- Performance Marketers: Add deep learning-driven retargeting and acquisition to hit ROAS targets at scale.
- Ecommerce Brands: Personalize shoppable creative and product recommendations to lift conversion.
- App Growth Teams: Run app-focused campaigns that drive installs, re-engagement, and in-app purchases.
- Retail and Marketplaces: Capture non-obvious converters and promote catalog depth efficiently.
- Brand and Media Teams: Leverage CTV/OTT, video, and premium display with product overlays for awareness and consideration.
How to Use RTB House?
- Integrate First-Party Signals: Connect pixel/app events and product feeds to activate privacy-safe performance data.
- Define Objectives: Set ROAS, CPA, or revenue goals across conversion, acquisition, engagement, or demand generation.
- Launch Deep Learning Campaigns: Deploy next-gen retargeting, dynamic product ads, and contextual targeting with IntentGPT.
- Optimize and Scale: Iterate creative, placements, and audiences to expand reach while maintaining efficiency.
- First-Party Advertising: Builds on durable, privacy-forward signals without data pooling or selling.
- Deep Learning Modeling: Surfaces non-obvious converters and complex paths to purchase missed by others.
- Shoppable Creative: On-brand units with live product recommendations across the journey.
- Contextual Intelligence: IntentGPT boosts engagement with high-quality, brand-safe environments.
- Stack-Compatible: Designed to complement existing tools, adding incremental revenue at set ROAS.
- Privacy-first approach with no selling or pooling of first-party data.
- Proven lift from finding non-obvious converters and unviewed product recommendations.
- High-quality shoppable and video creatives that align with brand standards.
- Works alongside existing ad tech to drive additive, measurable revenue.
- Requires quality first-party signals and feeds to reach full potential.
- Deep learning models may need ramp time for optimal performance.
- Premium placements and formats can increase media costs.
- Black-box modeling can limit transparency into individual decision factors.
Custom
Pricing information is not directly provided.
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Editorial Note
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
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