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Commit 2fcb1ab

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‎SQL_Query-1.sql

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/* Analyze the yearly performance of products by comparing their sales
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to both the average sales performance of the product and the previous year's sales */
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WITH yearly_product_sales as
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(
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select
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YEAR(f.order_date) AS ORDER_YEAR,
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p.product_name,
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SUM(f.sales_amount) AS CURRENT_SALES
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FROM gold.fact_sales f
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LEFT JOIN gold.dim_products p
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ON f.product_key=p.product_key
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WHERE f.order_date is not null
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GROUP BY
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YEAR(f.order_date),p.product_name
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)
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SELECT
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order_year,
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product_name,
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CURRENT_SALES,
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AVG(current_sales) OVER (PARTITION BY product_name) AS AVG_SALES,
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current_sales - AVG(current_sales) OVER (PARTITION BY product_name) AS DIFF_AVG,
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CASE WHEN current_sales - AVG(current_sales) OVER (PARTITION BY product_name) > 0 THEN 'Above Avg'
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WHEN current_sales - AVG(current_sales) OVER (PARTITION BY product_name) < 0 THEN 'Below Avg'
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ELSE 'AVG'
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END Avg_change
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FROM yearly_product_sales
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ORDER BY product_name,order_year

‎SQL_Query-2.sql

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/*Segment products into cost ranges and
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count how many products fall into each segment */
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WITH products_segments AS (
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SELECT
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product_key,product_name,cost,
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CASE WHEN cost < 100 THEN 'Below 100'
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WHEN cost BETWEEN 100 AND 500 THEN '100-500'
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WHEN cost BETWEEN 500 AND 1000 THEN '500-1000'
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ELSE 'Above 1000'
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END cost_range
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FROM gold.dim_products)
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SELECT
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cost_range,
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COUNT(product_key) AS total_products
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FROM products_segments
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GROUP BY cost_range
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ORDER BY total_products DESC

‎SQL_Query-3.sql

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/* Which Categories contribute the most to overall sales?*/
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WITH category_sales as (
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select p.category,
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SUM(s.sales_amount) AS Total_Sales
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FROM gold.fact_sales as s
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INNER JOIN gold.dim_products as p ON s.product_key=p.product_key
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GROUP BY p.category)
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SELECT
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category,
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total_sales,
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SUM(total_sales) OVER () overall_sales,
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CONCAT(ROUND((CAST (total_sales AS FLOAT) / SUM(total_sales) OVER ())*100,2), '%') as percentage_of_total
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FROM category_sales

‎SQL_Query-4.sql

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/* Group customers into three segments based on their spending behaviours:
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-VIP : Customers with at least 12months of history and spending more than 5000ドル.
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-REGULAR : Customers with at least 12 months of history but spending 5000$ or less
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-NEW : Customers with a lifespan less than 12 months.
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And find the total numbers od customers by each group */
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WITH customer_spending AS (
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SELECT
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c.customer_key,
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SUM(f.sales_amount) AS total_spending,
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MIN(order_date) first_order,
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MAX(order_date) last_order,
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DATEDIFF (month,MIN(order_date), MAX(order_date)) AS lifespan
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FROM [gold].[fact_sales] f
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LEFT JOIN gold.dim_customers c
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ON f.customer_key=c.customer_key
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GROUP BY c.customer_key
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)
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SELECT
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customer_segment,
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COUNT(customer_key) AS total_customers
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FROM (
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SELECT
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customer_key,
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CASE WHEN lifespan >= 12 AND total_spending > 5000 THEN 'VIP'
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WHEN lifespan >= 12 AND total_spending <= 5000 THEN 'Regular'
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ELSE 'New'
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END AS customer_segment
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FROM customer_spending ) t
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GROUP BY customer_segment
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ORDER BY total_customers DESC

