-- 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);
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']}")
