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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-f7fd86b243","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/domo-mmm-guide",":type":"snowflake-site/components/quickstart/quickstart-hero","isDeveloperGuidesPage":false,"quickstartHeroFirstCertifiedTag":{"tagText":"Partner 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Developers","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers","currentPage":false}]},"flexible_column_cont":{"id":"flexible-column-container-f0366d7786","propertiesId":"quickstart-template-main-flexible-container","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-7c675d2b7b",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"contentfragment":{"id":"contentfragment-8f3be45697","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EIn this quickstart, you'll learn how to use Domo to consolidate your marketing performance data from multiple sources and run Marketing Mix Modelling analysis using Domo MMM. By the end of this guide, you'll have a unified marketing dataset connected to an AI-powered analysis that shows which channels drive true incremental revenue, where budget is being wasted, and how to reallocate spend for maximum impact. You'll also be able to interrogate your model results using natural language through Snowflake CoWork, asking questions like "What happens if I cut social spend by 20%?" and receiving evidence-based answers in plain English.\u003C/p\u003E\n","\u003Ch3\u003EWhat is Domo MMM?\u003C/h3\u003E\n","\u003Cp\u003EEvery marketing team faces the same question from finance: which channels actually work? Platform metrics show clicks and conversions, but they cannot separate correlation from causation. They cannot tell you which sales would have happened anyway, or explain the impact of channels that don't have direct tracking.\u003C/p\u003E\n","\u003Cp\u003EDomo MMM answers these questions using Bayesian statistical modelling. It analyses your historical marketing spend alongside revenue data to isolate the incremental impact of each channel, accounting for seasonality, carryover effects, and diminishing returns. The result is a confidence interval for each channel's true return on investment, not a single point estimate that hides uncertainty.\u003C/p\u003E\n","\u003Cp\u003EWhat makes Domo MMM different is how you interact with these results. Traditional MMM outputs require a statistician to interpret. Domo MMM integrates with Snowflake CoWork to create a conversational layer over your model. Ask "Which channel should I prioritise next quarter?" or "Why is the model recommending I reduce TV spend?" and receive answers drawn directly from your analysis. The system understands marketing concepts like saturation, diminishing returns, and incremental contribution, translating statistical findings into decisions you can act on immediately.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EBasic understanding of Snowflake\u003C/li\u003E\u003Cli\u003EA Snowflake account. If you do not have a Snowflake account, you can register for a \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003Efree trial account\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003EA Domo account and a basic understanding of Domo\u003C/li\u003E\u003Cli\u003EAccess to data\u003C/li\u003E\u003Cli\u003EFamiliarity with marketing metrics and KPIs\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to request and deploy Domo MMM from Domo\u003C/li\u003E\u003Cli\u003EHow to initialise a dataset using Domo Connectors\u003C/li\u003E\u003Cli\u003EHow to prepare and structure your marketing data for MMM analysis\u003C/li\u003E\u003Cli\u003EHow to configure field mappings in Domo MMM\u003C/li\u003E\u003Cli\u003EHow to run and interpret Bayesian Marketing Mix Model results\u003C/li\u003E\u003Cli\u003EHow to use budget optimisation recommendations\u003C/li\u003E\u003Cli\u003EHow to set up and use Snowflake CoWork for natural language queries\u003C/li\u003E\u003Cli\u003EHow to generate AI-powered insights using Cortex Analysis\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake\u003C/a\u003E account\u003C/li\u003E\u003Cli\u003EA \u003Ca href=\"https://www.domo.com/\"\u003EDomo\u003C/a\u003E account with admin privileges\u003C/li\u003E\u003Cli\u003EMarketing performance data with revenue and channel spend (minimum 8 weeks, recommended 2 years)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Marketing Mix Modeling solution to analyze channel performance, view iROAS metrics with confidence intervals, decompose revenue by channel, and optimize budget allocation using AI-powered recommendations.\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EInstall\u003C/h2\u003E\n","\u003Ch3\u003ERequest Access\u003C/h3\u003E\n","\u003Cp\u003ERequest the Domo MMM app:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EOpen the Domo provider page in the \u003Cstrong\u003ESnowflake Marketplace\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003ELocate and click the Domo MMM app listing.\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ERequest\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EFill out then submit the request form.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe Domo team will review the request and contact you with more information.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant\u003C/strong\u003E: Domo MMM cannot be self-installed from the Snowflake Marketplace. You must contact Domo to provision the application for your instance.\u003C/p\u003E\n","\u003Ch3\u003EWhat Domo Configures For You\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EComponent\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDescription\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ECustom App\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDomo MMM application deployed to your instance\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESnowflake Connection\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECortex AI integration (if requested)\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPrepare Your Data\u003C/h2\u003E\n","\u003Cp\u003EBefore running your MMM analysis, you need to consolidate your marketing performance data into a single dataset. Most organisations have this data scattered across multiple platforms: revenue in their CRM or ERP, digital spend in Google and Meta, offline spend in separate tracking systems. Domo's connectors and Magic ETL bring these sources together into the unified structure that Domo MMM requires.