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leaders",{"id":537,"name":538,"alternativeText":37,"caption":37,"width":31,"height":31,"formats":37,"hash":539,"ext":353,"mime":354,"size":540,"url":541,"previewUrl":37,"provider":357,"provider_metadata":37,"createdAt":542,"updatedAt":542,"documentId":543,"publishedAt":360,"focalPoint":37},16238,"webinars.svg","webinars_b295fc2850",0.68,"https://s3.us-east-2.amazonaws.com/whatagraph.com/webinars_b295fc2850.svg","2026-01-14T13:40:51.403Z","risc1zt1hxx1o5hg2thlmsy8",{},{"data":546,"meta":646},[547],{"id":548,"documentId":549,"title":550,"slug":551,"card_description":552,"order":23,"roles":553,"tasks":556,"setup_minutes":37,"runs":558,"best_for":559,"createdAt":560,"updatedAt":561,"publishedAt":562,"icon":563,"agents":567,"seo":568,"content":592},101,"folpax22ruejg2fc1ijgfc50","Create custom metrics, dimensions, and blends","cross-channel-marketing-data","Describe the metrics and groupings your cross-channel data needs in plain words. Agents build them across your sources, and you check them before adding to reports.",[554,555],"Data / Analytics Lead","Paid Media Specialist",[557],"Reporting Production","On request, and when you onboard a new client","Data leads, analytics leads, PPC leads","2026-10-15T08:44:31.954Z","2026-10-15T09:07:53.151Z","2026-10-15T09:07:53.478Z",{"id":564,"documentId":565,"url":566,"alternativeText":37},17887,"m5o3t3o9djf1d3cuqa7hx8g9","https://s3.us-east-2.amazonaws.com/whatagraph.com/intersect_three_76e0b8d522.svg",[],{"id":569,"metaTitle":570,"metaDescription":571,"addNoIndex":17,"canonicalURL":37,"metaImage":572},3340,"Cross-channel marketing data with AI agents | Whatagraph","Build custom metrics, dimensions and blends for your cross-channel marketing data. AI agents set them up across every source, and your team checks them.",{"id":573,"name":574,"alternativeText":37,"caption":37,"width":575,"height":576,"formats":577,"hash":587,"ext":371,"mime":372,"size":588,"url":589,"previewUrl":37,"provider":357,"provider_metadata":37,"createdAt":590,"updatedAt":590,"documentId":591,"publishedAt":590,"focalPoint":37},17888,"Govern your cross-channel marketing data - IQ Agents.png",2400,1256,{"thumbnail":578},{"ext":371,"url":579,"etag":580,"hash":581,"mime":372,"name":582,"path":37,"size":583,"width":584,"height":585,"sizeInBytes":586},"https://s3.us-east-2.amazonaws.com/whatagraph.com/thumbnail_Govern_your_cross_channel_marketing_data_IQ_Agents_43eeab9712.png","ab8eaf2a5c9abfa7a6c97dc8e3a81b79","thumbnail_Govern_your_cross_channel_marketing_data_IQ_Agents_43eeab9712","thumbnail_Govern your cross-channel marketing data - IQ Agents.png",41.12,245,128,41124,"Govern_your_cross_channel_marketing_data_IQ_Agents_43eeab9712",1040.38,"https://s3.us-east-2.amazonaws.com/whatagraph.com/Govern_your_cross_channel_marketing_data_IQ_Agents_43eeab9712.png","2026-10-15T08:56:24.305Z","vy6lfhi8qdc0q04k9ems8e0j",[593,604,608,618],{"id":594,"text_above_title":37,"title":595,"subtext":596,"background":597,"cta":598,"__component":603},115,"Govern your cross-channel marketing data","Consolidate, define, and standardize your cross-channel marketing data in one place. Agents build custom metrics, dimensions, and blends, and add them directly to your reports, while your team signs off the definitions.","single-use-case",[599],{"id":600,"label":322,"url":601,"subtext":37,"style":602,"element_id":37,"color":37,"icon":37,"width":37},3938,"https://whatagraph.com/book-a-call","button","sections.hero-dark",{"id":605,"content":606,"__component":607},427,"\u003Ch2 id=\"what-is-cross-channel-marketing-data\">What is cross-channel marketing data?\u003C/h2>\u003Cp>Cross-channel marketing data is the performance data from every channel you run (ads, analytics, social, email, CRM and ecommerce) brought into one view, so you can compare channels and add them up. It's the first step before you create a \u003Ca href=\"/blog/articles/cross-channel-marketing-report\">cross-channel marketing report\u003C/a>.\u003C/p>\u003Cp>Consolidating the data in one place is just the beginning. The harder part is standardizing it so numbers mean the same thing everywhere, whether that’s in your external report, internal dashboard, or AI tools.\u003C/p>\u003Cp>Each ad platform names and counts things its own way. Google Ads records conversions within a \u003Ca href=\"https://support.google.com/google-ads/answer/3123169\" target=\"_blank\" rel=\"noopener noreferrer\">conversion window\u003C/a> that defaults to 30 days and can be set anywhere from 1 to 90. Other platforms use different windows and different names for the same action. For example, Google Ads calls ad spend “Cost”, while Meta Ads calls it “Amount spent”. Campaign names also vary depending on which platform you’re on.