Update #6 Marketing Campaign Analysis.sql

Author: SQLicious

RAW SQL/#6 Marketing Campaign Analysis.sql

@@ -1 +1,302 @@
+# Date created and Last ran : 10-20-2023 
+# Queries to solve Steel Data Challenge
+# Challenge 6 - Marketing Analysis
+
+USE steel_data ;
+-- Lets create the Tables Required
+-- Create the table 
+CREATE TABLE sustainable_clothing (
+product_id INT PRIMARY KEY,
+product_name VARCHAR(100),
+category VARCHAR(50),
+size VARCHAR(10),
+price FLOAT
+);
+-- Insert data into the table
+INSERT INTO sustainable_clothing (product_id, product_name, category, size, price)
+VALUES
+(1, 'Organic Cotton T-Shirt', 'Tops', 'S', 29.99),
+(2, 'Recycled Denim Jeans', 'Bottoms', 'M', 79.99),
+(3, 'Hemp Crop Top', 'Tops', 'L', 24.99),
+(4, 'Bamboo Lounge Pants', 'Bottoms', 'XS', 49.99),
+(5, 'Eco-Friendly Hoodie', 'Outerwear', 'XL', 59.99),
+(6, 'Linen Button-Down Shirt', 'Tops', 'M', 39.99),
+(7, 'Organic Cotton Dress', 'Dresses', 'S', 69.99),
+(8, 'Sustainable Swim Shorts', 'Swimwear', 'L', 34.99),
+(9, 'Recycled Polyester Jacket', 'Outerwear', 'XL', 89.99),
+(10, 'Bamboo Yoga Leggings', 'Activewear', 'XS', 54.99),
+(11, 'Hemp Overalls', 'Bottoms', 'M', 74.99),
+(12, 'Organic Cotton Sweater', 'Tops', 'L', 49.99),
+(13, 'Cork Sandals', 'Footwear', 'S', 39.99),
+(14, 'Recycled Nylon Backpack', 'Accessories', 'One Size', 59.99),
+(15, 'Organic Cotton Skirt', 'Bottoms', 'XS', 34.99),
+(16, 'Hemp Baseball Cap', 'Accessories', 'One Size', 24.99),
+(17, 'Upcycled Denim Jacket', 'Outerwear', 'M', 79.99),
+(18, 'Linen Jumpsuit', 'Dresses', 'L', 69.99),
+(19, 'Organic Cotton Socks', 'Accessories', 'M', 9.99),
+(20, 'Bamboo Bathrobe', 'Loungewear', 'XL', 69.99);
+-- Create the table
+CREATE TABLE marketing_campaigns (
+campaign_id INT PRIMARY KEY,
+campaign_name VARCHAR(100),
+product_id INT,
+start_date DATE,
+end_date DATE,
+FOREIGN KEY (product_id) REFERENCES sustainable_clothing (product_id)
+);
+-- Insert data into the table
+INSERT INTO marketing_campaigns (campaign_id, campaign_name, product_id, start_date, end_date)
+VALUES
+(1, 'Summer Sale', 2, '2023-06-01', '2023-06-30'),
+(2, 'New Collection Launch', 10, '2023-07-15', '2023-08-15'),
+(3, 'Super Save', 7, '2023-08-20', '2023-09-15');
+-- Create the table
+CREATE TABLE transactions2 (
+transaction_id INT PRIMARY KEY,
+product_id INT,
+quantity INT,
+purchase_date DATE,
+FOREIGN KEY (product_id) REFERENCES sustainable_clothing (product_id)
+);
+-- Insert data into the table
+INSERT INTO transactions2 (transaction_id, product_id, quantity, purchase_date)
+VALUES
+(1, 2, 2, '2023-06-02'),
+(2, 14, 1, '2023-06-02'),
+(3, 5, 2, '2023-06-05'),
+(4, 2, 1, '2023-06-07'),
+(5, 19, 2, '2023-06-10'),
+(6, 2, 1, '2023-06-13'),
+(7, 16, 1, '2023-06-13'),
+(8, 10, 2, '2023-06-15'),
+(9, 2, 1, '2023-06-18'),
+(10, 4, 1, '2023-06-22'),
+(11, 18, 2, '2023-06-26'),
+(12, 2, 1, '2023-06-30'),
