Sales Performance Analysis

Project Overview

This project provides a comprehensive analysis of a US company's sales data from 2017. The objective is to identify key revenue and profit drivers across products, sales channels, and regions to inform pricing, promotions, and expansion strategy.

Tech Stack

• Data Analysis: Python (Pandas, NumPy)
• Data Visualization: Matplotlib, Seaborn
• Business Intelligence: Power BI (Interactive Dashboards)
• Workflow: Jupyter Notebook for ETL and EDA

Dataset Details

The analysis uses a relational dataset consisting of:

• Sales Orders: Transactional data (quantities, prices, channels)
• Products: 30+ product types
• State Regions: Geography (States, Cities, Counties)
• Customers: 100+ corporate clients

Data Processing (ETL)

Steps performed using Python:

1. Cleaning: Handle missing values and standardize date formats
2. Merging: Consolidated multiple CSVs into a master dataset (sales.csv)
3. Feature Engineering:

   • Calculated Total Cost, and Profit

   • Derived Profit Margin %, Return ober Investmant (ROI) %, Cost to Profit Ratio, Average Orders per Customer, and % of Total Revenue per State

   • Extracted Order Month and Year for trend analysis

Key Insights & Findings

• Dominant Channel: The Wholesale channel contributes ~54% of total business volume
• Peak Performance: May is the highest-grossing month (~$26.35M)
• Geographical Leaders:
  • Region: West leads overall
  • State: California leads in sales volume
• Product Performance:
  • Top 3 Products: Product 25, Product 26, Product 13
  • Underperformers: Product 28, Product 10 (candidates for replacement or re-marketing)
• Top Customers: Aibox Company — top revenue (~$3.5M) and orders (139)

Dashboard Visualization

1. Navigation Page

2. Overview — Overview KPIs (Total Revenue, Total Profit, Sales Trends, Profit Margin, Return over Investment (ROI), etc.)

3. Product — Top products, Cost to Profit Ratio, Total Units Sold, Total Orders

4. Customers — Top Customers, Total Customers, Total Units Sold, Total Orders, Average Orders per Customer

5. Geographics — Geographic performance, % of Total Revenue, Number of States, Total Orders, Total Revenue

Recommendations

• Seasonal Promotions: Launch recovery campaigns in April and amplify January offers to smooth revenue swings.
• SKU Optimization: Double down on top products 26 & 25 and re-evaluate pricing or phase out low‑margin SKUs.
• Channel Expansion: Incentivize Export partnerships for high margins and introduce volume deals in Wholesale.
• Regional Investment: Replicate California’s success in other regions and boost marketing in the Northeast & Midwest.
• Margin Monitoring: Flag orders below 80 % margin and analyse cost drivers to uplift underperforming segments.

File Structure

├── Data/                       # Raw Xlsx file
├── Images/                     # Screenshots for README
├── Sales.ipynb                 # Notebook (Data Cleaning & EDA)
├── Sales.pbix                  # Power BI Dashboard file
├── sales.csv                   # Final cleaned master dataset
└── README.md                   # Project documentation

👤 Author

Loay Ayman

• LinkedIn: https://linkedin.com/in/loayayman
• Portfolio: https://loayayman.vercel.app/