2025年,AI代理将深刻改变广告技术行业。开发者利用Replit等工具快速构建应用与游戏,而XMP等媒介购买平台通过AI实时优化预算分配。用户获取(UA)经理可借助AI代理自动匹配需求与供应商,缩短销售周期;客户服务中,AI代理能根据具体问题定制响应,大幅提升效率。长期来看,AI将把创建十亿美元公司所需的人员从万名降至五千甚至五百,释放人力资源用于战略工作,改善利润率。
隐私保护成为2025年的焦点。消费者数据权利意识增强,尽管谷歌放弃淘汰第三方cookie,但其他浏览器已导致约40%的开放网络无法通过cookie寻址。这催生了隐私优先的定向方式,如匿名标识符或上下文定向,但数字广告从业者必须将隐私与合规置于2025年活动规划的核心。
广告主正拓展新渠道以应对隐私限制。互联电视(CTV)因消费者转向流媒体而获得更多媒体预算;电子商务广告主开始进入应用内及手游广告,尽管传统上此类广告的eCPM高达50-100美元,但成效付费模式降低了风险,使广告主愿意为下载及首次购买等成果支付20美元。
广告技术并购在2025年将加速。2024年Q3交易量同比增长118%,Outbrain以10亿美元收购Teads,Omnicom以135亿美元收购Interpublic,LoopMe收购ChartBoost以规避20-30%的交易所费用。行业信心恢复,预计将有更多IPO和收购,如SDK平台收购移动网页公司,或移动SDK网络收购CTV平台。
创意在定向受限的背景下愈发重要。AI解决了时间、人才和工具三大传统挑战:生成式AI可快速产出10、50甚至100个变体并进行测试,其新颖性和独特性本身就能吸引用户。2025年,AI生成的创意内容将大规模应用,成为广告主的核心竞争手段。
这篇文章在年底盘点时点发布,为广告投放从业者梳理了2025年关键趋势。值得关注的是,M&A活跃度回升(Q3交易量同比增118%)反映了行业信心恢复,尤其是Outbrain收购Teads等案例可能重塑竞争格局。此外,隐私合规从“威胁论”转向常态,40%开放网络无法通过cookie定位,促使UA经理必须将隐私优先纳入策略,而非被动应对。
AI Agent落地将降低初测新产品的交易成本,但实操中需警惕渠道适配性。整体而言,文章精准捕捉了从“技术驱动”到“规则+创意双轮驱动”的行业拐点。
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.
One person built, shipped, and marketed a mobile game in 14 days using AI tools, achieving 5,563 installs at $0.39 eCPI on $2,200 spend. MCPs (Model Context Protocol) were critical for agentic workflows. The AI agent CLAW managed ad campaigns via AppsFlyer MCP and BigQuery. Data Locker streamed raw data for analysis. Key takeaway: vendors must offer MCPs for fast, agentic data access; measurement stack (Data Locker, ROI 360, Creative Optimization) is essential for solo teams; human+AI beats AI alone.
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.
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.
AI is reshaping digital advertising as platforms like ChatGPT and Gemini become new discovery channels. Key findings: ChatGPT ad impressions surged 7x since March 2026, and AI-related ad spend tripled in Q1 2026. Early advertisers are concentrated in Shopping, Software, Travel, and Financial Services. AI assistants drive referral traffic to retailers, with Walmart and Target exceeding 1.5% GenAI share. Competition among AI platforms is intensifying, with Claude gaining professional users. For ad ops, integrating AI into media plans and optimizing for AI-driven discovery is critical.
Meta announces end-to-end creative AI tools enabling brand-aware ad generation, testing, and optimization for all marketers. Key updates include a unified Creator Marketing Hub combining Instagram and Facebook creator discovery, plus AI agents connecting customer conversations to conversions. A study of 1M+ campaigns shows $4.13 average revenue per dollar spent (up 25% since 2022). New features: brand memory for consistent creative, enhanced text generation, language translations (11 languages), and integrated creative approval workflows.
Non-gaming marketers like e-commerce, fintech, and subscription services are increasingly turning to mobile advertising, driven by rising costs on walled gardens. They are shifting from CPI to outcome-based models (e.g., ROAS, CPA), leveraging ML to find quality users beyond contextual placements. Key takeaways: ad platforms must enable direct revenue attribution, faster feedback loops, and product-first creative to serve these advertisers. The era of growth at any cost is giving way to quality-focused, intentional scaling.
Gen AI apps have become the primary growth engine of the non-gaming market, with revenue surging 232% YoY to $6.1 billion between Q2 2025 and Q1 2026. The US leads with 38% of global revenue, while Japan and Korea emerge as key growth markets. AI Assistants are increasingly concentrated, with ChatGPT dominating, but vertical segments like AI Companions, AI Agents, and AI Image & Video offer fragmented, high-growth opportunities. Lessons from Plaud highlight success through vertical focus, deep localization, and precision advertising. For ad ops, targeting vertical AI segments and localized user acquisition strategies present significant opportunities.
本文分析了可玩广告与视频广告在印度市场的表现,关键数据显示视频广告CTR(72%)略高于可玩广告(63%),且均远超全球平均水平。文章建议开发者重视可玩广告的预安装体验价值,优化视频广告的初始吸引力,并借助Playturbo工具高效制作创意...
代理型AI(Agentic AI)正在重塑媒体购买流程,将手动投放策略转向基于目标的自动化系统,让营销人员专注于更高层次的策略决策。关键数据显示,Google PMax和Meta Advantage+在成熟营销者中的采用率分别达到91%和8...
本文指出,随着Cookie受限和确定性身份可靠性下降,广告业正从基于上下文的精准定向转向基于概率的预测系统。关键优势在于通过SDK直接获取供应、降低延迟,并利用机器学习实现实时优化。实践意义是,具备预测能力的平台能突破传统内容场景,以更低成...
早期广告表现数据通常反映的是高意向用户群的初期反应,而非长期稳态。由于变现延迟和归因窗口不完整,Day 3的ROAS往往无法预示Day 30的真实价值,甚至可能误导优化方向。实现可持续扩量的关键在于平衡早期信号与充分的时间沉淀,让真实用户行...
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CPI和ROAS是两种不同的用户获取模型,分别适用于应用的不同阶段:CPI适合初期导量积累用户数据,ROAS适合成熟期追求LTV最大化。两者并行会误导算法降低效率,建议根据阶段选择单一模型。Hybrid ROAS是Mintegral的高级策...