Towards Data Science is a community-powered publication that showcases work in data science, machine learning and artificial intelligence. Every day newcomers, seasoned researchers and industry practitioners publish tutorials, research notes and real-world case studies that help the field move forward.

Towards Data Science

This post introduces hierarchical Bayesian modeling for estimating product-level price elasticities. The method incorporates data from various groupings to improve the estimation of individual-level effects, which is particularly beneficial for products with limited data. The article describes the modeling process, challenges of traditional approaches, and benefits of hierarchical Bayesian models, supported by Python code using the Numpyro library. Various applications of the method in retail demand forecasting and recommendation systems are also discussed.

Estimating Product-Level Price Elasticities Using Hierarchical Bayesian