Leveraging Predictive Analytics for Performance

Interpret the outputs of common predictive models (e.g., conversion probability, churn risk).

Evaluate a model's business value by interpreting performance metrics (e.g., precision, recall, AUC).

Translate churn risk scores into a targeted retention strategy and campaign brief.

Use feature importance outputs from a model to understand the key drivers of customer behavior.

Assess when a predictive model is reliable versus when human judgment and business context are needed.

Formulate a business case and project brief for commissioning a predictive analytics project.

Critique the deliverables of a data science project, focusing on the business implications of the findings.

Evaluate the strategic fit of AI-powered marketing tools based on their underlying predictive models.