Machine Learning based Framework for Robust Price-Sensitivity Estimation with Application to Airline Pricing

• URL: http://arxiv.org/abs/2205.01875v1
• Date: Wed, 4 May 2022 03:35:12 GMT
• Title: Machine Learning based Framework for Robust Price-Sensitivity Estimation with Application to Airline Pricing
• Authors: Ravi Kumar, Shahin Boluki, Karl Isler, Jonas Rauch and Darius Walczak
• Abstract summary: We consider the problem of dynamic pricing of a product in the presence of feature-dependent price sensitivity. We construct a flexible yet interpretable demand model where the price related part is parametric. The remaining (nuisance) part of the model is non-parametric and can be modeled via sophisticated ML techniques.
• Score: 20.5282398019991
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider the problem of dynamic pricing of a product in the presence of feature-dependent price sensitivity. Based on the Poisson semi-parametric approach, we construct a flexible yet interpretable demand model where the price related part is parametric while the remaining (nuisance) part of the model is non-parametric and can be modeled via sophisticated ML techniques. The estimation of price-sensitivity parameters of this model via direct one-stage regression techniques may lead to biased estimates. We propose a two-stage estimation methodology which makes the estimation of the price-sensitivity parameters robust to biases in the nuisance parameters of the model. In the first-stage we construct the estimators of observed purchases and price given the feature vector using sophisticated ML estimators like deep neural networks. Utilizing the estimators from the first-stage, in the second-stage we leverage a Bayesian dynamic generalized linear model to estimate the price-sensitivity parameters. We test the performance of the proposed estimation schemes on simulated and real sales transaction data from Airline industry. Our numerical studies demonstrate that the two-stage approach provides more accurate estimates of price-sensitivity parameters as compared to direct one-stage approach.

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