Powerful SQL Analytics Made Simple
Turn complex data questions into clear insights with our AI-powered SQL analysis tool. Perfect for analysts, data scientists, and business intelligence professionals.
Analysis Categories
1. Time Series Analysis
WITH monthly_sales AS (
SELECT
DATE_FORMAT(sale_date, '%Y-%m') as month,
SUM(amount) as revenue,
COUNT(*) as transaction_count
FROM sales
WHERE sale_date >= DATE_SUB(CURRENT_DATE, INTERVAL 2 YEAR)
GROUP BY month
)
SELECT
month,
revenue,
transaction_count,
LAG(revenue) OVER (ORDER BY month) as prev_month_revenue,
ROUND(
((revenue - LAG(revenue) OVER (ORDER BY month)) /
LAG(revenue) OVER (ORDER BY month) * 100),
2
) as month_over_month_growth,
LAG(revenue, 12) OVER (ORDER BY month) as prev_year_revenue,
ROUND(
((revenue - LAG(revenue, 12) OVER (ORDER BY month)) /
LAG(revenue, 12) OVER (ORDER BY month) * 100),
2
) as year_over_year_growth
FROM monthly_sales
ORDER BY month DESC
2. Cohort Analysis
WITH cohort_data AS (
SELECT
DATE_FORMAT(first_purchase_date, '%Y-%m') as cohort_month,
customer_id,
TIMESTAMPDIFF(MONTH,
first_purchase_date,
subsequent_purchase_date
) as month_number
FROM (
SELECT
customer_id,
MIN(purchase_date) as first_purchase_date,
purchase_date as subsequent_purchase_date
FROM purchases
GROUP BY customer_id, purchase_date
) purchase_dates
),
cohort_sizes AS (
SELECT
cohort_month,
COUNT(DISTINCT customer_id) as cohort_size
FROM cohort_data
GROUP BY cohort_month
)
SELECT
cd.cohort_month,
cs.cohort_size,
cd.month_number,
COUNT(DISTINCT cd.customer_id) as active_customers,
ROUND(
COUNT(DISTINCT cd.customer_id) / cs.cohort_size * 100,
2
) as retention_rate
FROM cohort_data cd
JOIN cohort_sizes cs ON cd.cohort_month = cs.cohort_month
GROUP BY cd.cohort_month, cs.cohort_size, cd.month_number
ORDER BY cd.cohort_month,
3. Customer Segmentation
WITH customer_metrics AS (
SELECT
customer_id,
DATEDIFF(CURRENT_DATE, MAX(purchase_date)) as recency,
COUNT(*) as frequency,
AVG(amount) as avg_purchase,
SUM(amount) as total_spent
FROM purchases
WHERE purchase_date >= DATE_SUB(CURRENT_DATE, INTERVAL 1 YEAR)
GROUP BY customer_id
),
customer_segments AS (
SELECT
customer_id,
NTILE(5) OVER (ORDER BY recency DESC) as r_score,
NTILE(5) OVER (ORDER BY frequency) as f_score,
NTILE(5) OVER (ORDER BY total_spent) as m_score
FROM customer_metrics
)
SELECT
CASE
WHEN (r_score + f_score + m_score) >= 13 THEN 'Champions'
WHEN (r_score + f_score + m_score) >= 10 THEN 'Loyal'
WHEN (r_score + f_score + m_score) >= 7 THEN 'Regular'
WHEN (r_score + f_score + m_score) >= 4 THEN 'At Risk'
ELSE 'Lost'
END as customer_segment,
COUNT(*) as customer_count,
ROUND(AVG(cm.total_spent), 2) as avg_customer_value
FROM customer_segments cs
JOIN customer_metrics cm USING (customer_id)
GROUP BY customer_segment
ORDER BY avg_customer_value DESC
4. Product Analysis
WITH product_metrics AS (
SELECT
p.product_id,
p.product_name,
p.category,
COUNT(DISTINCT s.sale_id) as sale_count,
SUM(s.quantity) as units_sold,
SUM(s.quantity * s.price) as revenue,
COUNT(DISTINCT s.customer_id) as unique_customers
FROM products p
JOIN sales s ON p.product_id = s.product_id
