Aligning creative elements (image, headline, offer, CTA) to audience motivations via AI—then adapting mid-flight—will substantially increase HVBs vs randomized rotation and sustain/improve over time.

This application of AI/ML works by identifying cohorts and optimizing pre-approved elements and creative to each. MMA's approach uses two ML techniques combined with unsupervised learning:

• One-Hot Encoding
• K-Modes Clustering

RCT Type: Person-level with 65/35 earn-learn

Creative: 36 versions × 4 sizes = 144 total

Flight: 6 weeks with GenAI creative refresh

KPIs: HVBs, Vehicle Details Page Views

Lyriq achieved +139% HVB and +138% VDP lift; Sierra achieved +228% HVB and +229% VDP lift (conversions per person). Lift increased as the AI learned; no creative wear-out observed.

• Message-to-mindset alignment mattered: luxury-focused creative beat performance-focused for Cadillac; mid-flight shift added late lift
• Changing ad copy mid-campaign revealed a new high-performing audience segment that contributed a 19% incremental KPI lift. GM and MMA also introduced a new ‘towing’-themed creative mid-campaign, based on what it had learned so far.
• GenAI analysis suggested storyboard changes (e.g., comfort/safety emphasis for retired empty nesters) while preserving performance angles for affluent professionals with families
• GM has industrialized an ~8-week test-to-deployment motion in always-on creative ops