M&S: Markdown Optimization with Causal ML

The dynamic retail environment presents unique challenges for causal inference in optimal pricing. Evolving business tactics, seasonality, changing product assortments, and censored demand create highly contextual, heterogeneous, and time-varying price response effects. In this presentation, we will demonstrate how causal ML is utilized for markdown optimization at Marks & Spencer. We will explain our approach to predicting price elasticities, accounting for uncertainty, and address the pitfalls of using aggregated data for causal inference. Our experiences illustrate how these methods can lead to more robust and effective decision-making processes in complex retail environments.

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  Mindaugas Zickus Lead Data Scientist, Marks and Spencer