AI decisioning that pairs creative messages to audience/context in real time will materially beat both randomized creative rotation and BAU rules-based optimization on buy-flow, sustain performance (reduced wear-out), and translate into incremental sales.
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
3-cell RCT: AI-On vs Random (AI-Off) vs BAU (Google)
Creative: 112 variants across 4 sizes, 3 audiences
Allocation: 65/35 earn-learn, 4-week flight
Primary KPI: Buy-flow conversions, sales matchback
| 112 Total Versions |
× | 1,852,200 Audience variations ✓ DMA ✓ Time ✓ Day of Week ✓ Device OS ✓ Connection Type ✓ Segment ✓ Other Data Signals |
= | 207,446,400 opportunities to find value in the interactions between message & audience |
AI-On delivered +272% vs Random and +92% vs BAU on buy-flow; sales matchback showed +88% lift and a strong ROI (~$15 returned per $1 incremental AI cost). Lift improved week-over-week with no creative wear-out observed, exceeding 500% by week 4 vs Random.
- First CAP study to link web optimization directly to verified sales
- AI dynamically found & scaled the winning message ("Upgrade Early"; >80% of impressions)
- No wear-out; lift climbed continuously through the flight
- Scaling is mostly org/process change, not tech. Points to a headless marketing future