MintegralMintegral

The Next (Agentic) Phase Of Media Buying Is Coming

By Phoena Pang·2026年7月9日·6 分钟阅读

摘要

代理型AI(Agentic AI)正在引发媒体购买的根本性变革,颠覆了传统手动上传出价和创意、监控表现并迭代的运营模式。新一代AI系统能够根据营销团队或AI创建的简报,自动处理受众定向、投放优化和出价管理,极大减少广告组搭建、受众分层和创意上传等重复性工作。

当前目标驱动自动化已广泛普及:2026年3月一项调查显示,Google PMax在高级绩效营销者中的规模化采用率达91%,Meta Advantage+达88%。代理型AI在此基础上进一步升级,通过实时读取性能信号、自动调整投放策略,并整合下游行为数据回传,实现从漏斗上层获客到下流转化的闭环优化,打破传统信号局限。

买家的角色将从手动执行转向策略制定与监督,重点包括:设定优化目标(如CPA、ROAS),设计增量测试,以及决定AI系统应信任哪些信号。同时,代理型AI要求广告主仔细评估解决方案的供应链——是否拥有直接供给或仅作为中介,以及预测模型是否基于充分的结果数据训练,以避免在机器速度下造成预算浪费。

通过将创意生成、预测算法和垂直供给整合,代理型AI能够跨渠道协调,利用API和开放协议实现生态级别优化。最终,媒体团队可将执行直接关联业务成果,预算随用户漏斗行为动态调整,定向基于真实用户价值而非假设,从而推动增量提效。

分析师点评

这篇文章的发布时间点值得关注,正值agentic AI从概念走向规模化部署的关键窗口。关键信号在于,文章将agentic AI与传统自动化明确区分为‘功能迭代’与‘阶跃变化’——传统自动化执行预设指令,而agentic AI自主决策并持续优化。对于UA经理而言,这意味着能力重心的转移:从手动搭建广告结构转向设定目标与评估信号质量。

文章隐含了一个行业背景:Google PMax和Meta Advantage+的高采纳率已为agentic范式铺平道路,但跨平台协调能力仍是当前供给方的分水岭。从趋势定位看,这与隐私限制下信号衰减的应对需求相呼应——agentic系统可能通过行为预测补偿丢失的定向能力。实操影响方面,团队需关注预测模型的训练数据与信号鲁棒性,而非仅关注自动化程度。

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