SQL Programming Resources

📈 Total Customers  📈 Active Customers  📈 Active Accounts  📈 Total Deposits  📈 Total Withdrawals  📈 Net Transaction Value  📈 Average Transaction Value  📈 Transaction Volume  📈 Customer Average Balance  📈 Total Deposits by Branch  📈 Branch-wise Transaction Volume  📈 Customer Segment Analysis  📈 Premium Customer Contribution  📈 High-Value Customers  📈 Monthly Transaction Growth  📈 Account Growth  📈 Loan Portfolio Value  📈 Average Loan Amount  📈 Loan Distribution by Type  📈 Active vs Closed Loans  📈 Customer Loan Exposure  📈 Deposit-to-Withdrawal Ratio  📈 Transaction Success Rate  📈 Dormant Account Analysis  📈 Unusual Transaction Detection  📈 Customer Profitability  📈 Executive Banking Dashboard  💡 Example 1: Total Deposits  SELECT SUM(amount) AS total_deposits  FROM transactions  WHERE transaction_type = 'Deposit' AND transaction_status = 'Success';  💡 Example 2: Customer Transaction Summary  SELECT c.customer_id, c.customer_name, COUNT(t.transaction_id) AS total_transactions, SUM(t.amount) AS total_transaction_value  FROM customers c  JOIN accounts a ON c.customer_id = a.customer_id  JOIN transactions t ON a.account_id = t.account_id  WHERE t.transaction_status = 'Success'  GROUP BY c.customer_id, c.customer_name  ORDER BY total_transaction_value DESC;  💡 Example 3: Branch-wise Deposits  SELECT b.branch_name, SUM(t.amount) AS total_deposits  FROM branches b  JOIN accounts a ON b.branch_id = a.branch_id  JOIN transactions t ON a.account_id = t.account_id  WHERE t.transaction_type = 'Deposit' AND t.transaction_status = 'Success'  GROUP BY b.branch_name  ORDER BY total_deposits DESC;  💡 Example 4: Identify High-Value Customers  SELECT c.customer_id, c.customer_name, SUM(a.current_balance) AS total_balance  FROM customers c  JOIN accounts a ON c.customer_id = a.customer_id  GROUP BY c.customer_id, c.customer_name  HAVING SUM(a.current_balance) > 500000  ORDER BY total_balance DESC;  💡 Example 5: Monthly Transaction Trend  SELECT DATE_TRUNC('month', transaction_date) AS month, COUNT(*) AS transaction_count, SUM(amount) AS transaction_value  FROM transactions  WHERE transaction_status = 'Success'  GROUP BY DATE_TRUNC('month', transaction_date)  ORDER BY month;  💡 Example 6: Rank Customers by Balance  SELECT c.customer_name, SUM(a.current_balance) AS total_balance, DENSE_RANK() OVER (ORDER BY SUM(a.current_balance) DESC) AS balance_rank  FROM customers c  JOIN accounts a ON c.customer_id = a.customer_id  GROUP BY c.customer_id, c.customer_name;  💡 Example 7: Identify Potentially Unusual Transactions  SELECT account_id, transaction_id, transaction_date, amount  FROM transactions  WHERE amount > 100000 AND transaction_status = 'Success'  ORDER BY amount DESC;  🎯 Key Insights You Can Derive  🔹 Which branches generate the highest transaction volume?  🔹 Which customer segments hold the most deposits?  🔹 Who are the highest-value customers?  🔹 Which account types have the highest activity?  🔹 How are deposits and withdrawals trending?  🔹 Which customers have significant loan exposure?  🔹 Which transactions may require additional investigation?  💼 Double Tap ❤️ For More

