Promograph: Solving Retail Promotion Waste with GNNs

Promograph uses Graph Neural Networks (GNNs) to model retail promotion waste by representing SKUs as nodes and co-purchase patterns as edges, enabling the propagation of discount impacts across the product graph. This graph-based architecture moves beyond standard tabular models like XGBoost to capture structural relationships in retail data, predicting volume changes for both promoted and non-promoted items.

Promograph: Solving Retail Promotion Waste with GNNs The technical bottleneck is that standard tabular models treat SKU relationships as independent variables. To actually model the ripple effect of a promotion on a category, you need Graph Neural Networks GNNs . By representing products as nodes and co-purchase patterns as edges, a GNN can propagate the impact of a discount across the entire product graph. For those trying to implement a similar AI workflow for demand forecasting, the shift from a standard XGBoost approach to a graph-based architecture usually looks like this: 1. Graph Construction: Map SKUs as nodes. Edges are weighted by the strength of the association lift/support in historical transaction data. 2. Feature Embedding: Nodes are initialized with static attributes category, brand, price point . 3. Message Passing: The GNN aggregates features from neighboring nodes, allowing the model to "understand" that a discount on pasta might spike the sales of a specific premium sauce. 4. Prediction: The final layer predicts the delta in volume for both the promoted and non-promoted nodes. This approach moves the needle from simple "A implies B" correlation to a structural understanding of the retail ecosystem. It's a deep dive into how geometric deep learning handles sparse, high-cardinality retail data far better than flat vectors. Next Open-Source AI: The Lobbying Conflict → /en/threads/3845/