The article compares in-app bidding and waterfall approaches for app publishers, highlighting that in-app bidding is gaining traction with about 80% of publishers using it. Waterfall requires manual pricing and sequential network calls, leading to latency and inefficiency, whereas bidding allows all buyers to bid simultaneously, driven by machine learning models that optimize in real-time. This reduces latency, improves ARPDAU, and frees up resources for other growth areas.
However, some publishers stick with waterfalls for control over pricing, especially for brand advertisers with different KPIs. Yet, poorly configured waterfalls can limit effectiveness and deter performance-based advertisers. In-app bidding provides cleaner data with single bid requests, enabling better ML model training and smarter bidding.
Moloco’s platform exemplifies this cycle: better performance leads to more learnings, smarter models, and increased ad spend, benefiting both advertisers and publishers. The shift towards bidding is expected to grow as more networks adopt it.
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.
The open internet offers incremental growth opportunities beyond walled gardens. Supply path optimization is complex, with intermediaries inflating costs. Moloco's in-app bidding SDK creates a direct publisher-marketer path, eliminating fees to improve ROI. It enhances ML predictions with high-quality signals, giving marketers better transparency and control over ad rendering for improved engagement.
A/B testing on Google Play and App Store optimizes app listings. Google tests icons, descriptions, graphics; Apple tests icons, screenshots, videos. Both improve conversion and retention but have limitations like single-variable focus and lack of post-install data. Adjust enhances analysis with attribution and analytics.
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.
Hitapps, a mobile game developer, uses in-app advertising and hybrid monetization to balance revenue and user experience. Early adoption of Moloco SDK boosted ARPDAU by 5-15%. Key challenges include privacy changes (ATT, iOS updates, Google's Privacy Sandbox) and loss of control in mediation. Hitapps evaluates partners based on demand quality, ease of integration, and ARPDAU uplift.
Sports publishers face unique monetization challenges due to event-driven traffic spikes. AI-native SDKs like Moloco optimize ad revenue in real-time without harming user experience. By connecting publishers to premium demand and leveraging machine learning, these tools help maximize yield during high-engagement moments. Key publishers (LiveScore, Sofascore, etc.) adopt Moloco to balance revenue growth with fan retention. The trend reflects a broader shift toward transparent, automated monetization across the app ecosystem.
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.
Cross-platform measurement resolves the common problem of fragmented, device-level reporting that inflates ROAS and misallocates budgets. By unifying customer identity across web, mobile, CTV, and other surfaces, marketers gain a single view of LTV and attribution. AppsFlyer provides this via CUID stitching and Product Line grouping, enabling real-time, deduplicated insights without manual BI work. Key benefits include accurate cross-platform ROAS, elimination of duplicate attribution, and reliable data for AI-driven optimization.
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