\u003C/p>\u003Cp>Modeling cross-channel marketing data means building one marketing data model on top of your sources: one definition per metric and one set of dimensions that works across every channel and every client. You build it from four parts:\u003C/p>\u003Cfigure class=\"table\">\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>Building block\u003C/th>\u003Cth>What it does\u003C/th>\u003Cth>Example\u003C/th>\u003C/tr>\u003C/thead>\u003Ctbody>\u003Ctr>\u003Ctd>Custom metric\u003C/td>\u003Ctd>A calculated number built from other metrics\u003C/td>\u003Ctd>Gross spend = spend / 0.8, or blended ROAS = total revenue / total spend\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Custom dimension\u003C/td>\u003Ctd>A grouping or label built from existing fields\u003C/td>\u003Ctd>Region, product and objective split out of campaign names\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Blend\u003C/td>\u003Ctd>Joins a few different sources on shared dimensions\u003C/td>\u003Ctd>Facebook and Instagram reach in one view, joined on date\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Source group\u003C/td>\u003Ctd>Adds up many similar sources into one\u003C/td>\u003Ctd>Every Google Ads account a client has, as one source\u003C/td>\u003C/tr>\u003C/tbody>\u003C/table>\u003C/figure>\u003Cp>Analytics tools have their own versions of these. GA4, for example, offers \u003Ca href=\"https://support.google.com/analytics/answer/14240153\" target=\"_blank\" rel=\"noopener noreferrer\">custom dimensions and metrics\u003C/a> with a cap of 50 event-scoped custom dimensions, 50 custom metrics and 5 calculated metrics on a standard property. Those only cover GA4 data, though. A cross-channel model has to work across every source at once.\u003C/p>\u003Ch3 id=\"how-do-agents-help-with-cross-channel-marketing-data-modeling\">How do agents help with cross-channel marketing data modeling?\u003C/h3>\u003Cp>AI agents build the model for you from a plain-language description. Whatagraph comes with three pre-made agents for this work:\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"eacf4d7557af0b1314c83c55321abb6bd\">\u003Cstrong>KPI\u003C/strong> builds custom metrics and custom dimensions. It checks whether an equivalent already exists before it creates anything, so you don't end up with four versions of ROAS.\u003C/li>\u003Cli data-list-item-id=\"e0a59acefffd949653833d17c27cb0220\">\u003Cstrong>Omni\u003C/strong> builds blends across channels. It joins sources on a shared date dimension and builds rate metrics like CTR on top of the blend, never inside it.\u003C/li>\u003Cli data-list-item-id=\"edd88685d1c524f92bee8eb3a61944f21\">\u003Cstrong>Rollup\u003C/strong> builds source groups. It waits for the data to load and checks the group in a widget before handing it back.\u003C/li>\u003C/ul>\u003Cp>Agents hand work to each other as they go, so a report-building agent can ask KPI for a field it needs. Every change is attributed to the agent that made it, and you decide which actions need your approval first.\u003C/p>\u003Ch2 id=\"modeling-cross-channel-marketing-data-manual-vs-with-ai-agents\">Modeling cross-channel marketing data: manual vs. with AI agents\u003C/h2>\u003Cp>With agents, you describe the definition once and the agent builds, checks and reuses it. If you’re doing it manually, you’ll need to rebuild the same fields for every source, client, and report.\u003C/p>\u003Cfigure class=\"table\">\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth> \u003C/th>\u003Cth>Manual\u003C/th>\u003Cth>With AI agents\u003C/th>\u003C/tr>\u003C/thead>\u003Ctbody>\u003Ctr>\u003Ctd>Time per build\u003C/td>\u003Ctd>Hours to days, repeated per client\u003C/td>\u003Ctd>Minutes. Four dimensions from campaign names took two minutes; a three-source CRM schema took about 30 minutes\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Where the formula lives\u003C/td>\u003Ctd>Inside each report or source\u003C/td>\u003Ctd>Once in the data layer, reused by every report\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Consistency across clients\u003C/td>\u003Ctd>Drifts as different people rebuild it\u003C/td>\u003Ctd>The same definition is copied to each new client\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Splitting campaign names\u003C/td>\u003Ctd>Regex or spreadsheet work\u003C/td>\u003Ctd>You describe the pattern; the agent writes the rules\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Duplicate metrics\u003C/td>\u003Ctd>Several slightly different versions pile up\u003C/td>\u003Ctd>KPI checks for an existing equivalent first\u003C/td>\u003C/tr>\u003Ctr>\u003Ctd>Who checks\u003C/td>\u003Ctd>Whoever built it, if anyone\u003C/td>\u003Ctd>The agent checks in a widget; you approve the plan and the result\u003C/td>\u003C/tr>\u003C/tbody>\u003C/table>\u003C/figure>\u003Cp>The manual route eats whole days. A marketer at a US agency said: \"I literally spent all day yesterday working on blended sources and source groups. That's all I did all day.