\u003C/p\u003E\n","\u003Cp\u003EOver the next few sections, you'll supercharge your marketing data by leveraging SQL in Snowflake alongside Domo's Magic ETL. Connect key sources such as Adobe Analytics, Google Analytics, Marketo, NetSuite, Salesforce, Facebook, and Instagram. Use customizable join logic and preparatory steps tailored to your data environment to build a cohesive, centralized data foundation. These preparatory steps allow for advanced attribution models and media mix analysis, ensuring you have the insights needed to optimize your marketing strategy.\u003C/p\u003E\n","\u003Cp\u003ELeverage the Domo Data Warehouse to access your data wherever it sits to transform & visualize.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/warehouse.png\" alt=\"assets/warehouse.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EAdd a DataSet using a Connector\u003C/h3\u003E\n","\u003Cp\u003EWhen you add a DataSet, you are automatically assigned as the DataSet owner. For information about changing the owner of a DataSet, see Changing the Owner of a DataSet.\u003C/p\u003E\n","\u003Cp\u003EYou can access the interface for adding Connector DataSets via the Appstore, the Data Center, or the menu.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETo add a DataSet using a Connector\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EChoose one of the following:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E(Conditional) If you want to connect to data via the \u003Cstrong\u003EAPPSTORE\u003C/strong\u003E, do the following:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESelect \u003Cem\u003EAppstore\u003C/em\u003E in the toolbar at the Features Section of the left menu\u003C/li\u003E\u003Cli\u003EUse the search bar to locate the Connector you want to connect to, then click it to open its details view.\u003C/li\u003E\u003Cli\u003EClick \u003Cem\u003EGet the Data\u003C/em\u003E.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E(Conditional) If you want to connect to data via the \u003Cstrong\u003EDATA CENTER\u003C/strong\u003E, do the following:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EGo to  \u003Cem\u003EDatasets\u003C/em\u003E in the Features Section at the left menu.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESelect Connect Data at the top menu\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIn the Connect Data submenu at the top of the screen, select \u003Cem\u003EConnectors\u003C/em\u003E, \u003Cem\u003EUpload a spreadsheet\u003C/em\u003E, depending on the connection type.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EYou can use the following table to learn more about these Connector types:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EConnector Type\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDescription\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EConnectors\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EA Connector for a third-party app in which data is stored in the cloud. Most of Domo's Connectors fall into this category. Examples include Facebook, Salesforce, Adobe Analytics, and so on.\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUpload a Spreadsheet\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EA Connector used to pull files into Domo. Examples include Excel, Google Sheets, and Box.\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EThe other  icons in this area denote other non-Connector methods for bringing data into Domo. Federated refers to federated DataSets, and Cloud Integration refers to native integration with different CDW (i.e. Snowflake).\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESelect the Connector type you want.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick the desired Connector tile.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cem\u003ENote: Popular Connectors are marked with a Preferred tag. This is also used when there are several different Connectors to the same data, such as Facebook. The most commonly used option will display the Preferred tag.\u003C/em\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESpecify the settings in each section. - Refer to the general information included in this topic and to the help in the specific data Connector.\n\u003Cem\u003EFor more information about configuring specific data Connectors, see Configuring Each Connector.\nAs applicable, click Connect, Next, or Save and open the next section.\u003C/em\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EWhen finished, click \u003Cem\u003ESave\u003C/em\u003E.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EYou are taken to the details view for the DataSet in the Data Center. For more information about this view, see Data Center Layout.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EConnector Settings\u003C/h3\u003E\n","\u003Cp\u003EAll \u003Ca href=\"https://domo-support.domo.com/s/topic/0TO5w000000ZammGAC/connect-data-to-domo?language=en_US\"\u003EConnector\u003C/a\u003E types in Domo have different options for setting up a DataSet.\u003C/p\u003E\n","\u003Cp\u003EMost Connectors require you to enter login credentials, an API key, a server URL, or a combination of these to access the Connector. If you cannot connect after entering your credentials, you have most likely entered incorrect credentials.\u003C/p\u003E\n","\u003Cp\u003EAfter you connect, you are usually asked for information about the data you want to pull and the desired format. Most Connectors have two or more associated report types. In addition, many Connectors request a timeframe for the data to be retrieved. You may also be asked to submit a query for retrieving data. For example, when connecting to JIRA you can enter a JQL query to retrieve data for a specified search filter.\u003C/p\u003E\n","\u003Cp\u003EFor most Connectors, you are also asked to schedule data updates. You can use basic scheduling, in which you select a single, specific unit of time (such as "Every hour") and enter the time of day when the update is to occur, if required. Or you can use advanced scheduling, in which you can select multiple update times.