Continuous Model Improvement

A forecast model that is not regularly retrained on new data degrades. Your sales process changes. Your market changes. Your team composition changes. A model trained on last year's data reflects last year's reality. The retraining cycle should be quarterly at minimum — incorporating the most recent quarter's outcomes to update weights, recalibrate probabilities, and identify new patterns. The retrained model is validated against a holdout set: deals from the most recent quarter that were not used in training. If the retrained model outperforms the previous version on the holdout set, it deploys. If it does not, investigate why.

1. Quarterly Retraining Add the most recent quarter's resolved deals to the training set. Retrain the model. Compare accuracy on a holdout set against the previous model. Deploy only if accuracy improves.
2. Feature Drift Analysis Check whether the importance of each input has shifted. If meeting frequency was the strongest predictor last quarter but stakeholder count is strongest now, your sales process may have changed. Understand the drift before relying on the new weights.
3. Model Documentation Record every model version: training data range, feature weights, holdout accuracy, and deployment date. This is the audit trail. When someone asks "why did the model score this deal at 34%?" the documentation provides the answer.