Retail AI Solutions for Demand Forecasting and Promotion Optimization
Explains how Rubikloud uses ML to improve retail demand forecasts, promotion planning, and margin outcomes at SKU-DC-customer granularity.
The document outlines Rubikloud's Price & Promotion Manager, an AI/ML engine that generates automated forecasts incorporating promotion effects, cannibalization, seasonality, price elasticity, and historical stockouts. It details use cases for overstock reduction, new SKU forecasting, and profitable promotion selection, with reported outcomes including 26% forecast accuracy gains on new items and $39M margin savings.
ML models factor promotion performance, cross-product effects, and residual basket data into forecasts
Promotion optimization identifies scenarios that maximize sales uplift and margin improvement
New SKU forecasting applies product attributes and NLP to raise accuracy by 26%
SKU-DC-customer level granularity supports targeted inventory and promotion decisions
Cloud-based, productized deployment delivers measurable results in months
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