WHERE s.sale_date >= DATE_SUB(CURRENT_DATE, INTERVAL 6 MONTH)
GROUP BY p.product_id, p.product_name, p.category
)
SELECT
product_name,
category,
sale_count,
units_sold,
revenue,
unique_customers,
ROUND(revenue / units_sold, 2) as avg_unit_price,
ROUND(units_sold / sale_count, 2) as avg_quantity_per_sale
FROM product_metrics
ORDER BY revenue DESC
Advanced Analytics Features
1. Predictive Analysis
WITH daily_sales AS (
SELECT
sale_date,
SUM(amount) as daily_revenue
FROM sales
WHERE sale_date >= DATE_SUB(CURRENT_DATE, INTERVAL 90 DAY)
GROUP BY sale_date
),
moving_averages AS (
SELECT
sale_date,
daily_revenue,
AVG(daily_revenue) OVER (
ORDER BY sale_date
ROWS BETWEEN 7 PRECEDING AND CURRENT ROW
) as week_moving_avg,
AVG(daily_revenue) OVER (
ORDER BY sale_date
ROWS BETWEEN 30 PRECEDING AND CURRENT ROW
) as month_moving_avg
FROM daily_sales
)
SELECT
sale_date,
daily_revenue,
week_moving_avg,
month_moving_avg,
ROUND(
(daily_revenue - week_moving_avg) / week_moving_avg * 100,
2
) as weekly_variance_pct
FROM moving_averages
ORDER BY sale_date DESC
2. Pattern Recognition
WITH purchase_patterns AS (
SELECT
customer_id,
EXTRACT(HOUR FROM purchase_time) as hour_of_day,
DAYNAME(purchase_date) as day_of_week,
COUNT(*) as purchase_count,
AVG(amount) as avg_purchase_amount
FROM purchases
WHERE purchase_date >= DATE_SUB(CURRENT_DATE, INTERVAL 3 MONTH)
GROUP BY
customer_id,
EXTRACT(HOUR FROM purchase_time),
DAYNAME(purchase_date)
)
SELECT
hour_of_day,
day_of_week,
COUNT(DISTINCT customer_id) as unique_customers,
SUM(purchase_count) as total_purchases,
ROUND(AVG(avg_purchase_amount), 2) as avg_transaction_value
FROM purchase_patterns
GROUP BY hour_of_day, day_of_week
ORDER BY total_purchases DESC
Visualization Integration
1. Chart Data Preparation
SELECT
DATE_FORMAT(date, '%Y-%m-%d') as date,
metric_name,
value
FROM metrics
WHERE date >= DATE_SUB(CURRENT_DATE, INTERVAL 30 DAY)
AND metric_name IN ('revenue', 'users', 'conversion_rate')
ORDER BY date,
2. Dashboard Metrics
SELECT
metric_name,
current_value,
previous_value,
ROUND(
((current_value - previous_value) / previous_value * 100),
2
) as change_percentage,
target_value,
ROUND(
(current_value / target_value * 100),
2
) as target_achievement
FROM (
SELECT
'Revenue' as metric_name,
SUM(CASE WHEN period = 'current' THEN value END) as current_value,
SUM(CASE WHEN period = 'previous' THEN value END) as previous_value,
MAX(target) as target_value
FROM kpi_data
WHERE metric_type = 'financial'
)
Best Practices
1. Data Preparation
Clean and validate data
Handle missing values
Standardize formats
Create analysis views
2. Performance Optimization
Use appropriate indexes
Optimize complex queries
Implement caching
Schedule heavy analysis
3. Analysis Workflow
Document assumptions
Version control queries
Test with sample data
Validate results
FAQs
Q: Can I export analysis results? A: Yes, export to CSV, Excel, or direct database connection.
Q: How often is data refreshed? A: Real-time analysis with configurable refresh intervals.
Getting Started
Connect your data source
Choose analysis type
Customize metrics
Generate insights
Export results