A Reference Point Logit Model for Estimating Substitution Probabilities Using Point of Sale Data

Abstract

This paper presents a practical approach to estimate the substitution probabilities between products at a retail store by using the store’s point of sale data and prospect theory based structural restrictions on the consumer choice behavior. The prospect theory based reference dependent preference structure imposed on the consumer choice behavior (a) accounts for how consumers make their original choice as well as how they substitute, (b) eliminates the IIA and IPS assumptions that the standard utility theory based models impose on consumer choice, and (c) alleviates the need for inventory information for estimating the substitution probabilities. Simulations have been used to show that the estimates of the substitution probabilities are efficient and are robust to stock-out rates, and two empirical studies are presented to illustrate the estimation process.