In 2025, Generative AI boosted mobile ecosystem growth, with app downloads doubling YoY. Retail Media impressions surged 165% YoY, peaking in October due to Microsoft Copilot at Costco. Eight Gen AI brands drove category growth, with Copilot dominating at 392M impressions, followed by Google AI and Adobe.
Most brands focused on Amazon and Best Buy, while Copilot expanded to Costco. Video channels like OTT and YouTube were key for demonstrating AI use cases. ChatGPT ran 100% of impressions on OTT, showcasing everyday tasks.
Anthropic used OTT and OffSite Video for aspirational positioning. Creatives highlighted common themes and distinct strategies, such as Microsoft's laptop productivity, Google's daily companion, and Adobe's agentic AI in Acrobat Studio.
A 165% year-over-year surge in retail media impressions from generative AI brands signals a structural shift: AI companies are treating retail media networks as core demand generation channels, not just ecommerce conversion tools. The concentration on Amazon and Best Buy (except Microsoft’s Costco play) reflects a strategic bet on high-intent, tech-savvy audiences. Video creatives—especially OTT—are being used to demonstrate complex use cases, from everyday productivity (Copilot, Google) to high-stakes problem-solving (Anthropic).
For ad ops and UA teams, the key takeaway is that retail media is evolving from product listing pages to full-funnel brand storytelling platforms. The divergence in creative positioning (practical vs. aspirational) offers a valuable competitive lens.
Additionally, the heavy allocation of impressions to video suggests that traditional search and display formats are insufficient for explaining novel AI capabilities. As generative AI becomes more commoditized, the retail media landscape is being reshaped, forcing monetization strategists to reconsider inventory packaging and measurement for narrative-driven campaigns. This development also highlights the growing symbiosis between AI adoption and retail media innovation—a trend that is already influencing ad spend allocation.
In 2025, non-game apps surpassed games in revenue, with total in-app spending hitting $167B. APAC publishers drove a $2.58B increase in gaming revenue. Short Drama and AI Assistant categories saw explosive growth, while Blinkit, Shopee, and DeepSeek led their sectors. For ad ops, this signals shifting user attention toward lifestyle, commerce, and AI tools, creating new inventory opportunities beyond gaming.
AI amplifies marketing's fragmentation tax—bad signals across platforms, channels, and tools produce faster wrong decisions. 62% of marketers cite data quality as top barrier to AI success. The fix is not more AI tools but governed signals, AI-ready data architecture (traceable, validated, privacy-compliant), and mobile-grade measurement applied universally. CMOs must prioritize foundation over hype to turn AI from liability into compounding advantage.
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.
Digital retail maturity shifts focus from downloads to omnichannel experiences, engagement, and ecosystems. Key data: 8.7B app downloads, 400B web visits, mobile 59% of web visits in Q1 2026. Competitive advantage comes from quick commerce, loyalty, content-led discovery, and connected in-store. For ad ops, prioritize engagement and frequency over acquisition; mobile is dominant; ecosystem expansion is critical.
MAMA SF 2025 emphasized that AI is reshaping consumer discovery and purchase behavior, with apps becoming essential owned infrastructure. Key insights for ad ops: measurement integrity is critical as AI automates budget decisions; 30% of campaigns are undervalued by last-touch models. Brands must measure total app value (direct revenue, influenced revenue, operational savings, LTV lift), often 5-6x ROI. AI's practical impact is eliminating friction through automation like natural language queries and AI-powered campaign checks. The marketer's role is evolving to owning end-to-end recommendations.
Ramadan drives high mobile engagement in the Gulf, but success hinges on pre-Ramadan acquisition for higher LTV and remarketing during the month. eCommerce peaks early; finance responds to mature market triggers; travel converts at Eid. Post-Ramadan, focus on retention over acquisition to stabilize. AI tools are operational but measurement lags. Key takeaway: plan early, leverage remarketing, and phase strategies by period.
The 2025 mobile market hit record highs across downloads, IAP revenue ($167B, +10.6% YoY), and time spent (5.3T hours). Non-game IAP surpassed games for the first time (+21% YoY). Generative AI apps surged, with downloads doubling to 3.8B and IAP tripling to $5B. Key ad ops insights: attention competition intensifies, UA shifts to lifetime value, and AI expands monetization opportunities across categories.
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.
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