+(13, 13, 1, '2023-06-30'),
+(14, 4, 1, '2023-07-04'),
+(15, 6, 2, '2023-07-08'),
+(16, 15, 1, '2023-07-08'),
+(17, 9, 2, '2023-07-12'),
+(18, 20, 1, '2023-07-12'),
+(19, 11, 1, '2023-07-16'),
+(20, 10, 1, '2023-07-20'),
+(21, 12, 2, '2023-07-24'),
+(22, 5, 1, '2023-07-29'),
+(23, 10, 1, '2023-07-29'),
+(24, 10, 1, '2023-08-03'),
+(25, 19, 2, '2023-08-08'),
+(26, 3, 1, '2023-08-14'),
+(27, 10, 1, '2023-08-14'),
+(28, 16, 2, '2023-08-20'),
+(29, 18, 1, '2023-08-27'),
+(30, 12, 2, '2023-09-01'),
+(31, 13, 1, '2023-09-05'),
+(32, 7, 1, '2023-09-05'),
+(33, 6, 1, '2023-09-10'),
+(34, 15, 2, '2023-09-14'),
+(35, 9, 1, '2023-09-14'),
+(36, 11, 2, '2023-09-19'),
+(37, 17, 1, '2023-09-23'),
+(38, 2, 1, '2023-09-28'),
+(39, 14, 1, '2023-09-28'),
+(40, 5, 2, '2023-09-30'),
+(41, 16, 1, '2023-10-01'),
+(42, 12, 2, '2023-10-01'),
+(43, 1, 1, '2023-10-01'),
+(44, 7, 1, '2023-10-02'),
+(45, 18, 2, '2023-10-03'),
+(46, 12, 1, '2023-10-03'),
+(47, 13, 1, '2023-10-04'),
+(48, 4, 1, '2023-10-05'),
+(49, 12, 2, '2023-10-05'),
+(50, 7, 1, '2023-10-06'),
+(51, 4, 2, '2023-10-08'),
+(52, 8, 2, '2023-10-08'),
+(53, 16, 1, '2023-10-09'),
+(54, 19, 1, '2023-10-09'),
+(55, 1, 1, '2023-10-10'),
+(56, 18, 2, '2023-10-10'),
+(57, 2, 1, '2023-10-10'),
+(58, 15, 2, '2023-10-11'),
+(59, 17, 2, '2023-10-13'),
+(60, 13, 1, '2023-10-13'),
+(61, 10, 2, '2023-10-13'),
+(62, 9, 1, '2023-10-13'),
+(63, 19, 2, '2023-10-13'),
+(64, 20, 1, '2023-10-14');
+
+-- Business Questions to Answer
+
+-- 1. How many transactions were completed during each marketing campaign?
+
+SELECT  mc.campaign_name,
+       COUNT(t.transaction_id) as transaction_count
+FROM 
+    marketing_campaigns mc
+JOIN 
+    transactions2 t ON mc.product_id = t.product_id
+GROUP BY 
+    mc.campaign_name;
+
+-- 2. Which product had the highest sales quantity?
+
+SELECT 
+    sc.product_name,
+    SUM(t.quantity) as total_quantity_sold
+FROM 
+    sustainable_clothing sc
+JOIN 
+    transactions2 t ON sc.product_id = t.product_id
+GROUP BY 
+    sc.product_name
+ORDER BY 
+    total_quantity_sold DESC
+LIMIT 1;
+
+-- 3. What is the total revenue generated from each marketing campaign?
+
+SELECT 
+    mc.campaign_name,
+    ROUND(SUM(t.quantity * sc.price),2) as total_revenue
+FROM 
+    marketing_campaigns mc
+JOIN 
+    transactions2 t ON mc.product_id = t.product_id
+JOIN 
+    sustainable_clothing sc ON mc.product_id = sc.product_id
+GROUP BY 
+    mc.campaign_name;
+
+-- 4. What is the top-selling product category based on the total revenue generated?
+SELECT 
+    sc.category,
+    ROUND(SUM(t.quantity * sc.price),2) as total_revenue
+FROM 
+    sustainable_clothing sc
+JOIN 
+    transactions2 t ON sc.product_id = t.product_id
+GROUP BY 
+    sc.category
+ORDER BY 
+    total_revenue DESC
+LIMIT 1;
+
+-- 5. Which products had a higher quantity sold compared to the average quantity sold?
