# DISCO: An End-to-End Bandit Framework for Personalised Discount Allocation

arXiv: https://arxiv.org/abs/2406.06433 · HTML: https://mlsanity.com/paper/2406.06433/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 10 Jun 2024.

## Builds on (most cited first, 9 of 9)

- [Off-Policy Evaluation for Large Action Spaces via Embeddings](https://mlsanity.com/paper/2202.06317/) (2022)
- [Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting](https://mlsanity.com/paper/1902.01520/) (2019)
- [Contextual Bandits with Large Action Spaces: Made Practical](https://mlsanity.com/paper/2207.05836/) (2022)
- [Efficient Contextual Bandits with Continuous Actions](https://mlsanity.com/paper/2006.06040/) (2020)
- [Markdowns in E-Commerce Fresh Retail: A Counterfactual Prediction and Multi-Period Optimization Approach](https://mlsanity.com/paper/2105.08313/) (2021)
- [A Map of Bandits for E-commerce](https://mlsanity.com/paper/2107.00680/) (2021)
- [Dynamic Pricing for Airline Ancillaries with Customer Context](https://mlsanity.com/paper/1902.02236/) (2019)
- [Promotheus: An End-to-End Machine Learning Framework for Optimizing Markdown in Online Fashion E-commerce](https://mlsanity.com/paper/2207.01137/) (2022)
- [Modeling Price Elasticity for Occupancy Prediction in Hotel Dynamic Pricing](https://mlsanity.com/paper/2208.03135/) (2022)

## Built on by (most cited first, 3 of 3)

- [Scalable and Interpretable Contextual Bandits: A Literature Review and Retail Offer Prototype](https://mlsanity.com/paper/2505.16918/) (2025)
- [Budget-Constrained Causal Bandits: Bridging Uplift Modeling and Sequential Decision-Making](https://mlsanity.com/paper/2604.26169/) (2026)
- [Bandit Simulation for Average Reward Inference](https://mlsanity.com/paper/2606.00913/) (2026)

## Abstract (arXiv)

Personalised discount codes provide a powerful mechanism for managing customer relationships and operational spend in e-commerce. Bandits are well suited for this product area, given the partial information nature of the problem, as well as the need for adaptation to the changing business environment. Here, we introduce DISCO, an end-to-end contextual bandit framework for personalised discount code allocation at ASOS. DISCO adapts the traditional Thompson Sampling algorithm by integrating it within an integer program, thereby allowing for operational cost control. Because bandit learning is often worse with high dimensional actions, we focused on building low dimensional action and context representations that were nonetheless capable of good accuracy. Additionally, we sought to build a model that preserved the relationship between price and sales, in which customers increasing their purchasing in response to lower prices ("negative price elasticity"). These aims were achieved by using radial basis functions to represent the continuous (i.e. infinite armed) action space, in combination with context embeddings extracted from a neural network. These feature representations were used within a Thompson Sampling framework to facilitate exploration, and further integrated with an integer program to allocate discount codes across ASOS's customer base. These modelling decisions result in a reward model that (a) enables pooled learning across similar actions, (b) is highly accurate, including in extrapolation, and (c) preserves the expected negative price elasticity. Through offline analysis, we show that DISCO is able to effectively enact exploration and improves its performance over time, despite the global constraint. Finally, we subjected DISCO to a rigorous online A/B test, and find that it achieves a significant improvement of >1% in average basket value, relative to the legacy systems.