# Business Metrics Kpis > Use when defining and tracking business metrics and KPIs. - Skill: `loopyluci/business-metrics-kpis` (Agent Skill) - Install (CLI): `npx skillmds@latest add loopyluci/business-metrics-kpis` - Raw SKILL.md: https://api.skillmd.com/api/skills/loopyluci/business-metrics-kpis/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - License: MIT - Author: LoopyLuci (https://skillmd.com/u/loopyluci) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/loopyluci/business-metrics-kpis --- # Business Metrics and KPIs Defining, tracking, and analyzing key business metrics and KPIs — from financial metrics through marketing, sales, product, and customer success KPIs. ## When to Use - Defining KPIs for a new business or department - Building executive dashboards and reports - Setting OKRs and tracking progress - Analyzing business performance across functions - Making data-driven decisions based on metrics ## KPI Framework ``` Input Metrics (leading) → Process Metrics → Output Metrics (lagging) (activities) (quality) (results) ``` ## Metric Definitions by Department ```python from typing import Dict, List, Optional from datetime import datetime, timedelta class KPIEngine: """Define and track metrics across departments.""" METRICS_LIBRARY = { 'revenue': { 'MRR': 'Monthly Recurring Revenue', 'ARR': 'Annual Run Rate (MRR × 12)', 'ARPU': 'Average Revenue Per User', 'LTV': 'Customer Lifetime Value', 'Gross_Margin': 'Revenue - COGS / Revenue', }, 'growth': { 'CAC': 'Customer Acquisition Cost', 'LTV_CAC': 'LTV to CAC Ratio (>3 is healthy)', 'Payback_Period': 'Months to recover CAC', 'Net_Revenue_Retention': 'Revenue retention including expansions', }, 'sales': { 'Pipeline_Value': 'Total value of open deals', 'Win_Rate': 'Deals won / deals closed', 'Sales_Cycle': 'Average days from lead to close', 'Quota_Attainment': '% of reps hitting quota', }, 'marketing': { 'MQLs': 'Marketing Qualified Leads', 'SQLs': 'Sales Qualified Leads', 'MQL_to_SQL': 'Conversion rate from MQL to SQL', 'CPL': 'Cost Per Lead', 'ROAS': 'Return on Ad Spend', 'Organic_Traffic': 'SEO-driven website traffic', }, 'product': { 'DAU_MAU': 'Daily active / monthly active users', 'Retention_Rate': '% users returning after N days', 'Feature_Adoption': '% users using a feature', 'NPS': 'Net Promoter Score', 'Churn_Rate': '% customers lost per period', }, 'customer_success': { 'Churn': 'Customer churn rate', 'Expansion_MRR': 'Revenue from upsells/cross-sells', 'Health_Score': 'Composite customer health metric', 'CSAT': 'Customer satisfaction score', 'First_Response_Time': 'Avg time to respond to support', }, } @staticmethod def define_okr(objective: str, key_results: List[Dict]) -> Dict: """Define OKR (Objectives and Key Results).""" return { 'objective': objective, 'key_results': [ { 'kr': kr.get('name', ''), 'current_value': kr.get('current', 0), 'target_value': kr.get('target', 100), 'progress_pct': round((kr.get('current', 0) / max(kr.get('target', 1), 1)) * 100, 1), 'owner': kr.get('owner', ''), } for kr in key_results ], 'confidence': 7, # 1-10 scale 'quarter': f"Q{(datetime.now().month - 1) // 3 + 1} {datetime.now().year}", } ``` ## Dashboard Builder ```python class DashboardBuilder: """Build business dashboards with metrics from multiple sources.""" DASHBOARD_TEMPLATES = { 'executive': { 'title': 'Executive Dashboard', 'sections': [ { 'name': 'Revenue', 'metrics': ['MRR', 'ARR', 'LTV', 'CAC', 'Gross_Margin'], 'visuals': ['big_number', 'trend_line'], }, { 'name': 'Growth', 'metrics': ['New Customers', 'Churn Rate', 'Net Revenue Retention'], 'visuals': ['trend_line', 'bar_chart'], }, { 'name': 'Sales', 'metrics': ['Pipeline Value', 'Win Rate', 'Sales Cycle'], 'visuals': ['gauge', 'funnel'], }, ], }, 'marketing': { 'title': 'Marketing Dashboard', 'sections': [ { 'name': 'Traffic & Leads', 'metrics': ['Website Traffic', 'MQLs', 'SQLs', 'SQL to Revenue'], 