This project analyzes real-world Amazon sales data to uncover key business insights across sales performance, customer behavior, shipping efficiency, promotional impact, and profitability. It replicates a full analytics workflow—from data cleaning and SQL querying to dashboard creation in Power BI
- Sales Trends: Total revenue, top-performing products, and seasonal peaks
- Customer Behavior: Top locations, B2B vs B2C mix, cancellation patterns
- Shipping & Fulfilment: Performance benchmarking - Amazon vs Merchant
- Product Insights: most popular categories & styles
- Promotions & Seasonality: Impact of campaigns and holiday-driven sales
- Cancellations & Returns: rates across products and categories
The goal was to replicate a al business analytics workflow: cleaning data, writing SQL queries, and building a structured analysis that could feed directly into a reporting dashboard.
- SQL: Data cleaning, transformation, and analysis
- Power BI: Interactive dashboards and visual storytelling
- Excel: Initial data exploration and validation
Order-level Amazon sales data on kaggle with key fields:
- Order Details: Order ID, Date, SKU, Category, Style, Fulfillment, Status
- Financial: Quantity, Amount, Currency
- Shipping Info: City, State, Country, Postal Code
- Extras: Promotion details, B2B flag
You can find the file for the dashboard here: amazon_sales.pbix.
You can find all the Query here: amazon_sales.sql
Advanced SQL Queries: Leveraged CTEs, aggregations, conditional logic, and date functions to derive actionable insights from raw sales data. Data Cleaning & Validation: Filtered out canceled orders and nulls to ensure high data quality. KPI Development:** Measured business-critical metrics like Total Revenue, Average Order Value (AOV), Cancellation Rate, and Promotion ROI. Customer & Market Segmentation: Identified top-performing geographies, customer demographics (B2B vs. B2C), and category-level performance. Trend & Seasonality Analysis: Uncovered monthly sales patterns, product seasonality, and cancellation trends. Operational Insights: Benchmarked Amazon vs. Merchant fulfillment performance to evaluate efficiency. Scalable Query Design: Built modular SQL blocks for reuse across reporting and dashboard pipelines.
Interactive Dashboards: Designed insight-rich dashboards with cards, bar charts, and time-series visuals. Data Storytelling: Translated complex metrics into intuitive visuals that highlight performance, risks, and opportunities. KPI Tracking: Showcased key business indicators (Revenue, AOV, Cancellation Rate, Fulfillment Mix). Segmentation Analysis: Visualized B2B vs. B2C mix, top categories, styles, and geographic hotspots. Trend Exploration: Used time-series and stacked charts to highlight seasonal shifts and cancellation drivers. Promotion Effectiveness: Identified revenue uplift from high-performing promotions. User-Centric Design: Built dashboards with clarity, interactivity, and color-coded layouts for decision-maker impact.
- 💰 Total Revenue: ₹72M generated from successful orders, with an Average Order Value of ₹695
- 🏢 B2B Dominance: 99.34% of orders are B2B, indicating a strong enterprise customer base
- 📦 Fulfillment Split: 70.93% of orders fulfilled by merchants vs 29.07% by Amazon—suggesting operational reliance on third-party sellers
- 📊 Top Categories by Order Volume:
- Set and Kurta lead with 41K and 40K orders respectively
- Western Dress, Top, Ethnic Dress, and Blouse follow with smaller volumes
- 📆 Peak Sales Months: April and May each generated ~₹28M in sales, followed by June (~₹25M)
- 🌍 Key Markets: Bengaluru, Mumbai, Pune, Kolkata, Hyderabad, and New Delhi are top-performing cities
- 👗 Top Styles: Style codes like JNE3797 and JNE3405 dominate order volume, useful for inventory planning
- ❌ Cancellation Insights:
- ₹6.92M in cancelled sales
- 18K cancelled orders out of 120K total, yielding a 15% cancellation rate
- April saw the highest cancellation volume
- 🎯 Promotion Impact: “IN Core Free Shi” drove the highest sales among all promotions
- 📈 Seasonal Trends by Category:
- Kurta peaked in March with ₹33K in sales
- Other categories like Saree, Set, and Top show varied monthly performance
This project demonstrates end-to-end business analytics using SQL and Power BI. I analyzed Amazon sales data to uncover key insights on revenue, customer behavior, fulfillment, promotions, and market trends. The work highlights my ability to write scalable SQL queries, design interactive dashboards, and translate complex data into actionable business insights that drive decision-making.