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What is in a Price? Estimating Willingness-to-Pay with Bayesian Hierarchical Models

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  • Srijesh Pillai
  • Rajesh Kumar Chandrawat

Abstract

For premium consumer products, pricing strategy is not about a single number, but about understanding the perceived monetary value of the features that justify a higher cost. This paper proposes a robust methodology to deconstruct a product's price into the tangible value of its constituent parts. We employ Bayesian Hierarchical Conjoint Analysis, a sophisticated statistical technique, to solve this high-stakes business problem using the Apple iPhone as a universally recognizable case study. We first simulate a realistic choice based conjoint survey where consumers choose between different hypothetical iPhone configurations. We then develop a Bayesian Hierarchical Logit Model to infer consumer preferences from this choice data. The core innovation of our model is its ability to directly estimate the Willingness-to-Pay (WTP) in dollars for specific feature upgrades, such as a "Pro" camera system or increased storage. Our results demonstrate that the model successfully recovers the true, underlying feature valuations from noisy data, providing not just a point estimate but a full posterior probability distribution for the dollar value of each feature. This work provides a powerful, practical framework for data-driven product design and pricing strategy, enabling businesses to make more intelligent decisions about which features to build and how to price them.

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  • Srijesh Pillai & Rajesh Kumar Chandrawat, 2025. "What is in a Price? Estimating Willingness-to-Pay with Bayesian Hierarchical Models," Papers 2509.11089, arXiv.org.
    • Handle: RePEc:arx:papers:2509.11089
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    References listed on IDEAS

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