\"\u003C/p>\u003Cp>Building by hand still makes sense in two cases: a one-off metric for a single report, and a definition your team hasn't agreed on yet. Agree on the definition first. An agent will build whatever you describe, including the wrong thing.\u003C/p>\u003Ch2 id=\"case-studies-how-teams-use-ai-agents-to-model-cross-channel-marketing-data\">Case studies: how teams use AI agents to model cross-channel marketing data\u003C/h2>\u003Ch3 id=\"gkv\">GKV\u003C/h3>\u003Cp>GKV is a communications agency based in the US. Anna Alexiadi Smyrniotaki, Director of Analytics at GKV, uses agents to create custom metrics, custom dimensions and source groups.\u003C/p>\u003Cp>Before agents, every source group meant the same rebuild. Duplicating a group didn't copy its custom metrics, so she created CTR and a handful of other metrics by hand, group after group. Now she asks an agent to create the source group and its metrics in the same request. \u003C/p>\u003Cp>Anna Alexiadi Smyrniotaki said: \u003C/p>\u003Cblockquote class=\"quote\">\u003Cdiv class=\"quote__body\">\u003Cspan class=\"quote__text\">\"There's... same... four or five metrics that I have to create again and again, but... I no longer do that manually. Using the agents to create custom metrics and dimensions has been... amazing.\"\u003C/span>\u003C/div>\u003C/blockquote>\u003Ch3 id=\"a-consumer-electronics-brand\">A consumer electronics brand\u003C/h3>\u003Cp>A consumer electronics brand was switching CRM platforms. Before switching off the old one, its marketing analytics manager had to unify three sets of customer data: a data dump from the old CRM, a spreadsheet of historical CRM data and the live CRM source that was about to be switched off.\u003C/p>\u003Cp>The fields overlapped, but the same metric often had a different name in each file, and the spreadsheets had to match the live source's schema so the history would survive.\u003C/p>\u003Cp>He used agents to clean the data and map schemas instead. He gave the agents a job title, the outcome he wanted and the context only he had, and asked for a plan before any work. Here’s the prompt he used:\u003C/p>\u003Cp>\u003Cimg src=\"https://media.whatagraph.com/prompt_for_CRM_Nothing_848a86d87c.png\" srcset=\"https://media.whatagraph.com/prompt_for_CRM_Nothing_848a86d87c.png?width=350 350w, https://media.whatagraph.com/prompt_for_CRM_Nothing_848a86d87c.png?width=700 700w, https://media.whatagraph.com/prompt_for_CRM_Nothing_848a86d87c.png?width=420 440w, https://media.whatagraph.com/prompt_for_CRM_Nothing_848a86d87c.png?width=840 880w, https://media.whatagraph.com/prompt_for_CRM_Nothing_848a86d87c.png?width=768 768w, https://media.whatagraph.com/prompt_for_CRM_Nothing_848a86d87c.png?width=1536 1536w, https://media.whatagraph.com/prompt_for_CRM_Nothing_848a86d87c.png?width=992 992w, https://media.whatagraph.com/prompt_for_CRM_Nothing_848a86d87c.png?width=1984 1984w, https://media.whatagraph.com/prompt_for_CRM_Nothing_848a86d87c.png?width=600 1200w, https://media.whatagraph.com/prompt_for_CRM_Nothing_848a86d87c.png?width=1200 2400w, https://media.whatagraph.com/prompt_for_CRM_Nothing_848a86d87c.png?width=720 1440w, https://media.whatagraph.com/prompt_for_CRM_Nothing_848a86d87c.png?width=1440 2880w\" sizes=\"(max-width: 350px) 350px, (max-width: 440px) 440px, (max-width: 768px) 768px, (max-width: 992px) 992px, (max-width: 1200px) 1200px, (min-resolution: 192dpi) and (max-width: 350px) 700px, (min-resolution: 192dpi) and (max-width: 420px) 840px, (min-resolution: 192dpi) and (max-width: 768px) 1536px, (min-resolution: 192dpi) and (max-width: 992px) 1984px, (min-resolution: 192dpi) and (max-width: 1200px) 2400px, (min-resolution: 192dpi) 2880px, 1440px\" loading=\"lazy\" fetchpriority=\"auto\" decoding=\"async\" alt=\"prompt for CRM - Nothing.png\">\u003C/p>\u003Cp>The agents came back with an implementation plan.\u003C/p>\u003Cfigure class=\"image\">\u003Cimg src=\"https://media.whatagraph.com/implementation_plan_for_Nothing_7c4afaf0b8.png\" srcset=\"https://media.whatagraph.com/implementation_plan_for_Nothing_7c4afaf0b8.png?width=350 350w, https://media.whatagraph.com/implementation_plan_for_Nothing_7c4afaf0b8.png?width=700 700w, https://media.whatagraph.com/implementation_plan_for_Nothing_7c4afaf0b8.png?width=420 440w, https://media.whatagraph.com/implementation_plan_for_Nothing_7c4afaf0b8.png?width=840 880w, https://media.whatagraph.com/implementation_plan_for_Nothing_7c4afaf0b8.png?width=768 768w, https://media.whatagraph.com/implementation_plan_for_Nothing_7c4afaf0b8.png?width=1536 1536w, https://media.whatagraph.com/implementation_plan_for_Nothing_7c4afaf0b8.png?width=992 992w, https://media.whatagraph.com/implementation_plan_for_Nothing_7c4afaf0b8.png?width=1984 1984w, https://media.whatagraph.com/implementation_plan_for_Nothing_7c4afaf0b8.png?width=600 1200w, https://media.whatagraph.com/implementation_plan_for_Nothing_7c4afaf0b8.png?width=1200 2400w, https://media.whatagraph.com/implementation_plan_for_Nothing_7c4afaf0b8.png?width=720 