Optimizing Pricing Strategy Based on Demand Elasticity for Café Menu Items
Python | Data Manipulation (pandas, numpy) | Data Visualization (matplotlib, seaborn) | Statistical Modeling (Linear, Polynomial, Spline Regression) | Machine Learning (Scikit-learn) | Price Elasticity Modeling | Profit Maximization | Experiment Tracking (ML Foundry) | Model Evaluation (RMSE, MAE, MSE, R²) | Data Imputation | Business Strategy | Economic Forecasting | Pricing Strategy Optimization
Project Description
This project focused on optimizing the pricing strategy for various café items by analyzing their demand elasticity. The goal was to determine the ideal pricing points that would maximize profitability while considering consumer sensitivity to price changes. By leveraging advanced data analytics and predictive modeling techniques, I evaluated the relationship between price and quantity sold for items like burgers, coke, lemonade, and coffee. Statistical methods such as Ordinary Least Squares (OLS) regression were used to assess price elasticity, revealing how consumers react to price changes.
The analysis provided critical insights into the optimal pricing strategy, guiding data-driven decision-making for maximizing profits. The findings indicated that demand for different products varied significantly, with some items demonstrating highly elastic demand, where even slight price increases could lead to substantial decreases in sales, while others showed more inelastic demand.
Project Skills
Statistical Analysis: Applied OLS regression models to calculate price elasticity of demand for each café item. Techniques such as p-value assessment, R-squared analysis, and Durbin-
Watson statistics were used to validate the model’s effectiveness.
Predictive Modeling: Built and tested models to predict the impact of price changes on sales volume, helping to determine the optimal pricing that maximizes profitability while minimizing lost sales.
Data Visualization: Created scatterplots, histograms, and regression plots using Seaborn and Matplotlib to visualize the relationship between price and quantity sold, and to communicate findings to stakeholders.
Business Economics: Utilized demand elasticity principles to assess consumer price sensitivity and advised on data-driven pricing strategies to maximize profit margins.
Optimization Techniques: Used predictive models to estimate profit maximization points for each product based on variable costs and demand elasticity.
Data Wrangling and Exploration: Cleaned and structured data from multiple sources, including sales, transactions, and external factors like temperature and holidays, to ensure accurate analysis.
Project Demostrates
In this project, I utilized OLS regression to calculate the price elasticity for each café item, offering valuable insights into consumer behavior. For instance, the price elasticity for "Coke" was found to be -7.46, meaning that a 1% increase in price would result in a 7.46% decrease in the quantity sold. By identifying the optimal prices for all café items, the project successfully maximized profit potential. For example, the optimal price for the "Burger_1070" product was calculated to be $17.45, leading to an estimated profit of $554.14. These pricing strategies ensure profitability while maintaining a competitive market presence. Throughout the analysis, key business and economic principles such as marginal cost analysis, consumer behavior, and demand forecasting were applied to understand the relationship between pricing and consumer purchasing decisions. The result was the development of revenue-increasing strategies that do not compromise customer retention. Furthermore, I provided café management with data-driven recommendations for adjusting their pricing strategy, ensuring that future decisions are grounded in real-time data and predictive analysis for continued profitability.