kscz0000 KPI 仪表盘设计
设计高效关键绩效指标（KPI）仪表盘的全面模式，驱动业务决策。
不使用此技能的情况
- 任务与 KPI 仪表盘设计无关
- 需要此范围之外的其他领域或工具
使用说明
- 明确目标、约束和所需输入。
- 应用相关最佳实践并验证结果。
- 提供可操作的步骤和验证方法。
- 如需详细示例，请打开
resources/implementation-playbook.md。
使用此技能的情况
- 设计高管仪表盘
- 选择有意义的 KPI
- 构建实时监控面板
- 创建部门级指标视图
- 改进现有仪表盘布局
- 建立指标治理体系
核心概念
1. KPI 框架
| 层级 | 关注点 | 更新频率 | 受众 |
|---|---|---|---|
| 战略层 | 长期目标 | 月度/季度 | 高管 |
| 战术层 | 部门目标 | 周度/月度 | 管理者 |
| 运营层 | 日常运营 | 实时/每日 | 团队 |
2. SMART KPI
Specific: Clear definition
Measurable: Quantifiable
Achievable: Realistic targets
Relevant: Aligned to goals
Time-bound: Defined period
3. 仪表盘层级
├── 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
各部门常用 KPI
销售 KPI
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
市场 KPI
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
产品 KPI
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
财务 KPI
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
仪表盘布局模式
模式 1：高管摘要
┌─────────────────────────────────────────────────────────────┐
│ 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 │
└─────────────────────────────────────────────────────────────┘
模式 2：SaaS 指标仪表盘
┌─────────────────────────────────────────────────────────────┐
│ 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% │ │
│ └──────────┴──────────┴──────────┴──────────────────────┘ │
└─────────────────────────────────────────────────────────────┘
模式 3：实时运营
┌─────────────────────────────────────────────────────────────┐
│ 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 │
└─────────────────────────────────────────────────────────────┘
实现模式
KPI 计算的 SQL
-- 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 仪表盘代码（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']}")
最佳实践
应做
- 限制为 5-7 个 KPI — 聚焦关键指标
- 展示上下文 — 对比、趋势、目标值
- 使用一致的配色 — 红色=不良，绿色=良好
- 支持下钻 — 从摘要到明细
- 按需更新 — 匹配指标频率
不应做
- 不要展示虚荣指标 — 聚焦可操作的数据
- 不要过度拥挤 — 留白有助于理解
- 不要使用 3D 图表 — 它们会扭曲感知
- 不要隐藏计算方法 — 记录计算逻辑
- 不要忽视移动端 — 确保响应式设计
参考资料
局限性
- 仅在任务明确匹配上述范围时使用此技能。
- 不要将输出视为环境特定验证、测试或专家评审的替代方案。
- 如果缺少必要输入、权限、安全边界或成功标准，请停下来请求澄清。