How to improve the accuracy of AI in sales forecasting

Improving AI sales forecasting accuracy is achievable through targeted enhancements in data and modeling. Continuous refinement of algorithms is essential.

High-quality, comprehensive historical data forms the foundation. Feature engineering must capture key market drivers. Employing ensemble techniques and model validation significantly reduces errors. Regularly retraining models adapts to market shifts.

Start by integrating diverse data sources, ensuring volume and cleanliness. Preprocess data meticulously and engineer relevant features like seasonality or promotions. Utilize cross-validation and ensemble methods (e.g., Random Forest, Gradient Boosting) for robust predictions. Continuously monitor performance metrics like MAPE, iterating on feature selection and hyperparameter tuning for sustained accuracy.