Retail discount optimization

A data-driven analysis of discount strategies for a U.S. supermarket chain, identifying optimal pricing approaches that balance customer acquisition with profitability. This project demonstrates strategic use of business intelligence tools to solve real-world retail challenges.

Key Achievement:

Identified discount optimization strategies that could increase profit margins from 12% to 18% without sacrificing sales volume.

Business Problem

Retail supermarkets face a critical challenge: while discounts attract customers, excessive discounting erodes profit margins. This analysis addresses:

• What is the relationship between discount level and profit margin?
• Which product categories benefit most from discounting?
• How do geographic differences influence discount effectiveness?

Dataset

• Source: Kaggle Superstore Sales Dataset
• Size: 9,994 sales transactions
• Scope: Multiple U.S. stores across different regions
• Key Variables: Sales, Profit, Discount, Category, Sub-category, Region, State, Quantity

Tools and Technologies

• Microsoft Power BI: Data visualization and analysis
• Data Analysis: Segmentation, calculated fields, KPI development
• Business Intelligence: Dashboard design and stakeholder reporting

Key Findings

1. Category Performance

• Technology: Maintains strong margins (15-20%) with moderate discounts due to high price sensitivity
• Furniture: Suffers from excessive discounting (18% average) - reducing unnecessary sales volume
• Office Supplies: Shows minimal response to discounts - customers purchase regardless

2. Geographic Insights

• Top Performers: California, New York, Washington maintain healthy margins with modest discounts
• Risk States: Several states show negative profits at 25%+ discount levels
• Regional Patterns: All regions profit most from 0-10% discount ranges

3. Critical Thresholds

• Optimal Range: 0-10% discounts generate consistent profitability across all regions
• Caution Zone: 15-20% discounts work only for specific products in Western region
• Loss Zone: 25%+ discounts consistently generate losses and should be eliminated

Strategic Recommendations

Immediate Actions

• Eliminate 25%+ discounts: Stop all deep discounting immediately
• Cap Furniture discounts at 8-10% (down from 18%)
• Minimize Office Supplies discounts: demand is price-inelastic
• Maintain Technology discounts at moderate levels for price-sensitive customers

Regional Strategy

• Implement California's successful discount model across other states
• Customize discount levels based on regional performance data
• Deploy an automated monitoring system for real-time optimization

Expected Impact

• Profit Margin Increase: 12% → 18% (+50% improvement)
• Sales Volume: Maintained at current levels
• Strategic Positioning: Data-driven pricing vs. blanket discounting

Visualizations

Presentation

Discount Optimization Strategy Presentation.pdf

View Project Presentation Video

Acknowledgments

Dataset: Roopa Calistus (Kaggle)