BigBasket E-Commerce Analytics – Product Assortment & Brand Insights

📌 Overview

This project analyzes BigBasket’s e-commerce product catalog to understand category-wise assortment depth, brand-level concentration, and pricing distribution, with the goal of supporting assortment planning, brand strategy analysis, and catalog optimization.

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📂 Dataset

• Source: BigBasket product catalog dataset
• Records: ~27,500 products
• Key Attributes: Category, sub-category, brand, sale price, market price, discount, ratings

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🔍 Analysis Focus

The analysis focuses on:

• Category-level product distribution
• Brand-wise product concentration
• Pricing and discount patterns across the catalog

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📊 Exploratory Data Analysis (Key Findings)

1️⃣ Brand-wise Product Assortment

Insight:
A small number of brands contribute a large share of products across multiple categories, indicating high brand concentration within the catalog and potential dependence on a limited set of brands.

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2️⃣ Category-wise Product Assortment

Insight:
Categories such as Beauty & Hygiene and Gourmet & World Food show deeper assortments, while categories like Baby Care and Fruits & Vegetables have relatively fewer product listings.

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📈 Power BI Dashboard

A Power BI dashboard was built to provide a consolidated view of:

• Category-wise product distribution
• Brand-level product concentration
• Price band segmentation and discount patterns

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🛠 Tools & Technologies

• Python (Pandas, NumPy, Matplotlib, Seaborn)
• Jupyter Notebook for exploratory data analysis
• Power BI for interactive dashboarding

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💼 Business Impact

• Identified brand concentration patterns across categories
• Highlighted categories with high and low assortment depth
• Supported data-driven decisions for catalog optimization and pricing review