---
name: kpi-dashboard-design
description: Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use this skill when building an executive SaaS metrics dashboard tracking MRR, churn, and LTV/CAC ratios; designing an operations center with live service health and request throughput; creating a cohort retention analysis view for a product team; or debugging a dashboard where metrics contradict each other due to inconsistent calculation methodology.
---

# KPI Dashboard Design

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

## When to Use This Skill

- 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

```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
```

### Marketing KPIs

```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
```

### Product KPIs

```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
```

### Finance KPIs

```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
```

## 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

```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
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

## Troubleshooting

### MRR shown on dashboard contradicts finance's number

The most common cause is inconsistent treatment of annual plans. Finance may prorate to a daily rate while the dashboard normalizes to monthly. Align on a single formula and document it directly on the dashboard card:

```sql
-- Explicit formula shown in tooltip / data dictionary
-- Annual plans: divide total contract value by 12
-- Quarterly plans: divide by 3
-- Monthly plans: use as-is
CASE subscription_interval
    WHEN 'monthly'   THEN amount
    WHEN 'quarterly' THEN amount / 3.0
    WHEN 'yearly'    THEN amount / 12.0
END AS normalized_mrr
```

### Dashboard shows green but product team reports users complaining

The dashboard likely tracks system uptime (a lagging indicator) but not user-facing quality metrics. Add customer-perceived metrics alongside infrastructure metrics:

| Infrastructure (green) | User-perceived (add these) |
|---|---|
| API uptime 99.9% | P95 page load time |
| Error rate 0.1% | Task completion rate |
| Queue depth normal | Support ticket volume |

### Retention cohort looks flat — no variation between cohorts

Check whether the cohort query is partitioning by signup month correctly. A common bug is using `created_at::date` instead of `DATE_TRUNC('month', created_at)`, which groups by day and produces cohorts too small to show trends:

```sql
-- Wrong: too granular, cohorts are too small
DATE_TRUNC('day', created_at) AS cohort_date

-- Correct: monthly cohorts
DATE_TRUNC('month', created_at) AS cohort_month
```

### Real-time dashboard hammers the database

A live dashboard refreshing every 10 seconds with complex cohort SQL will degrade production query performance. Separate OLAP workloads from OLTP by writing pre-aggregated metrics to a summary table via a scheduled job, and have the dashboard read from that:

```python
# Scheduled every 5 minutes via cron/Celery
def refresh_mrr_summary():
    conn.execute("""
        INSERT INTO kpi_snapshot (metric, value, snapshot_at)
        SELECT 'mrr', SUM(...), NOW()
        FROM subscriptions WHERE status = 'active'
        ON CONFLICT (metric) DO UPDATE SET value = EXCLUDED.value
    """)
```

### Alert thresholds fire constantly, team ignores them

Static thresholds set once and never reviewed cause alert fatigue. Use dynamic thresholds based on rolling averages so alerts fire only when the metric deviates significantly from its own baseline:

```python
# Alert if current value is > 2 standard deviations from 30-day rolling mean
def is_anomalous(current: float, history: list[float]) -> bool:
    mean = statistics.mean(history)
    stdev = statistics.stdev(history)
    return abs(current - mean) > 2 * stdev
```

## Related Skills

- `data-storytelling` - Turn dashboard findings into narratives that drive executive decisions