🚀 SQL Project Series #36 Banking Customer & Transaction Analytics 🏦 Analyze customers, accounts, transactions, branches, and loan activity using SQL to understand customer behavior, transaction trends, account profitability, and banking operations. 🎯 Business Objectives ✅ Analyze customer activity ✅ Monitor account balances ✅ Track deposits and withdrawals ✅ Identify high-value customers ✅ Analyze branch performance ✅ Detect unusual transaction patterns ✅ Measure loan performance ✅ Build banking dashboards 📂 Step 1: Create Database CREATE DATABASE banking_analytics_db; USE banking_analytics_db; 📂 Step 2: Create Customers Table CREATE TABLE customers ( customer_id INT PRIMARY KEY, customer_name VARCHAR(100), city VARCHAR(50), customer_segment VARCHAR(30), signup_date DATE ); 📂 Step 3: Create Branches Table CREATE TABLE branches ( branch_id INT PRIMARY KEY, branch_name VARCHAR(100), city VARCHAR(50) ); 📂 Step 4: Create Accounts Table CREATE TABLE accounts ( account_id INT PRIMARY KEY, customer_id INT, branch_id INT, account_type VARCHAR(30), opening_date DATE, current_balance DECIMAL(15,2), account_status VARCHAR(20), FOREIGN KEY (customer_id) REFERENCES customers(customer_id), FOREIGN KEY (branch_id) REFERENCES branches(branch_id) ); 📂 Step 5: Create Transactions Table CREATE TABLE transactions ( transaction_id INT PRIMARY KEY, account_id INT, transaction_date DATETIME, transaction_type VARCHAR(30), amount DECIMAL(15,2), transaction_status VARCHAR(20), FOREIGN KEY (account_id) REFERENCES accounts(account_id) ); 📂 Step 6: Create Loans Table CREATE TABLE loans ( loan_id INT PRIMARY KEY, customer_id INT, loan_type VARCHAR(50), loan_amount DECIMAL(15,2), interest_rate DECIMAL(5,2), loan_status VARCHAR(20), FOREIGN KEY (customer_id) REFERENCES customers(customer_id) ); 📂 Step 7: Insert Sample Customers INSERT INTO customers VALUES (1,'Rahul Sharma','Mumbai','Premium','2022-01-10'), (2,'Priya Verma','Delhi','Mass Affluent','2022-04-15'), (3,'Amit Patel','Pune','Premium','2023-02-20'), (4,'Sneha Joshi','Bangalore','Mass Market','2023-06-05'), (5,'Rohan Gupta','Hyderabad','Premium','2024-01-18'); 📂 Step 8: Insert Sample Branches INSERT INTO branches VALUES (101,'Mumbai Central','Mumbai'), (102,'Connaught Place','Delhi'), (103,'Pune Central','Pune'), (104,'Bangalore Main','Bangalore'); 📂 Step 9: Insert Sample Accounts INSERT INTO accounts VALUES (1001,1,101,'Savings','2022-01-10',250000,'Active'), (1002,2,102,'Current','2022-04-15',520000,'Active'), (1003,3,103,'Savings','2023-02-20',175000,'Active'), (1004,4,104,'Savings','2023-06-05',85000,'Active'), (1005,5,101,'Current','2024-01-18',750000,'Active'); 📂 Step 10: Insert Sample Transactions INSERT INTO transactions VALUES (5001,1001,'2025-01-05 10:15:00','Deposit',50000,'Success'), (5002,1001,'2025-01-07 14:30:00','Withdrawal',15000,'Success'), (5003,1002,'2025-01-08 11:20:00','Deposit',120000,'Success'), (5004,1003,'2025-01-10 09:45:00','Withdrawal',25000,'Success'), (5005,1004,'2025-01-12 16:10:00','Deposit',30000,'Success'), (5006,1005,'2025-01-15 13:25:00','Withdrawal',85000,'Success'), (5007,1003,'2025-01-18 18:40:00','Transfer',45000,'Success'); 📂 Step 11: Insert Sample Loans INSERT INTO loans VALUES (9001,1,'Home Loan',5000000,8.25,'Active'), (9002,2,'Personal Loan',800000,11.50,'Active'), (9003,3,'Car Loan',1200000,9.10,'Active'), (9004,4,'Personal Loan',500000,12.00,'Closed'), (9005,5,'Business Loan',3000000,10.25,'Active'); 🧠 SQL Concepts You'll Practice ✔ INNER JOIN ✔ LEFT JOIN ✔ GROUP BY ✔ HAVING ✔ CASE WHEN ✔ CTEs ✔ Subqueries ✔ Window Functions ✔ RANK() ✔ DENSE_RANK() ✔ LAG() ✔ Date Functions ✔ Conditional Aggregation ✔ Financial KPI Calculations 📊 Business KPIs You Can Build

