DEMAND ESTIMATION AND PRICING IN E-COMMERCE AND HEALTH INSURANCE

Abstract

Demand estimation is a crucial aspect of revenue management. Its primary objective is to leverage historical data to forecast future demand and inform operational decisions. The accuracy of demand estimation directly impacts a firm's revenue, as it involves analyzing consumer behavior to predict future patterns and make informed operational choices. However, there is no one-size-fits-all solution that can be universally applied across all scenarios. In this thesis, we narrow down our focus to the E-commerce and insurance industries, where we tackle the specific challenges of demand estimation and pricing, respectively. We will explore the prevalent demand modeling approach, the prediction of future demand and the design of product prices, with adaptation to different contexts.