Creative
Creative tested against what actually drives performance.
Creative developed, measured, and iterated against real attribution data across every channel we run. Not network-reported engagement metrics or creative gut feel. AI helps surface what to scale, cut, and test next based on what actually drove the customer to convert.
What We Run
Creative built for every channel, tested against attribution truth.
Most creative decisions get made on instinct, brand preference, or engagement data. A video gets high view-through. An image drives clicks. But when you reconcile against what actually drove the customer to convert, the winners and losers often look completely different.
We develop creative across every channel we run and pull live creative directly from the networks into Atrilyx for attribution-based measurement. Creative performance ties back to what actually drove the customer to convert, not network-reported engagement metrics.
What’s included
- Ad creative development across paid search, paid social, programmatic, and CTV + OTT
- Landing page creative and conversion flow development
- Creative testing architecture — A/B and multivariate
- Direct creative import from ad networks into Atrilyx for attribution-based measurement
- AI-generated creative variants informed by attribution performance data
- Automated creative fatigue detection and variant recommendations
- Top performer identification and scaling recommendations
- Cross-channel creative consistency and adaptation
- Video, static, and motion creative production
- Creative briefing and strategy aligned to performance goals
How We Run It
Creative decisions made against attribution truth, not creative opinion.
Most creative processes end at launch. An asset goes live. Engagement metrics come back. The creative that gets scaled is often the one the brand likes best, not the one that actually drove the customer to convert.
Every asset we launch gets pulled back into Atrilyx and measured against real attribution data. Creative fatigue detection runs on conversion contribution data, not engagement metrics — the AI flags when a creative's attribution performance starts to slip before the platform dashboard shows it, and surfaces variant recommendations grounded in what actually drove the conversion.
Proof
Performance, proven against attribution truth.
Real client results, reconciled through Atrilyx.
Common Questions
Why do network-reported engagement metrics fail to predict creative performance?
Network-reported engagement metrics (click-through rates, video completion rates, view-through rates) measure how users interact with an ad, not whether the ad drove a conversion. A creative asset with high engagement can drive clicks that do not convert. A creative with lower engagement can drive the customers most likely to purchase. Optimizing creative against engagement metrics produces assets that perform well on platform dashboards while potentially underperforming against actual revenue and conversion goals.
What is attribution-informed creative development?
Attribution-informed creative development is the process of building, testing, and iterating creative assets based on what the attribution data shows actually drove customers to convert. Not platform engagement scores or subjective creative opinion. It connects creative performance to real conversion outcomes by pulling live creative from ad networks into Atrilyx, measuring each asset against attributed conversion behavior, and using those signals to inform what gets scaled, what gets cut, and what gets tested next.
How does AI assist in creative testing and iteration?
AI surfaces patterns in creative performance data that would take significant manual analysis to identify: which creative combinations are driving conversions, which audiences are responding to which formats, which messages are fatiguing, and what the attribution data suggests testing next. It then generates new creative variants informed by those attribution patterns. The AI does not replace creative judgment. It accelerates the feedback loop between what is running and what is working.
What is creative fatigue and how does attribution detect it?
Creative fatigue occurs when an audience has been exposed to the same creative asset frequently enough that performance begins to decline. Attribution-informed fatigue detection identifies when a creative's conversion contribution is declining relative to its spend, not just when its engagement metrics slip. This catches fatigue earlier and more accurately because it connects the creative directly to downstream revenue rather than surface-level interaction signals.