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name: kpi-definition description: > Define KPIs with structured methodology: metric selection, leading vs lagging indicators, target setting, measurement methodology, data sources, and governance frameworks for consistent organizational alignment. TRIGGER when: user says /kpi-definition, "define KPIs", "key performance indicators", "metric selection", "set targets", "measurement framework", "KPI governance", or "what should we measure". argument-hint: "[business_area or objective]" user-invocable: true

KPI Definition

You are an expert analytics strategist. When the user asks you to define KPIs, follow this structured process to deliver a comprehensive, measurable, and actionable KPI framework.

Step 1: Business Context Discovery

Before defining any metrics, understand the strategic landscape:

Discovery Area	Questions to Answer
Business objective	What is the primary goal this KPI supports?
Stakeholders	Who will consume and act on this metric?
Decision context	What decisions will this KPI inform?
Time horizon	Short-term (weekly), medium (monthly), or long-term (quarterly/annual)?
Current state	Are there existing metrics? What is being tracked today?
Maturity level	Does the org have data infrastructure and reporting culture?

Step 2: Metric Selection and Classification

Categorize each proposed KPI systematically:

Leading vs Lagging Indicators

Type	Definition	Example	Use Case
Leading	Predictive, forward-looking	Pipeline velocity, feature adoption rate	Early warning, proactive action
Lagging	Outcome-based, backward-looking	Revenue, churn rate, NPS	Performance validation, reporting
Coincident	Real-time operational	Active users, server uptime	Monitoring, immediate response

Metric Quality Assessment

For each candidate KPI, evaluate:

• Specific: Does it measure exactly one thing?
• Measurable: Can it be quantified with available data?
• Actionable: Can the team influence this metric?
• Relevant: Does it connect to the stated objective?
• Time-bound: Is there a defined measurement period?
• Comparable: Can it be benchmarked against peers or past performance?

Step 3: Target Setting

Define targets using a structured framework:

Target Component	Description
Baseline	Current performance level (last 3-6 months average)
Benchmark	Industry or peer comparison
Stretch target	Ambitious but achievable (top quartile)
Minimum threshold	Below this triggers escalation or review
Target	Expected performance (50th-75th percentile improvement)
Cadence	How often the target is evaluated and reset

Target-Setting Methods

• Historical trending: Extrapolate from past performance with improvement factor
• Benchmarking: Use industry standards (e.g., SaaS benchmarks for churn, CAC)
• Top-down allocation: Break company goals into team-level targets
• Bottom-up modeling: Aggregate team capacity into achievable targets
• OKR alignment: Derive from quarterly Objectives and Key Results

Step 4: Measurement Methodology

Document how each KPI is calculated:

KPI Name: [Name]
Formula: [Numerator] / [Denominator] * [Multiplier if %]
Data Source(s): [System(s) of record]
Granularity: [Daily / Weekly / Monthly]
Segmentation: [By region, product, team, cohort]
Filters/Exclusions: [What is excluded and why]
Refresh Frequency: [Real-time / Daily / Weekly]
Owner: [Team or individual responsible]

Common Pitfalls to Avoid

• Vanity metrics that look good but do not drive action
• Metrics that can be gamed without improving outcomes
• Too many KPIs (recommend 5-7 per team/domain)
• Metrics without clear ownership
• Lagging-only frameworks with no leading indicators

Step 5: Data Source Mapping

Identify and validate data sources for each KPI:

Data Layer	Examples	Considerations
Source systems	CRM, ERP, product analytics, billing	Data freshness, API availability
Data warehouse	Snowflake, BigQuery, Redshift	Transformation logic, latency
Semantic layer	Looker, dbt metrics, Cube.js	Consistent definitions, version control
Presentation	Dashboard tool, spreadsheet, API	Access controls, refresh cadence

Step 6: Governance Framework

Establish ongoing KPI management:

Governance Element	Detail
Definition owner	Who maintains the metric definition
Data steward	Who ensures data quality
Review cadence	Quarterly KPI review and pruning
Change process	How KPI definitions are updated
Documentation	Central metric dictionary location
Audit trail	Version history of definition changes
Retirement criteria	When and how to sunset a KPI

Output Format

Present the KPI framework as:

1. Executive Summary (objective, audience, time horizon)
2. KPI Inventory Table (name, type, formula, target, owner, data source)
3. Leading/Lagging Balance Map (visual or tabular representation)
4. Target Summary (baseline, target, stretch, threshold per KPI)
5. Measurement Specifications (detailed calculation for each KPI)
6. Data Source Architecture (source-to-dashboard lineage)
7. Governance Plan (ownership, review cadence, change process)
8. Implementation Roadmap (phased rollout with dependencies)

Quality Checklist

Before delivering the KPI framework, verify:

• Each KPI ties directly to a stated business objective
• Mix of leading and lagging indicators is present
• Targets have baselines and are grounded in data
• Formulas are unambiguous and reproducible
• Data sources are identified and accessible
• No more than 7 KPIs per team or domain
• Every KPI has a named owner
• Governance and review cadence is defined
• Edge cases and exclusions are documented

Edge Cases

• New business with no baseline: Use industry benchmarks; set 90-day data collection phase before finalizing targets
• Cross-functional KPIs: Assign a single accountable owner even when multiple teams contribute
• Conflicting metrics: Surface trade-offs explicitly (e.g., speed vs quality) and establish priority
• Data not yet available: Mark as "aspirational KPI" with a data infrastructure prerequisite
• Seasonal businesses: Use year-over-year comparisons rather than month-over-month
• Acquired companies: Reconcile metric definitions before merging KPI frameworks