This project analyzes multi-year sales data (2019–2024) from five e-commerce vendors to uncover platform-specific trends, customer behavior, and revenue dynamics using data-driven techniques.
- Identify long-term trends, seasonality, and shifts in customer preferences.
- Apply quantitative methods to understand product-level and platform-level performance.
- Compare platform characteristics: Amazon, Flipkart, Meesho, and others.
- Exploratory Data Analysis (EDA): Identified seasonal patterns, sales cycles, and vendor-specific behaviors.
- Pareto Analysis (80/20 Rule): Used Python to determine the top 20% of products contributing to 80% of revenues.
- Customer Segmentation: Behavioral insights based on purchase patterns and platform preferences.
- Amazon: Strong year-round dominance with a diverse product base.
- Flipkart: Notable spikes during festive sales and flash events.
- Meesho: Highly price-sensitive customer base with rapid sales cycles.
- Python (Pandas, NumPy, Matplotlib, Seaborn)