AI Creative Effectiveness (ACE) uses artificial intelligence to analyze creative performance patterns and provide pre-launch scoring and recommendations. By learning from your past campaigns, ACE extracts brand-specific success drivers and applies them to new assets—checking brand/platform compliance, predicting performance, and suggesting edits before you go live. This pre-optimization approach helps teams prioritize high-potential assets and fix issues early, reducing costly revisions and improving campaign outcomes.
Research Objective
Validate that AI scoring correlates with in-market performance and that AI-guided revisions improve outcomes.
ACE aims to demonstrate that:
- AI scores accurately predict creative effectiveness (CTR, CVR, ROAS)
- Assets revised per AI recommendations outperform originals
- Pre-launch optimization reduces production cycles and rework
- The process integrates smoothly into existing creative workflows
Research Questions
- Which visual, copy, format, and layout attributes most influence predicted effectiveness?
- How do AI scores compare to expert/human evaluations and to pre‑test norms?
- Can AI guidance systematically improve results by audience cohort (e.g., segment‑specific variants)?
- How much production time/cost can be reduced by closing the loop before launch?
Methodology & Approach
| Modeling | The AI is trained on your brand's historical creative assets and their associated performance data to learn your unique drivers of success. |
| Process |
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