1440w, https://media.whatagraph.com/implementation_plan_for_Nothing_7c4afaf0b8.png?width=1440 2880w\" sizes=\"(max-width: 350px) 350px, (max-width: 440px) 440px, (max-width: 768px) 768px, (max-width: 992px) 992px, (max-width: 1200px) 1200px, (min-resolution: 192dpi) and (max-width: 350px) 700px, (min-resolution: 192dpi) and (max-width: 420px) 840px, (min-resolution: 192dpi) and (max-width: 768px) 1536px, (min-resolution: 192dpi) and (max-width: 992px) 1984px, (min-resolution: 192dpi) and (max-width: 1200px) 2400px, (min-resolution: 192dpi) 2880px, 1440px\" loading=\"lazy\" fetchpriority=\"auto\" decoding=\"async\" alt=\"implementation plan for Nothing.png\">\u003C/figure>\u003Cp>He added his own mapping definitions and two extra metrics, then approved it. About 30 seconds later, the agents returned an audit of all three schemas and a mapping plan built around the existing production schema.\u003C/p>\u003Cp>He also wanted the CRM data reported next to paid media, so a metric like clicks would fill in for both in the same table. The agents mapped the CRM metrics onto the same universal fields the paid channels already use.\u003C/p>\u003Cfigure class=\"image\">\u003Cimg src=\"https://media.whatagraph.com/Universal_mapping_schema_for_cross_channel_analytics_Whatagraph_IQ_Agents_a17e707161.png\" srcset=\"https://media.whatagraph.com/Universal_mapping_schema_for_cross_channel_analytics_Whatagraph_IQ_Agents_a17e707161.png?width=350 350w, https://media.whatagraph.com/Universal_mapping_schema_for_cross_channel_analytics_Whatagraph_IQ_Agents_a17e707161.png?width=700 700w, https://media.whatagraph.com/Universal_mapping_schema_for_cross_channel_analytics_Whatagraph_IQ_Agents_a17e707161.png?width=420 440w, https://media.whatagraph.com/Universal_mapping_schema_for_cross_channel_analytics_Whatagraph_IQ_Agents_a17e707161.png?width=840 880w, https://media.whatagraph.com/Universal_mapping_schema_for_cross_channel_analytics_Whatagraph_IQ_Agents_a17e707161.png?width=768 768w, https://media.whatagraph.com/Universal_mapping_schema_for_cross_channel_analytics_Whatagraph_IQ_Agents_a17e707161.png?width=1536 1536w, https://media.whatagraph.com/Universal_mapping_schema_for_cross_channel_analytics_Whatagraph_IQ_Agents_a17e707161.png?width=992 992w, https://media.whatagraph.com/Universal_mapping_schema_for_cross_channel_analytics_Whatagraph_IQ_Agents_a17e707161.png?width=1984 1984w, https://media.whatagraph.com/Universal_mapping_schema_for_cross_channel_analytics_Whatagraph_IQ_Agents_a17e707161.png?width=600 1200w, https://media.whatagraph.com/Universal_mapping_schema_for_cross_channel_analytics_Whatagraph_IQ_Agents_a17e707161.png?width=1200 2400w, https://media.whatagraph.com/Universal_mapping_schema_for_cross_channel_analytics_Whatagraph_IQ_Agents_a17e707161.png?width=720 1440w, https://media.whatagraph.com/Universal_mapping_schema_for_cross_channel_analytics_Whatagraph_IQ_Agents_a17e707161.png?width=1440 2880w\" sizes=\"(max-width: 350px) 350px, (max-width: 440px) 440px, (max-width: 768px) 768px, (max-width: 992px) 992px, (max-width: 1200px) 1200px, (min-resolution: 192dpi) and (max-width: 350px) 700px, (min-resolution: 192dpi) and (max-width: 420px) 840px, (min-resolution: 192dpi) and (max-width: 768px) 1536px, (min-resolution: 192dpi) and (max-width: 992px) 1984px, (min-resolution: 192dpi) and (max-width: 1200px) 2400px, (min-resolution: 192dpi) 2880px, 1440px\" loading=\"lazy\" fetchpriority=\"auto\" decoding=\"async\" alt=\"Universal mapping schema for cross-channel analytics - Whatagraph IQ Agents.png\">\u003C/figure>\u003Cp>Then three pre-made agents at Whatagraph split the work amongst themselves without being instructed to do so.\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e6432ffa914e0b6fa2ce77743d4230af9\">Roster, the agent that connects and checks data sources, planned the mapping and ran the audits.\u003C/li>\u003Cli data-list-item-id=\"ee84e35fbf4d2336d16d9a10e282f717c\">KPI built five unified fields.\u003C/li>\u003Cli data-list-item-id=\"ed92a57c08113e8d816c587b02a6f9e63\">Rollup grouped the sources into one total CRM source group.\u003C/li>\u003C/ul>\u003Cp>The entire job only took about 30 minutes. The brand's marketing analytics manager said:\u003C/p>\u003Cblockquote class=\"quote\">\u003Cdiv class=\"quote__body\">\u003Cspan class=\"quote__text\">“IQ Agents saved me a ton of time and work. I now have all the data speaking the same language, and all I had to do was prompt.”\u003C/span>\u003C/div>\u003C/blockquote>\u003Cp>The team now has one set of definitions across CRM and paid media, and the next source they connect maps onto it.\u003C/p>\u003Ch3 id=\"a-performance-agency\">A performance agency\u003C/h3>\u003Cp>A performance agency needed to group a client's Shopify products into product types for its dashboards. The product names didn't follow a pattern, so simple \"contains\" rules wouldn't sort them.