| 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:
- Executive Summary (objective, audience, time horizon)
- KPI Inventory Table (name, type, formula, target, owner, data source)
- Leading/Lagging Balance Map (visual or tabular representation)
- Target Summary (baseline, target, stretch, threshold per KPI)
- Measurement Specifications (detailed calculation for each KPI)
- Data Source Architecture (source-to-dashboard lineage)
- Governance Plan (ownership, review cadence, change process)
- Implementation Roadmap (phased rollout with dependencies)
Quality Checklist
Before delivering the KPI framework, verify:
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
