GoogleGoogle

Make AI Max work for your business with new testing and planning tools.

By Brandon Ervin·2026年8月20日·1 分钟阅读

摘要

文章介绍了 Google AI Max 的更新,旨在帮助广告主更高效地测试和规划投放策略。首先,AI Max 在此前的一键实验基础上,推出了跨多个 Search 广告系列的 A/B 测试功能,允许广告主同时测试不同的预算和 ROAS 目标。该功能计划于 9 月上线,能够直观展示预算调整对广告系列底部漏斗指标的真实影响,从而为规模化投放决策提供数据支撑。

其次,针对依赖特定品牌或位置控制的广告主,AI Max 实验新增了在启用这些控件时运行测试的能力。这解决了此前无法在保留原有定向约束下验证 AI Max 效果的痛点,使测试结果更具实际指导意义,确保广告主在提升投放灵活性的同时,不牺牲既有的品牌安全或地域策略。

第三,Performance Planner 的升级允许广告主预览竞价或预算目标调整对现有广告系列可能产生的影响。通过一键操作,建议的调整可直接应用于广告系列,简化了从规划到执行的流程,帮助广告主快速响应市场变化并优化预算分配。

这些工具共同强化了 Google AI 驱动的广告管理闭环,从实验设计到效果预测,再到策略落地,为广告主提供了一条清晰的增量提效路径。文章最后提到,如需了解更全面的行业最佳实践和 AI 规模化表现方法,可关注 Rethink 2026 活动的线上直播。

分析师点评

值得关注的是,该更新将 AI Max 从‘一键实验’推向‘多活动、多目标’的批量测试,并允许带约束条件(如品牌/位置)运行。这表明平台在追求自动化与可控性之间的平衡——广告主既能享受 AI 的扩展能力,又不失去护栏。关键信号在于,Performance Planner 的‘一键应用’打通了规划与执行,使得 ROI 目标调整可以快速落地。

放在隐私信号减少的背景下,这种基于建模的预测和实验能力正成为平台标配,也是广告主在预算分配中增加对 AI 系统信任的底层基础。对于 UA 团队而言,需要关注的是实验设计中的统计显著性,特别是多活动交叉测试中如何避免代理冲突;而变现侧则需留意 ROI 目标设定与预算上限的联动效应。九月上线也意味着 Q4 预算规划前,平台为规模化测试提供了新工具。

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