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SELECT d.department_name, COUNT(e.employee_id) AS employee_count FROM departments d LEFT JOIN employees e ON d.department_id = e.department_id GROUP BY d.department_name ORDER BY employee_count DESC; 💡 Example 2: Average Salary by Department SELECT d.department_name, ROUND(AVG(e.salary), 2) AS average_salary FROM employees e JOIN departments d ON e.department_id = d.department_id GROUP BY d.department_name ORDER BY average_salary DESC; 💡 Example 3: Identify Highest-Paid Employees SELECT employee_name, job_title, salary FROM employees ORDER BY salary DESC LIMIT 10; 💡 Example 4: Calculate Attrition Rate SELECT ROUND(100.0 * SUM(CASE WHEN employment_status = 'Resigned' THEN 1 ELSE 0 END) / COUNT(*), 2) AS attrition_rate FROM employees; 💡 Example 5: Calculate Attendance Rate SELECT employee_id, ROUND(100.0 * SUM(CASE WHEN attendance_status = 'Present' THEN 1 ELSE 0 END) / COUNT(*), 2) AS attendance_rate FROM attendance GROUP BY employee_id; 💡 Example 6: Rank Employees by Salary SELECT employee_name, department_id, salary, DENSE_RANK() OVER (PARTITION BY department_id ORDER BY salary DESC) AS salary_rank FROM employees; 💡 Example 7: Find Employees Who Were Promoted SELECT e.employee_name, p.old_job_title, p.new_job_title, p.promotion_date FROM employees e JOIN promotions p ON e.employee_id = p.employee_id ORDER BY p.promotion_date; 🎯 Key Insights You Can Derive 🔹 Which departments have the highest headcount? 🔹 Which departments have the highest attrition? 🔹 Which roles have the highest salaries? 🔹 Which employees have been promoted? 🔹 What is the average employee tenure? 🔹 Which departments have attendance problems? 🔹 How quickly is the organization growing? 🔹 Where are the biggest employee retention challenges? 💼 Double Tap ❤️ For More

🚀 SQL Project Series #35 HR & Employee Analytics 👥 Analyze employees, departments, salaries, attendance, promotions, and attrition using SQL to understand workforce trends and improve HR decision-making. 🎯 Business Objectives ✅ Analyze employee headcount ✅ Track employee attrition ✅ Analyze salary distribution ✅ Measure department performance ✅ Identify high-performing employees ✅ Analyze promotions and tenure ✅ Track attendance ✅ Build HR analytics dashboards 📂 Step 1: Create Database CREATE DATABASE hr_analytics_db; USE hr_analytics_db; 📂 Step 2: Create Departments Table CREATE TABLE departments ( department_id INT PRIMARY KEY, department_name VARCHAR(100), location VARCHAR(50) ); 📂 Step 3: Create Employees Table CREATE TABLE employees ( employee_id INT PRIMARY KEY, employee_name VARCHAR(100), department_id INT, job_title VARCHAR(100), hire_date DATE, salary DECIMAL(12,2), employment_status VARCHAR(20), FOREIGN KEY (department_id) REFERENCES departments(department_id) ); 📂 Step 4: Create Attendance Table CREATE TABLE attendance ( attendance_id INT PRIMARY KEY, employee_id INT, attendance_date DATE, attendance_status VARCHAR(20), FOREIGN KEY (employee_id) REFERENCES employees(employee_id) ); 📂 Step 5: Create Promotions Table CREATE TABLE promotions ( promotion_id INT PRIMARY KEY, employee_id INT, promotion_date DATE, old_job_title VARCHAR(100), new_job_title VARCHAR(100), FOREIGN KEY (employee_id) REFERENCES employees(employee_id) ); 📂 Step 6: Insert Sample Departments INSERT INTO departments VALUES (101,'Data Analytics','Mumbai'), (102,'Finance','Delhi'), (103,'Human Resources','Bangalore'), (104,'Technology','Pune'), (105,'Operations','Hyderabad'); 📂 Step 7: Insert Sample Employees INSERT INTO employees VALUES (1,'Rahul Sharma',101,'Data Analyst','2022-01-10',850000,'Active'), (2,'Priya Verma',102,'Financial Analyst','2021-05-15',920000,'Active'), (3,'Amit Patel',104,'Software Engineer','2020-08-20',1200000,'Active'), (4,'Sneha Joshi',103,'HR Specialist','2023-02-10',650000,'Active'), (5,'Rohan Gupta',105,'Operations Analyst','2022-11-05',750000,'Resigned'); 📂 Step 8: Insert Sample Attendance INSERT INTO attendance VALUES (1,1,'2025-01-02','Present'), (2,1,'2025-01-03','Present'), (3,2,'2025-01-02','Present'), (4,2,'2025-01-03','Absent'), (5,3,'2025-01-02','Present'), (6,4,'2025-01-02','Present'), (7,5,'2025-01-02','Absent'); 📂 Step 9: Insert Sample Promotions