Case study
Drivers of demand
Building a causal model to identify the drivers of demand for a product.
Challenge
Solution
Sales quantities show significant fluctuations over time.
Client wants to understand why this is the case and how to stabilize and increase demand.
Impact
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.