Same banner, 300% conversion variance. The system now explains why — before launch.
Visually similar creatives were producing wildly different results. moat8 built a deep visual analysis system that maps creative elements to ROMI and converts top performers into reusable production blueprints.
Banners differing by 10% in detail were producing 300% variance in conversion.
The performance marketing team was running A/B tests to learn what worked — but the learning came after the spend. Creatives that looked nearly identical were producing conversion rates that varied by up to 300%. The patterns were invisible to the human eye and only emerged after thousands of dollars in test budget. There was no way to predict winners before launch.
Deep visual analysis that reads what the human eye misses.
Creative corpus ingestion
Historical ad library loaded with performance data — impressions, CTR, ROMI per creative
Visual feature extraction
AI decomposes each creative into granular elements: color zones, text placement, focal point, contrast ratios, compositional structure
ROMI correlation mapping
Statistical model finds which visual features consistently correlate with high-conversion outcomes
Pattern library generation
Top-performing feature combinations described in structured format — usable as creative briefs or generation prompts
Pre-launch scoring
New creatives scored against the model before any spend — ranked by predicted performance
Brief-to-output loop
Designers and AI generation tools receive scored briefs — production time drops because direction is clear from the start