\u003C/p>\u003Cp>The agency's reporting analyst asked an agent to write the categorization prompt, then had it build the custom dimension. It sorted every product into about 10 categories, and it could tell phone cases apart from screen protectors without being told.\u003C/p>\u003Cp>The agency's reporting analyst said: \"for Shopify, we have a lot of product line items that we need to group into different product types. So it's not really uniform... I think this is 10 categories. And it actually created this one for me.\"\u003C/p>\u003Ch3 id=\"an-agency-with-multilingual-client-reports\">An agency with multilingual client reports\u003C/h3>\u003Cp>An agency that runs client reports in several languages needed readable GA4 events. Event IDs meant nothing to the client, so the agency had an agent build a custom dimension that maps each event ID to a plain label. A marketer at the agency said: \"I never manually setup of the custom dimensions because the agents are too good.\"\u003C/p>\u003Ch3 id=\"a-uk-social-media-agency\">A UK social media agency\u003C/h3>\u003Cp>A UK social media agency wanted total social reach across Facebook and Instagram, organic and paid, in one view. An agent built the cross-channel blend and worked out the agency's own \"effectiveness rate\" (interactions divided by reach).\u003C/p>\u003Cp>The agency's social lead said: \"I made it work out the engagement rate, which was like individual interactions... divided by reach, which we actually call effectiveness rate... Normally I’d have to do that manually.\"\u003C/p>\u003Ch2 id=\"how-to-analyze-cross-channel-marketing-data-with-ai-agents\">How to analyze cross-channel marketing data with AI agents\u003C/h2>\u003Cp>You analyze cross-channel marketing data in two passes: model it first, then ask questions of the model. Here's how that works with agents in Whatagraph.\u003C/p>\u003Col>\u003Cli data-list-item-id=\"e2a9396a36437ec81e2a82e8d0d0a4789\">\u003Cstrong>Connect your sources.\u003C/strong> The Roster agent connects, tags and sets the currency on each source. Everything lands in the \u003Ca href=\"/data-hub\">Data Hub\u003C/a>, the data layer your reports and agents read from.\u003C/li>\u003Cli data-list-item-id=\"e8555f4313bd6e0a1c71e713b5d47283c\">\u003Cstrong>Write down your definitions.\u003C/strong> List each formula, the markup per client, the pattern your campaign names follow and which conversions count. This becomes the agent's brief.\u003C/li>\u003Cli data-list-item-id=\"e9be5f50f89f272826be1f14c3972a22d\">\u003Cstrong>Ask KPI for custom metrics and dimensions.\u003C/strong> Describe the field in plain words, like \"split campaign names into region, development, objective and format.\" An agent built those four dimensions from a housebuilder's campaign names in two minutes.\u003C/li>\u003Cli data-list-item-id=\"e0050d906ca89e644171a05a3f59d4727\">\u003Cstrong>Ask Omni to blend channels, or Rollup to group accounts.\u003C/strong> Use a blend to put a few different channels side by side. Use a source group to add up many accounts of the same kind.\u003C/li>\u003Cli data-list-item-id=\"e298f1ae28f67eb169a6f7fa1149b638a\">\u003Cstrong>Approve the plan, then check the values.\u003C/strong> The agent shows what it'll create before it builds. Afterwards, compare a widget's totals with the platform for last month.\u003C/li>\u003Cli data-list-item-id=\"e9a5186b9284067c54a02cbc8e3f812ee\">\u003Cstrong>Ask questions of the modeled data.\u003C/strong> The Insight agent answers questions like \"what was CPA by channel last month?\" from the same definitions your reports use.\u003C/li>\u003Cli data-list-item-id=\"e055b39839b767ca68be17052730553fd\">\u003Cstrong>Reuse the setup for the next client.\u003C/strong> The Intake agent copies your blends and custom fields to each new client, so the model doesn't get rebuilt from scratch.\u003C/li>\u003C/ol>\u003Cp>Here's a brief you can copy and adapt:\u003C/p>\u003Cdiv class=\"prompt-box\">\u003Cbutton class=\"prompt-box__copy\" type=\"button\" aria-label=\"Copy prompt\" data-tooltip=\"Copy\">\u003Csvg class=\"prompt-box__icon\" width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" aria-hidden=\"true\" focusable=\"false\">\u003Crect x=\"3.75\" y=\"6.75\" width=\"13\" height=\"13\" rx=\"1.5\"/>\u003Cpath d=\"M7.25 3.25h11.5a1.5 1.5 0 0 1 1.5 1.5v11.5\"/>\u003C/svg>\u003C/button>\u003Cspan class=\"prompt-box__status\" role=\"status\">\u003C/span>\u003Cdiv class=\"prompt-box__content\">\u003Cp>You're our data modeling specialist for [client name].\u003Cbr>\u003Cbr>Goal: one set of cross-channel metrics and dimensions for [client name] that every report can use.\u003Cbr>\u003Cbr>Sources: [Google Ads account], [Meta Ads account], [GA4 property], [CRM source or sheet].\u003Cbr>\u003Cbr>Definitions:\u003Cbr>- Gross spend = spend / [0.8]\u003Cbr>- Conversions = [conversion action names] only\u003Cbr>- Campaign names follow [pattern, e.g. Region_Product_Objective_Format].