AI Pricing Optimization
Pricing optimization is the highest-ROI predictive problem in commerce — a 1% lift in price intelligence often beats a 5% lift in marketing. OctOpus builds the model that powers it: estimate price elasticity per SKU, predict demand at each candidate price, surface the optimal price under business constraints (margin floor, competitive ceiling).
What the model learns
Price elasticity per SKU or segment. Cross-elasticity between substitute and complementary products. Time-varying demand (seasonality, promotional uplift, day-of-week patterns). Competitor-price sensitivity when competitor data is available. Inventory-aware constraints. The agent picks LightGBM with monotonicity constraints for elasticity, NeuralForecast for time-varying demand, and stacks for the final price recommendation.
What you need
A historical transactions CSV (SKU, date, price, units sold), at minimum. Optional but powerful: competitor prices, promotions calendar, weather, customer segment. OctOpus profiles the data, detects which features carry signal, and trains accordingly. No price-science PhD required.
Deploy
The trained model becomes a prediction API. Plug it into your pricing dashboard or replenishment system. Re-train monthly on fresh data — OctOpus runs the same loop end-to-end on each refresh.
Key capabilities
- Price elasticity model per SKU or segment.
- Time-varying demand with seasonality and promotions.
- Margin and competitive-ceiling constraints honoured.
- Reproducible: every experiment's train.py is saved.
- Prediction API for live pricing decisions.