'visuals': ['trend_line', 'funnel'], }, { 'name': 'Channel Performance', 'metrics': ['ROAS', 'CPL', 'CPA', 'Conversion Rate'], 'visuals': ['bar_chart'], }, ], }, 'product': { 'title': 'Product Dashboard', 'sections': [ { 'name': 'Engagement', 'metrics': ['DAU/MAU', 'Retention Rate', 'Session Duration'], 'visuals': ['trend_line', 'cohort'], }, { 'name': 'Health', 'metrics': ['NPS', 'CSAT', 'Churn', 'Feature Adoption'], 'visuals': ['gauge', 'bar_chart'], }, ], }, } @staticmethod def build_dashboard(template_name: str = 'executive') -> Dict: """Build a dashboard from a template.""" template = DashboardBuilder.DASHBOARD_TEMPLATES.get(template_name, DashboardBuilder.DASHBOARD_TEMPLATES['executive']) dashboard = { 'title': template['title'], 'last_updated': datetime.now().isoformat(), 'sections': [], } for section in template['sections']: metrics_data = [] for metric in section['metrics']: # Look up metric definition for dept, metrics in KPIEngine.METRICS_LIBRARY.items(): if metric in metrics: metrics_data.append({ 'name': metric, 'description': metrics[metric], 'current_value': None, 'previous_value': None, 'change_pct': None, 'status': 'pending', }) dashboard['sections'].append({ 'name': section['name'], 'metrics': metrics_data, 'visuals': section['visuals'], }) return dashboard ``` ## Metric Health Check ```python class MetricHealth: """Check metric health against targets and benchmarks.""" @staticmethod def check(metric_name: str, current_value: float, target_value: float, benchmark: float = None) -> Dict: """Evaluate metric health.""" pct_of_target = round(current_value / max(target_value, 1) * 100, 1) if target_value > 0: # Higher is better status = '✅ On track' if current_value >= target_value else '⚠️ Below target' else: # Lower is better (churn, cost) status = '✅ On track' if current_value <= abs(target_value) else '⚠️ Above target' result = { 'metric': metric_name, 'current': current_value, 'target': target_value, 'progress_pct': pct_of_target, 'status': status, } if benchmark: vs_benchmark = round((current_value - benchmark) / benchmark * 100, 1) result['benchmark'] = benchmark result['vs_benchmark_pct'] = vs_benchmark result['benchmark_status'] = 'Above' if vs_benchmark > 0 else 'Below' return result @staticmethod def health_score(metrics: List[Dict]) -> int: """Calculate composite business health score (0-100).""" if not metrics: return 0 on_track = sum(1 for m in metrics if m.get('status', '').startswith('✅')) return round(on_track / len(metrics) * 100) ``` ## Common Pitfalls 1. **Vanity metrics** — metrics that look good but don't drive decisions (page views, downloads); focus on actionable metrics 2. **Too many KPIs** — tracking 50 metrics dilutes focus; identify 5-7 "one metric that matters" 3. **Not benchmarking** — a metric in isolation means nothing; compare to industry, past periods, targets 4. **Confusing correlation with causation** — two metrics moving together doesn't mean one caused the other 5. **Manual reporting** — manually exported spreadsheets are slow and error-prone; automate dashboards 6. **Metric hoarding** — data without decisions is waste; each metric should have an associated action ## Verification Checklist - [ ] North Star metric identified (one metric that matters most) - [ ] 5-7 key KPIs defined per department - [ ] Leading and lagging indicators balanced - [ ] Targets set for each KPI (with benchmark data) - [ ] Dashboard built and automated (no manual reporting) - [ ] Monthly/quarterly review cadence established - [ ] OKRs defined and aligned with KPIs - [ ] Metric definitions documented (so everyone measures the same thing) - [ ] Action triggers defined (what happens when a metric goes red) ## See Also - crm-sales-pipeline — sales pipeline metrics - digital-marketing-strategy — marketing ROI metrics - website-analytics-tracking — website performance metrics - conversion-rate-optimization — conversion metrics