\u003Cbr>Split them into dimensions for [region], [product], [objective] and [format].\u003Cbr>\u003Cbr>Rules:\u003Cbr>- Before you create a metric or dimension, check whether one with the same meaning exists. If it does, reuse it.\u003Cbr>- Build rate metrics (CTR, CPA, ROAS) on top of the blend or source group, from summed values. Never inside the blend.\u003Cbr>- Join channels on date.\u003Cbr>- Don't change or delete existing metrics, dimensions, blends or source groups that reports use. If one needs to change, ask me first.\u003Cbr>- If a value doesn't map cleanly, list it instead of guessing.\u003Cbr>- If a source returns no data, flag it.\u003Cbr>\u003Cbr>Before you build anything, show me the plan: what you'll create, what you'll reuse and the formula for each metric.\u003Cbr>\u003Cbr>When you're done, check each new field in a widget for last month and compare the totals with the platform. Write a short summary of what you built and what I should review. Don't add the fields to client reports or share anything with the client.\u003C/p>\u003C/div>\u003C/div>\u003Ch2 id=\"how-to-create-a-cross-channel-marketing-data-dashboard-with-agents\">How to create a cross-channel marketing data dashboard with agents\u003C/h2>\u003Cp>Once the data is modeled, you can ask an agent for a cross-channel dashboard in plain language. In Whatagraph, that's Recap, the pre-made agent that builds reports and dashboards from a short prompt. It builds on top of your metrics, dimensions and blends.\u003C/p>\u003Col>\u003Cli data-list-item-id=\"ef73778522a8eb65e334d73fc7956973c\">\u003Cstrong>Describe the dashboard.\u003C/strong> Name the client, the channels, the metrics and the filters you want.\u003C/li>\u003Cli data-list-item-id=\"ef3293c5119e4b5669d55b9c24a5f48cb\">\u003Cstrong>Let the agent fill gaps.\u003C/strong> If the dashboard needs a metric that doesn't exist yet, the agent creates it from your definitions or asks another agent to.\u003C/li>\u003Cli data-list-item-id=\"e0de5eaec0ed2ed360f12fdfc66ee6815\">\u003Cstrong>Use your custom dimensions as filters.\u003C/strong> Region, product type or campaign objective become dropdowns on the dashboard.\u003C/li>\u003Cli data-list-item-id=\"eae522ea2a118a139dea21226e40aeb95\">\u003Cstrong>Apply the client's branding.\u003C/strong> Add the logo, colors and fonts.\u003C/li>\u003Cli data-list-item-id=\"ea091560dc102831bc79b51ffcabfb3b4\">\u003Cstrong>Share it.\u003C/strong> Send a live link with a password, or schedule it by email.\u003C/li>\u003C/ol>\u003Cp>An example prompt:\u003C/p>\u003Cdiv class=\"prompt-box\">\u003Cbutton class=\"prompt-box__copy\" type=\"button\" aria-label=\"Copy prompt\" data-tooltip=\"Copy\">\u003Csvg class=\"prompt-box__icon\" width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\" aria-hidden=\"true\" focusable=\"false\">\u003Crect x=\"3.75\" y=\"6.75\" width=\"13\" height=\"13\" rx=\"1.5\"/>\u003Cpath d=\"M7.25 3.25h11.5a1.5 1.5 0 0 1 1.5 1.5v11.5\"/>\u003C/svg>\u003C/button>\u003Cspan class=\"prompt-box__status\" role=\"status\">\u003C/span>\u003Cdiv class=\"prompt-box__content\">\u003Cp>Build a one-page cross-channel overview for [client name] covering [Google Ads], [Meta Ads] and [GA4] for [last 30 days].\u003Cbr>\u003Cbr>Show total spend, gross spend, blended ROAS and CPA by channel, plus a trend line for conversions.\u003Cbr>\u003Cbr>Add filters for [region] and [objective].\u003Cbr>\u003Cbr>Use only existing metrics and dimensions, and tell me if one is missing.\u003C/p>\u003C/div>\u003C/div>\u003Cp>After an agent split the housebuilder's campaign names into four dimensions, it built a five-tab report that used the new dimensions as filters, so the team could look at one region at a time.\u003C/p>\u003Ch2 id=\"best-tools-for-cross-channel-marketing-data-collection-and-analysis\">Best tools for cross-channel marketing data collection and analysis\u003C/h2>\u003Cp>The right tool depends on whether you need to run campaigns across channels or report on them. A few options:\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e0a1b953480db817f22a29513da4455eb\">\u003Cstrong>Whatagraph:\u003C/strong> cross-channel reporting and marketing intelligence. One governed data foundation holds your custom metrics, dimensions, blends and source groups, and AI agents build and maintain them.\u003C/li>\u003Cli data-list-item-id=\"e15143ecec7589eb6c4bfcc6c937f5a0d\">\u003Cstrong>HubSpot Marketing Hub:\u003C/strong> running and tracking campaigns across email, social, ads and your website, with CRM data built in.\u003C/li>\u003Cli data-list-item-id=\"eddcda7786d9efb3ce4aca651be88405a\">\u003Cstrong>Salesforce Marketing Cloud:\u003C/strong> cross-channel marketing for large enterprises.\u003C/li>\u003Cli data-list-item-id=\"e7c44dbb96460baf6495bc11cef1811ee\">\u003Cstrong>SE Ranking:\u003C/strong> SEO and search visibility data.