This involves predicting the increase in demand due to marketing campaigns, discounts, or events. It’s more complex than baseline forecasting due to variability and short time frames. Underestimating promotional demand can lead to stockouts, while overestimating can cause overstock and markdowns. Both erode margins and customer satisfaction. Inventory optimization platforms apply uplift factors and past promotional data to refine forecasts. Machine learning models help identify what type of promotion, channel, or timing yields the most accurate demand prediction.