INSERT INTO promotions VALUES (1,1,'2024-06-01','Junior Data Analyst','Data Analyst'), (2,3,'2024-01-15','Software Engineer','Senior Software Engineer'); 🧠 SQL Concepts You'll Practice ✔ INNER JOIN ✔ LEFT JOIN ✔ GROUP BY ✔ HAVING ✔ CASE WHEN ✔ Aggregate Functions ✔ CTEs ✔ Subqueries ✔ Window Functions ✔ RANK() ✔ DENSE_RANK() ✔ LAG() ✔ Date Functions ✔ Conditional Aggregation 📊 Business KPIs You Can Build 📈 Total Employees 📈 Active Employees 📈 Employee Attrition Rate 📈 Monthly Hiring Rate 📈 Monthly Attrition Rate 📈 Employee Retention Rate 📈 Average Salary 📈 Median Salary 📈 Salary by Department 📈 Salary by Job Title 📈 Gender Distribution 📈 Employee Tenure 📈 Average Tenure 📈 Promotion Rate 📈 Employees Promoted 📈 Attendance Rate 📈 Absenteeism Rate 📈 Department Headcount 📈 Department Attrition Rate 📈 Highest Paid Employees 📈 Salary Distribution 📈 Hiring Trend 📈 Employee Growth 📈 Executive HR Dashboard 💡 Example 1: Department-wise Employee Count

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🚀 SQL Project Series #34 Food & Grocery Delivery Analytics 🛒 Analyze customers, stores, products, orders, deliveries, and payments to understand sales performance, customer behavior, delivery efficiency, and operational costs. 🎯 Business Objectives ✅ Analyze order and revenue trends ✅ Identify top-selling products ✅ Measure customer retention ✅ Analyze store performance ✅ Track delivery efficiency ✅ Identify peak ordering periods ✅ Monitor cancellations and refunds ✅ Optimize product and store performance 📂 Database Setup CREATE DATABASE grocery_delivery_db; USE grocery_delivery_db; Tables Created: customers → customer_id, customer_name, city, signup_date stores → store_id, store_name, city, store_type products → product_id, product_name, category, price orders → order_id, customer_id, store_id, order_date, order_status, delivery_time_minutes, delivery_fee order_items → order_item_id, order_id, product_id, quantity, unit_price Sample data for 5 customers, 4 stores, 5 products, 5 orders already included. 🧠 SQL Concepts You'll Practice ✔ INNER JOIN, LEFT JOIN ✔ Aggregate Functions, GROUP BY, HAVING ✔ CASE WHEN, CTEs, Subqueries ✔ Window Functions ✔ Date & Time Functions ✔ Conditional Aggregation 📊 Business KPIs You Can Build 📈 Total Orders, Completed Orders, Cancelled Orders, Cancellation Rate 📈 Total Revenue, AOV, Average Basket Size, Items Sold 📈 Revenue by Category, Store, City 📈 Top-Selling / Low-Selling Products 📈 Customer Lifetime Value, Repeat Purchase Rate, Retention Rate 📈 Average Delivery Time, On-Time Delivery Rate 📈 Peak Ordering Hour, Peak Ordering Day, Monthly Revenue Growth 📈 Delivery Fee Revenue, Customer Acquisition Trend 📈 Executive Grocery Delivery Dashboard 💡 Example Queries 1. Total Revenue SELECT SUM(oi.quantity * oi.unit_price) AS total_revenue FROM orders o JOIN order_items oi ON o.order_id = oi.order_id WHERE o.order_status = 'Delivered'; 2. Top-Selling Products SELECT p.product_name, SUM(oi.quantity) AS units_sold FROM products p JOIN order_items oi ON p.product_id = oi.product_id JOIN orders o ON oi.order_id = o.order_id WHERE o.order_status = 'Delivered' GROUP BY p.product_name ORDER BY units_sold DESC LIMIT 10; 3. Average Order Value WITH order_values AS ( SELECT o.order_id, SUM(oi.quantity * oi.unit_price) AS order_value FROM orders o JOIN order_items oi ON o.order_id = oi.order_id WHERE o.order_status = 'Delivered' GROUP BY o.order_id ) SELECT ROUND(AVG(order_value), 2) AS average_order_value FROM order_values; 4. Repeat Customers SELECT customer_id, COUNT(order_id) AS total_orders FROM orders WHERE order_status = 'Delivered' GROUP BY customer_id HAVING COUNT(order_id) > 1; 5. Revenue by Store SELECT