\u003C/li>\u003C/ul>\u003Cp>See our full comparison of \u003Ca href=\"/blog/articles/cross-channel-marketing-platforms\">cross-channel marketing platforms\u003C/a>.\u003C/p>\u003Ch2 id=\"what-your-team-still-owns\">What your team still owns\u003C/h2>\u003Cp>Agents build the model. Your team owns what goes into it and what goes out.\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e7afd8d393cd48754ad720407cd3a2714\">\u003Cstrong>The definitions.\u003C/strong> Which conversions count, the markup per client, what engagement means for each client. An agent can't decide these.\u003C/li>\u003Cli data-list-item-id=\"e7eaeb758f1e6787cd5f4e588d8a89c21\">\u003Cstrong>The check.\u003C/strong> Compare new fields against the platform before they reach a client report.\u003C/li>\u003Cli data-list-item-id=\"ed62fbfbfae7d6cce8f0a846b55d0d547\">\u003Cstrong>Approval on shared fields.\u003C/strong> Changing a metric that 40 reports use changes 40 reports. Keep those changes on approval.\u003C/li>\u003Cli data-list-item-id=\"eb646226f5e264ecfbb69f3262aaceac0\">\u003Cstrong>Naming conventions going forward.\u003C/strong> Dimension rules only work while campaigns follow the pattern.\u003C/li>\u003C/ul>\u003Cp>A marketing lead at a global company's in-house team said: \"I modified just there with IQ, and then I checked it manually myself...\"\u003C/p>\u003Cp>This is the same discipline as \u003Ca href=\"/blog/articles/marketing-data-governance\">marketing data governance\u003C/a>: agree the definitions, write them down and check that the numbers match.\u003C/p>\u003Cp>Before a new field goes live, check that:\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"eded9bd345e91e9ee891e1d20cbee3401\">the formula matches what you agreed with the client\u003C/li>\u003Cli data-list-item-id=\"ebef7bdcef5b4cd78fd2fd793cfd0e820\">last month's totals match the platform\u003C/li>\u003Cli data-list-item-id=\"e6b01129efadf94113c59a00e596b26da\">no older version of the same metric is still in use\u003C/li>\u003Cli data-list-item-id=\"e1ec299b87a0fd1cf14cd2985d56fe059\">every value in a new dimension has a category, with nothing left blank\u003C/li>\u003C/ul>\u003Ch2 id=\"challenges-in-cross-channel-marketing-data-management\">Challenges in cross-channel marketing data management\u003C/h2>\u003Cp>Most cross-channel data problems come from definitions that don't line up. Here are the common ones and how to fix each.\u003C/p>\u003Cp>\u003Cstrong>Rates that average averages.\u003C/strong> Put CTR or conversion rate inside a blend and you get an average of each channel's rate, not the true rate. A PPC team lead at a German agency said: \"I don't have a working summary row for relative KPIs like click rate or conversion rate.\" Fix: calculate rates on top of the blend from summed clicks, impressions and conversions. Omni does this by default.\u003C/p>\u003Cp>\u003Cstrong>Near-duplicate metrics.\u003C/strong> Every rebuild adds another version of CPM or ROAS, and nobody knows which one is right. Fix: one definition per metric, clearly named. KPI checks for an existing equivalent before it creates one.\u003C/p>\u003Cp>\u003Cstrong>Metrics that don't carry over when you copy.\u003C/strong> Custom metrics live on the source, so duplicating a blend or source group leaves them behind. An account team at a Dutch agency said: \"when you duplicate the blend, the metrics are not there because metrics is created on the source.\" Anna Alexiadi Smyrniotaki at GKV said: \"if I duplicate a source group, it doesn't duplicate its custom [metrics] with it.\" Fix: ask an agent to duplicate the blend or group together with its metrics.\u003C/p>\u003Cp>\u003Cstrong>Messy or changing campaign names.\u003C/strong> The information is there, but inside a string. Fix: describe the naming pattern and let an agent turn it into dimension rules. Review any values it can't map.\u003C/p>\u003Cp>\u003Cstrong>Different conversion windows.\u003C/strong> Platforms count conversions over different periods, so totals don't line up. Fix: agree which conversion actions count per client and build one conversions metric from those.\u003C/p>\u003Cp>\u003Cstrong>Blend joins.\u003C/strong> Blends match rows on fields that have to hold the same values in every source, and the join type decides which rows survive. Looker Studio, for example, supports \u003Ca href=\"https://docs.cloud.google.com/data-studio/how-blends-work\" target=\"_blank\" rel=\"noopener noreferrer\">five join types\u003C/a>, from inner to cross joins. A marketer at a property marketing company said: \"it was, like, select to join, but, like, there was also select to join here. And then the whole outside, inside, something, something.\" Fix: join on date and let Omni handle the join rules. Our guide to \u003Ca href=\"/blog/articles/data-blending-looker-studio\">blending data in Looker Studio\u003C/a> shows how this works by hand.\u003C/p>\u003Cp>\u003Cstrong>Data that's still loading.