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本页介绍了您可以从 Cortex Looker 中获取哪些有关跨媒体和产品关联分析的数据洞见。借助此 Looker 块，您可以将来自多个付费媒体平台（包括 Google Ads、Meta、TikTok 和 YouTube [通过 DV360]）的广告系列数据整合到 BigQuery 中，并利用 Google Cloud Cortex Framework for Marketing 提供的预打包提取数据流和报告视图，全面了解广告系列支出和效果。
此流水线还包含一个选项，可在 Vertex AI 上使用 Gemini 文本生成模型，将媒体广告系列的文本表示形式与单个产品层次结构节点进行匹配。例如，名为 “BMX - Reels - Reach” 的广告系列与产品层次结构节点 ['Fitness & Sports', 'Bicycles', 'Special Bikes','BMX Bikes'] 匹配。
查看与特定产品类别和产品相关的广告系列在各个平台上的展示次数和点击次数的概要明细。
可用的数据洞见
Cortex Framework 中用于跨媒体和产品关联分析的 Looker 代码块可提供以下分析洞见。
大致了解效果和互动指标，包括：
- 总展示次数
- 总点击次数
- 点击率 (CTR)
- 总支出
- 每千次展示费用 (CPM)
- 每次点击费用 (CPC)
- 按月和媒体平台划分的支出
- 累计每月支出（总计和按媒体平台）
- 按时间顺序查看广告系列
- 按媒体平台、广告系列和国家/地区划分的展示次数、点击次数、点击率和每千次展示费用
- 按广告系列和国家/地区的详细效果
所需数据
按照 Cortex Framework 的安装说明获取此分块所需的 BigQuery 数据集。
代码库
如需访问用于跨媒体和产品关联分析的 Cortex Looker 块，请访问其官方 GitHub 代码库：block-cortex-cross-media。此代码库包含基本视图、探索和信息中心，可让您将数据无缝集成到 Looker 环境中。这些资源为您提供了坚实的基础，可帮助您根据自己的具体需求创建自定义报告、可视化图表和信息中心。
部署
如需了解如何部署适用于跨媒体和产品关联分析的 Cortex Looker 块，请参阅为 Cortex Framework 部署 Looker 块。
其他注意事项
如需优化用于跨媒体和产品关联数据分析的 Looker 代码块的性能和功能，请考虑以下事项：
- Liquid 模板语言：某些常量、视图、探索和信息中心使用 Liquid 模板语言。如需了解详情，请参阅 Looker 的液体变量参考文档。
- 取消隐藏其他维度和测量参数：为简单起见，系统会隐藏许多维度和测量参数。如果您发现缺少任何有价值的信息，请在相关视图中将字段的
hidden 参数值设置为 No。
如未另行说明，那么本页面中的内容已根据知识共享署名 4.0 许可获得了许可，并且代码示例已根据 Apache 2.0 许可获得了许可。有关详情，请参阅 Google 开发者网站政策。Java 是 Oracle 和/或其关联公司的注册商标。
最后更新时间 (UTC)：2025-08-18。
[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["很难理解","hardToUnderstand","thumb-down"],["信息或示例代码不正确","incorrectInformationOrSampleCode","thumb-down"],["没有我需要的信息/示例","missingTheInformationSamplesINeed","thumb-down"],["翻译问题","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC)：2025-08-18。"],[[["\u003cp\u003eThis Looker Block provides a unified view of campaign performance and spend across multiple paid media platforms like Google Ads, Meta, TikTok, and YouTube (with DV360), using data consolidated in BigQuery.\u003c/p\u003e\n"],["\u003cp\u003eIt offers insights into overall campaign performance, including total impressions, clicks, CTR, spend, CPM, and CPC.\u003c/p\u003e\n"],["\u003cp\u003eYou can analyze media platform performance and monthly spend trends, with a detailed breakdown by platform and country.\u003c/p\u003e\n"],["\u003cp\u003eThe Looker Block allows campaigns to be matched to specific product hierarchies using a Gemini text generation model on Vertex AI.\u003c/p\u003e\n"],["\u003cp\u003eThe block's resources, including views, Explores, and dashboards, are available on the block-cortex-cross-media GitHub repository, enabling users to customize reports and visualizations.\u003c/p\u003e\n"]]],[],null,["# Looker Block for Cross Media & Product Connected Insights\n\nLooker Block for Cross Media \\& Product Connected Insights\n==========================================================\n\nThis page describes the insights you can get from the Cortex Looker Block\nfor Cross Media \\& Product Connected Insights. With this Looker Block\nyou can get a comprehensive view of your campaign spend and performance by\ncombining your campaign data from multiple paid media platforms\nincluding Google Ads, Meta, TikTok and YouTube (with DV360) into\nBigQuery with pre-packaged ingestion pipelines and reporting views\nprovided by [Google Cloud Cortex Framework for Marketing](/cortex/docs/overview).\n\nThis pipeline also includes an option to use a Gemini text generation\nmodel on Vertex AI to match textual representations of media campaigns\nwith a single product hierarchy node. For example, a campaign named\n*\"BMX - Reels - Reach\"* matches the product hierarchy node\n`['Fitness & Sports', 'Bicycles', 'Special Bikes','BMX Bikes']`.\n\nSee a\nhigh level breakdown of volume of impressions and clicks from each platform for\ncampaigns relating to specific product category and product.\n\nAvailable insights\n------------------\n\nThe Looker Block for Cross Media \\& Product Connected Insights in\nCortex Framework offers the following insights.\n\nOverall campaign performance\n----------------------------\n\nOverview of high-level performance and engagement metrics including:\n\n- Total impressions\n- Total clicks\n- Click through rate (CTR)\n- Total Spend\n- Cost per Mille (CPM)\n- Cost per click (CPC)\n\n### Media platform performance and spend by month\n\n- Spend by month and media platform\n- Cumulative monthly spend in total and by media platform\n\n### Campaign performance\n\n- Campaigns in chronological view\n- Impressions, clicks, click through rate and cost per mille by media platform, campaign, and country\n- Detailed performance by campaign and country\n\nRequired Data\n-------------\n\nGet the required BigQuery datasets for this block by\nfollowing the installation instructions for\n[Cortex Framework](https://github.com/GoogleCloudPlatform/cortex-data-foundation).\n\nRepository\n----------\n\nTo access the Cortex Looker Block for Cross Media \\& Product Connected\nInsights, visit its official GitHub repository: [**block-cortex-cross-media**](https://github.com/looker-open-source/block-cortex-cross-media).\nThis repository contains essential views, Explores and dashboards that enable\nyou to seamlessly integrate data into your Looker environment.\nThese resources provide a solid foundation for creating custom reports,\nvisualizations, and dashboards tailored to your specific needs.\n\nDeployment\n----------\n\nFor instructions about how to deploy the Cortex Looker Block for\nCross Media \\& Product Connected Insights, see\n[Deploy Looker Blocks for Cortex Framework](/cortex/docs/looker-block-deployment).\n\n### Other Considerations\n\nFor optimizing the performance and functionality of your\nLooker Block for Cross Media \\& Product Connected Insights\nconsider the following:\n\n- **Liquid Templating Language** : Some constants, views, Explores and dashboards use liquid templating language. For more information, see Looker's [Liquid Variable Reference](/looker/docs/liquid-variable-reference) documentation.\n- **Unhide additional dimensions and measures** : Many dimensions and measures are hidden for simplicity. If you find anything valuable missing, set field's `hidden` parameter value to `No` in the relevant views."]]
