Measure the value of AI marketing content and make more informed spend decisions. Use Claude and realistic data to link assets to results, compare models, and check claims before you report them. Throughout the course, you’ll explore: Data Modeling: Build asset tags, UTM rules, and fact tables that link AI content to business KPIs. Attribution: Compare credit across touchpoints with rule-based models, Markov chains, and GA4. Lift Testing: Design holdout and geo tests to assess sales that would not happen without a campaign.
Uplift Modeling: Score customers and use Qini curves to choose who receives AI-personalized offers. Marketing Mix: Fit models with adstock and saturation, read response curves, and assess budget shifts. ROI Scorecards: Calibrate credit with lift results, reconcile model estimates, and report confidence. By the end of the course, you’ll be able to build a traceable data model, evaluate AI campaign impact, and create an ROI scorecard. You’ll distinguish credit from causal evidence and verify AI outputs against the numbers.