\u003C/strong> A new source group takes time to fill. Fix: wait for the data to load, then check. Rollup waits and validates the group in a widget before handing it back.\u003C/p>\u003Cp>\u003Cstrong>Platform changes.\u003C/strong> When a platform renames or retires a metric, every report that uses it needs a fix. The owner of a French agency said: \"Facebook or Instagram decides to change... you have to change in all the reports... really... exhausting.\" Fix: point reports at one custom metric, so a platform change means one edit to the definition.\u003C/p>","sections.rich-text-content",{"id":609,"title":610,"subtext":611,"subtextBottom":37,"titleSize":612,"type":613,"subtextStyle":612,"disableTopMargin":17,"disableBottomMargin":17,"text_above_title":37,"CTA":614,"cta_collection":37,"__component":617},649,"Use agents to govern your cross-channel marketing data","","normal","dark 2",[615],{"id":616,"label":322,"url":601,"subtext":37,"style":602,"element_id":37,"color":37,"icon":37,"width":37},3939,"sections.headline",{"id":619,"description":37,"title":37,"items":620,"__component":645},1281,[621,625,629,633,637,641],{"id":622,"question":623,"answer":624},7802,"Why is data important in cross-channel marketing strategies?","\u003Cp>Data is how you decide where the next dollar goes. A cross-channel strategy only works if you can compare channels and add them up, and that needs one data model underneath. Without it, each platform reports on its own definitions: different conversion windows, different names for the same action and rates you can't combine. Budget decisions then rest on numbers that don't mean the same thing. With shared definitions, a question like \"which channel brought in customers at the lowest cost last quarter?\" has one answer, whoever asks it.\u003C/p>",{"id":626,"question":627,"answer":628},7803,"What's the difference between cross-channel and multi-channel marketing data?","\u003Cp>Multi-channel marketing data is each channel's data kept on its own: Google Ads numbers in Google Ads, Meta numbers in Meta, each on its own definitions. Cross-channel marketing data is the same data joined and normalized, so the channels can be compared and added up in one view. The difference is the model in the middle. Cross-channel data needs shared metric definitions, shared dimensions such as region or product, and rules for joining sources, like matching on date and calculating rates from totals.\u003C/p>",{"id":630,"question":631,"answer":632},7804,"Is cross-channel marketing data useful for small businesses?","\u003Cp>Yes, and the setup is smaller. A small business might run Google Ads, Meta and GA4 and nothing else. Start with one blend of those sources and three metrics: total spend, conversions and cost per conversion, calculated from the totals. That's enough to see which channel earns its budget. AI agents help most here, because the setup work usually needs a data person most small teams don't have. You describe the metric, and the agent builds and checks it.\u003C/p>",{"id":634,"question":635,"answer":636},7805,"What are the trends in cross-channel marketing data analytics?","\u003Cp>Three changes stand out. First, definitions now live in one governed data layer that reports, dashboards and AI tools all read, so the number in a client report matches the number an AI assistant gives you. Second, AI agents are taking over the modeling work itself: building custom metrics, splitting campaign names into dimensions and grouping accounts. Third, AI assistants like Claude and ChatGPT can now read marketing data through MCP connectors, which makes shared definitions matter even more.\u003C/p>",{"id":638,"question":639,"answer":640},7806,"How do you integrate cross-channel marketing data with CRM systems?","\u003Cp>Connect the CRM as a source, or upload its exports as a sheet if you're working with historical data. Then map the CRM fields onto the same fields your ad channels use, so a metric like clicks or conversions fills in for both in one table. Group the CRM sources together if you have more than one. A consumer electronics brand did this with agents in about 30 minutes: three CRM sources, five unified fields and one source group, reported next to paid media.\u003C/p>",{"id":642,"question":643,"answer":644},7807,"How is cross-channel marketing data analysis different from standard digital marketing analysis?","\u003Cp>Standard digital marketing analysis reads one platform's numbers on that platform's definitions: Google Ads CPA in Google Ads, Meta ROAS in Meta. Cross-channel analysis compares and adds up channels, so it needs shared definitions first. Rates have to come from summed totals, conversions have to mean the same action on every platform and reach can't simply be added. Skip that step and the totals look right but aren't.\u003C/p>","sections.faq",{"pagination":647},{"page":5,"pageSize":147,"pageCount":5,"total":5},1792055478740]