Liftoff has launched Cortex, a next-generation machine learning platform that leverages neural network technology to analyze larger datasets, improving decision-making and ROI for advertisers. Key advancements include processing 10x more training data, enabling 10x faster experimentation cycles, and achieving 6x faster model training. This results in more accurate predictions, quicker product releases, and better adaptation to market changes.
Partners like Playlinks and Bigo Live report improved ROAS, lower CPI, and higher user retention. Cortex is now widely used by Liftoff advertisers, with further innovations like SKAdNetwork models planned for late 2024.
First-party data, collected directly from users with consent, is crucial for marketers due to privacy regulations limiting third-party data. It enables accurate personalization, compliance, and cost savings. Key steps include ethical collection, maintaining clean data, and using it internally for product/marketing optimization and externally via commerce media networks.
This guide helps app marketers select a Mobile Measurement Partner (MMP) by covering essential features like privacy-first measurement, unified attribution, fraud protection, and advanced analytics. It emphasizes choosing an MMP that integrates easily, scales with business growth, and provides reliable data for optimizing marketing ROI across teams.
The open internet presents unique challenges for performance advertising: fragmented identity, closed first-price auctions, and non-stationary supply. Moloco's CARA compound architecture tackles this with six integrated technical domains—Campaign Automation, Supply, Ad Recommendations, Bidding, Creative, and Signals—running on a unified ML infrastructure. Key insights for ad ops: the system continuously learns from every interaction, uses knowledge distillation to serve real-time predictions under 10ms latency, and validates improvements through rigorous live experiments. In 2025, 65 validated model updates reduced CPA by 17% and improved ROAS by 27%. The key takeaway: compound AI architectures that connect prediction, bidding, creative, and data can unlock measurable performance gains beyond walled gardens.
In-app bidding is increasingly preferred over waterfall due to efficiency, with around 80% of publishers now using it. It reduces latency, manual work, and improves ARPDAU by enabling simultaneous bids from all buyers. ML models in platforms like Moloco optimize bids in real-time, while waterfalls allow manual pricing control but risk inefficiency and reduced advertiser interest.
The article outlines five key mobile app market predictions for 2025, emphasizing AI/ML maturation, consumer-driven data control, the limitations of GenAI for measurement, growth of alternative app stores, and increased M&A activity. Ad ops decision-makers should prepare for scaled AI adoption, adopt multiple measurement frameworks to navigate privacy regulations, leverage ML models for privacy-compliant insights, explore emerging app distribution channels, and consider strategic acquisitions for market expansion.
Next-gen campaign optimization combines attribution, incrementality testing, and MMM for holistic insights. AI-driven tools like pLTV and deep linking automate analysis, reduce waste, and improve ROI. This scalable, privacy-compliant approach future-proofs marketing success.
Non-gaming apps can reduce CAC by advertising on gaming platforms, reaching 3.3 billion monthly players. Creative formats like playable and rewarded video ads boost conversions. Key challenges include identifying high-value users and allocating sufficient budget for algorithm optimization. Successful examples include Buddy AI and food delivery apps.
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