Superstore Sales Analysis with SQL

Retail dataset of a global superstore for 4 years. The dataset can be found here Kaggle

The SQL project involves an extensive analysis of the Superstore Sales Dataset to extract actionable insights crucial for business strategy and decision-making. The analysis encompasses various aspects including revenue generation, sales metrics, customer behavior and geographical distribution.

Key Achievements:

1. Revenue Analysis:

   • Calculated total revenue generated.
   • Identified highest and lowest revenue figures.

2. Sales Metrics:

   • Determined the total number of sales made.
   • Determined the number of cities where the business operates.

3. Customer Insights:

   • Identified customers responsible for the highest and lowest sales.

Highest Sales Lowest Sales

4. Geographical Analysis:

   • Analyzed sales performance in Kentucky.
   • Determined the total revenue generated in the state.

5. Aggregated Analysis:

   • Utilized SQL queries to aggregate sales trends by category, state, region, ship modes, and segment.

Category State Region ShipModes and their preference by customers Segment

6. Interactive Dashboard:

   • Developed an interactive dashboard using Microsoft Excel and Microsoft Power BI reporting tools to present insights for ease of access and visualization.

Microsoft Excel Microsoft Power BI

Project Methodology:

1. Data Preparation:

   • Cleaned and formatted the Superstore Sales Dataset for analysis.
   • Converted date fields to appropriate SQL date format.
   • Handled null values appropriately, either by replacing them or excluding them from analysis.

Microsoft Excel Employed functions such as SUM(), MAX(), MIN(), COUNT(), SUMIF(), COUNTIF(), etc

2. Analysis and Calculation:

   • Utilized SQL functions and queries to compute revenue figures, sales metrics, and customer insights.

3. Aggregated Analysis:

   • Constructed SQL queries to aggregate and visualize data for various dimensions such as category, sub-category, state, etc.

Outcomes and Impact:

• Provided actionable insights into revenue generation and sales performance.
• Identified key trends and patterns in customer behavior and geographical distribution.
• Enabled stakeholders to make informed decisions regarding product categories, geographic focus, and customer segmentation, thereby enhancing data-driven decision-making capabilities within the organization.