This project involves data preparation, customer analytics, experimentation design, and commercial insights generation for a chips retail brand. It was carried out as part of an internship with Quantium, focusing on real-world customer purchasing behavior and store performance analysis.
- Task 1: Data Preparation and Customer Analytics
- Task 2: Experimentation and Uplift Testing
- Task 3: Strategic Insights and Client Reporting
- Clean and preprocess the transaction and customer datasets.
- Merge datasets for comprehensive analysis.
- Explore customer purchasing behavior.
- Total sales & average transaction value.
- Sales drivers by brand, packet size, customer segment.
- Geographic and demographic customer segmentation.
- Data visualizations and trend discovery.
- Select suitable control stores for each trial store.
- Design and implement a comparison framework.
- Assess the impact of layout trials on sales.
- Match stores using pre-trial sales patterns and customer characteristics.
- Evaluate trial impact using uplift and time-series visualizations.
- Generate visual comparisons and statistical insights.
- Business-ready client report using the Pyramid Principle.
- Insights on:
- Customer segment targeting
- Packet size optimization
- Store layout impact
- Visual dashboards and actionable recommendations.
- Python (pandas, numpy, matplotlib, seaborn)
- Jupyter Notebooks
- SQL (for initial exploration if needed)
- Plotly / Seaborn for interactive visualizations
- Excel/PowerPoint for final client presentation
This project helps the category team at Quantium:
- Understand chip-buying behavior by customer segment
- Evaluate the effectiveness of store layout trials
- Make data-driven decisions for H2 strategy
Harsh Nagar
Email: [harshnagar05631@gmail.com]
LinkedIn: [www.linkedin.com/in/harsh-nagar-8b17bb23a]