Sql Project :- Consumer Goods Ad-hoc Insights

Consumer Goods Analysis

Objectives

This project is designed to demonstrate SQL skills and techniques typically used by data analysts to explore and analyze consumer Goods. The project involves setting up a database, performing data analysis, and answering (specific business questions) Ad-hoc requests through SQL queries.

Data Analysis & Findings

The following SQL queries were developed to answer Ad-hoc requests

1.Provide the list of markets in which customer "Atliq Exclusive" operates its business in the APAC region.

#  Query:
SELECT DISTINCT market 
FROM dim_customer
WHERE  customer = "Atliq Exclusive" AND region = "APAC"
ORDER BY market;

2.What is the percentage of unique product increase in 2021 vs. 2020? The final output contains these fields,unique_products_2020 ,unique_products_2021 ,percentage_chg

#  Query:
WITH Cte1 AS
(
SELECT COUNT(DISTINCT product_code) AS unique_products_2020
FROM fact_sales_monthly
WHERE fiscal_year = "2020"),
Cte2 AS
(
SELECT COUNT(DISTINCT product_code) AS unique_products_2021
FROM fact_sales_monthly
WHERE fiscal_year = "2021")

SELECT C1.unique_products_2020 ,C2.unique_products_2021,
ROUND(((C2.unique_products_2021 - C1.unique_products_2020)/C1.unique_products_2020 *100),2) AS percentage_chg
FROM Cte1 as C1
JOIN Cte2 as C2;

3.Provide a report with all the unique product counts for each segment and sort them in descending order of product counts.The final output contains 2 fields, segment,product_count

#  Query:
SELECT segment,COUNT(DISTINCT product_code) AS product_counts
FROM dim_product
GROUP BY segment
ORDER BY product_counts DESC; 

4.Follow-up: Which segment had the most increase in unique products in 2021 vs 2020? The final output contains these fields, segment ,product_count_2020 ,product_count_2021 ,difference

#  Query:
WITH Cte1 AS
(
SELECT p.segment,  COUNT(DISTINCT f.product_code) AS product_count_2020
FROM fact_sales_monthly f
JOIN dim_product p 
ON f.product_code = p.product_code
WHERE f.fiscal_year = "2020"
GROUP BY p.segment
),
Cte2 AS
(
SELECT p.segment,  COUNT(DISTINCT f.product_code) AS product_count_2021
FROM fact_sales_monthly f
JOIN dim_product p 
ON f.product_code = p.product_code
WHERE f.fiscal_year = "2021"
GROUP BY p.segment
)
SELECT C2.segment,C1.product_count_2020, C2.product_count_2021,
(C2.product_count_2021-C1.product_count_2020) AS difference
FROM Cte1 AS C1
JOIN Cte2 AS C2
	ON C1.segment = C2.segment
ORDER BY difference DESC;

5.Get the products that have the highest and lowest manufacturing costs.The final output should contain these fields,product_code,product,manufacturing_cost

#  Query:
SELECT MAX(manufacturing_cost) AS Max_cost, MIN(manufacturing_cost) AS Min_cost
FROM fact_manufacturing_cost;

SELECT p.product_code,p.product,m.manufacturing_cost 
FROM dim_product p
JOIN fact_manufacturing_cost m
ON p.product_code = m.product_code
WHERE m.manufacturing_cost = (SELECT MAX(manufacturing_cost) FROM fact_manufacturing_cost)
OR
m.manufacturing_cost = (SELECT MIN(manufacturing_cost) FROM fact_manufacturing_cost)
ORDER BY m.manufacturing_cost DESC;

6.Generate a report which contains the top 5 customers who received an average high pre_invoice_discount_pct for the fiscal year 2021 and in the Indian market. The final output contains these fields,customer_code,customer,average_discount_percentage

#  Query:
SELECT p.customer_code , c.customer , ROUND(AVG(p.pre_invoice_discount_pct)*100,2) AS average_discount_percentage
FROM fact_pre_invoice_deductions p
JOIN dim_customer c
ON p.customer_code = c.customer_code
WHERE p.fiscal_year='2021' AND c.market = "India"
GROUP BY c.customer_code, c.customer
ORDER BY average_discount_percentage DESC
LIMIT 5;

