Demand Planning for DTC Channels: Statistical Models
Aaron Ethridge
Last Update 9 days ago
Statistical forecasting models help accurately predict consumer demand patterns for direct-to-consumer (DTC) sales channels.
Follow these step-by-step instructions to set up your DTC forecasts in Planster using statistical methods:
From the left-side menu, click on Demand Plan.
Select Direct-to-Consumer (DTC) to begin.
Choose the product or products you wish to forecast.
Ensure historical sales data is available for accurate statistical analysis.
Planster provides several statistical forecasting methods suited for DTC channels:
Prophet: Ideal for capturing seasonal effects and trend changes.
Linear: Good for steady growth or predictable sales.
Exponential Smoothing: Excellent for smoothing out irregular sales fluctuations.
ARIMA: Best when sufficient historical data exists (preferably 2+ years).
After selecting a model, Planster automatically generates a demand forecast.
Carefully review the resulting forecast chart to verify accuracy and reasonableness.
If needed, manually adjust forecasts based on known factors like upcoming promotions, market shifts, or product launches.
Use the edit feature to update specific weeks or periods.
Once satisfied with the accuracy of your forecast, confirm and publish your demand plan.
Your finalized forecasts directly influence your inventory purchase recommendations.
Regularly review forecasts against actual sales data.
Adjust models and parameters periodically to reflect current trends and market changes.
If you have questions or require assistance selecting or optimizing your DTC forecasting models, contact Planster Support—we're here to help!