AppsFlyerAppsFlyer

Introducing AppsFlyer MCP: Marketing Intelligence for the AI Era

By Oryan Aviner·2025年7月17日·8 分钟阅读

摘要

AppsFlyer正式发布Model Context Protocol (MCP),这是一项将营销数据与AI代理无缝连接的开源协议。通过MCP,营销人员可直接在其偏好的LLM(如Claude、ChatGPT、Gemini)中,用自然语言提问并获取实时归因、ROAS、LTV等数据,无需仪表盘或数据工程师支持。MCP充当桥梁,将自然语言转换为AppsFlyer API调用(涵盖归因、分析、受众、OneLink等),并返回结构化结果。MCP不仅支持人类即时查询,还可赋能AI代理自主执行任务,例如监控UA活动、自动优化出价或管理受众。

MCP的差异化优势在于其底层数据质量。AppsFlyer的数据经过防欺诈和隐私保护处理,被超过7000个品牌信赖,确保每次查询都基于准确、合规的信息。这解决了行业内数据孤岛和延迟问题,使决策从“等待数据”转变为“即时洞察”。首席产品官Barak Witkowski强调,MCP让营销数据成为AI时代真正的战略资产,而非技术负担。

在应用场景方面,MCP覆盖了四个关键领域:一是营销绩效分析,可实时查询渠道ROAS、高LTV活动,或让AI代理自动监测并建议优化;二是受众管理,支持查询受众定义、重叠分析,并通过AI代理自动同步更新;三是链接治理,可审计OneLink模板,由代理持续监控链接合规性;四是应用配置与帮助中心,快速检索设置细节或由代理检测配置错误。这些用例直击性能营销、留存、CRM和martech团队的痛点。

MCP的战略意义在于推动营销从“工具驱动”向“AI协同”转型。其开放协议允许客户在此基础上构建自定义AI代理,如媒体组合优化或自动受众管理器,实现规模化智能编排。AppsFlyer将其AI战略总结为三个支柱:清洁的AI-ready数据、辅助工具和自主代理,而MCP正是这三者的集中体现。未来,MCP将支持预测性洞察和代理驱动的自动化,进一步释放营销团队的创造力和效率。目前,MCP以Beta版本向现有客户开放,新客户可预约演示体验。

猜你喜欢

AppsFlyerAppsFlyer

Winning mobile banking users – your ultimate guide to trust and growth in the age of privacy

Banking apps are vital digital channels requiring granular measurement to optimize user acquisition, engagement, and retention amid strict privacy regulations. Key challenges include measuring sensitive conversions, preventing fraud, and personalizing experiences without compromising compliance. Granular event tracking, deep linking, and anti-fraud solutions are essential. Banks must measure early-funnel milestones, re-activate dormant users, and leverage owned media for cost-effective re-engagement. Advanced attribution methods like SKAdNetwork, probabilistic modeling, and data clean rooms help navigate privacy changes. Effective measurement drives long-term customer value and validates mobile's impact on business outcomes.

Lesia Kupriienko·2025年10月13日·22 分钟阅读阅读文章 →
AppsFlyerAppsFlyer

Your AI is making marketing decisions on bad data – here’s how to tell

Most marketing AI fails due to poor data foundations: fragmented, unstructured, or inconsistent data leads to flawed insights. AI needs governed, contextual, and real-time data to function reliably. For ad ops decision-makers, ensuring data completeness, consistency across sources, and governance is critical before scaling AI. Richer, well-documented data improves attribution, fraud detection, and automation. The key takeaway: AI is only as smart as the data it consumes.

Eden Kalderon·2025年9月29日·7 分钟阅读阅读文章 →
AppsFlyerAppsFlyer

Cross-channel marketing analytics: how it works in 2026

Cross-channel marketing analytics isn't about putting Meta, Google, and TikTok numbers side by side—they often double-count the same customer journey. Fragmented identity is the real culprit; without a first-party Customer User ID, attribution measures platform credit, not customer value. The article explains that deduplicating conversions across mobile, web, and CTV can lift attributed revenue by 30–60% and improve ROAS by 20%. It walks through attribution models, warns against platform-native analytics, and advises using an independent MMP for true cross-channel measurement. Ad ops takeaway: fix identity resolution first, because AI-driven optimization and budget allocation depend on trustworthy, deduplicated data.

Rachel Siegman·2026年9月3日·14 分钟阅读阅读文章 →
MolocoMoloco

Inside Moloco's CARA: the AI that compounds

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.

