Key Metrics to Track for Capturing Events and Seasonality Impact on Demand Predictions for Growing Brands

Many growing brands rely on a single accuracy metric such as MAPE to evaluate forecasting performance. However, traditional metrics often fail to capture the impact of seasonal demand cycles or promotional events.

This can create a false sense of confidence in demand predictions while inventory risk remains hidden at SKU-level granularity.

Weighted Mean Absolute Percentage Error (WMAPE) accounts for forecast error across high-volume SKUs, ensuring that demand spikes during events receive appropriate weighting.

Tracking WMAPE enables planners to evaluate forecast reliability during peak demand windows.

Bias metrics expose systematic over- or under-forecasting associated with seasonal demand cycles.

High bias during promotional periods often indicates poor event capture in forecasting workflows.

Error contribution highlights which SKUs generate the highest forecast deviations during seasonal demand variability.

Tracking this metric allows planners to prioritize event-driven demand drivers for improvement.

Service-level metrics link forecast performance to fulfillment outcomes.

Monitoring service levels during promotional campaigns helps planners evaluate the effectiveness of event-aware forecasting.

Metrics such as Days Inventory Outstanding (DIO) and Inventory Turnover reflect how well demand predictions align with seasonal consumption patterns.

Improved alignment reduces overstock and stockout risk.

Tracking metrics that capture event-driven variability enables growing brands to improve demand predictions sustainably.

Behavior-aware performance indicators provide more actionable insights than traditional accuracy metrics.