Moloco今日正式推出代理商合作计划(Agency Partner Program),这是一项旨在深化与全球代理商合作的新举措,帮助代理商利用Moloco Ads为广告主驱动可盈利的绩效成果。该计划首批已吸纳来自北美、EMEA、拉美和亚太地区的超过十几家创始合作伙伴,覆盖移动应用内广告和Performance CTV两大领域,重点在于通过更紧密的协作,共同构建、营销并实现业务增长。
在日益碎片化的广告生态中,代理商面临着触达新受众、证明投放效果以及实现差异化的多重压力。随着广告主将预算从搜索和社交渠道向外拓展,代理商需要发掘下一个高潜力渠道并将其转化为可衡量的增量增长。Moloco的合作计划正是为此设计,为代理商提供教育培训、专属支持以及与Moloco产品和市场团队更深入的协作机会,帮助他们在提升客户价值的同时也实现自身业务增长。
Moloco首席商务官Sunil Rayan表示,代理商在帮助营销人员应对快速变化的广告格局中扮演着关键角色,公司致力于为合作伙伴提供所需的协作与支持,从教育、产品协作到联合市场推广,共同帮助营销人员解锁新的增长机会。首批合作伙伴中,PMG、M+C Saatchi Performance、Dentsu、SplitMetrics、RocketLab等均对合作计划表示高度认可,强调Moloco在突破围墙花园、获取高价值用户以及提升D30 ROAS等方面的实际成效。
合作伙伴反馈显示,Moloco的机器学习技术已成为他们优化跨渠道投放、提升广告变现效率的重要工具。例如,SplitMetrics提到Moloco在金融科技应用上持续带来D30 ROAS提升,Addict Mobile和Admiral Media则强调其在多个垂直领域(尤其是约会类应用)的强劲表现。CyberZ和GatherOne则看重Moloco的AI驱动广告投放对LTV的最大化价值,以及团队提供的快速响应和深度支持。
整体而言,Moloco的代理商合作计划不仅是一个资源整合平台,更是一种战略协同机制。通过与代理商在技术、数据和市场层面的深度绑定,Moloco正助力代理商在效果营销时代构建新的竞争力,同时为广告主提供更可持续的增量增长路径。这一计划的扩展,也反映出广告技术行业从工具提供向生态共建的转型趋势。
Moloco 推出代理商合作计划,关键信号在于 AdTech 的渠道策略正在从直客向代理商体系延伸。值得关注的是,其强调的“独立应用生态”和 Performance CTV,恰好切中广告主在围墙花园之外寻求增量、以及电视预算数字化转型的当下需求。隐私政策收紧后,信号质量下降,机器学习优化成为投放效率的核心,Moloco 借此将技术能力包装为代理商可输出的服务模块。
对从业者而言,这反映出平台间竞争已从单纯买量能力扩展到代理商的赋能体系,运营团队在评估渠道时,除了 ROI 表现,也需考量平台对代理商的生态支持力度。
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.
Customer lifetime value (LTV) is a critical long-term metric for app success, but most marketers measure it per-device, understating true value by 2-5x. Cross-platform LTV stitches together web, app, CTV, and more, attributing all revenue back to the original acquisition campaign. Key drivers include retention (5% increase boosts profits up to 95%), purchase frequency, average order value, and acquisition quality. To improve LTV, focus on retention, cross-platform adoption, and optimizing acquisition by predicted LTV rather than CPI.
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.
TikTok Ads is courting new advertisers with tiered ad credits (spend $100/$500/$1500, get same in credit) plus expert support for the top tier, but credits expire by end of 2023. Decision-makers should note strict eligibility: only self-serve SMB accounts, no agency-created or TikTok Shop accounts, one account per business, and a 30-day spend window. Research from Circana, GroupM/KIKO, and Samba TV indicates TikTok often outperforms traditional attribution models. Salesforce CRM integration and Canva creative tools reduce friction, while quarterly safety reports strengthen brand protection. Overall, incentivized testing, robust measurement, and enhanced integrations make TikTok a viable paid social channel for SMBs.
European finance app installs hit 960M in 2025 but grew only 0.4%. BNPL apps grew 40% while crypto fell 35%, signaling a shift to utility. Neobanks win acquisition; traditional banks win retention (1.5-2x Day 30 rates). Web-to-app drives 41.8% of conversions but most brands can't measure the handoff. Nearly 1 in 2 investment app installs in Western Europe is fraudulent, distorting CPI and ROAS. Winning brands prioritize engagement, fraud detection, and cross-platform measurement.
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.
Data collaboration platforms are consolidating under ad-centric owners, threatening measurement neutrality. Publicis bought LiveRamp, WPP acquired InfoSum, and LiveRamp absorbed Habu, leaving AppsFlyer as the only major independent player. Brands must vet partners for conflicts: does the platform or its parent benefit from ad spend? Without independence, budget allocation and ROAS calculations may reflect agency incentives over actual performance. Key questions: revenue from ads, cross-channel attribution consistency, data governance, and auditable methodology.
AI is reshaping consumer behavior, with 80% of Google searches ending without a click and half of consumers using AI for product research. This disrupts traditional channels like search (CPC up 10-25%) and affiliate marketing (revenues down 7%). Meanwhile, mobile apps and CTV offer stable, high-engagement alternatives. Advertisers should diversify away from disrupted channels, targeting the independent app ecosystem where Day 30 ROAS can be 116% higher. Key metrics: organic direct traffic share (target >51%) and disrupted channel spend share (target <34%).
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