Tal Shaked·2026年8月7日·14 分钟阅读阅读文章 →
TikTokTikTok

The AI Uprising in Advertising: TikTok is Leading the Charge | TikTok For Business Blog

TikTok's full-funnel automation, integrating creative, media, and measurement, addresses fragmentation in AI tools. Brands using Smart+ and GMV Max see improved ROAS and CPA. Case studies show Naturium achieved 3.5x ROAS, PHLUR 191% higher ROAS, and Leatherman 97% revenue increase. Symphony and Content Suite enable scalable, authentic content. The key is pairing automation with strategic storytelling.

Pierson Krass·2025年10月13日·7 分钟阅读阅读文章 →
AppsFlyerAppsFlyer

The Ultimate Creative Optimization Masterclass

AppsFlyer's Creative Optimization tool centralizes creative performance data, detects fatigue early, and enables cross-geo/network comparisons. AI-powered tagging dissects ads by elements like tone, content, and timing, revealing why ads succeed. This eliminates guesswork, improves budget allocation, and accelerates ad iteration for UA teams.

AppsFlyer·2025年9月21日·6 分钟阅读阅读文章 →
AdjustAdjust

How AI is reshaping the mobile growth stack | Adjust

AI is transforming mobile growth stacks from reactive, fragmented systems into unified, predictive platforms. Marketers move from manual dashboard analysis to conversational AI that delivers instant insights and proactive optimization. Predictive AI flags risks early, enabling faster decisions and reducing wasted spend. The shift empowers marketers to focus on strategy rather than data assembly, with tools like Adjust Growth Copilot providing a single interface for querying, analyzing, and optimizing performance in real time.

lagging metrics and reacting·2025年8月26日·4 分钟阅读阅读文章 →
MetaMeta

Introducing Business AI: Empowering Every Business to Grow in the AI Era

Meta launches Business AI, a turnkey sales concierge for WhatsApp, Messenger, Facebook/Instagram ads, and websites. Early adopters like Julep (13% ROAS lift) and Solgaard (6x higher conversion rates) show strong results. Setup is stress-free—AI learns from existing posts and ads. Business AI is free for ads and affordable for messaging/websites. For ad ops decision-makers, this means scalable, 24/7 personalized customer engagement that drives conversions and lowers costs, with easy integration and no technical expertise required.

2025年10月2日·5 分钟阅读阅读文章 →

更多来自 AppsFlyer

Cross-channel marketing analytics: how it works in 2026

真正的跨渠道营销分析并非把Meta与Google的报表并列展示,而是通过统一的身份识别(CUID)将同一客户贯通各渠道,在归因前解决重复计数问题,否则只是渠道聚合而非跨渠道分析。据AppsFlyer数据,统一归因能帮助品牌实现30%以上的归...

2026年9月3日·14 分钟阅读

How an eCommerce Loyalty Platform Cut Time-to-Purchase by 66%

本文通过澳大利亚电商忠诚度平台的案例,展示了AppsFlyer与Braze集成如何将深度链接、行为归因和个性化编排融为一体,实现从安装到首次购买时间缩短66%的显著成效。关键数据包括推送收入提升500%、邮件和内容卡片收入增长50-80%,...

2026年8月26日·9 分钟阅读

Five marketing lessons from football’s biggest tournament

足球顶级赛事揭示了移动营销的五个关键教训:注意力呈3分钟爆发式碎片化,而非持续第二屏;购买行为与注意力不同步,中场休息是转化高峰;全球赛事不等于全球行为,本地法规、基础设施、生活习惯塑造差异;情感参与度比观众规模更能驱动互动,胜负难料的比赛...

2026年7月28日·10 分钟阅读

Marketing attribution: what it is and how to measure it

营销归因是确定哪些渠道和广告活动真正驱动转化的关键,缺乏独立测量层会导致预算决策被last-click偏差扭曲,使真正有效的渠道被削减。多触点归因模型(如位置模型)比单触点更准确,但需要更多数据支持;AppsFlyer的归因方案可恢复30-...

2026年7月9日·15 分钟阅读

The Optimal Tech Stack for the AI Era

文章指出AI时代营销技术栈的核心问题在于测量层与激活层脱节,四个结构性缺陷(平台碎片化、渠道孤岛、漏斗盲区、测量-激活断层)导致信号失真与决策偏差。关键洞察是传统营销云以激活为中心,但AI优化依赖独立、一致的信号层,AppsFlyer通过跨...

2026年7月8日·7 分钟阅读

6 reasons why mobile apps set the gold standard for omni-channel measurement

移动应用因率先解决隐私、欺诈和平台碎片化等挑战,建立了全渠道测量的黄金标准。例如,iOS 14.5后移动广告支出持续增长,而欺诈检测显示约15%的安装为虚假。其他渠道必须借鉴移动经验,通过独立归因、信号治理等基础设施,才能实现可信任的跨平台...

2026年7月5日·8 分钟阅读