文章第一部分指出消费者存在两种搜索模式,第二部分证明社交搜索不仅捕获需求,更能创造需求,且获客效率高于搜索。最后一部分回答了预算转移是否合理的核心问题。
趋势一:搜索预算正从搜索栏向外扩展。Google在美搜索广告份额将从十年前的67.1%降至2026年的不足50%,而非Google搜索广告支出将在2028年超过1000亿美元。然而,92%消费者使用社交平台获取产品信息,高于搜索引擎的79%,品牌当前预算结构仍过度偏向需求捕获,低估了需求创造。
趋势二:短视频成为新的搜索结果。在购物旅程中,Reels是用户最常遇到且认为最有帮助的格式。在Meta生态中,视觉内容不是文本结果页的附加,而是核心体验。将Reels作为可搜索资产而非单纯品牌曝光手段的品牌,正在赢得发现时刻。
趋势三:AI延长而非终结购物旅程。49%消费者已使用AI助手进行产品发现,70%计划在未来6个月内增加AI购物使用,71%在看到AI摘要后仍会点击进入。AI提供选项,社交提供验证。Meta的Shopping Mode结合AI检索与视觉社交上下文,并直接从产品目录中提取信息,因此目录准确性成为可发现性策略。
趋势四:增量数据支持立即转移。独立增量测量公司Measured分析了2025年200多个北美广告主的1万多个广告系列,采用基于地理位置的测试-对照组实验。结果发现,Meta在新客获取上的iROAS是搜索的2.3倍,在年支出5000万美元以上的品牌中,效率优势达46%。广告主应基于边际效率重新平衡预算,运行增量测试量化空间,将产品目录作为搜索资产,投资视觉证明和社交验证,并以地理测试增量作为测量标准。
值得关注的是,这篇文章出现在搜索广告格局加速分化的关键时刻:Google份额跌破50%的临界点与AI搜索兴起叠加,正在重塑‘搜索’的定义。文章从竞争视角切入,指出Meta不仅通过社交内容承接AI生成的消费意图,更以视觉验证和社交信任补足了传统搜索缺失的‘证明’环节。从趋势定位看,它将AI视为搜索行为的延长线而非终结者,反驳了‘AI会减少广告接触’的流行焦虑。
实操层面,第三方增量数据(如新客iROAS优于搜索2.3倍)为预算再分配提供了可量化依据,但文中强调的‘基于边际效率测试’才是关键——这暗示了行业从平台归因向因果测量的范式转移。对UA经理而言,这意味着搜索策略必须从‘关键词匹配’转向‘情境与信任的资产化’。
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.
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.
Meta introduces the Holiday Insights Center, offering data-driven strategies for small businesses to maximize holiday sales. Key insights: 85% of shoppers buy in-store after seeing products on social media; 59% message businesses during holidays; AI adoption is rising among shoppers and can streamline operations; 94% of shoppers use creator content for guidance. Advertising ROI is strong: $4 back per $1 spent. Actionable steps include optimizing social profiles, enabling messaging tools, leveraging AI, collaborating with creators, and updating data setups like Meta Pixel and Conversions API. The free Holiday Playbook provides step-by-step guidance.
Omnichannel analytics unifies customer behavior and campaign performance across mobile, web, CTV, and AI search into one identity, one attribution model, and one ROAS number. The core problem: a single customer seen on three surfaces becomes three users, inflating performance and corrupting attribution, LTV, and retention. Stitching channel reports into one dashboard is not unification—each channel keeps its own attribution logic, so conversions get double-counted and upper-funnel spend looks unproductive. For ad ops leaders, the takeaway is that identity resolution (deterministic plus probabilistic, privacy-safe) must happen at the data layer before any dashboard exists. Platform choice hinges on surface coverage, identity resolution, compliance, incrementality testing, and migration support.
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.
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.
Meta’s Advertising Week New York updates center on AI-driven campaign automation and faster creative production. Ads Creative Studio is expanding with performance insights. Meta AI business assistant gains creative insights, deeper business memory, and agentic abilities to plan, create, optimize, and schedule tasks, with advertiser permissions and approvals. AI-enabled audience discovery arrives in Detailed Targeting, and Customer Lifecycle Strategy helps prioritize new customers. Generative AI video expands for static assets, catalog product video, and smart recommendations. Direct checkout tests launch in Meta Business Agent on Messenger, Meta AI, and Muse. For ad ops, these tools promise efficiency, but governance and testing remain critical.
AppsFlyer’s 2026 creative data shows video and static image ads reward opposite creative qualities, so ad ops teams should plan, budget, and test them separately. Video spend is concentrating: gaming’s top 2% of video creatives captured 60.7% of spend, up from 53.5%, while image spend spread out. Top spenders are testing far more variations, widening the testing gap. Action/Strategy and Puzzle/Lifestyle show format-specific tone and social-proof reversals; Finance is the exception. Teams should use AI-assisted variation, analyze video winners deeply, keep broad image testing, and measure performance at attribute level.
旺季投放的本质不是盯着后台调参,而是给竞价系统写一份清晰的"brief"(简报)——优化目标、出价强度、归因口径三条线决定了系统去找哪一类人;去年 42% 的消费者声称要少花钱,实际支出却增加了 4.2%,说明"谨慎型计划者"与"果断型买家...
文章以经营12年的女装品牌JaeLuxe Shoetique为例,说明单人创始团队如何借助Muse for Small Business把网站、邮件自动化、Meta广告投放和财务分析等分散工作整合为可执行的“虚拟团队”,从而清理积压并备战Q...
Meta 广告投放系统的算法进化让创意多样性从"素材供给"变成直接的成效杠杆,Nest Commerce 以 AI 创意方案 Nest AI Creative 将单一 brief 批量转化为平台原生变体,把创意生产从成本中心重构为效率驱动引...
文章指出,广告投放的核心原则之一,是在看效果时必须区分“本来就会发生的转化”与广告真正带来的增量,因此增量测试(incrementality testing)正从一次性项目变为代理商的标准作业流程——通过将广告从随机选定的对照组中撤出、再对...
Meta 在 Brand Building Summit 上集中发布并测试多项品牌广告新品,围绕短视频形态与文化热点时刻,帮助广告主与代理商抢占用户注意力并沉淀长期品牌记忆。测试数据显示,Ads Super Burst 带来广告回忆度中位数...
文章以特拉华州第六代家族农场 Bennett Orchards 为例,说明在无冷链缓冲、全年收入押注 3—4 月几个霜冻夜的极端波动业务中,Muse for Small Business 通过接入分钟级温度记录仪与多源天气数据、自动生成简报...