Moloco has globally launched its AI-powered re-engagement models, designed to boost ROAS and lower CPA for app marketers, now generally available. Early results show up to 15% higher ROAS and 12% lower CPA. Re-engagement is increasingly vital as conversions grow 10x faster than user acquisition, with 5x more cost-effectiveness and stronger ROAS.
On Moloco's platform, re-engaged users yield 3x higher ARPU than UA users. However, retention challenges persist: 77% of users drop off within 3 days of install, and 30-day uninstall rates in non-gaming categories are high (e.g., Dating 61%, Lifestyle 49%). The new models feature shorter learning periods for faster optimal performance, enhanced targeting of high-intent lapsed users, and flexible goals like ROAS, CPA, and app open.
Moloco's top five gaming partners now invest 3x more in re-engagement, achieving greater returns with less investment. Moloco offers a single platform for full-funnel growth, enabling combined UA and re-engagement campaigns. Results include a trading app cutting first-deposit CPA by 50% and a gaming studio seeing 4x ROAS improvement and 3.5x CPA drop.
Re-engagement is essential for maximizing LTV and marketing efficiency.
LLMs like ChatGPT and Gemini are reshaping mobile app discovery, with traditional search volume expected to decline 25% by 2026. These AI platforms act as answer engines, delivering direct app recommendations to users. For ad ops, this shift requires optimizing for LLM visibility through structured content and reputation management. While native ad formats are in early testing on platforms like Perplexity and Gemini, early adoption can secure high-intent placements. Marketers should track AI-driven traffic and align discovery strategies across ASO, SEO, and LLMs to stay competitive in an AI-first environment.
AI personalization is now essential for mobile marketing, with 71% of consumers expecting tailored experiences. This article outlines how AI enhances audience intelligence, creative personalization via DCO and GenAI, engagement timing, and measurement. Marketers should start small with focused A/B tests, prioritize user value, and collaborate across UA, CRM, and product. Key challenges include privacy, overpersonalization, and model bias. Adjust's Growth Copilot offers AI-driven analytics to streamline decision-making.
Aura Remarketing re-engages inactive app users via native on-device notifications, targeting high-value segments like in-app purchasers. It diversifies UA strategy by reactivating lapsed users (90% churn within 30 days). Key targeting: user type, demographics, device, download recency, engagement, and post-install behavior. Requires a performance link, redirect link, user criteria, bid, and KPIs. Currently supports notifications; future placements include Game Spotlight. No additional privacy steps needed.
Marketing attribution is critical for connecting spend to revenue, but platform self-reporting and last-click bias distort budget decisions. Single-touch models (first/last-click) are simple but miss the full journey; multi-touch models (position-based, data-driven) are more accurate but require robust data. Mobile attribution is particularly challenging due to ATT, SKAdNetwork, and cross-platform gaps, necessitating a mobile measurement partner (MMP) for independent, deduplicated measurement. Clean attribution data is essential for AI-driven optimization—bad signals lead to bad decisions. Starting with position-based attribution and incrementality testing provides a practical foundation.
Consumer app ad revenue is growing rapidly, projected to surpass gaming by 2026. Key challenges include user reaction to ads and internal team alignment. Solutions involve constant A/B testing, open-minded cross-team communication, and balancing ad frequency with user experience. Subscription tiers and ad formats like rewarded interstitials, banners, and in-line ads offer strong revenue opportunities. Blocking sensitive ad categories and using big networks with strict vetting are crucial. Gamification can boost engagement and ad placements.
AppLovin explains its AI-driven advertising platform, Axon 2, which has quadrupled ad spend to a ~$10B run rate. The engine uses five data buckets—no hidden data—and relies on sophisticated models with a reinforcement loop. For decision-makers, key insights: Axon drives incremental revenue, not cannibalization; compliance with ATT and no persistent IDs; web attribution uses first-party cookies; and the rapid learning loop adapts to any vertical. The article emphasizes data minimalism and world-class tech as the competitive moat.
Mintegral launches IAP ROAS optimization for precise in-app purchase targeting. The feature supports D0 and D7 windows, AI-powered bidding, and hybrid goals. Results show improved user acquisition scale and ROAS. Setup requires data integration, realistic goals, and careful optimization during learning phases (5-7 days for D0, 15-20 for D7). Best practices include limiting ROAS target changes to twice weekly with ±10% adjustments.
Moloco's In-App Bidding SDK achieved Google AdMob and Ad Manager certification, expanding its reach to four major platforms. With ~500 publisher integrations, including Voodoo and CrazyMaple, the SDK showed key improvements: bid error rates dropped from ~40% to under 1% on iOS, rewarded video skip-timer boosted ROAS by 8%, and smarter caching increased share of voice by up to 10%. The fully self-serve platform offers direct access to ~$2B in advertiser demand, enabling publishers to maximize revenue without intermediary fees.
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