kpi-dashboard-design

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KPI Dashboard Design

KPI仪表盘设计

Comprehensive patterns for designing effective Key Performance Indicator (KPI) dashboards that drive business decisions.

用于设计可驱动业务决策的高效关键绩效指标（KPI）仪表盘的综合模式。

When to Use This Skill

何时使用此方法

Designing executive dashboards
Selecting meaningful KPIs
Building real-time monitoring displays
Creating department-specific metrics views
Improving existing dashboard layouts
Establishing metric governance

设计高管仪表盘
选择有意义的KPI指标
构建实时监控展示界面
创建部门专属指标视图
优化现有仪表盘布局
建立指标治理机制

Core Concepts

核心概念

1. KPI Framework

1. KPI框架

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

层级	关注重点	更新频率	受众
战略层	长期目标	每月/每季度	高管
战术层	部门目标	每周/每月	管理者
运营层	日常运营情况	实时/每日	执行团队

2. SMART KPIs

Specific: Clear definition
Measurable: Quantifiable
Achievable: Realistic targets
Relevant: Aligned to goals
Time-bound: Defined period

Specific：定义清晰
Measurable：可量化
Achievable：目标实际可行
Relevant：与业务目标对齐
Time-bound：有明确时间周期

3. Dashboard Hierarchy

3. 仪表盘层级结构

├── 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

├── 高管摘要（1页）
│   ├── 4-6个核心KPI指标
│   ├── 趋势指示器
│   └── 关键告警
├── 部门视图
│   ├── 销售仪表盘
│   ├── 营销仪表盘
│   ├── 运营仪表盘
│   └── 财务仪表盘
└── 详细钻取页
    ├── 单个指标详情
    └── 根因分析

Common KPIs by Department

各部门常见KPI指标

Sales KPIs

销售KPI

yaml

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

yaml

营收指标:
  - Monthly Recurring Revenue (MRR)
  - Annual Recurring Revenue (ARR)
  - Average Revenue Per User (ARPU)
  - 营收增长率

销售管道指标:
  - 销售管道总价值
  - 赢单率
  - 平均客单价
  - 销售周期长度

活动指标:
  - 每位销售的通话/邮件数量
  - 安排的演示次数
  - 发送的提案数量
  - 成交率

Marketing KPIs

营销KPI

yaml

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

yaml

获客指标:
  - Cost Per Acquisition (CPA)
  - Customer Acquisition Cost (CAC)
  - 线索数量
  - Marketing Qualified Leads (MQL)

用户参与指标:
  - 网站流量
  - 转化率
  - 邮件打开/点击率
  - 社交媒体互动量

投资回报率:
  - 营销ROI
  - 营销活动表现
  - 渠道归因
  - CAC回收期

Product KPIs

产品KPI

yaml

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

yaml

使用指标:
  - Daily/Monthly Active Users (DAU/MAU)
  - 会话时长
  - 功能 adoption率
  - 用户粘性（DAU/MAU）

质量指标:
  - Net Promoter Score (NPS)
  - Customer Satisfaction (CSAT)
  - 缺陷/问题数量
  - 问题解决时长

增长指标:
  - 用户增长率
  - 激活率
  - 留存率
  - 流失率

Finance KPIs

财务KPI

yaml

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

yaml

盈利能力:
  - 毛利率
  - 净利润率
  - EBITDA
  - 营业利润率

流动性:
  - 流动比率
  - 速动比率
  - 现金流
  - 营运资金

运营效率:
  - 人均营收
  - 运营费用率
  - 应收账款周转天数
  - 库存周转率

Dashboard Layout Patterns

仪表盘布局模式

Pattern 1: Executive Summary

模式1：高管摘要仪表盘

┌─────────────────────────────────────────────────────────────┐
│  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        │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│  高管仪表盘                        [时间范围 ▼]  │
├─────────────┬─────────────┬─────────────┬─────────────────┤
│   营收      │   利润      │  用户数量    │    NPS评分      │
│   $2.4M     │    $450K    │    12,450   │       72        │
│   ▲ 12%     │    ▲ 8%     │    ▲ 15%    │     ▲ 5分      │
├─────────────┴─────────────┴─────────────┴─────────────────┤
│                                                             │
│  营收趋势                    │  按产品划分营收               │
│  ┌───────────────────────┐       │  ┌──────────────────┐   │
│  │    /\    /\          │       │  │ ████████ 45%     │   │
│  │   /  \  /  \    /\   │       │  │ ██████   32%     │   │
│  │  /    \/    \  /  \  │       │  │ ████     18%     │   │
│  │ /            \/    \ │       │  │ ██        5%     │   │
│  └───────────────────────┘       │  └──────────────────┘   │
│                                                             │
├─────────────────────────────────────────────────────────────┤
│  🔴 告警：流失率超过阈值(>5%)              │
│  🟡 警告：支持工单量超出平均值20%        │
└─────────────────────────────────────────────────────────────┘

