AtliQ Mart Analysis

Project Overview

AtliQ Mart Analysis is a comprehensive promotional performance evaluation project for AtliQ Mart, a retail giant with over 50 supermarkets across southern India. The project analyzes the effectiveness of promotions run during Diwali 2023 and Sankranti 2024 on AtliQ branded products, providing actionable insights to the sales director for future promotional planning.

Key Insights

• Store Performance Analysis:
  • Identified top 10 stores by Incremental Revenue (IR) from promotions
  • Analyzed bottom 10 stores by Incremental Sold Units (ISU)
  • Explored performance variations across different cities
  • Discovered common characteristics among top-performing stores
• Promotion Type Analysis:
  • Determined top 2 promotion types for highest Incremental Revenue
  • Identified bottom 2 promotion types by impact on Incremental Sold Units
  • Compared performance between discount-based, BOGOF, and cashback promotions
  • Revealed promotions that balanced increased sales with healthy margins
• Product and Category Analysis:
  • Highlighted product categories with highest sales lift from promotions
  • Analyzed specific products' responses to different promotion types
  • Established correlation between product categories and promotion effectiveness

Colab Notebook Preview

Technical Implementation

The project utilizes Python for data analysis and visualization, with a focus on the following techniques:

• City-wise revenue analysis using bar and line charts
• Creation of calculated variables (ISU, IR, percentages) for comprehensive metrics
• Visualization of category sales dynamics with donut and pie charts
• Revenue trend summarization by stores in tabular format
• Promotional type frequency analysis using value_counts() and waterfall charts
• Product performance assessment by campaign and promotion type
• Distribution visualization of sales and revenue by category using various chart types

Key Learnings

This project provided valuable experience in:

• Advanced data manipulation using pandas library
• Creating calculated variables on the fly (similar to measures in Power BI)
• Designing effective visualizations with dual y-axes and annotations
• Building various chart types for different data representation needs
• Presenting complex metrics in concise, readable tabular formats
• Leveraging value_counts() for category analysis
• Comparing pre and post-promotion metrics for data-driven decision making