{"id":16495,"date":"2026-06-29T07:02:28","date_gmt":"2026-06-29T07:02:28","guid":{"rendered":"https:\/\/e-dialog.group\/blog\/mmm-for-everyone-meridian-comes-to-google-analytics\/"},"modified":"2026-06-29T07:42:25","modified_gmt":"2026-06-29T07:42:25","slug":"mmm-for-everyone-meridian-comes-to-google-analytics","status":"publish","type":"post","link":"https:\/\/e-dialog.group\/en\/blog\/mmm-for-everyone-meridian-comes-to-google-analytics\/","title":{"rendered":"MMM for everyone? Meridian comes to Google Analytics!"},"content":{"rendered":"<div id=\"basic-content-block_872462bba9db4f0d46d8653b468b3a17\" class=\"basic-content block block--basic-content\" data-title=\"\">\n  <div class=\"basic-content__content content\">\n    <p><a href=\"https:\/\/e-dialog.group\/en\/google-sales-partner\/google-analytics-360\/\">Google Analytics 360<\/a> is increasingly developing into a one stop shop for media planning and performance analyses &ndash; based on MMM-like statistics. With the latest updates, Google is bringing functionalities for <a href=\"https:\/\/support.google.com\/analytics\/answer\/14896117#modelmethodology\">cross-channel media and budget planning<\/a>, as well as performance forecasting, directly into the tool. <\/p>\n<p>&nbsp;<\/p>\n<h2>What do the new integrations in GA360 mean?<\/h2>\n<p>Previously, merging cost and performance data from various platforms was often a manual or tool-intensive process. The new update fundamentally changes this: <\/p>\n  <\/div>\n<\/div>  <div id=\"small-ul-block_378e4df6b57988d5ea2484c74ad33d11\" class=\"small-ul block block--small-ul block--no-margin small-ul--one-column\" data-title=\"\">\n    <ul class=\"small-ul__content content\">\n              <li class=\"content__li li\">\n                      <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewbox=\"0 0 24 24\">\n              <circle fill=\"#0045a5\" cx=\"12\" cy=\"12\" r=\"11.77\"><\/circle>\n              <polyline fill=\"none\" stroke=\"#fff\" stroke-linecap=\"round\" stroke-linejoin=\"round\" stroke-width=\"1.5\" points=\"18 7.25 11 16.75 6 12.75\"><\/polyline>\n            <\/svg>\n            <strong>Native Connections: <\/strong>Integrations launched at the end of 2025 for Meta, TikTok, Pinterest, Reddit, Snapchat and more will become highly relevant.                  <\/li>\n              <li class=\"content__li li\">\n                      <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewbox=\"0 0 24 24\">\n              <circle fill=\"#0045a5\" cx=\"12\" cy=\"12\" r=\"11.77\"><\/circle>\n              <polyline fill=\"none\" stroke=\"#fff\" stroke-linecap=\"round\" stroke-linejoin=\"round\" stroke-width=\"1.5\" points=\"18 7.25 11 16.75 6 12.75\"><\/polyline>\n            <\/svg>\n            <strong>Historical Data:<\/strong> There is now a seamless and automated integration for Impressions, Clicks &amp; Cost Data for the previous 2 years.                  <\/li>\n              <li class=\"content__li li\">\n                      <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewbox=\"0 0 24 24\">\n              <circle fill=\"#0045a5\" cx=\"12\" cy=\"12\" r=\"11.77\"><\/circle>\n              <polyline fill=\"none\" stroke=\"#fff\" stroke-linecap=\"round\" stroke-linejoin=\"round\" stroke-width=\"1.5\" points=\"18 7.25 11 16.75 6 12.75\"><\/polyline>\n            <\/svg>\n            <strong>Offline Channels:<\/strong> Offline channel data can also be imported.                  <\/li>\n          <\/ul>\n  <\/div>\n<div id=\"basic-content-block_3ffd2cca3031997bc9148d94f6dccbab\" class=\"basic-content block block--basic-content block--no-margin\" data-title=\"\">\n  <div class=\"basic-content__content content\">\n    <p>By importing this data, you gain the reason to have Non-Google Impressions, Clicks and Cost in GA360, which is the reporting of Sessions &amp; Key Events. This allows for a clean, unified view of metrics like Cost-per-Session or ROAS across different platforms directly alongside your website data. These would still be impacted by GA360&rsquo;s click-attribution, but would drastically reduce manual efforts to aggregate data of different sources.  <\/p>\n<p>While we at e-dialog had predicted that these data integrations would constitute the basis for MMM-like functions &ndash; we see exactly this happening now.<\/p>\n<p>As an agency we get similar questions each year:<\/p>\n  <\/div>\n<\/div>  <div id=\"small-ul-block_b3a1343d32e2b17165ac7b351cfcde1a\" class=\"small-ul block block--small-ul small-ul--one-column\" data-title=\"\">\n    <ul class=\"small-ul__content content\">\n              <li class=\"content__li li\">\n                      <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewbox=\"0 0 24 24\">\n              <circle fill=\"#0045a5\" cx=\"12\" cy=\"12\" r=\"11.77\"><\/circle>\n              <polyline fill=\"none\" stroke=\"#fff\" stroke-linecap=\"round\" stroke-linejoin=\"round\" stroke-width=\"1.5\" points=\"18 7.25 11 16.75 6 12.75\"><\/polyline>\n            <\/svg>\n            How much marketing budget is needed?                  <\/li>\n              <li class=\"content__li li\">\n                      <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewbox=\"0 0 24 24\">\n              <circle fill=\"#0045a5\" cx=\"12\" cy=\"12\" r=\"11.77\"><\/circle>\n              <polyline fill=\"none\" stroke=\"#fff\" stroke-linecap=\"round\" stroke-linejoin=\"round\" stroke-width=\"1.5\" points=\"18 7.25 11 16.75 6 12.75\"><\/polyline>\n            <\/svg>\n            How do we allocate the marketing budget?                  <\/li>\n              <li class=\"content__li li\">\n                      <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewbox=\"0 0 24 24\">\n              <circle fill=\"#0045a5\" cx=\"12\" cy=\"12\" r=\"11.77\"><\/circle>\n              <polyline fill=\"none\" stroke=\"#fff\" stroke-linecap=\"round\" stroke-linejoin=\"round\" stroke-width=\"1.5\" points=\"18 7.25 11 16.75 6 12.75\"><\/polyline>\n            <\/svg>\n            Can we measure what impact awareness and branding measures have?                  <\/li>\n          <\/ul>\n  <\/div>\n<div id=\"basic-content-block_23f3ab4086e551b725aef4a1aa268d7c\" class=\"basic-content block block--basic-content\" data-title=\"\">\n  <div class=\"basic-content__content content\">\n    <p>Without MMM these questions are a nightmare to answer. In advertising tools, forecasts scream for more money, the <a href=\"https:\/\/e-dialog.group\/en\/marketing-performance-measurement\/attribution\/\">attribution<\/a> says that each channel is working incredibly well and GA360&rsquo;s attribution only shows good figures for click-based channels like SEA, Shopping etc. <\/p>\n<p>By integrating <a href=\"https:\/\/support.google.com\/analytics\/answer\/17025764\">Meridian like modelings<\/a> and insights, answering the above questions will be much more feasible and GA360 become the marketing measurement power house it always strived to be. How do we answer these questions in the near future? <\/p>\n  <\/div>\n<\/div><div id=\"basic-content-block_9beb52aedf9159f31b1407f6045bc439\" class=\"basic-content block block--basic-content block--no-margin\" data-title=\"\">\n  <div class=\"basic-content__content content\">\n    <h2>The GA360 Scenario Planner for Cross-Channel Budgeting<\/h2>\n<p>The most exciting new feature is the Scenario Planner. It is designed to optimize your Marketing Budget by allowing you to evaluate your Monthly\/Quarterly\/Yearly Budget for your Primary Channel Grouping. Just this can already answer two of the above questions on how to set budgets. Besides optimizing the marketing budgets, the scenario planner also allows players to play around with budgets to see how a lower\/higher budget would impact overall CPA\/ROAS per channel in your channel grouping.   <\/p>\n<p>This last bit also allows to break down the performance of channels that have performed poorly with the click-attribution as it is entirely modeled &ndash; like awareness or offline measures.