Product Sales Performance Analysis

Project Title: Product Sales Performance Analysis

Link notion page: (English version) (versi Bahasa Indonesia)

Dashboard Preview:

Description: This case study presents a comprehensive analysis of a synthetic Product Sales Dataset containing 200,000 transaction records across multiple regions in the United States. The project was designed to simulate a real-world business analytics scenario and demonstrate an end-to-end data analytics workflow. The analysis covers the full pipeline, starting from raw data ingestion and cleaning, followed by PostgreSQL data warehouse design using a star schema, and culminating in an interactive Power BI dashboard. The dashboard enables stakeholders to monitor sales performance, identify high-performing products and regions, and support data-driven business decision-making.

Dataset: https://www.kaggle.com/datasets/yashyennewar/product-sales-dataset-2023-2024

Analytical Approach

Methods

The analysis followed a structured, end-to-end analytics methodology:

• Data Ingestion Raw CSV data was loaded into PostgreSQL using a dedicated raw schema.
• Data Cleaning & Validation (ETL) Data quality checks and transformations were performed in a staging schema, including:
  • Date format standardization
  • Handling missing and invalid values
  • Numeric validation for revenue, profit, and quantity
  • Text normalization for categorical fields
• Data Warehouse Design A star schema was implemented to support analytical queries efficiently:
  • Fact table: fact_orders
  • Dimension tables: dim_dates, dim_customers, dim_products
• Business Intelligence & Visualization The curated data warehouse was connected to Power BI to create an interactive, insight-driven dashboard.

Dashboard Structure

1. Summary Overview Key KPIs: Total Revenue, Total Profit, Profit Margin, AOV, Total Orders Revenue & Profit trend over time Revenue distribution by region Revenue by product category
2. Product & Customer Insights Top 10 products by revenue and profit Revenue by sub-category Revenue distribution by customer state Customer and order-level performance metrics

Tools

• PostgreSQL — Data storage, Data cleaning, validation, transformation, dimensional modeling, ETL processing, and data warehouse implementation
• Power BI — Data modeling, DAX calculations, and dashboard visualization

Key Insight

• Revenue is dominated by the Electronics category, contributing over 40% (≈57M) of total revenue, driven by high-value products such as laptops, smartphones, and wearables.
• The overall profit margin of 22.15% indicates a healthy business performance, with opportunities for further optimization.
• The East region is the largest revenue contributor (45M), followed by the West and Central regions, indicating where marketing and inventory focus should be prioritized.
• California stands out as the highest-revenue state, while several states show underperformance and potential for market expansion.
• Tempur-Pedic Mattress and Instant Pot are the top revenue-generating products, highlighting the strong impact of premium-priced items.
• Certain apparel sub-categories generate relatively low revenue, suggesting possible inefficiencies in product assortment.

Business Recommendations

• Focus on High-Value Categories: Allocate more resources to Electronics and high-margin home products to maximize revenue and profitability.
• Regional Optimization: Strengthen marketing campaigns and inventory availability in the East and West regions while developing targeted growth strategies for underperforming regions.
• Upselling & Bundling Strategies: Leverage top-selling, high-priced products with complementary add-ons or bundles to increase average order value.
• Category Rationalization: Review low-performing apparel categories for potential stop-loss decisions, rebranding, or bundling promotions.
• Geographic Expansion: Develop localized marketing strategies to improve penetration in lower-performing states and reduce over-reliance on top states.