UM-Model 1 enables marketing systems to reason about causality, perform counterfactual inference, and plan optimal campaigns—capabilities that fundamentally differentiate it from pattern-matching systems.
Pattern Matching vs Causal Reasoning
Observes correlation: "Sales increased 20% during campaign period"
Campaign caused 20% increase. ROI = 300% ✗
Ignores seasonality, competitor actions, and economic trends. Correlation ≠ Causation.
Builds causal graph: Season → Sales, Economy → Sales, Campaign → Sales
"What would sales have been WITHOUT the campaign?" Uses UCIE algorithm
Campaign caused only 8% increase. True ROI = 120% ✓
Explore how UM-Model 1 analyzes marketing campaigns with causal reasoning
Concrete examples from actual marketing campaigns
60% of attributed sales were actually driven by seasonal trends and competitor price changes
High-intent users were already planning to purchase. Email timing was correlated but not causal
Viral content was driven by external influencer mentions, not paid promotion. Saved $320K
TV ads created awareness (causal), social media captured existing demand. 3.2x ROI improvement
Ask "What if?" questions about your marketing campaigns
Adjust parameters and click "Run Analysis" to see counterfactual predictions