AI for Promotion Planning
AI-driven promotion planning enables organizations to understand and predict how pricing, campaigns, and marketing actions influence demand. By combining advanced statistical and machine learning models, these systems capture complex interactions between variables such as price, timing, advertising, and competition. They support ROI estimation, causal inference, and what-if simulations, allowing teams to evaluate both planned and past promotions with measurable attribution.
Next-Generation AI Promotion Planning
This approach overcomes the limitations of traditional models, which struggle to represent real-world promotional dynamics. The result is more accurate demand forecasting, better stock planning, and data-backed marketing decisions that improve efficiency and profitability.
Challenge
Promotion planning is often driven by intuition, legacy rules, and spreadsheet-based analysis. It is hard to isolate true uplift, account for cannibalization, or quantify the long-term impact of repeated campaigns across channels and categories.
Outcome
With AI-driven promotion planning, commercial teams gain a rigorous, experiment-ready framework that quantifies incremental lift, optimizes budgets, and simulates alternative scenarios. This enables a closed-loop process where every campaign becomes a structured learning opportunity that improves future decisions and overall marketing ROI.
Data Consolidation
Integrates historical sales, price, and promotional data with contextual factors such as advertising, seasonality, and competition into a unified modeling layer.
Recurrent Demand Modeling
Estimates baseline demand under normal (non-promotional) conditions using advanced AI-based regression and time series models that capture trends and seasonality.
Performance Monitoring
Tracks forecast accuracy and promotion impact to continuously refine model performance, ensuring that each campaign improves the next.
Simulation & Scenario Analysis
Enables what-if simulations so marketing and pricing teams can test different campaign structures, discounts, and timing before launch.
Forecast Integration
Combines baseline and promotion effects to predict demand under both regular and campaign conditions, feeding directly into demand planning and replenishment systems.
Promotion Impact Estimation
Measures the proportional effect of promotion variables — primarily price and advertising — on sales volume, enabling robust ROI and attribution analysis at product, store, and campaign level.