广告营销工具
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SEOSEO付费获客付费获客程序化广告网站变现程序化App 获客App 变现网站变现关键词研究搜索意图App 获客ROASCPAApp 变现CPCLTV联盟营销eCPMRPM零售媒体营销归因转化追踪创意情报MMPHeader BiddingDSPSSPRTB广告可见率填充率ASOSKAdNetworkARPDAU激励视频广告聚合联盟营销创意测试A/B 测试再营销相似受众广告优化品牌安全供应路径

创意情报

MagicBrief

付费

创意情报与广告灵感平台,帮助 DTC 品牌发现、收藏并分析 Meta、TikTok 等平台的优秀广告,整理灵感库并生成创意 brief。

调研广告灵感创意情报灵感库

主要用途

MagicBrief is a creative research and workflow tool that helps brands study the ads competitors and category leaders are running, then turn that inspiration into structured creative briefs. Users browse and search across Meta and TikTok ad libraries, save standout ads into organized boards and swipe files, and tag them by format, hook, or angle. AI features summarize what makes an ad work and help draft briefs, while integrations let teams pull in performance context and collaborate with designers. Rather than just tracking spend like a pure ad-intelligence database, MagicBrief focuses on the creative production pipeline, shortening the path from competitive research to the next batch of concepts a team actually ships.

所在链路

MagicBrief sits at the research and creative stages, feeding competitive inspiration directly into the brief-and-produce workflow before ads launch.

核心功能

  • Searchable ad discovery across Meta and TikTok ad libraries
  • Boards and swipe files to organize saved ads by angle or format
  • AI-assisted creative analysis and brief generation
  • Tagging by hook, format, and creative concept
  • Collaboration features connecting marketers and designers
  • Integrations that bring ad-account context into creative planning

适合谁用

  • DTC brands building a repeatable creative testing pipeline
  • Performance creative teams researching competitor ads
  • Agencies briefing designers from organized swipe files

新手提示

  1. Build themed boards by competitor, hook, or funnel stage early so your swipe file stays usable as it grows.
  2. Use the ad libraries to spot longevity, since ads running for months are usually winners worth studying.
  3. Treat AI-generated briefs as a starting draft and pair them with your own performance data before committing to production.
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