7.Get the complete report of the Gross sales amount for the customer “Atliq Exclusive” for each month.This analysis helps to get an idea of low and high-performing months and take strategic decisions.The final report contains these columns:Month,Year,Gross sales Amount

#  Query:
SELECT 
	MONTHNAME(s.date) AS Month,
    YEAR(s.date) AS YEAR,
    CONCAT(ROUND(SUM(g.gross_price * s.sold_quantity)/1000000,2)," M" )AS Gross_sales_Amount
FROM fact_gross_price g
JOIN fact_sales_monthly s 
	ON g.product_code = s.product_code AND g.fiscal_year=s.fiscal_year
JOIN dim_customer c
	ON  s.customer_code = c.customer_code
WHERE c.customer = "Atliq Exclusive" 
GROUP BY s.date,s.fiscal_year
ORDER BY YEAR;

8.In which quarter of 2020, got the maximum total_sold_quantity? The final output contains these fields sorted by the total_sold_quantity,Quarter,total_sold_quantity

#  Query:
SELECT 
CASE 
	WHEN MONTH(date) IN (9,10,11) THEN "Q1"
    WHEN MONTH(date) IN (12,1,2) THEN "Q2"
    WHEN MONTH(date) IN (3,4,5) THEN "Q3"
    WHEN MONTH(date) IN (6,7,8) THEN "Q4"
END AS Quarters, CONCAT(ROUND(SUM(sold_quantity)/1000000,2),' M') AS total_sold_quantity
FROM fact_sales_monthly
WHERE fiscal_year='2020'
GROUP BY Quarters;

9.Which channel helped to bring more gross sales in the fiscal year 2021 and the percentage of contribution? The final output contains these fields,channel,gross_sales_mln,percentage

#  Query:
WITH Cte AS
(
SELECT 
	c.channel,
    ROUND((SUM(g.gross_price * s.sold_quantity)/1000000),2)  AS gross_sales_mln
FROM   fact_gross_price g 
JOIN   fact_sales_monthly s
	ON g.product_code = s.product_code AND g.fiscal_year=s.fiscal_year
JOIN dim_customer c
	ON  s.customer_code = c.customer_code
GROUP BY c.channel
)
SELECT 
	channel,
	CONCAT(gross_sales_mln,' M') as gross_sales_mln,
    ROUND((( gross_sales_mln / SUM(gross_sales_mln) OVER())*100),2) AS  percentage
FROM Cte
ORDER BY percentage DESC;

10.Get the Top 3 products in each division that have a high total_sold_quantity in the fiscal_year 2021? The final output contains these fields,division,product_code,product,total_sold_quantity,rank_order

#  Query:
WITH Cte1 AS
(
SELECT p.division,p.product_code,p.product, SUM(s.sold_quantity) AS total_sold_quantity
FROM fact_sales_monthly s 
JOIN dim_product p
ON s.product_code = p.product_code
WHERE s.fiscal_year = '2021'
GROUP BY p.division,p.category,p.product_code,p.product
),
Cte2 AS
(
SELECT *,
RANK() OVER(PARTITION BY division ORDER BY  total_sold_quantity DESC ) AS rank_order
FROM Cte1 
)
SELECT *
FROM  Cte2
WHERE rank_order <=3;

Reports

𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐂𝐨𝐦𝐩𝐚𝐫𝐢𝐬𝐨𝐧:Evaluated company performance.

𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐒𝐞𝐠𝐦𝐞𝐧𝐭𝐬:Analyzed different product segments.

𝐒𝐚𝐥𝐞𝐬 𝐂𝐨𝐦𝐩𝐚𝐫𝐢𝐬𝐨𝐧 (𝟐𝟎𝟐𝟎 𝐯𝐬. 𝟐𝟎𝟐1:Identified trends and changes in sales.

Customer Analysis: Identified top customers who receive highest average pre invoice discount percentage.

𝐂𝐨𝐬𝐭 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬:Identified highest and lowest cost products.