Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources
🚀 SQL Project Series #5
E-Commerce Sales Analysis – Advanced Business Analytics
In this part, we'll solve real-world business problems that Data Analysts encounter while working with customer, sales, and product data.
31. Calculate Customer Lifetime Value (CLV)
SELECT
o.customer_id,
SUM(oi.quantity * oi.unit_price) AS customer_lifetime_value
FROM orders o
JOIN order_items oi
ON o.order_id = oi.order_id
GROUP BY o.customer_id
ORDER BY customer_lifetime_value DESC;
32. Calculate Repeat Purchase Rate
WITH customer_orders AS (
SELECT
customer_id,
COUNT(*) AS total_orders
FROM orders
GROUP BY customer_id
)
SELECT
ROUND(
100.0 *
COUNT(CASE WHEN total_orders > 1 THEN 1 END) /
COUNT(*),
2
) AS repeat_purchase_rate
FROM customer_orders;
33. Find New vs Returning Customers
WITH first_order AS (
SELECT
customer_id,
MIN(order_date) AS first_order_date
FROM orders
GROUP BY customer_id
)
SELECT
CASE
WHEN o.order_date = f.first_order_date
THEN 'New Customer'
ELSE 'Returning Customer'
END AS customer_type,
COUNT(*) AS total_orders
FROM orders o
JOIN first_order f
ON o.customer_id = f.customer_id
GROUP BY customer_type;
34. Find Customer Retention by Month
WITH monthly_orders AS (
SELECT DISTINCT
customer_id,
DATE_TRUNC('month', order_date) AS order_month
FROM orders
)
SELECT
order_month,
COUNT(DISTINCT customer_id) AS active_customers
FROM monthly_orders
GROUP BY order_month
ORDER BY order_month;
35. Find Customers Who Purchased from Multiple Categories
SELECT
o.customer_id,
COUNT(DISTINCT p.category) AS categories_purchased
FROM orders o
JOIN order_items oi
ON o.order_id = oi.order_id
JOIN products p
ON oi.product_id = p.product_id
GROUP BY o.customer_id
HAVING COUNT(DISTINCT p.category) > 1;
36. Find the Most Frequently Purchased Product Pair
SELECT
oi1.product_id AS product₁,
oi2.product_id AS product₂,
COUNT(*) AS purchase_count
FROM order_items oi1
JOIN order_items oi2
ON oi1.order_id = oi2.order_id
AND oi1.product_id < oi2.product_id
GROUP BY oi1.product_id, oi2.product_id
ORDER BY purchase_count DESC
LIMIT 10;
37. Calculate Average Days Between Orders
WITH customer_orders AS (
SELECT
customer_id,
order_date,
LAG(order_date) OVER (
PARTITION BY customer_id
ORDER BY order_date
) AS previous_order
FROM orders
)
SELECT
customer_id,
ROUND(
AVG(order_date - previous_order),
2
) AS avg_days_between_orders
FROM customer_orders
WHERE previous_order IS NOT NULL
GROUP BY customer_id;
38. Find the Fastest Growing Product Category
WITH monthly_category_sales AS (
SELECT
DATE_TRUNC('month', o.order_date) AS month,
p.category,
SUM(oi.quantity * oi.unit_price) AS revenue
FROM orders o
JOIN order_items oi
ON o.order_id = oi.order_id
JOIN products p
ON oi.product_id = p.product_id
GROUP BY month, p.category
)
SELECT
month,
category,
revenue,
revenue -
LAG(revenue) OVER (
PARTITION BY category
ORDER BY month
) AS revenue_growth
FROM monthly_category_sales;
39. Identify Customers at Risk of Churn
SELECT
customer_id,
MAX(order_date) AS last_order_date
FROM orders
GROUP BY customer_id
HAVING MAX(order_date) <
CURRENT_DATE - INTERVAL '90 days';
40. Perform RFM Analysis
SELECT
customer_id,
CURRENT_DATE - MAX(order_date) AS recency,
COUNT(order_id) AS frequency,
SUM(oi.quantity * oi.unit_price) AS monetary
FROM orders o
JOIN order_items oi
ON o.order_id = oi.order_id
GROUP BY customer_id
ORDER BY monetary DESC;
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