s.store_name, SUM(oi.quantity * oi.unit_price) AS revenue FROM stores s JOIN orders o ON s.store_id = o.store_id JOIN order_items oi ON o.order_id = oi.order_id WHERE o.order_status = 'Delivered' GROUP BY s.store_name ORDER BY revenue DESC; 6. Cancellation Rate SELECT ROUND(100.0 * SUM(CASE WHEN order_status = 'Cancelled' THEN 1 ELSE 0 END) / COUNT(*), 2) AS cancellation_rate FROM orders; 7. Peak Ordering Hours SELECT EXTRACT(HOUR FROM order_date) AS order_hour, COUNT(*) AS total_orders FROM orders WHERE order_status = 'Delivered' GROUP BY EXTRACT(HOUR FROM order_date) ORDER BY total_orders DESC;

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WITH ranked_videos AS (     SELECT         video_title,         category,         views,         ROW_NUMBER() OVER (             PARTITION BY category             ORDER BY views DESC         ) AS rn     FROM videos ) SELECT     video_title,     category,     views FROM ranked_videos WHERE rn = 1; 💡 Example 4: Calculate Channel Performance  SELECT     c.channel_name,     COUNT(v.video_id) AS total_videos,     SUM(v.views) AS total_views,     SUM(v.likes) AS total_likes,     SUM(v.comments) AS total_comments FROM channels c LEFT JOIN videos v     ON c.channel_id = v.channel_id GROUP BY c.channel_name ORDER BY total_views DESC; 💡 Example 5: Analyze Publishing Performance  SELECT     EXTRACT(DOW FROM publish_date) AS day_of_week,     COUNT(*) AS total_videos,     ROUND(AVG(views), 0) AS avg_views FROM videos GROUP BY EXTRACT(DOW FROM publish_date) ORDER BY avg_views DESC; 💡 Example 6: Rank Videos by Views  SELECT     video_title,     views,     DENSE_RANK() OVER (         ORDER BY views DESC     ) AS view_rank FROM videos; 🎯 Key Insights You Can Derive 🔹 Which videos generate the most views?  🔹 Which content categories perform best?  🔹 Which videos have high engagement but relatively low views?  🔹 What publishing days generate the most views?  🔹 Which channels have the strongest audience engagement?  🔹 Which content contributes most to subscriber growth?  🔹 Which videos should be promoted further?  💼 This project is especially useful for Data Analysts, Product Analysts, Growth Analysts, Marketing Analysts, and Business Intelligence professionals working with content, media, and digital platforms. Double Tap ❤️ For More

🚀 SQL Project Series #33 YouTube Channel Analytics 📺 Analyze videos, creators, views, watch time, engagement, and subscriber growth using SQL to understand content performance and audience behavior. 🎯 Business Objectives ✅ Analyze video performance ✅ Track subscriber growth ✅ Measure audience engagement ✅ Identify top-performing content ✅ Compare video categories ✅ Analyze watch time ✅ Identify high-performing creators ✅ Discover the best publishing times 📂 Step 1: Create Database CREATE DATABASE youtube_analytics_db; USE youtube_analytics_db; 📂 Step 2: Create Channels Table CREATE TABLE channels ( channel_id INT PRIMARY KEY, channel_name VARCHAR(100), category VARCHAR(50), country VARCHAR(50), created_date DATE ); 📂 Step 3: Create Videos Table CREATE TABLE videos ( video_id INT PRIMARY KEY, channel_id INT, video_title VARCHAR(200), category VARCHAR(50), publish_date DATETIME, duration_minutes DECIMAL(6,2), views BIGINT, likes INT, comments INT, shares INT, FOREIGN KEY (channel_id) REFERENCES channels(channel_id) ); 📂 Step 4: Create Subscribers Table CREATE TABLE subscribers ( subscriber_id INT PRIMARY KEY, channel_id INT, subscribe_date DATE, unsubscribe_date DATE, FOREIGN KEY (channel_id) REFERENCES channels(channel_id) ); 📂 Step 5: Insert Sample Channels INSERT INTO channels VALUES (1,'Data Simplifier','Education','India','2023-01-10'), (2,'Tech