+SELECT 
+    sc.product_name,
+    SUM(t.quantity) as total_quantity_sold
+FROM 
+    sustainable_clothing sc
+JOIN 
+    transactions2 t ON sc.product_id = t.product_id
+GROUP BY 
+    sc.product_name
+HAVING 
+    total_quantity_sold > (SELECT AVG(quantity) FROM transactions2)
+ORDER BY total_quantity_sold DESC;    
+
+-- 6. What is the average revenue generated per day during the marketing campaigns?
+SELECT 
+    mc.campaign_name,
+    ROUND(AVG(t.quantity * sc.price),2) as average_daily_revenue
+FROM 
+    marketing_campaigns mc
+JOIN 
+    transactions2 t ON mc.product_id = t.product_id
+JOIN 
+    sustainable_clothing sc ON mc.product_id = sc.product_id
+GROUP BY 
+    mc.campaign_name
+ORDER BY 
+    mc.campaign_name;
+
+-- 7. What is the percentage contribution of each product to the total revenue?
+WITH product_revenue AS (
+    SELECT 
+        sc.product_name,
+        ROUND(SUM(t.quantity * sc.price),2) as total_revenue
+    FROM 
+        sustainable_clothing sc
+    JOIN 
+        transactions2 t ON sc.product_id = t.product_id
+    GROUP BY 
+        sc.product_name
+)
+SELECT 
+    product_name,
+    total_revenue,
+    ROUND((total_revenue / (SELECT SUM(total_revenue) FROM product_revenue)) * 100,2) as percentage_contribution
+FROM 
+    product_revenue
+ORDER BY 
+    total_revenue DESC;
+
+-- 8. Compare the average quantity sold during marketing campaigns to outside the marketing campaigns
+WITH campaign_quantity AS (
+    SELECT mc.campaign_name,
+           AVG(t.quantity) as average_quantity_sold
+    FROM 
+           marketing_campaigns mc
+    JOIN 
+           transactions2 t ON mc.product_id = t.product_id
+    GROUP BY 
+           mc.campaign_name
+),
+outside_campaign_quantity AS (
+    SELECT 'Outside Campaigns' as campaign_name,
+        AVG(t.quantity) as average_quantity_sold
+    FROM 
+        transactions2 t
+    WHERE 
+        t.product_id NOT IN (SELECT product_id FROM marketing_campaigns)
+)
+SELECT * FROM campaign_quantity
+UNION ALL
+SELECT * FROM outside_campaign_quantity;
+
+-- 9. Compare the revenue generated by products inside the marketing campaigns to outside the campaigns
+WITH campaign_revenue AS (
+    SELECT 
+        mc.campaign_name,
+        ROUND(SUM(t.quantity * sc.price),2) as total_revenue
+    FROM 
+        marketing_campaigns mc
+    JOIN 
+        transactions2 t ON mc.product_id = t.product_id
+    JOIN 
+        sustainable_clothing sc ON mc.product_id = sc.product_id
+    GROUP BY 
+        mc.campaign_name
+),
+outside_campaign_revenue AS (
+    SELECT 
+        'Outside Campaigns' as campaign_name,
+        ROUND(SUM(t.quantity * sc.price),2) as total_revenue
+    FROM 
+        transactions2 t
+    JOIN 
+        sustainable_clothing sc ON t.product_id = sc.product_id
+    WHERE 
+        t.product_id NOT IN (SELECT product_id FROM marketing_campaigns)
+)
+SELECT * FROM campaign_revenue
+UNION ALL
+SELECT * FROM outside_campaign_revenue;
+
+-- 10. Rank the products by their average daily quantity sold
+WITH product_avg_quantity AS (
+    SELECT 
+        sc.product_name,
+        ROUND(AVG(t.quantity),2) as average_daily_quantity_sold
+    FROM 
+        transactions2 t
+    JOIN 
+        sustainable_clothing sc ON t.product_id = sc.product_id
+    GROUP BY 
+        sc.product_name
+)
+SELECT * 
+FROM product_avg_quantity
+ORDER BY average_daily_quantity_sold DESC;