{"ID":2871243,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.11089","arxiv_id":"2509.11089","title":"What is in a Price? Estimating Willingness-to-Pay with Bayesian Hierarchical Models","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.","short_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 Hi...","url_abs":"https://arxiv.org/abs/2509.11089","url_pdf":"https://arxiv.org/pdf/2509.11089v1","authors":"[\"Srijesh Pillai\",\"Rajesh Kumar Chandrawat\"]","published":"2025-09-14T04:39:35Z","proceeding":"stat.AP","tasks":"[\"stat.AP\",\"cs.LG\",\"econ.EM\",\"stat.ML\"]","methods":"[]","has_code":false}