‎SQL_Query-5.sql

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/*
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CUSTOMER REPORT
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Purpose:
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- This report consolidates key customer metrics and behaviours.
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Highlights:
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1. Gathers essential fields such as names,ages,and transaction details.
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2. Segments customers into categories (VIP,Regular,New) and age groups.
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3.Aggregates customer-level metrics:
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-total orders
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-total sales
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-total quantity purchased
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-total products
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-lifespan (in months)
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4.Calculates valueable KPIs:
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- recency(months since last order)
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-average order value
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-average monthly spend
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*/
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CREATE VIEW gold.report_customers AS
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WITH base_query AS (
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/* Base query : Retrieves core olumns from tables */
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SELECT
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f.order_number,
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f.product_key,
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f.order_date,
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f.sales_amount,
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f.quantity,
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c.customer_key,
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c.customer_number,
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CONCAT(c.first_name, '' , c.last_name) AS customer_name,
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DATEDIFF(year, c.birthdate, GETDATE()) age
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FROM
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gold.fact_sales f
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LEFT JOIN gold.dim_customers c
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ON c.customer_key=f.customer_key
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WHERE order_date IS NOT NULL
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)
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,customer_aggregation AS (
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SELECT
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customer_key,
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customer_number,
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customer_name,
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age,
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COUNT(DISTINCT order_number) AS total_orders,
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SUM(sales_amount) AS total_sales,
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SUM(quantity) AS total_quantity,
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COUNT(DISTINCT product_key) AS total_products,
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MAX(order_date) AS last_order_date,
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DATEDIFF(month,MIN(order_date), MAX(order_date)) AS lifespan
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FROM base_query
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GROUP BY
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customer_key,
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customer_number,
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customer_name,
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age
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)
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SELECT
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customer_key,
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customer_number,
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customer_name,
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age,
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CASE
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WHEN age < 20 THEN 'Under 20'
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WHEN age between 20 and 29 THEN '20-29'
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WHEN age between 30 and 39 THEN '30-39'
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WHEN age between 40 and 49 THEN '40-49'
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ELSE '50 and above'
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END AS age_group,
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CASE
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WHEN lifespan >= 12 AND total_sales > 5000 THEN 'VIP'
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WHEN lifespan >= 12 AND total_sales <= 5000 THEN 'Regular'
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ELSE 'New'
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END AS customer_segment,
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last_order_date,
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DATEDIFF(month,last_order_date,GETDATE()) AS resency,
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total_orders,
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total_sales,
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total_quantity,
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total_products,
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lifespan,
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-- Compute average order value( AVO)
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CASE WHEN total_sales = 0 THEN 0
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ELSE total_sales / total_orders
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END AS avg_order_value,
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-- Compute average monthly spend
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CASE WHEN lifespan = 0 THEN total_sales
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ELSE total_sales / lifespan
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END AS avg_monthly_spend
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FROM customer_aggregation

‎SQL_Query-6.sql

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/*
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PRODUCT REPORT
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Purpose:
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- This report consolidates key product metrics and behaviours.
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Highlights:
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1. Gathers essential fields such as product name,category,subcategory and cost.
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2. Segments products by revenue to identify High-Performers,Midd-Range,or Low-Range
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3.Aggregates product-level metrics:
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-total orders
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-total sales
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-total quantity sold
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-total customers (unique)
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-lifespan (in months)
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4.Calculates valueable KPIs:
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- recency(months sincelast sale)
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-average order revenue (AOR)
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-average monthly revenue
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*/
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CREATE VIEW gold.report_products AS
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WITH base_query AS (
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SELECT
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s.order_number,
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s.order_date,
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s.customer_key,
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s.sales_amount,
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s.quantity,
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p.product_key,
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p.product_name,
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p.category,
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p.subcategory,
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p.cost
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FROM gold.fact_sales s
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LEFT JOIN gold.dim_products as p
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ON p.product_key=s.product_key
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WHERE order_date IS NOT NULL
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)
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,
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product_aggregations AS
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(
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SELECT
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product_key,
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product_name,
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category,
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subcategory,
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cost,
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DATEDIFF(MONTH,MIN(order_date), MAX(order_date)) AS lifespan,
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MAX(order_date) as last_sale_date,
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COUNT(DISTINCT order_number) AS total_orders,
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COUNT(DISTINCT customer_key) AS total_customers,
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SUM(sales_amount) AS total_sales,
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SUM(quantity) AS total_quantity,
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ROUND(AVG(CAST(sales_amount as FLOAT) / NULLIF(quantity, 0)),1) AS avg_selling_price
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FROM base_query
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GROUP BY
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product_key,
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product_name,
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category,
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subcategory,
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cost
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)
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SELECT
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product_key,
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product_name,
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category,
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subcategory,
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cost,
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last_sale_date,
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DATEDIFF(MONTH, last_sale_date,GETDATE()) AS recency_in_months,
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CASE
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WHEN total_sales > 50000 THEN 'High Performer'
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WHEN total_sales >= 10000 THEN 'Mid Range'
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ELSE 'Low Performer'
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END AS product_segment,
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lifespan,
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total_orders,
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total_customers,
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total_sales,
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total_quantity,
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avg_selling_price,
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-- Average ORDER Revenue
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CASE
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WHEN total_orders = 0 THEN 0
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ELSE total_sales / total_orders
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END AS avg_order_revenue,
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--Average Monthly Revenue
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CASE
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WHEN lifespan = 0 THEN total_sales
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ELSE total_sales / lifespan
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END AS avg_monthly_revenue
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FROM product_aggregations

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