Research: Estimating Price Elasticity and Optimizing Dynamic Pricing Strategies Through...

Research question: How can price elasticity be accurately estimated and leveraged to optimize dynamic pricing strategies in digital marketplaces using experimental and econometric methods?

Background & Motivation: Dynamic pricing is increasingly utilized in digital marketplaces to maximize revenue and adapt to real-time market changes. Accurate estimation of price elasticity—the responsiveness of demand to price changes—is critical for effective dynamic pricing but remains methodologically challenging in practice.

Research Gap: While prior studies have explored price elasticity using historical data, fewer have conducted controlled field experiments in real-world digital environments to test and refine dynamic pricing strategies. There is a need to bridge econometric estimation with experimental validation to inform actionable pricing decisions.

Approach & Expected Contribution: This project reviews the literature on price elasticity estimation and dynamic pricing, formulates testable hypotheses, and designs online field experiments (e.g., A/B or multi-arm bandit tests) on a digital platform or using publicly available datasets (such as Airline Reporting Corporation or Kaggle e-commerce data). Econometric models (e.g., logit/probit, regression discontinuity) will be applied to estimate elasticity, with results compared to baseline approaches. The research aims to provide actionable guidelines for practitioners on integrating estimation and experimentation in pricing decisions.

Why it Matters: The findings will inform both scholarly understanding and managerial practice, contributing to more robust, data-driven pricing strategies that can improve profitability and market competitiveness in digital sales channels.