亚马逊在CES上宣布推出Amazon Retail Ad Service,将其自有的站内广告技术打包给其他零售商使用,核心功能包括基于机器学习的上下文相关广告投放、原生需求集成和广告管理工具,目前仅支持Sponsored Product格式,处于内测阶段。该服务旨在帮助中小零售商提升广告变现能力,同时为亚马逊聚合更多广告库存,强化其与谷歌、苹果等竞争对手的对抗地位,但大型零售商因数据安全与利益冲突风险多未参与。
亚马逊约68%的利润来自广告,其站内广告因高利润率、品牌安全、第一方数据精准定向及闭环归因优势日益重要。通过推出ARAS,亚马逊希望吸引更多零售商使用其技术,但自身利益优先于合作伙伴——其自有站点广告利润率是第三方零售商的5倍,存在明显的竞争冲突风险,类似此前谷歌和苹果的垄断诉讼。
数据隐私是零售商核心担忧:ARAS基于AWS,零售商需登录管理数据,并在AWS Clean Rooms中完成广告测量,尽管环境安全,但可能导致敏感客户数据泄露,削弱零售商的竞争护城河。亚马逊的广告主网络虽能带来额外需求,但多数大型RMN倾向维护直客关系,且已有Skai、Pacvue、Criteo等无利益冲突的聚合平台可选。
独立AI原生平台如Moloco提供了第三条道路——利用ML自动化实现个性化投放与规模化运营,同时保证数据主权、竞争中立与广告主直营关系。零售商应选择这类技术伙伴,在提升广告提效和增量回报的同时,避免将核心数据交给最大竞争对手,从而构建可持续的零售媒体广告业务。
Retail media networks (RMNs) are poised for major growth in 2025, with personalized, AI-driven onsite ads becoming top priority. Advertisers demand performance-based outcomes like CPO and tROAS, while retailers invest in self-serve platforms and go-to-market teams. Key shifts include mid-funnel formats, regional variations (US in-store, EU onsite), and tech partnerships to scale. RMNs that combine ML personalization with streamlined operations will dominate.
Retailers building retail media networks (RMNs) can learn from Google, Meta, and Amazon by leveraging first-party data, machine learning, self-service automation, and outcomes-based performance. Key insights include using purchase intent signals and loyalty data for personalization, investing in AI for targeting and optimization, automating campaign management to scale advertiser participation, and moving to outcome-based pricing like closed-loop attribution. These strategies transform RMNs into high-margin ad platforms that deliver value for brands and shoppers.
Onsite retail media ads remain the most critical driver of RMN growth, accounting for over 80% of ad spending. They offer higher ROAS, better margins, and brand safety. Leading RMNs like Amazon and Walmart generate most media revenue from onsite. Growth can be unlocked through ML optimization, self-serve platforms, and outcomes-based campaigns, even without massive traffic increases.
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.
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.
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.
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.
TikTok For Business is courting new advertisers with a tiered credit promotion: spend $100/$500/$1,500 and receive equivalent ad credits, with the top tier adding 1:1 expert support. For ad ops decision-makers, the surrounding content underscores a strategic shift: marketers should embrace marketing mix modeling (MMM) rather than last-touch ROAS, leverage full-funnel AI automation, and use seasonal/industry playbooks (beauty, fashion, sports) to align creative with intent. Key takeaway: combine offer-based trial with longer-horizon measurement and structured content planning to maximize TikTok ad efficiency.
Moloco宣布推出代理商合作计划,旨在帮助代理商通过Moloco Ads为客户实现可衡量的绩效增长,首期已吸引全球超过十几家创始合作伙伴。该计划提供教育、专属支持及与产品团队更紧密的协作,助力代理商在移动应用广告和Performance ...
Moloco公开其广告AI系统CARA的技术架构,旨在解决开放互联网效果广告中身份碎片化、信号稀疏和实时竞价等挑战,通过六个技术域的复合设计实现持续学习与优化。2025年,CARA经实验验证的65项模型改进为广告主降低CPI 14%、降低C...
本文通过Moloco与BCG的AI Disruption Index研究,揭示AI正快速重塑数字发现格局,信息型web流量大幅下滑,而交易型相对稳健。数据显示,新闻、教育、健康健身等网站流量同比分别下降26%、22%和16%,但app端活跃...
体育类App的用户参与呈事件驱动特性,比赛等突发流量高峰为广告变现带来机遇与挑战。Moloco SDK通过AI实时优化广告竞价,帮助LiveScore等头部App在维持用户体验的同时提升增量收入。该技术反映了行业向AI-native变现的转...
AI disrupts digital advertising by reshaping consumer behavior, with 80% of Google searches now zero-click and nearly ha...
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