微信扫一扫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
Use resources/metric-queries.sql for executable normalized-input examples. users contains one row per customer, events contains user/month observations, spend contains source spend rows, and monthly_revenue contains one reconciled amount per month. Represent months as an integer year × 12 + month − 1 so offsets do not wrap after December.
The queries keep the original cohort denominator, deduplicate activity before counting, aggregate spend before joining customers, and return NULL for undefined growth or acquisition cost. Missing cohort/month combinations need an explicit reporting calendar if zero-activity rows must be displayed. Normalize subscription intervals, currency, refunds and revenue recognition before producing monthly revenue; these examples do not define accounting policy.
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")
# Synthetic layout illustration; this is not a connected or filterable dashboard.
# Header with illustrative date selector
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='ME'),
'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
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.