A Prior-Predictive Monte Carlo Framework for Pricing Complex Data Products in Data-Poor Markets

TL;DR

This paper introduces a Bayesian Monte Carlo framework for pricing complex data products in markets with limited transaction data, providing probabilistic price ranges and ensuring fair, consistent valuation.

Contribution

It presents a novel prior-predictive Monte Carlo method that generates probabilistic price bands for data products without relying on extensive empirical data.

Findings

Produces stable probabilistic price bands (P5/P50/P95)

Enables auditable and repeatable pricing processes

Bridges traditional experience-based and data-driven pricing methods

Abstract

Pricing advanced data products - particularly in complex fields such as semiconductor manufacturing - is a fundamentally challenging task due to the sparsity of publicly available transaction data, and its frequent heterogeneity and confidentiality. While data value depends on multiple interacting factors, such as technical sophistication, quality, utility, and licensing rights, traditional pricing methods tend to rely on ad-hoc heuristics or require massive amounts of historical transaction data. In an increasingly data-based economy, we introduce a prior-predictive Monte Carlo framework that enables the generation of fair, consistent, and justified price ranges for data products in the absence of empirical data. By simulating many plausible pricing 'worlds' and deal configurations, the framework produces stable probabilistic price bands (e.g., P5/P50/P95) rather than single point estimates, creating an auditable and repeatable probabilistic pricing system with business realism enforced via constraint-truncated priors. The proposed model bridges traditional data pricing rooted in professional experience with a data-based approach that also allows for classical Bayesian updating as more transaction data is accumulated.