The article emphasizes that retail media networks (RMNs) can leverage commerce audiences—combining purchase behavior and shopping intent—to outperform traditional targeting. Nearly 20% of programmatic ad spend flows through RMNs, making first-party data a premium asset. Moloco offers three targeting pathways: Smart Targeting uses transformer models for real-time, automated optimization, ideal for less mature advertisers; Custom Targeting allows precise segments based on events (purchases, item views), item metadata (category, brand, seller), and time periods; Predefined Targeting lets retailers upload curated audience sets, enabling one-click access and flexible logic (include all/any).
Audience Administration provides centralized control over access, supporting tiered models (open, restricted, per account) and bulk operations. Key data points: 20% programmatic spend share, closed-loop attribution for ROAS, and incremental revenue from premium segments. Actionable takeaways: retailers should sync audiences from CDPs or CSV files, combine targeting types, and align access with business rules.
Benefits include simplified operations via Smart Targeting, strategic monetization of high-value data, and improved advertiser satisfaction. For advertisers, benefits include precision targeting, flexibility, and enhanced campaign efficiency. Implementation is flexible, scalable for all retailer sizes.
Web-to-app strategies boost conversions by 77% and achieve 13.6% average paying user rate. Brands like adidas saw 2.4x higher ROAS from deep-linked users, while AirAsia improved bookings by 19%. Key challenges include measurement gaps, siloed teams, and onboarding friction. Solutions involve Google Ads Web-to-App Install and Web to App Connect with AppsFlyer Smart Banners and deep linking. Actionable steps: set tracking, import conversions, activate smart bidding, and deep link users.
Commerce media, projected to reach $140B in 2025, leverages retailers' first-party transaction data and AI to deliver targeted, performance-driven ads. Privacy regulations and high-margin revenue potential drive growth. Advertisers benefit from closed-loop measurement and high-intent audiences. Retailers gain 70-90% margins. AI enables real-time personalization, automated bidding, and closed-loop optimization. For ad ops, investing in first-party data activation and AI-native platforms is critical to capture this expanding market.
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.
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.
Influencer marketing drives app growth by building trust and authenticity beyond traditional UA. Budgeting should start with target markets, CPM benchmarks, and a 25% uplift in daily organic installs. Choose creators based on data: audience demographics, recent views, and content alignment. Measure performance with granular attribution links (e.g., AppsFlyer OneLink) to track installs, conversions, and ROI. Avoid vanity metrics; focus on CVR, retention, and long-tail effects. Start with small campaigns to gather benchmarks before scaling.
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.
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.
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.
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