🛍️ Retail Promotion Analysis

A complete Retail Promotion Analytics project that evaluates the effectiveness of promotional campaigns using Python. This project includes exploratory data analysis (EDA), business insights, and visualizations to answer client business questions.

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📌 Project Overview

Promotional campaigns are widely used to increase sales and revenue in the retail industry. This project analyzes promotional campaign data to measure their impact on product sales, revenue, and customer purchasing behavior.

The analysis addresses seven client business requests using data visualization and business metrics such as Incremental Revenue (IR%) and Incremental Sold Units (ISU%).

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📂 Repository Structure

RetailPromotionAnalysis/
│
├── datasets/
│   ├── dim_campaigns.csv
│   ├── dim_products.csv
│   ├── dim_stores.csv
│   └── fact_events.csv
│
├── resources/
│   ├── dim_campaigns.jpg
│   ├── dim_products.jpg
│   ├── dim_stores.jpg
│   ├── fact_events.jpg
│   ├── Q1_Stores_by_City.png
│   ├── Q2_Sankranti_Category_Contribution.png
│   ├── Q3_Correlation_Heatmap.png
│   ├── Q4_Grocery_&_Staples.png
│   ├── Q4_Home_Care.png
│   ├── Q4_Combo1.png
│   ├── Q4_Home_Appliances.png
│   ├── Q4_Personal_Care.png
│   ├── Q5_ISU_by_City.png
│   ├── Q6_Hyderabad_IR_vs_ISU.png
│   └── Q7_Bengaluru_Revenue_Comparison.png
│
├── RetailPromotionAnalysis.ipynb
├── client_requests.pdf
├── Solutions.pdf
├── README.md

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🎯 Business Objectives

The project aims to answer the following business questions:

1. Analyze the distribution of stores across cities.
2. Measure category-wise contribution during the Sankranti campaign.
3. Examine the relationship between product price and quantity sold after promotion.
4. Study the distribution of quantity sold before promotions across product categories.
5. Compare Incremental Sold Units Percentage (ISU%) across cities.
6. Analyze Incremental Revenue (IR%) vs Incremental Sold Units (ISU%) for different promotion types in Hyderabad.
7. Compare revenue before and after promotions across product categories in Bengaluru.

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📊 Project Visualizations

Question	Visualization
Q1	Bar Chart – Number of Stores by City
Q2	Pie Chart – Category-wise Contribution
Q3	Correlation Heatmap
Q4	Histograms by Product Category
Q5	Line Chart – ISU% Across Cities
Q6	Scatter Plot – IR% vs ISU%
Q7	Clustered Bar Chart – Revenue Comparison

All generated charts are available in the resources/ directory.

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📈 Key Business Insights

🏪 Store Distribution

• Bengaluru has the highest number of retail stores.
• Chennai and Hyderabad follow closely, indicating strong market presence.

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🛒 Sankranti Campaign

• Grocery & Staples contributed over 70% of total quantity sold after promotion.
• Home Appliances ranked second in contribution.

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💰 Price vs Sales

• A weak positive correlation (~0.27) exists between post-promotion base price and quantity sold.
• Product pricing alone does not significantly influence sales volume.

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📦 Product Categories

• Grocery & Staples consistently exhibit the highest demand.
• Personal Care products contribute the lowest sales volume.

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📈 ISU% Analysis

• Madurai achieved the highest Incremental Sold Units Percentage.
• Visakhapatnam recorded the smallest improvement after promotions.

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🎯 Promotion Effectiveness

• BOGOF generated the highest increase in units sold.
• 500 Cashback produced the highest increase in revenue.

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💵 Revenue Analysis

• Combo1 products experienced the highest revenue growth after promotions.
• Personal Care showed a decline in revenue, indicating scope for improving promotional strategies.

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📁 Dataset Description

Dataset	Description
dim_campaigns.csv	Campaign information
dim_products.csv	Product details
dim_stores.csv	Store information
fact_events.csv	Promotional sales transactions

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🛠️ Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Jupyter Notebook
• Git
• GitHub

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🚀 Getting Started

Clone the Repository

git clone https://github.com/vijaysamco/RetailPromotionAnalysis.git

Navigate to the Project

cd RetailPromotionAnalysis

Install Dependencies

pip install pandas numpy matplotlib

Run the Python notebooks or scripts to reproduce the analysis.

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📄 Project Report

A comprehensive report containing:

• Business Questions
• Methodology
• Visualizations
• Business Insights
• Conclusions

is available in:

📘 Solutions.pdf

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📸 Resources

All visualizations generated during the analysis are stored in the resources/ folder.

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🎯 Conclusion

This project demonstrates how data analytics can evaluate the success of retail promotional campaigns using business metrics such as Incremental Revenue (IR%) and Incremental Sold Units (ISU%). The findings provide actionable insights into customer purchasing behavior, campaign effectiveness, and regional performance, enabling informed business decisions.

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🤝 Connect

If you found this project useful, feel free to ⭐ star the repository and share your feedback.