World','Technology','India','2022-08-15'), (3,'Finance Explained','Finance','USA','2021-05-20'), (4,'Travel Diaries','Travel','India','2023-04-12'); 📂 Step 6: Insert Sample Videos INSERT INTO videos VALUES (101,1,'SQL Interview Questions','Education','2025-01-05 10:00:00',12,15000,900,120,80), (102,1,'Power BI Dashboard Tutorial','Education','2025-01-08 18:00:00',18,22000,1400,180,150), (103,2,'Best AI Tools','Technology','2025-01-10 12:00:00',10,35000,2800,310,420), (104,3,'How to Invest','Finance','2025-01-12 09:00:00',15,28000,2100,250,300), (105,4,'Top Places in India','Travel','2025-01-15 20:00:00',14,19000,1300,170,200); 📂 Step 7: Insert Sample Subscribers INSERT INTO subscribers VALUES (1001,1,'2025-01-01',NULL), (1002,1,'2025-01-03',NULL), (1003,1,'2025-01-05','2025-03-01'), (1004,2,'2025-01-02',NULL), (1005,3,'2025-01-04',NULL), (1006,4,'2025-01-10',NULL); 🧠 SQL Concepts You'll Practice ✔ Joins ✔ GROUP BY ✔ HAVING ✔ CASE WHEN ✔ CTEs ✔ Subqueries ✔ Window Functions ✔ Ranking ✔ Date & Time Functions ✔ Conditional Aggregation 📊 Business KPIs You Can Build 📈 Total Channels 📈 Total Videos 📈 Total Views 📈 Total Likes 📈 Total Comments 📈 Total Shares 📈 Total Subscribers 📈 Subscriber Growth Rate 📈 Subscriber Churn Rate 📈 Average Views per Video 📈 Average Likes per Video 📈 Average Comments per Video 📈 Engagement Rate 📈 Like-to-View Ratio 📈 Comment-to-View Ratio 📈 Share-to-View Ratio 📈 Watch Time 📈 Average Video Duration 📈 Top 10 Videos by Views 📈 Top Videos by Engagement 📈 Top Performing Categories 📈 Channel-wise Performance 📈 Views by Publishing Day 📈 Views by Publishing Hour 📈 Monthly Views Growth 📈 Subscriber Growth by Month 📈 Content Performance Dashboard 💡 Example 1: Find Top 5 Videos by Views SELECT video_title, views FROM videos ORDER BY views DESC LIMIT 5; 💡 Example 2: Calculate Engagement Rate SELECT video_title, views, likes, comments, shares, ROUND( 100.0 * (likes + comments + shares) / NULLIF(views, 0), 2 ) AS engagement_rate FROM videos ORDER BY engagement_rate DESC; 💡 Example 3: Find Top Video in Each Category

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🧠 SQL Concepts You'll Practice ✔ Joins ✔ GROUP BY ✔ HAVING ✔ CASE WHEN ✔ CTEs ✔ Subqueries ✔ Window Functions ✔ LAG() ✔ LEAD() ✔ Date Functions ✔ Conditional Aggregation 📊 Business KPIs You Can Build 📈 Monthly Recurring Revenue (MRR) 📈 Annual Recurring Revenue (ARR) 📈 Average Revenue Per User (ARPU) 📈 Customer Lifetime Value (CLV) 📈 Monthly Customer Churn Rate 📈 Revenue Churn Rate 📈 Customer Retention Rate 📈 New Customers, New MRR 📈 Expansion MRR, Contraction MRR, Churned MRR 📈 Net Revenue Retention (NRR), Gross Revenue Retention (GRR) 📈 Upgrade Rate, Downgrade Rate 📈 Plan-wise Revenue, Active Subscriptions, Cancelled Subscriptions 📈 Payment Success Rate, Failed Payment Rate 📈 Monthly Revenue Growth, Revenue Contribution by Plan 📈 Customer Segmentation 💡 Example 1: Calculate MRR SELECT     SUM(p.monthly_price) AS mrr FROM subscriptions s JOIN plans p ON s.plan_id = p.plan_id WHERE s.subscription_status = 'Active'; 💡 Example 2: Revenue by Plan SELECT     p.plan_name,     COUNT(s.subscription_id) AS active_subscriptions,     SUM(p.monthly_price) AS monthly_revenue FROM subscriptions s JOIN plans p ON s.plan_id = p.plan_id WHERE s.subscription_status = 'Active' GROUP BY p.plan_name ORDER BY monthly_revenue DESC; 💡 Example 3: Identify Churned Customers SELECT     customer_id,     subscription_id,     end_date FROM subscriptions WHERE subscription_status = 'Cancelled'; 💡 Example 4: Calculate Upgrade vs Downgrade Count SELECT     change_type,     COUNT(*) AS total_changes FROM subscription_changes GROUP BY change_type; 💡 Example 5: Calculate Average Revenue Per Customer SELECT     