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Case study

Drivers of demand

Building a causal model to identify the drivers of demand for a product.



  • Sales quantities show significant fluctuations over time.

  • Client wants to understand why this is the case and how to stabilize and increase demand.


Step 1 - Data refinement

  • Combine and clean existing data sources (sales quantities, pricing, marketing activity, etc.).

  • Enrich existing dataset with additional external data (competitor pricing, macroeconomic metrics, Google trends, etc.).

Step 2 - Causal Model

  • Leverage human expert knowledge from sales team combined with algorithmic to create a DAG (directed acyclic graph) representing the causal dynamics behind demand.

  • Based on the DAG and enriched dataset, create a Causal AI to model demand.

  • Identified and explained main drivers behind demand.

  • Robust prediction model to forecast demand.

  • Identified optimal product pricing to maximize profit.

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