# Model Distillation for Revenue Optimization: Interpretable Personalized Pricing

arXiv: https://arxiv.org/abs/2007.01903 · HTML: https://mlsanity.com/paper/2007.01903/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2020.

## Built on by (most cited first, 24 of 24)

- [Scalable Optimal Multiway-Split Decision Trees with Constraints](https://mlsanity.com/paper/2302.06812/) (2023)
- [Loss Functions for Discrete Contextual Pricing with Observational Data](https://mlsanity.com/paper/2111.09933/) (2021)
- [When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction](https://mlsanity.com/paper/2206.02058/) (2022)
- [Constrained Prescriptive Trees via Column Generation](https://mlsanity.com/paper/2207.10163/) (2022)
- [Participatory Personalization in Classification](https://mlsanity.com/paper/2302.03874/) (2023)
- [Regulatory Instruments for Fair Personalized Pricing](https://mlsanity.com/paper/2202.04245/) (2022)
- [Interpretable Personalized Experimentation](https://mlsanity.com/paper/2111.03267/) (2021)
- [Implementing Reinforcement Learning Datacenter Congestion Control in NVIDIA NICs](https://mlsanity.com/paper/2207.02295/) (2022)
- [Optimizing over an ensemble of neural networks](https://mlsanity.com/paper/2112.07007/) (2021)
- [Constraint Learning to Define Trust Regions in Predictive-Model Embedded Optimization](https://mlsanity.com/paper/2201.04429/) (2022)
- [Convex Surrogate Loss Functions for Contextual Pricing with Transaction Data](https://mlsanity.com/paper/2202.10944/) (2022)
- [Learning Prescriptive ReLU Networks](https://mlsanity.com/paper/2306.00651/) (2023)
- [Interpretable Price Bounds Estimation with Shape Constraints in Price Optimization](https://mlsanity.com/paper/2405.14909/) (2024)
- [Policy Trees for Prediction: Interpretable and Adaptive Model Selection for Machine Learning](https://mlsanity.com/paper/2405.20486/) (2024)
- [DeepRule: An Integrated Framework for Automated Business Rule Generation via Deep Predictive Modeling and Hybrid Search Optimization](https://mlsanity.com/paper/2512.03607/) (2025)
- [Practical Policy Optimization with Personalized Experimentation](https://mlsanity.com/paper/2303.17648/) (2023)
- [PresAIse, A Prescriptive AI Solution for Enterprises](https://mlsanity.com/paper/2402.02006/) (2024)
- [Robust personalized pricing under uncertainty of purchase probabilities](https://mlsanity.com/paper/2407.15332/) (2024)
- [From approximation error to optimality gap -- Explaining the performance impact of opportunity cost approximation in integrated demand management and vehicle routing](https://mlsanity.com/paper/2412.13851/) (2024)
- [The Value of AI Advice: Personalized and Value-Maximizing AI Advisors Are Necessary to Reliably Benefit Experts and Organizations](https://mlsanity.com/paper/2412.19530/) (2024)
- [PrefBench: Evaluating Zero-Shot LLM Agents in Hidden-Preference Personalized Pricing Negotiations](https://mlsanity.com/paper/2605.22855/) (2026)
- [Insurance Pricing Optimization via Off-Policy Evaluation](https://mlsanity.com/paper/2605.28327/) (2026)
- [Counterfactual Optimal Action Trees (COAT): Interpretable Prescriptive Policies from Observational Data](https://mlsanity.com/paper/2607.14318/) (2026)
- [Learning Choice Model Trees for Feature-Based Multi-Product Pricing: Exact Optimization and Field Evidence](https://mlsanity.com/paper/2609.16952/) (2026)

## Abstract (arXiv)

Data-driven pricing strategies are becoming increasingly common, where customers are offered a personalized price based on features that are predictive of their valuation of a product. It is desirable for this pricing policy to be simple and interpretable, so it can be verified, checked for fairness, and easily implemented. However, efforts to incorporate machine learning into a pricing framework often lead to complex pricing policies which are not interpretable, resulting in slow adoption in practice. We present a customized, prescriptive tree-based algorithm that distills knowledge from a complex black-box machine learning algorithm, segments customers with similar valuations and prescribes prices in such a way that maximizes revenue while maintaining interpretability. We quantify the regret of a resulting policy and demonstrate its efficacy in applications with both synthetic and real-world datasets.