Insights from Retail Store Dataset
This project involves a comprehensive analysis of the retail store dataset using SQL. The dataset consists of transaction information, including sale dates, times, customer demographics, categories, quantities, and sales values. The goal of the project is to explore and analyze sales data to identify trends, customer behavior, and sales performance across different categories and time periods. The analysis provides valuable insights into the retail store's operations, helping to understand customer preferences and optimize sales strategies.
Total Number of Transactions: The dataset contains a total count of transactions after cleaning and deleting null values.
Total Sales: The overall number of sales transactions recorded in the dataset.
Unique Customers: The analysis identifies the number of unique customers who made purchases.
Different Categories: The dataset includes various product categories, which are listed and explored.
Sales on Specific Date: The sales transactions made on '2022-11-05' are retrieved, providing insights into daily sales.
Clothing Category Sales in Nov-2022: Analysis of transactions in the 'Clothing' category with quantities greater than 1 in November 2022.
Total Sales by Category: Calculation of the total sales value for each product category.
Average Age of Beauty Category Customers: Determination of the average age of customers who purchased items in the 'Beauty' category.
High-Value Transactions: Identification of transactions where the total sale value exceeds 1000.
Transactions by Gender and Category: Analysis of the total number of transactions made by each gender in each product category.
Monthly Average Sales: Calculation of the average sales for each month, highlighting the best-selling months in each year.
Top 5 Customers: Identification of the top 5 customers based on the highest total sales.
Unique Customers by Category: The number of unique customers who purchased items from each product category.
Sales Shifts: Analysis of the number of orders categorized into different shifts: Morning, Afternoon, and Evening.
Parth Arora