kpi-dashboard-design

仓库: aj-geddes/useful-ai-prompts
安装量: 129
排名: #6669

安装

npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill kpi-dashboard-design

KPI Dashboard Design Comprehensive patterns for designing effective Key Performance Indicator (KPI) dashboards that drive business decisions. Do not use this skill when The task is unrelated to kpi dashboard design You need a different domain or tool outside this scope Instructions Clarify goals, constraints, and required inputs. Apply relevant best practices and validate outcomes. Provide actionable steps and verification. If detailed examples are required, open resources/implementation-playbook.md . Use this skill when Designing executive dashboards Selecting meaningful KPIs Building real-time monitoring displays Creating department-specific metrics views Improving existing dashboard layouts Establishing metric governance Core Concepts 1. KPI Framework Level Focus Update Frequency Audience Strategic Long-term goals Monthly/Quarterly Executives Tactical Department goals Weekly/Monthly Managers Operational Day-to-day Real-time/Daily Teams 2. SMART KPIs Specific: Clear definition Measurable: Quantifiable Achievable: Realistic targets Relevant: Aligned to goals Time-bound: Defined period 3. Dashboard Hierarchy ├── Executive Summary (1 page) │ ├── 4-6 headline KPIs │ ├── Trend indicators │ └── Key alerts ├── Department Views │ ├── Sales Dashboard │ ├── Marketing Dashboard │ ├── Operations Dashboard │ └── Finance Dashboard └── Detailed Drilldowns ├── Individual metrics └── Root cause analysis Common KPIs by Department Sales KPIs Revenue Metrics : - Monthly Recurring Revenue (MRR) - Annual Recurring Revenue (ARR) - Average Revenue Per User (ARPU) - Revenue Growth Rate Pipeline Metrics : - Sales Pipeline Value - Win Rate - Average Deal Size - Sales Cycle Length Activity Metrics : - Calls/Emails per Rep - Demos Scheduled - Proposals Sent - Close Rate Marketing KPIs Acquisition : - Cost Per Acquisition (CPA) - Customer Acquisition Cost (CAC) - Lead Volume - Marketing Qualified Leads (MQL) Engagement : - Website Traffic - Conversion Rate - Email Open/Click Rate - Social Engagement ROI : - Marketing ROI - Campaign Performance - Channel Attribution - CAC Payback Period Product KPIs Usage : - Daily/Monthly Active Users (DAU/MAU) - Session Duration - Feature Adoption Rate - Stickiness (DAU/MAU) Quality : - Net Promoter Score (NPS) - Customer Satisfaction (CSAT) - Bug/Issue Count - Time to Resolution Growth : - User Growth Rate - Activation Rate - Retention Rate - Churn Rate Finance KPIs Profitability : - Gross Margin - Net Profit Margin - EBITDA - Operating Margin Liquidity : - Current Ratio - Quick Ratio - Cash Flow - Working Capital Efficiency : - Revenue per Employee - Operating Expense Ratio - Days Sales Outstanding - Inventory Turnover Dashboard Layout Patterns Pattern 1: Executive Summary ┌─────────────────────────────────────────────────────────────┐ │ EXECUTIVE DASHBOARD [Date Range ▼] │ ├─────────────┬─────────────┬─────────────┬─────────────────┤ │ REVENUE │ PROFIT │ CUSTOMERS │ NPS SCORE │ │ $2.4M │ $450K │ 12,450 │ 72 │ │ ▲ 12% │ ▲ 8% │ ▲ 15% │ ▲ 5pts │ ├─────────────┴─────────────┴─────────────┴─────────────────┤ │ │ │ Revenue Trend │ Revenue by Product │ │ ┌───────────────────────┐ │ ┌──────────────────┐ │ │ │ /\ /\ │ │ │ ████████ 45% │ │ │ │ / \ / \ /\ │ │ │ ██████ 32% │ │ │ │ / \/ \ / \ │ │ │ ████ 18% │ │ │ │ / \/ \ │ │ │ ██ 5% │ │ │ └───────────────────────┘ │ └──────────────────┘ │ │ │ ├─────────────────────────────────────────────────────────────┤ │ 🔴 Alert: Churn rate exceeded threshold (>5%) │ │ 🟡 Warning: Support ticket volume 20% above average │ └─────────────────────────────────────────────────────────────┘ Pattern 2: SaaS Metrics Dashboard ┌─────────────────────────────────────────────────────────────┐ │ SAAS METRICS Jan 2024 [Monthly ▼] │ ├──────────────────────┬──────────────────────────────────────┤ │ ┌────────────────┐ │ MRR GROWTH │ │ │ MRR │ │ ┌────────────────────────────────┐ │ │ │ $125,000 │ │ │ /── │ │ │ │ ▲ 8% │ │ │ /────/ │ │ │ └────────────────┘ │ │ /────/ │ │ │ ┌────────────────┐ │ │ /────/ │ │ │ │ ARR │ │ │ /────/ │ │ │ │ $1,500,000 │ │ └────────────────────────────────┘ │ │ │ ▲ 15% │ │ J F M A M J J A S O N D │ │ └────────────────┘ │ │ ├──────────────────────┼──────────────────────────────────────┤ │ UNIT ECONOMICS │ COHORT RETENTION │ │ │ │ │ CAC: $450 │ Month 1: ████████████████████ 100% │ │ LTV: $2,700 │ Month 3: █████████████████ 85% │ │ LTV/CAC: 6.0x │ Month 6: ████████████████ 80% │ │ │ Month 12: ██████████████ 72% │ │ Payback: 4 months │ │ ├──────────────────────┴──────────────────────────────────────┤ │ CHURN ANALYSIS │ │ ┌──────────┬──────────┬──────────┬──────────────────────┐ │ │ │ Gross │ Net │ Logo │ Expansion │ │ │ │ 4.2% │ 1.8% │ 3.1% │ 2.4% │ │ │ └──────────┴──────────┴──────────┴──────────────────────┘ │ └─────────────────────────────────────────────────────────────┘ Pattern 3: Real-time Operations ┌─────────────────────────────────────────────────────────────┐ │ OPERATIONS CENTER Live ● Last: 10:42:15 │ ├────────────────────────────┬────────────────────────────────┤ │ SYSTEM HEALTH │ SERVICE STATUS │ │ ┌──────────────────────┐ │ │ │ │ CPU MEM DISK │ │ ● API Gateway Healthy │ │ │ 45% 72% 58% │ │ ● User Service Healthy │ │ │ ███ ████ ███ │ │ ● Payment Service Degraded │ │ │ ███ ████ ███ │ │ ● Database Healthy │ │ │ ███ ████ ███ │ │ ● Cache Healthy │ │ └──────────────────────┘ │ │ ├────────────────────────────┼────────────────────────────────┤ │ REQUEST THROUGHPUT │ ERROR RATE │ │ ┌──────────────────────┐ │ ┌──────────────────────────┐ │ │ │ ▁▂▃▄▅▆▇█▇▆▅▄▃▂▁▂▃▄▅ │ │ │ ▁▁▁▁▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁ │ │ │ └──────────────────────┘ │ └──────────────────────────┘ │ │ Current: 12,450 req/s │ Current: 0.02% │ │ Peak: 18,200 req/s │ Threshold: 1.0% │ ├────────────────────────────┴────────────────────────────────┤ │ RECENT ALERTS │ │ 10:40 🟡 High latency on payment-service (p99 > 500ms) │ │ 10:35 🟢 Resolved: Database connection pool recovered │ │ 10:22 🔴 Payment service circuit breaker tripped │ └─────────────────────────────────────────────────────────────┘ Implementation Patterns SQL for KPI Calculations -- Monthly Recurring Revenue (MRR) WITH mrr_calculation AS ( SELECT DATE_TRUNC ( 'month' , billing_date ) AS month , SUM ( CASE subscription_interval WHEN 'monthly' THEN amount WHEN 'yearly' THEN amount / 12 WHEN 'quarterly' THEN amount / 3 END ) AS mrr FROM subscriptions WHERE status = 'active' GROUP BY DATE_TRUNC ( 'month' , billing_date ) ) SELECT month , mrr , LAG ( mrr ) OVER ( ORDER BY month ) AS prev_mrr , ( mrr - LAG ( mrr ) OVER ( ORDER BY month ) ) / LAG ( mrr ) OVER ( ORDER BY month ) * 100 AS growth_pct FROM mrr_calculation ; -- Cohort Retention WITH cohorts AS ( SELECT user_id , DATE_TRUNC ( 'month' , created_at ) AS cohort_month FROM users ) , activity AS ( SELECT user_id , DATE_TRUNC ( 'month' , event_date ) AS activity_month FROM user_events WHERE event_type = 'active_session' ) SELECT c . cohort_month , EXTRACT ( MONTH FROM age ( a . activity_month , c . cohort_month ) ) AS months_since_signup , COUNT ( DISTINCT a . user_id ) AS active_users , COUNT ( DISTINCT a . user_id ) :: FLOAT / COUNT ( DISTINCT c . user_id ) * 100 AS retention_rate FROM cohorts c LEFT JOIN activity a ON c . user_id = a . user_id AND a . activity_month