Pattern 2: SaaS Metrics Dashboard

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%                 │ │
│  └──────────┴──────────┴──────────┴──────────────────────┘ │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│  SaaS指标面板                     2024年1月  [月度 ▼]     │
├──────────────────────┬──────────────────────────────────────┤
│  ┌────────────────┐  │  MRR增长趋势                          │
│  │      MRR       │  │  ┌────────────────────────────────┐  │
│  │    $125,000    │  │  │                          /──   │  │
│  │     ▲ 8%       │  │  │                    /────/      │  │
│  └────────────────┘  │  │              /────/            │  │
│  ┌────────────────┐  │  │        /────/                  │  │
│  │      ARR       │  │  │   /────/                       │  │
│  │   $1,500,000   │  │  └────────────────────────────────┘  │
│  │     ▲ 15%      │  │  1  2  3  4  5  6  7  8  9 10 11 12月 │
│  └────────────────┘  │                                      │
├──────────────────────┼──────────────────────────────────────┤
│  单位经济效益      │  群组留存率                    │
│                      │                                      │
│  CAC:     $450       │  第1个月: ████████████████████ 100%  │
│  LTV:     $2,700     │  第3个月: █████████████████    85%   │
│  LTV/CAC: 6.0x       │  第6个月: ████████████████     80%   │
│                      │  第12个月: ██████████████      72%   │
│  回收期: 4个月   │                                      │
├──────────────────────┴──────────────────────────────────────┤
│  流失分析                                             │
│  ┌──────────┬──────────┬──────────┬──────────────────────┐ │
│  │ 总流失率 │ 净流失率 │ 客户流失率 │ 客户拓展率            │ │
│  │ 4.2%     │ 1.8%     │ 3.1%     │ 2.4%                 │ │
│  └──────────┴──────────┴──────────┴──────────────────────┘ │
└─────────────────────────────────────────────────────────────┘

Pattern 3: Real-time Operations

模式3：实时运营监控仪表盘

┌─────────────────────────────────────────────────────────────┐
│  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          │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│  运营监控中心                    实时 ● 最后更新: 10:42:15 │
├────────────────────────────┬────────────────────────────────┤
│  系统健康状态             │  服务状态                │
│  ┌──────────────────────┐  │                                │
│  │   CPU    内存    磁盘 │  │  ● API网关      健康    │
│  │   45%    72%    58%  │  │  ● 用户服务     健康    │
│  │   ███    ████   ███  │  │  ● 支付服务     性能下降   │
│  │   ███    ████   ███  │  │  ● 数据库         健康    │
│  │   ███    ████   ███  │  │  ● 缓存            健康    │
│  └──────────────────────┘  │                                │
├────────────────────────────┼────────────────────────────────┤
│  请求吞吐量        │  错误率                    │
│  ┌──────────────────────┐  │  ┌──────────────────────────┐  │
│  │ ▁▂▃▄▅▆▇█▇▆▅▄▃▂▁▂▃▄▅ │  │  │ ▁▁▁▁▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁  │  │
│  └──────────────────────┘  │  └──────────────────────────┘  │
│  当前值: 12,450 请求/秒     │  当前值: 0.02%                │
│  峰值: 18,200 请求/秒        │  阈值: 1.0%               │
├────────────────────────────┴────────────────────────────────┤
│  近期告警                                              │
│  10:40  🟡 支付服务延迟过高（p99 > 500ms）    │
│  10:35  🟢 已解决: 数据库连接池恢复正常     │
│  10:22  🔴 支付服务熔断机制触发          │
└─────────────────────────────────────────────────────────────┘

Implementation Patterns

实现模式

SQL for KPI Calculations

KPI计算SQL示例

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);

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 Dashboard Code (Streamlit)

Python仪表盘代码（Streamlit实现）

python

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")

python

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)

col1, col2 = st.columns(2)

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']}")

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Best Practices

最佳实践

Do's

建议做法

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

限制KPI数量在5-7个 - 聚焦核心指标
展示上下文信息 - 包含对比数据、趋势和目标值
使用一致的配色规则 - 红色代表异常，绿色代表正常
支持钻取功能 - 从摘要视图到详细数据
匹配更新频率 - 指标更新周期与业务需求一致

Don'ts

避免事项

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

不展示虚荣指标 - 聚焦可落地的行动数据
不过度拥挤布局 - 留白提升可读性
不使用3D图表 - 易造成数据感知偏差
不隐藏计算逻辑 - 文档化指标计算方法
不忽略移动端适配 - 确保响应式设计

kpi-dashboard-design

Original

Translation

KPI Dashboard Design

KPI仪表盘设计

When to Use This Skill

何时使用此方法

Core Concepts

核心概念

1. KPI Framework

1. KPI框架

2. SMART KPIs

2. SMART KPIs

3. Dashboard Hierarchy

3. 仪表盘层级结构

Common KPIs by Department

各部门常见KPI指标

Sales KPIs

销售KPI

Marketing KPIs

营销KPI

Product KPIs

产品KPI

Finance KPIs

财务KPI

Dashboard Layout Patterns

仪表盘布局模式

Pattern 1: Executive Summary

模式1：高管摘要仪表盘

Pattern 2: SaaS Metrics Dashboard

模式2：SaaS指标仪表盘

Pattern 3: Real-time Operations

模式3：实时运营监控仪表盘

Implementation Patterns

实现模式

SQL for KPI Calculations

KPI计算SQL示例

Python Dashboard Code (Streamlit)

Python仪表盘代码（Streamlit实现）

Header with date filter

Header with date filter

KPI Cards

KPI Cards

Charts

Charts

Cohort Heatmap

Cohort Heatmap

Alerts Section

Alerts Section

Best Practices

最佳实践

Do's

建议做法

Don'ts

避免事项

Resources

参考资源