Applied Data Analytics for Impact

Introduction Many predictive models claim to forecast customer or donor behaviour, but struggle to influence real decisions. Scores are produced, yet no one knows how to act on them. Models perform well in validation but degrade quietly over time. Predictions explain what might happen, but not why or what to do next . The problem is rarely algorithm choice. It’s that predictive modelling is treated as an isolated exercise rather than a behavioural decision system . Why prediction is important Predictive models increasingly influence: targeting and prioritisation retention strategies resource allocation long term engagement planning When models are poorly designed: stakeholders lose trust bias and leakage go unnoticed models become brittle as behaviour shifts analytics teams spend more time defending outputs than improving them Well designed behavioural models do the opposite. They create shared understanding, support action, and adapt as pattern...