Define leading indicators that predict a lagging outcome (revenue, retention, LTV)

Inputs Required

Before starting, collect:

  • Business function: The area — e.g., ecommerce/DTC, retention/CRM, paid acquisition, merchandising, supply chain/ops, customer support
  • Team or role: Who owns these metrics day-to-day
  • Primary goal: The outcome this function is accountable for — stated as a target if possible (e.g., "grow repeat purchase rate," "lower CAC by 15%")
  • Time horizon: Quarterly / annual / campaign-length
  • Current metrics tracked: What they measure today (to identify gaps and vanity metrics)
  • Data sources available: Shopify/commerce platform, ad platforms, GA4, CRM, warehouse, etc.
  • Business model detail: One-time vs. subscription, AOV range, and channel mix if known

If time horizon or business model detail is missing, assume quarterly reporting for a DTC one-time-purchase brand and note the assumption.

Instructions

Step 1: Anchor the Goal and Pick the North Star

Restate the goal as a measurable outcome, then identify the single metric that best represents success for this function.

  • The North Star must be outcome-oriented, not activity-oriented (e.g., repeat purchase rate, not emails sent)
  • It must be directly influenceable by the named team — not a company-wide metric they can't move
  • Name it, define it, and state the target if one was given; if not, propose a defensible target range

Step 2: Classify Metrics Into Four Tiers

Sort every recommended metric into one of four roles. Every metric named must be tagged with its tier.

  • Primary KPIs (1–2 metrics): Define success. If these move the right way, the goal is being met.
  • Supporting metrics (3–5 metrics): The measurable components that mathematically drive the primary KPI. If a KPI is a ratio or product, its supporting metrics are its inputs.
  • Leading indicators (2–4 metrics): Early signals that predict the primary KPI before it fully lands — used to intervene early.
  • Guardrail metrics (2–3 metrics): Metrics that must NOT get worse while chasing the goal (e.g., margin while chasing revenue, CAC while chasing scale, refund rate while chasing conversion).

Step 3: Build the Metric Detail Table

For every metric across all four tiers, provide:

  1. Metric name and tier
  2. Definition: What it measures in one plain sentence
  3. Formula: The actual calculation (e.g., Repeat Purchase Rate = customers with 2+ orders ÷ total customers)
  4. Benchmark range: A realistic DTC/consumer-goods range with the caveat that benchmarks vary by category
  5. Cadence: Real-time / daily / weekly / monthly / quarterly
  6. Data source: Where it's pulled from, based on the sources the user listed

Step 4: Flag Vanity and Gap Metrics

Audit the user's current metric list:

  • Vanity metrics to demote: Metrics that feel good but don't drive decisions for this goal (e.g., total followers, gross impressions, total sessions with no conversion tie). Explain why each is vanity for this specific goal.
  • Critical gaps: Metrics they should be tracking but aren't — with the decision each would unlock.

Step 5: Recommend the Dashboard Layout

Specify how these metrics should be arranged for the team:

  • Top row: primary KPIs with target vs. actual
  • Second row: leading indicators (the "watch these" row)
  • Third row: supporting metric breakdown
  • Footer: guardrail metrics with threshold alerts
  • Note review cadence per section (which numbers get looked at daily vs. in the monthly review)

Output Format

  • Structure: North Star statement → four-tier metric detail table → vanity/gap audit → dashboard layout recommendation
  • Length: 800–1,200 words
  • Format: Prose for the North Star and audit sections; a structured table for the metric details; a labeled list for the dashboard layout
  • Sections: North Star KPI → Metric Set (table, grouped by tier) → Vanity Metrics to Demote → Critical Gaps → Recommended Dashboard Layout → Review Cadence
  • Tone: Analytics lead — precise, decision-first, benchmark-literate; opinionated about what not to measure

Quality Checklist

Before delivering output, verify:

  • [ ] Exactly one North Star KPI is named (not a list) and it is influenceable by the named team
  • [ ] Every metric is tagged with one of the four tiers (Primary / Supporting / Leading / Guardrail)
  • [ ] Every metric has a real formula — not just a definition
  • [ ] Supporting metrics mathematically connect to the primary KPI (they're inputs, not unrelated numbers)
  • [ ] At least one guardrail metric protects margin, CAC, or another cost/quality dimension
  • [ ] The vanity-metric section explains why each metric is vanity for this goal specifically — not in general

Notes

  • Supporting metrics should decompose the KPI. If the primary KPI is LTV, supporting metrics are AOV, purchase frequency, and margin — not a random assortment of ecommerce stats.
  • Leading indicators earn their place only if they genuinely predict the lagging KPI; add-to-cart rate leads conversion, first-week engagement leads retention.
  • Benchmarks for DTC vary sharply by category (beauty vs. apparel vs. supplements). Always frame benchmark ranges as directional, and tell the user to validate against their own trailing 12-month baseline.
  • The most common failure in DTC metric sets is chasing revenue or conversion without a margin or CAC guardrail — always include one.

How to use this skill

This file is a Claude Code skill in Anthropic's standard format — markdown with YAML frontmatter at the top.

Installing in Claude Code:

  • ~/.claude/skills/<folder-name>/SKILL.md — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects)
  • <your-project>/.claude/skills/<folder-name>/SKILL.md — auto-loads only when working in that project (recommended for project-specific skills)

Restart Claude Code or open a new session to register.

Using in Claude.ai, ChatGPT, Gemini, or other AI tools:

  1. Drag this file into a new chat, or copy and paste its contents
  2. Send your task or request as your next message — the AI will apply the skill where it fits

For Claude Code skill details: https://docs.anthropic.com/en/docs/claude-code/skills