Optimal pricing strategies adapt to changing buyer behavior
Optimal Contextual Pricing in Non-Stationary Environments
Machine Learning
Summary
This paper looks at how sellers can set the best prices when buyers’ preferences and values change over time. Buyers have different features that influence how much they’re willing to pay, but these can shift unpredictably. The authors create algorithms that adjust prices using limited feedback about whether a buyer purchased or not. Their methods work well even when buyer preferences change and they show mathematically that these strategies are nearly the best possible among similar approaches.
What this means in practice
- •For ecommerce platform engineers: Design pricing systems that adapt to shifting customer preferences using limited purchase data.
- •For online marketplace operators: Manage prices dynamically to maintain revenue when buyer behaviors evolve over time.
Authors
Yiding Feng, Alireza Rezaeimoghadam
Abstract
We study contextual dynamic pricing in a non-stationary environment. In each round, a seller observes a buyer's context, posts a price, and receives only binary purchase feedback. The buyer's value is linear in the context and an unobserved feature vector that may change over time, subject to a known total variation budget $V$. We measure dynamic regret against the first-best benchmark that extracts each buyer's full value. For identical contexts, we develop a deterministic algorithm that combines binary-search exploration, lower-envelope pricing, and periodic and inconsistency-triggered restarts. It achieves regret $O((\sqrt{TV}+1)\log T)$. With heterogeneous contexts, a new geometric difficulty arises: small feature changes can leave a relaxed feasible set narrow but far from the current feature vector. We address this difficulty through a multiscale shallow-pricing algorithm that maintains relaxed feasible sets and enclosing ellipsoids at several variation scales. Larger scales provide reliable localization certificates, while the smallest active scale determines the exploitation price. The algorithm achieves dynamic regret $O((d^2+d^{3/2}\sqrt{TV})\log^2 T)$, where $d$ is the context dimension. For fixed $d$, both guarantees match the lower bound $Ω(\sqrt{TV}+\log\log T)$ up to logarithmic factors in $T$, establishing near-optimality among deterministic algorithms for every $V\in[0,T]$, including the stationary case.