Predictive Modeling for Sales Optimization and Pricing Strategies

This thesis explores the application of predictive modeling for optimizing sales and pricing strategies. It begins by establishing the fundamental concepts of predictive modeling and its role in informing pricing decisions. Two prominent methods for demand forecasting at various price points are then examined, linear regression and price elasticity of demand. The thesis further goes through the practical application of these techniques within a corporate setting. Specifically, it explores the functionalities and benefits of Oracle Retail Predictive Application Server (RPAS). This software plays a crucial role in our company’s pricing strategy, assortment planning, and Merchandise Financial Planning (MFP). The concluding section details the internship project undertaken by this study, utilizing RPAS for pricing optimization. This project exemplifies the practical application of the theoretical concepts discussed earlier. The results and learnings from the project contribute valuable insights into the effectiveness of predictive modeling for sales and pricing strategies.