Price Optimization
Dynamic Price Optimization for Retail Products
Client
Self Development
YeAr
2025
Category
Machine Learning & Predictive Modeling
Service
Optimizing Operations
Tools / Languages Used
- Python (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn)
- SQL for data extraction
- Tableau for visualization
- Jupyter Notebook for experimentation
Technical Skills
- Data preprocessing and feature engineering
- Regression modeling (Linear, Ridge, Lasso)
- Price elasticity and demand modeling
- Cross-validation and model performance evaluation
- Data visualization and reporting
Soft Skills
- Business problem framing
- Cross-functional collaboration with marketing and sales teams
- Translating technical insights into business recommendations
- Iterative problem solving and stakeholder communication
Step 1: Exploratory Data ANalysis
- Analyzed 2 years of historical sales and pricing data across 50+ SKUs.
- Identified correlations between price, seasonality, promotions, and sales volume.
- Discovered that discount campaigns drove short-term sales spikes but eroded profit margins by ~8%.
- Visualized trends to communicate findings with stakeholders using Tableau dashboards.
Step 2: Solution Design
- Designed a price elasticity model to quantify how sensitive demand is to price changes for each product category.
- Selected relevant features such as competitor prices, historical demand, marketing spend, and seasonality.
- Used Ridge Regression to avoid overfitting while capturing relationships between variables.
- Defined business constraints (e.g., minimum margin thresholds, price floors) to ensure realistic recommendations.
Step 3: Model Assessment
- Evaluated models using Mean Absolute Percentage Error (MAPE) and Adjusted R².
- Achieved an average MAPE of 6.3% across categories.
- Validated results with the business team to confirm interpretability and practical usability.
Step 4: Results / How It’s Used
Built the model for self development. No further deployments associated.
Built the model for self development. No further deployments associated.
