Retail Sales and Customer Demographics Data Analysis Project

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Project Overview This project focuses on exploratory data analysis (EDA) of a Retail Sales and Customer Demographics Dataset. The dataset simulates a retail environment, including key features such as customer demographics and transactional data. The goal of this project is to extract meaningful insights about customer behavior, sales trends, and product preferences, which can be leveraged to enhance retail strategies.

Dataset Description The dataset captures customer purchases in a fictional retail environment with the following columns:

Transaction_ID: Unique identifier for each transaction. Date: Date of the transaction. Customer_ID: Unique identifier for each customer. Gender: Gender of the customer. Age: Age of the customer. Product_Category: Category of the product purchased. Quantity: Number of items bought. Price per Unit: The price of each product unit. Total_Amount: The total transaction amount, calculated as Quantity * Price per Unit. The dataset simulates typical retail scenarios and allows for various analyses, including demographic influences on sales and product preferences.

Project Objectives The primary objectives of this project include:

1.Sales Analysis:

Analyze sales performance over time. Identify the top-selling product categories. Determine sales trends by product category.

2.Customer Demographics Insights:

Examine the relationship between Gender and Spending. Investigate how Age affects purchasing behavior. Explore customer preferences based on demographics.

3.Top Spenders Analysis:

Identify the highest spenders (customers with the most total spend). Calculate average spend per transaction by customer demographics.

4.Transaction Analysis:

Analyze the number of transactions per customer. Understand the distribution of purchases by product category and time of year.

5.Product Preferences:

Discover which products are popular among different customer segments (e.g., by age and gender). Explore product preferences based on seasonal trends.

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Technical Approach This project will be executed using the following tools and steps:

1. Data Exploration and Cleaning: SQL in BigQuery will be used to query, clean, and aggregate the data for analysis. Excel will be used for initial data inspection and some manual data processing if necessary. Tableau will be employed for data visualization to uncover insights and present results in an interactive dashboard.

2. Data Analysis Steps:Customer Segmentation:Group customers by Customer_ID and analyze their total spend, average age, and transaction frequency using SQL.

3. Sales Trend Analysis:Use SQL queries to aggregate sales over time, broken down by Product_Category, Age, and GendeR.

4.Demographic Analysis: Analyze relationships between Gender, Age, and Total_Spent using SQL queries and Tableau visualizations.

5.Top Buyers/Spenders Identification: Use SQL to rank customers based on total spending and the number of transactions.

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SELECT * FROMretail-sales-project-438517.Retail_data_01.Retail_Sales` LIMIT 1000;

--Analyze how Age and Gender influence spending--

SELECT Gender, AVG(Total_Amount) as Avg_Spend, AVG(Age) as Avg_Age FROM retail-sales-project-438517.Retail_data_01.Retail_Sales GROUP BY Gender;

--Identify trends by examining sales over time--

SELECT EXTRACT(YEAR FROM Date) AS Year, SUM(Total_Amount) AS Total_Sales FROM retail-sales-project-438517.Retail_data_01.Retail_Sales GROUP BY Year;

--Examine which product categories generate the most revenue--

SELECT Product_Category, SUM(Total_Amount) AS Total_Revenue FROM retail-sales-project-438517.Retail_data_01.Retail_Sales GROUP BY Product_Category;

--Explore Time-Based Trends: Group transactions by month/year to see sales trend--

SELECT EXTRACT(MONTH FROM Date) AS month, SUM(Total_Amount) AS monthly_sales FROM retail-sales-project-438517.Retail_data_01.Retail_Sales GROUP BY month ORDER BY month;

--Finding the top 10 spenders by calculating the total amount spent per customer--

SELECT Customer_ID, SUM(Total_Amount) AS Total_Spent, COUNT(Transaction_ID) AS Number_of_Transactions FROM retail-sales-project-438517.Retail_data_01.Retail_Sales WHERE Date >= '2023-01-01' AND Date <= '2024-01-01' -- Filter by date range GROUP BY Customer_ID ORDER BY Total_Spent DESC LIMIT 10;

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FOR VISUALIZATION VISIT (RETAIL PROJECT.xlsx)