ROUND(SUM(p.monthly_price) / COUNT(DISTINCT s.customer_id), 2) AS arpu FROM subscriptions s JOIN plans p ON s.plan_id = p.plan_id WHERE s.subscription_status = 'Active'; 💡 Example 6: Rank Customers by MRR WITH customer_mrr AS (     SELECT         s.customer_id,         SUM(p.monthly_price) AS mrr     FROM subscriptions s     JOIN plans p ON s.plan_id = p.plan_id     WHERE s.subscription_status = 'Active'     GROUP BY s.customer_id ) SELECT     customer_id,     mrr,     DENSE_RANK() OVER (ORDER BY mrr DESC) AS revenue_rank FROM customer_mrr; 🎯 Key Insights You Can Derive 🔹 Which subscription plan generates the most revenue? 🔹 Which plan has the highest churn? 🔹 How much MRR comes from new customers? 🔹 How much revenue is lost through cancellations? 🔹 Which customers have upgraded or downgraded? 🔹 Is revenue growing month over month? 🔹 Which customers contribute the most recurring revenue? 💼 This project is especially useful for Data Analysts, Product Analysts, Revenue Analysts, Growth Analysts, and Business Intelligence professionals working with SaaS and subscription-based businesses. Double Tap ❤️ For More

🚀 SQL Project Series #32 SaaS Subscription & Revenue Analytics 💻💰 Analyze subscriptions, plans, payments, upgrades, downgrades, and churn to understand how a SaaS business grows revenue and retains customers. 🎯 Business Objectives ✅ Analyze subscription growth ✅ Calculate MRR and ARR ✅ Track upgrades and downgrades ✅ Measure customer churn ✅ Analyze revenue by plan ✅ Calculate ARPU and CLV ✅ Identify high-value customers ✅ Measure monthly revenue growth 📂 Step 1: Create Database CREATE DATABASE saas_revenue_db; USE saas_revenue_db; 📂 Step 2: Create Customers Table CREATE TABLE customers ( customer_id INT PRIMARY KEY, customer_name VARCHAR(100), company_name VARCHAR(100), signup_date DATE, country VARCHAR(50) ); 📂 Step 3: Create Plans Table CREATE TABLE plans ( plan_id INT PRIMARY KEY, plan_name VARCHAR(50), monthly_price DECIMAL(10,2) ); 📂 Step 4: Create Subscriptions Table CREATE TABLE subscriptions ( subscription_id INT PRIMARY KEY, customer_id INT, plan_id INT, start_date DATE, end_date DATE, subscription_status VARCHAR(20), FOREIGN KEY (customer_id) REFERENCES customers(customer_id), FOREIGN KEY (plan_id) REFERENCES plans(plan_id) ); 📂 Step 5: Create Subscription Changes Table CREATE TABLE subscription_changes ( change_id INT PRIMARY KEY, subscription_id INT, change_date DATE, old_plan_id INT, new_plan_id INT, change_type VARCHAR(20), FOREIGN KEY (subscription_id) REFERENCES subscriptions(subscription_id), FOREIGN KEY (old_plan_id) REFERENCES plans(plan_id), FOREIGN KEY (new_plan_id) REFERENCES plans(plan_id) ); 📂 Step 6: Create Payments Table CREATE TABLE payments ( payment_id INT PRIMARY KEY, subscription_id INT, payment_date DATE, amount DECIMAL(10,2), payment_status VARCHAR(20), FOREIGN KEY (subscription_id) REFERENCES subscriptions(subscription_id) ); 📂 Step 7: Insert Sample Customers INSERT INTO customers VALUES (1,'Rahul Sharma','DataPro','2025-01-05','India'), (2,'Priya Verma','TechLabs','2025-01-10','India'), (3,'Amit Patel','FinTech Solutions','2025-01-15','India'), (4,'Sneha Joshi','CloudWorks','2025-02-01','India'), (5,'Rohan Gupta','Analytics Hub','2025-02-10','India'); 📂 Step 8: Insert Sample Plans INSERT INTO plans VALUES (101,'Basic',499), (102,'Professional',999), (103,'Business',2499), (104,'Enterprise',4999); 📂 Step 9: Insert Sample Subscriptions INSERT INTO subscriptions VALUES (1001,1,102,'2025-01-05',NULL,'Active'), (1002,2,101,'2025-01-10','2025-04-10','Cancelled'), (1003,3,104,'2025-01-15',NULL,'Active'), (1004,4,103,'2025-02-01',NULL,'Active'), (1005,5,102,'2025-02-10',NULL,'Active'); 📂 Step 10: Insert Sample Subscription Changes INSERT INTO subscription_changes VALUES (1,1001,'2025-03-01',101,102,'Upgrade'), (2,1004,'2025-04-01',103,104,'Upgrade'), (3,1005,'2025-05-01',102,101,'Downgrade'); 📂 Step 11: Insert Sample Payments INSERT INTO payments VALUES (501,1001,'2025-02-01',999,'Paid'), (502,1002,'2025-02-01',499,'Paid'), (503,1003,'2025-02-01',4999,'Paid'), (504,1004,'2025-02-01',2499,'Paid'), (505,1005,'2025-02-01',999,'Paid');

