A Data Analytics Case Study on Quick Commerce Sector in India.
This case study analyzes delivery, category performance, and order data of the Indian Quick Commerce (Q-Commerce) sector.
The goal is to uncover operational insights, understand delivery performance, and recommend strategies for improving profitability and customer experience.
- What product categories and discount strategies drive higher average order value in Q-commerce
- What is the revenue contribution of each product category?
- How does dark store performance impact delivery efficiency?
- What strategic actions can improve delivery time and profitability?
The dataset used in this project contains:
- Order Details: Order ID, Category, Order Time, Delivery Time
- Customer Info: Customer ID , Name , Gender , City
- Cost & Revenue Metrics: Revenue, Gross Revenue, Net Revenue
- Delivery Metrics: Delivery Time (minutes), Delivery Zone
- Performance Indicators: Dark Store ID, Delivery Status
- Python: Data manipulation and analysis
- Pandas: Data cleaning, aggregation
- Matplotlib & Seaborn: Data visualization
- Jupyter Notebook: Analysis and visualization interface
- 📈 Product Categories: Groceries and Personal Care contribute most to revenue, but groceries often see net losses when heavy discounts are applied
- 🌦️ Weather Impact: Rainy and stormy days correlate with longer delivery delays and increased operational costs.
- 🏬 Dark store performance directly affects delivery efficiency, highlighting the importance of strategic dark store placement.
- 📦 Low AOV Drives Margin Pressure: A large volume of low-ticket orders, especially under ₹300, contributes to poor unit economics due to high fulfillment and delivery costs.
- Expand dark store presence in high-demand zones to optimize delivery times.
- Focus on top-performing product categories to boost profitability.
- Optimize delivery routes in cities with longer delivery times.
- Regularly monitor dark store performance for continuous improvement.
- Increase Average Order Value (AOV) ,Encourage larger baskets through bundling, cross-selling, and minimum order thresholds to improve profit per delivery.
This project provided data-driven insights into the operational challenges and opportunities of the Q-Commerce sector in India.
With a focus on delivery efficiency, category performance, and dark store optimization, these findings can help businesses achieve operational excellence and strategic growth.
Qcommerce_India_Case_Study_EDA.ipynb— Complete analysis notebookQcommerce_case_study.csv- DatasetREADME.md— Project overview and insights
Premveer Yadav — Data Analytics Enthusiast LinkedIn Profile | GitHub Profile
This project is licensed under the MIT License.