<\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"text-decoration: underline;\">Screenshot:<\/span> An example of a Scenario report in Google Analytics 360: While the optimal budget for the selected time frame is projected to be most efficient with 110.176$ we put in our budget of 190.000$ and can see that this additional budget is less efficient and ROAS decreases substantially (by 33.82%). <small>Source: e-dialog<\/small><\/p>\n  <\/div>\n<\/div><div id=\"image-fullscreen-block_40b8763bbe38a78b0a11fa60754258c2\" class=\"image-fullscreen block block--image-fullscreen\" data-title=\"\">\n  <div class=\"image-fullscreen__content content\">\n    <img loading=\"lazy\" decoding=\"async\" width=\"1085\" height=\"1063\" src=\"https:\/\/e-dialog.group\/wp-content\/uploads\/2026\/06\/Meridian-GA360-Screenshot.png\" class=\"content__img img img--desktop\" alt=\"\" srcset=\"https:\/\/e-dialog.group\/wp-content\/uploads\/2026\/06\/Meridian-GA360-Screenshot.png 1085w, https:\/\/e-dialog.group\/wp-content\/uploads\/2026\/06\/Meridian-GA360-Screenshot-300x294.png 300w, https:\/\/e-dialog.group\/wp-content\/uploads\/2026\/06\/Meridian-GA360-Screenshot-1024x1003.png 1024w, https:\/\/e-dialog.group\/wp-content\/uploads\/2026\/06\/Meridian-GA360-Screenshot-768x752.png 768w\" sizes=\"auto, (max-width: 1085px) 100vw, 1085px\">    <img loading=\"lazy\" decoding=\"async\" width=\"1085\" height=\"1063\" src=\"https:\/\/e-dialog.group\/wp-content\/uploads\/2026\/06\/Meridian-GA360-Screenshot.png\" class=\"content__img img img--mobile\" alt=\"\" srcset=\"https:\/\/e-dialog.group\/wp-content\/uploads\/2026\/06\/Meridian-GA360-Screenshot.png 1085w, https:\/\/e-dialog.group\/wp-content\/uploads\/2026\/06\/Meridian-GA360-Screenshot-300x294.png 300w, https:\/\/e-dialog.group\/wp-content\/uploads\/2026\/06\/Meridian-GA360-Screenshot-1024x1003.png 1024w, https:\/\/e-dialog.group\/wp-content\/uploads\/2026\/06\/Meridian-GA360-Screenshot-768x752.png 768w\" sizes=\"auto, (max-width: 1085px) 100vw, 1085px\">  <\/div>\n<\/div><div id=\"basic-content-block_09ab609a8ff6d99898801cd72862b95a\" class=\"basic-content block block--basic-content block--no-margin\" data-title=\"\">\n  <div class=\"basic-content__content content\">\n    <p>Currently these modeling functions are still being developed and alphas. We can expect the roll out of Meridian functionalities to be greater than scenario and projection reports we see today. At the Google Analytics Conference in 2026, further details were discussed that allow us to get a glimpse of what&rsquo;s coming:  <\/p>\n  <\/div>\n<\/div>  <div id=\"small-ul-block_eea01493a0503adfe52a3c08374f4be3\" class=\"small-ul block block--small-ul small-ul--one-column\" data-title=\"\">\n    <ul class=\"small-ul__content content\">\n              <li class=\"content__li li\">\n                      <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewbox=\"0 0 24 24\">\n              <circle fill=\"#0045a5\" cx=\"12\" cy=\"12\" r=\"11.77\"><\/circle>\n              <polyline fill=\"none\" stroke=\"#fff\" stroke-linecap=\"round\" stroke-linejoin=\"round\" stroke-width=\"1.5\" points=\"18 7.25 11 16.75 6 12.75\"><\/polyline>\n            <\/svg>\n            <strong>GA360 will use the click-attribution figures as priors<\/strong> (a starting point) for the modeling but it is intended that these priors can be adjusted by users in the future.                  <\/li>\n              <li class=\"content__li li\">\n                      <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewbox=\"0 0 24 24\">\n              <circle fill=\"#0045a5\" cx=\"12\" cy=\"12\" r=\"11.77\"><\/circle>\n              <polyline fill=\"none\" stroke=\"#fff\" stroke-linecap=\"round\" stroke-linejoin=\"round\" stroke-width=\"1.5\" points=\"18 7.25 11 16.75 6 12.75\"><\/polyline>\n            <\/svg>\n            <strong>The Google Search Volume will be integrated as control variables<\/strong> for demand changes for an advertiser&rsquo;s brand and category, just like in real Meridian projects. This integration helps to correct the inflated SEA and Shopping Performance and also captures seasonality changes.                   <\/li>\n          <\/ul>\n  <\/div>\n<div id=\"basic-content-block_55d5839ad7581066eb0a1db51a4a3516\" class=\"basic-content block block--basic-content block--no-margin\" data-title=\"\">\n  <div class=\"basic-content__content content\">\n    <h2>How does it work? What is different about this? <\/h2>\n<p><a href=\"https:\/\/e-dialog.group\/en\/google-sales-partner\/meridian\/\">Meridian<\/a> &ndash; and MMM in general are based on regressions. Regressions are a statistical term for the causal relationship between variables. If a simple regression figures out the relationship between one input (like Facebook spend) and your sales, <a href=\"https:\/\/e-dialog.group\/en\/ai-in-marketing\/marketing-mix-modeling\/\">Marketing Mix Modeling (MMM)<\/a> takes that exact same math and applies it to all your inputs at the same time.  <\/p>\n<p>It looks at your spend across every single channel &ndash; like Google, Meta, TikTok, and even offline ads &ndash; alongside outside factors like seasonality or holidays. By looking at how all these different inputs interact, <span style=\"text-decoration: underline;\">MMM untangles the messy web of your marketing to show you exactly which specific channel deserves the credit for your final output (sales)<\/span>, telling you exactly where your budget is working the hardest. While in the past, MMM were therefore only pursued by larger companies and corporations, this direct integration also allows small to medium advertisers to gain access to these substantial MMM insights. <\/p>\n<p>&nbsp;<\/p>\n<h2>Prerequisites: It&rsquo;s Not Plug-and-Play<\/h2>\n<p>However, the magic doesn&rsquo;t happen entirely on its own. For the digital connection to work and the model to be accurate, some technical homework must be done: <\/p>\n  <\/div>\n<\/div>  <div id=\"small-ul-block_5ccc3fe06ebf0731a8be4c18fcf0eb78\" class=\"small-ul block block--small-ul small-ul--one-column\" data-title=\"\">\n    <ul class=\"small-ul__content content\">\n              <li class=\"content__li li\">\n                      <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewbox=\"0 0 24 24\">\n              <circle fill=\"#0045a5\" cx=\"12\" cy=\"12\" r=\"11.77\"><\/circle>\n              <polyline fill=\"none\" stroke=\"#fff\" stroke-linecap=\"round\" stroke-linejoin=\"round\" stroke-width=\"1.5\" points=\"18 7.25 11 16.75 6 12.75\"><\/polyline>\n            <\/svg>\n            <strong>Consistent UTMs:<\/strong> A consistent UTM structure is required. You must use a single unique value for utm_medium, and a single unique utm_source value for each publisher platform. In discussions with Google at the Analytics Conference in Vienna, 2026 it was acknowledged that in times of Artificial Intelligence, aligning previous unanimous utm parameters should be possible. If this feature will come is, however, unclear. For now, having a clear utm structure is therefore recommended.                      <\/li>\n              <li class=\"content__li li\">\n                      <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewbox=\"0 0 24 24\">\n              <circle fill=\"#0045a5\" cx=\"12\" cy=\"12\" r=\"11.77\"><\/circle>\n              <polyline fill=\"none\" stroke=\"#fff\" stroke-linecap=\"round\" stroke-linejoin=\"round\" stroke-width=\"1.5\" points=\"18 7.25 11 16.75 6 12.75\"><\/polyline>\n            <\/svg>\n            <strong>Currency Alignment:<\/strong> The currency of the connected non-Google data sources matches GA360 otherwise a work-around through e.g. Google Sheets needs to be used.                  <\/li>\n              <li class=\"content__li li\">\n                      <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewbox=\"0 0 24 24\">\n              <circle fill=\"#0045a5\" cx=\"12\" cy=\"12\" r=\"11.77\"><\/circle>\n              <polyline fill=\"none\" stroke=\"#fff\" stroke-linecap=\"round\" stroke-linejoin=\"round\" stroke-width=\"1.5\" points=\"18 7.25 11 16.75 6 12.75\"><\/polyline>\n            <\/svg>\n            <strong>Key Events:<\/strong> If not already done, a conversion (or key event) needs to be defined.                  <\/li>\n              <li class=\"content__li li\">\n                      <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewbox=\"0 0 24 24\">\n              <circle fill=\"#0045a5\" cx=\"12\" cy=\"12\" r=\"11.77\"><\/circle>\n              <polyline fill=\"none\" stroke=\"#fff\" stroke-linecap=\"round\" stroke-linejoin=\"round\" stroke-width=\"1.5\" points=\"18 7.25 11 16.75 6 12.75\"><\/polyline>\n            <\/svg>\n            <strong>Channel Grouping:<\/strong> If not already done, prepare your channel grouping in a way that it would allow the granularity you would need for your performance evaluation. We suggest e.g. to split Social Media into the different platforms and Search into Brand\/Generic.                   <\/li>\n              <li class=\"content__li li\">\n                      <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewbox=\"0 0 24 24\">\n              <circle fill=\"#0045a5\" cx=\"12\" cy=\"12\" r=\"11.77\"><\/circle>\n              <polyline fill=\"none\" stroke=\"#fff\" stroke-linecap=\"round\" stroke-linejoin=\"round\" stroke-width=\"1.5\" points=\"18 7.25 11 16.75 6 12.75\"><\/polyline>\n            <\/svg>\n            <strong>Google Ads Link:<\/strong> Make sure your Google Ads Account is linked to your GA360 Property.                  <\/li>\n              <li class=\"content__li li\">\n                      <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewbox=\"0 0 24 24\">\n              <circle fill=\"#0045a5\" cx=\"12\" cy=\"12\" r=\"11.77\"><\/circle>\n              <polyline fill=\"none\" stroke=\"#fff\" stroke-linecap=\"round\" stroke-linejoin=\"round\" stroke-width=\"1.5\" points=\"18 7.25 11 16.75 6 12.75\"><\/polyline>\n            <\/svg>\n            <strong>1- 2 years of KPI &amp; cost: <\/strong>The model needs at least 1 year of conversion\/revenue data and cost from your Google and non-Google sources. The model does not know if the data it has is complete. Make sure all channels are connected to achieve reliable results. Up to two years of data can be used for model training.                     <\/li>\n              <li class=\"content__li li\">\n                      <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewbox=\"0 0 24 24\">\n              <circle fill=\"#0045a5\" cx=\"12\" cy=\"12\" r=\"11.77\"><\/circle>\n              <polyline fill=\"none\" stroke=\"#fff\" stroke-linecap=\"round\" stroke-linejoin=\"round\" stroke-width=\"1.5\" points=\"18 7.25 11 16.75 6 12.75\"><\/polyline>\n            <\/svg>\n            <strong>(optional) offline Channel: Prepare offline channel data (like e.g. TV or radio) to be able to upload it into GA360. Instead of monthly data, try to break it down further.  <\/strong>                  <\/li>\n          <\/ul>\n  <\/div>\n<div id=\"basic-content-block_61179a22b1ee239584034fc7ec58582f\" class=\"basic-content block block--basic-content\" data-title=\"\">\n  <div class=\"basic-content__content content\">\n    <h2>Summary, Outlook and Critique:<\/h2>\n<p>We at e-dialog expect these functionalities to be rolled out much further in GA360 to e.g. allow for side-to-side comparisons of Attribution vs. Modeling. This most exciting update for Google Analytics makes MMM insights available to a very broad audience and gives it major advantages over its free version and other website analytic tools. Also, while Google&rsquo;s Meridian is an open-source MMM and therefore transparent, this platform-native modeling should be taken with a grain of salt. Self-contained models naturally tend to favor their own ecosystem and setting independent priors and testing incrementality will be essential to counteract potential platform-bias.   <\/p>\n  <\/div>\n<\/div><div id=\"teaser-slim-block_1772c2b854ab54811a846ed9c9f657b7\" class=\"teaser-slim block block--teaser-slim\" data-title=\"\">\n  <div class=\"teaser-slim__content content\">\n    <div class=\"content__img\">\n              <img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"550\" src=\"https:\/\/e-dialog.group\/wp-content\/uploads\/2026\/06\/GA4-Meridian_Gemini_Generated_Image_7hvvib7hvvib7hvv_processed-e1782716443393-1024x550.png\" class=\"attachment-large size-large\" alt=\"\" srcset=\"https:\/\/e-dialog.group\/wp-content\/uploads\/2026\/06\/GA4-Meridian_Gemini_Generated_Image_7hvvib7hvvib7hvv_processed-e1782716443393-1024x550.png 1024w, https:\/\/e-dialog.group\/wp-content\/uploads\/2026\/06\/GA4-Meridian_Gemini_Generated_Image_7hvvib7hvvib7hvv_processed-e1782716443393-300x161.png 300w, https:\/\/e-dialog.group\/wp-content\/uploads\/2026\/06\/GA4-Meridian_Gemini_Generated_Image_7hvvib7hvvib7hvv_processed-e1782716443393-768x412.png 768w, https:\/\/e-dialog.group\/wp-content\/uploads\/2026\/06\/GA4-Meridian_Gemini_Generated_Image_7hvvib7hvvib7hvv_processed-e1782716443393-1536x824.png 1536w, https:\/\/e-dialog.group\/wp-content\/uploads\/2026\/06\/GA4-Meridian_Gemini_Generated_Image_7hvvib7hvvib7hvv_processed-e1782716443393-2048x1099.png 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\">          <\/div>\n    <div class=\"content__info info\">\n              <svg class=\"info__decoration\" width=\"267\" height=\"127\" viewbox=\"0 0 267 127\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n          <path d=\"M151.031 29C218.027 29 272.138 83.0603 272.138 149.5C272.138 215.94 218.027 270 151.031 270C84.0352 270 29.9248 215.94 29.9248 149.5C29.9248 83.0603 84.0352 29 151.031 29Z\" stroke=\"#FBC105\" stroke-width=\"58\"><\/path>\n        <\/svg>\n                    <div class=\"info__sl\">Let&rsquo;s explore together how you can use the new MMM forecasts in Google Analytics 360 to your advantage when planning your budget.<\/div>\n            <h2 class=\"info__hl\">Are your marketing budgets future-proof yet?<\/h2>      <a href=\"https:\/\/e-dialog.group\/en\/contact-form\/\" target=\"_self\" class=\"info__cta\">\n        Contact us\n      <\/a>    <\/div>\n  <\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Google Analytics 360 is undergoing a major transformation by integrating Meridian, Google&#8217;s advanced Marketing Mix Modeling (MMM) engine, into a native cross-channel forecasting tool. This game-changing update automatically fuses data from Google, non-Google, and offline channels, allowing marketers to accurately measure brand awareness and simulate budget changes via a new Scenario Planner. By breaking down the barriers to elite data science, the platform empowers businesses of all sizes to stop guessing and optimize their true cross-channel ROAS. While highly valuable, unlocking this predictive power requires strict technical preparation &#8211; including clean UTM structures and historical data &#8211; alongside independent testing to ensure objective results.   <\/p>\n","protected":false},"author":3,"featured_media":16494,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[445,448],"channel":[452,455,456,457],"goal":[465],"technology":[36],"c-year":[432],"class_list":["post-16495","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","category-strategy","channel-display","channel-search","channel-shopping","channel-social","goal-optimize-marketing-budgets","technology-google-analytics","c-year-432"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.5 (Yoast SEO v27.9) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Meridian in Google Analytics: MMM for everyone? - e-dialog<\/title>\n<meta name=\"description\" content=\"Meridian brings Marketing Mix Modeling (MMM) natively to Google Analytics 360. 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