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🎯 Key Insights You Can Derive 🔹 Which suppliers contribute the most to procurement spending? 🔹 Which suppliers frequently deliver late? 🔹 Which products have the highest price variance? 🔹 Which suppliers offer the most competitive prices? 🔹 Which products are purchased most frequently? 🔹 Where can procurement costs be reduced? 🔹 Which suppliers may represent supply-chain risk?  This project is especially useful for Data Analysts, Procurement Analysts, Supply Chain Analysts, Operations Analysts, and Business Intelligence professionals. Double Tap ❤️ For More

🚀 SQL Project Series #31: Supply Chain & Procurement Analytics 🚚 Analyze suppliers, purchase orders, deliveries, procurement costs, and supplier performance using SQL to identify cost-saving opportunities and improve supply chain efficiency. 🎯 Business Objectives ✅ Analyze purchase orders ✅ Track supplier performance ✅ Measure procurement spending ✅ Identify delayed deliveries ✅ Analyze purchase costs ✅ Monitor order fulfillment ✅ Evaluate supplier quality ✅ Identify cost-saving opportunities 📂 Step 1: Create Database CREATE DATABASE procurement_db; USE procurement_db; 📂 Step 2: Create Suppliers Table CREATE TABLE suppliers ( supplier_id INT PRIMARY KEY, supplier_name VARCHAR(100), city VARCHAR(50), supplier_category VARCHAR(50) ); 📂 Step 3: Create Products Table CREATE TABLE products ( product_id INT PRIMARY KEY, product_name VARCHAR(100), category VARCHAR(50), standard_cost DECIMAL(10,2) ); 📂 Step 4: Create Purchase Orders Table CREATE TABLE purchase_orders ( po_id INT PRIMARY KEY, supplier_id INT, po_date DATE, expected_date DATE, actual_delivery_date DATE, po_status VARCHAR(30), FOREIGN KEY (supplier_id) REFERENCES suppliers(supplier_id) ); 📂 Step 5: Create Purchase Order Items Table CREATE TABLE purchase_order_items ( po_item_id INT PRIMARY KEY, po_id INT, product_id INT, quantity INT, unit_cost DECIMAL(10,2), FOREIGN KEY (po_id) REFERENCES purchase_orders(po_id), FOREIGN KEY (product_id) REFERENCES products(product_id) ); 📂 Step 6: Insert Sample Suppliers INSERT INTO suppliers VALUES (1,'ABC Suppliers','Mumbai','Electronics'), (2,'Global Traders','Delhi','Office Supplies'), (3,'Prime Distributors','Pune','Electronics'), (4,'Reliable Wholesale','Bangalore','Furniture'), (5,'Metro Supplies','Hyderabad','General'); 📂 Step 7: Insert Sample Products INSERT INTO products VALUES (101,'Laptop','Electronics',55000), (102,'Monitor','Electronics',15000), (103,'Keyboard','Accessories',1200), (104,'Office Chair','Furniture',6500), (105,'Printer','Office Equipment',18000); 📂 Step 8: Insert Sample Purchase Orders INSERT INTO purchase_orders VALUES (1001,1,'2025-01-05','2025-01-10','2025-01-09','Delivered'), (1002,2,'2025-01-07','2025-01-12','2025-01-15','Delivered'), (1003,3,'2025-01-10','2025-01-18','2025-01-22','Delayed'), (1004,4,'2025-01-12','2025-01-20','2025-01-19','Delivered'), (1005,5,'2025-01-15','2025-01-25',NULL,'Pending'); 📂 Step 9: Insert Sample Purchase Order Items INSERT INTO purchase_order_items VALUES (1,1001,101,20,54000), (2,1001,102,30,14500), (3,1002,103,100,1100), (4,1003,101,15,53500), (5,1003,105,10,17500), (6,1004,104,40,6200), (7,1005,105,20,17200);