= c . cohort_month GROUP BY c . cohort_month , EXTRACT ( MONTH FROM age ( a . activity_month , c . cohort_month ) ) ORDER BY c . cohort_month , months_since_signup ; -- Customer Acquisition Cost (CAC) SELECT DATE_TRUNC ( 'month' , acquired_date ) AS month , SUM ( marketing_spend ) / NULLIF ( COUNT ( new_customers ) , 0 ) AS cac , SUM ( marketing_spend ) AS total_spend , COUNT ( new_customers ) AS customers_acquired FROM ( SELECT DATE_TRUNC ( 'month' , u . created_at ) AS acquired_date , u . id AS new_customers , m . spend AS marketing_spend FROM users u JOIN marketing_spend m ON DATE_TRUNC ( 'month' , u . created_at ) = m . month WHERE u . source = 'marketing' ) acquisition GROUP BY DATE_TRUNC ( 'month' , acquired_date ) ; Python Dashboard Code (Streamlit) import streamlit as st import pandas as pd import plotly . express as px import plotly . graph_objects as go st . set_page_config ( page_title = "KPI Dashboard" , layout = "wide" )

Header with date filter

col1 , col2 = st . columns ( [ 3 , 1 ] ) with col1 : st . title ( "Executive Dashboard" ) with col2 : date_range = st . selectbox ( "Period" , [ "Last 7 Days" , "Last 30 Days" , "Last Quarter" , "YTD" ] )

KPI Cards

def metric_card ( label , value , delta , prefix = "" , suffix = "" ) : delta_color = "green" if delta

= 0 else "red" delta_arrow = "▲" if delta = 0 else "▼" st . metric ( label = label , value = f" { prefix } { value : ,.0f } { suffix } " , delta = f" { delta_arrow } { abs ( delta ) : .1f } %" ) col1 , col2 , col3 , col4 = st . columns ( 4 ) with col1 : metric_card ( "Revenue" , 2400000 , 12.5 , prefix = "$" ) with col2 : metric_card ( "Customers" , 12450 , 15.2 ) with col3 : metric_card ( "NPS Score" , 72 , 5.0 ) with col4 : metric_card ( "Churn Rate" , 4.2 , - 0.8 , suffix = "%" )

Charts

col1 , col2 = st . columns ( 2 ) with col1 : st . subheader ( "Revenue Trend" ) revenue_data = pd . DataFrame ( { 'Month' : pd . date_range ( '2024-01-01' , periods = 12 , freq = 'M' ) , 'Revenue' : [ 180000 , 195000 , 210000 , 225000 , 240000 , 255000 , 270000 , 285000 , 300000 , 315000 , 330000 , 345000 ] } ) fig = px . line ( revenue_data , x = 'Month' , y = 'Revenue' , line_shape = 'spline' , markers = True ) fig . update_layout ( height = 300 ) st . plotly_chart ( fig , use_container_width = True ) with col2 : st . subheader ( "Revenue by Product" ) product_data = pd . DataFrame ( { 'Product' : [ 'Enterprise' , 'Professional' , 'Starter' , 'Other' ] , 'Revenue' : [ 45 , 32 , 18 , 5 ] } ) fig = px . pie ( product_data , values = 'Revenue' , names = 'Product' , hole = 0.4 ) fig . update_layout ( height = 300 ) st . plotly_chart ( fig , use_container_width = True )

Cohort Heatmap

st . subheader ( "Cohort Retention" ) cohort_data = pd . DataFrame ( { 'Cohort' : [ 'Jan' , 'Feb' , 'Mar' , 'Apr' , 'May' ] , 'M0' : [ 100 , 100 , 100 , 100 , 100 ] , 'M1' : [ 85 , 87 , 84 , 86 , 88 ] , 'M2' : [ 78 , 80 , 76 , 79 , None ] , 'M3' : [ 72 , 74 , 70 , None , None ] , 'M4' : [ 68 , 70 , None , None , None ] , } ) fig = go . Figure ( data = go . Heatmap ( z = cohort_data . iloc [ : , 1 : ] . values , x = [ 'M0' , 'M1' , 'M2' , 'M3' , 'M4' ] , y = cohort_data [ 'Cohort' ] , colorscale = 'Blues' , text = cohort_data . iloc [ : , 1 : ] . values , texttemplate = '%{text}%' , textfont = { "size" : 12 } , ) ) fig . update_layout ( height = 250 ) st . plotly_chart ( fig , use_container_width = True )

Alerts Section

st . subheader ( "Alerts" ) alerts = [ { "level" : "error" , "message" : "Churn rate exceeded threshold (>5%)" } , { "level" : "warning" , "message" : "Support ticket volume 20% above average" } , ] for alert in alerts : if alert [ "level" ] == "error" : st . error ( f"🔴 { alert [ 'message' ] } " ) elif alert [ "level" ] == "warning" : st . warning ( f"🟡 { alert [ 'message' ] } " ) Best Practices Do's Limit to 5-7 KPIs - Focus on what matters Show context - Comparisons, trends, targets Use consistent colors - Red=bad, green=good Enable drilldown - From summary to detail Update appropriately - Match metric frequency Don'ts Don't show vanity metrics - Focus on actionable data Don't overcrowd - White space aids comprehension Don't use 3D charts - They distort perception Don't hide methodology - Document calculations Don't ignore mobile - Ensure responsive design Resources Stephen Few's Dashboard Design Edward Tufte's Principles Google Data Studio Gallery

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