Nonlinear pricing improves revenue and speeds up data sales
Non-Linear Pricing Restores Tractability for a Data Seller
Computer Science and Game Theory
Summary
Many data sellers want to set prices for different datasets to make the most money, but buyers have limited budgets and care about data accuracy. Previous research showed that using simple linear prices makes finding the best prices very hard. This paper shows that if sellers use more flexible, non-linear pricing rules, they can make more money and find the best prices much faster. The authors also show these better price rules have a simple shape and tested their idea using real data simulations.
What this means in practice
- •For data marketplace operators: Design pricing schemes that increase revenue and allow efficient computation of optimal prices for multiple datasets under buyer budget limits.$Commercial implications: Enables building advanced pricing products for data sellers that outperform and compute faster than traditional linear pricing.
- •For software pricing teams: Create flexible pricing models that balance buyer budgets and usage to boost sales and simplify optimization challenges.
Tested on simulated data.
Authors
Bhaskar Ray Chaudhury, Jugal Garg, Eklavya Sharma, Jiaxin Song
Abstract
We consider a data seller who designs pricing mechanisms over multiple datasets to maximize revenue from budget-constrained buyers. The seller offers multiple datasets and assigns each a pricing function that maps the quantity purchased to a total payment. The goal is to design these pricing functions to maximize revenue, anticipating that buyers---who trade off accuracy gains against cost---choose bundles optimally subject to their budget constraints. Prior work [Chaudhury et al., 2026] studies such optimal pricing under the restriction that each dataset is assigned a linear price, and shows that computing optimal linear prices is computationally intractable. In contrast, we allow each dataset to be priced via a general function and show that this additional flexibility can not only increase the revenue but also restore tractability, yielding a surprising simultaneous improvement in economic performance and computational efficiency. Even when pricing functions are only required to be monotone and lower-continuous, optimal pricing admits a highly structured and simple form: each pricing function is piecewise linear and convex (PLC), and the optimal solution can be computed in polynomial time. Moreover, the total number of kinks across all pricing functions is bounded by the number of buyers. Consequently, when datasets significantly outnumber buyers, most pricing functions are effectively linear. We further empirically study the structure of optimal pricing by analyzing the number of kinks and the revenue gap between optimal nonlinear pricing and optimal linear pricing on simulations generated from a real dataset.