\u003C/p\u003E\n","\u003Ch3\u003EConnector Credentials\u003C/h3\u003E\n","\u003Cp\u003EIf required, specify the credentials for connecting to the data provider. If available, you can select an account or create an account to use in connecting. For more information about accounts, see \u003Ca href=\"https://domo-support.domo.com/s/article/360042926054\"\u003EManage Connector Accounts\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003ESome Connectors, such as Google Drive, use OAuth to connect. This means that you only need to enter your credentials once for a given account. In the future, when you go to create a DataSet using this Connector account, your credentials are passed in automatically. Other Connectors do not use OAuth, so you must enter your credentials each time you create a DataSet using this Connector account.\u003C/p\u003E\n","\u003Ch4\u003EConnector Details\u003C/h4\u003E\n","\u003Cp\u003EMost Connectors include a Details settings category. Here you usually specify options like the report to run, the timeframe for the data, a data query for pulling specific information from a database, and so on. If a query is required, the type of query you need to use depends on the Connector type and the source data in your system.\u003C/p\u003E\n","\u003Cp\u003EClick \u003Cem\u003ELoad Preview\u003C/em\u003E to verify that your data is accessible. If connection errors occur, verify the specified connection information.\u003C/p\u003E\n","\u003Ch3\u003EConnector Scheduling\u003C/h3\u003E\n","\u003Cp\u003EIn the \u003Cstrong\u003EScheduling\u003C/strong\u003E settings category, you can specify the update schedule, retry settings, and update method you want for this DataSet.\nYou can use either basic or advanced scheduling for connectors.\u003C/p\u003E\n","\u003Ch4\u003EBasic scheduling\u003C/h4\u003E\n","\u003Cp\u003EIn the \u003Cstrong\u003EBasic Scheduling\u003C/strong\u003E tab, you can create a basic update schedule in which you specify a predefined update interval for this DataSet (such as "every Monday at 10:00 AM").\u003C/p\u003E\n","\u003Cp\u003EBy default, schedules are set from the current time. Update intervals include every hour, day, weekday, week, month, and manually. Schedule times are based on UTC and will also show what time that is for you based on your Company Time Zone setting.\nFor hour, day, and week options, you can specify the interval (every # hours/days/weekdays) and the start period.\u003C/p\u003E\n","\u003Cp\u003E\u003Cem\u003ENote: If you set a Connector schedule using the hourly method, the end time is not inclusive. For example, if the schedule is set to hourly with the active hours set to run 8 AM UTC to 7 AM UTC it will skip the 7 AM UTC run because the end hour is not treated as inclusive.\nIf you select Manually for your update interval, you can instruct Domo to send you a notification when the data has not been updated for a given period of time. Time periods range from one hour to three months.\u003C/em\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/connector.png\" alt=\"assets/connector.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cem\u003ENote: If you need your DataSet to update faster than every 15 minutes, please reach out to your account team for evaluation.\u003C/em\u003E\u003C/p\u003E\n","\u003Ch4\u003EUpdate Method\u003C/h4\u003E\n","\u003Cp\u003EWhen creating or editing a DataSet, you can specify whether to append or replace data when updates occur. The update options are found at the bottom of the Basic Scheduling and Advanced Scheduling tabs.\n| Option | Description |\n|----|----|\n| Replace | Replace the current version of the data with a new version of the data. Previous versions are preserved. |\n| Append | Add data to the current version of the data, increasing the size of the DataSet. |\nUpsert | Update DataSets with restated data to ensure you have the most up-to-date information. Available for selected connectors only. For a list of available connectors, see \u003Ca href=\"https://domo-support.domo.com/s/article/360043430733\"\u003EDataSet Update Methods\u003C/a\u003E. |\nPartition | Select a rolling window of data to keep, making it easier to focus on relevant data. Available for selected connectors only. For a list of available connectors, see \u003Ca href=\"https://domo-support.domo.com/s/article/360043430733\"\u003EDataSet Update Methods\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EAdvanced Scheduling\u003C/h3\u003E\n","\u003Cp\u003EIn the \u003Cstrong\u003EAdvanced Scheduling\u003C/strong\u003E tab, you have more control over when data is updated than you do when using basic scheduling. You can create schedules by month, day of the month, or day of the week. You can even specify which days of the week out of the month you want to update (for example, every second and fourth Sunday).\u003C/p\u003E\n","\u003Cp\u003EYou can indicate whether updates are done on a set interval (such as "every 15 minutes," "every 8 hours," etc.) or at a specified time. You can also set the start time (based on the current minute). If you want, you can set the update schedule to start immediately.\u003C/p\u003E\n","\u003Cp\u003E\u003Cem\u003ENote: If you need your DataSet to update faster than every 15 minutes, please reach out to your account team for evaluation.\nSchedule times are based on UTC but can be seen in your timezone.\u003C/em\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/advanced_connector.png\" alt=\"assets/advanced_connector.png\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/advanced_connector2.png\" alt=\"assets/advanced_connector2.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EConnector Error handling\u003C/h3\u003E\n","\u003Cp\u003ERetry settings determine whether Domo should attempt to retry if updates fail for this DataSet and, if so, the frequency and maximum number of retries. These settings apply only to scheduled runs, not manual runs. You access the retry options dialog by selecting \u003Cem\u003EAlways retry when an update fails\u003C/em\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/error_handling_connector.png\" alt=\"assets/error_handling_connector.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe options in this dialog are as follows:\n| Option | Description |\n|----|----|\n| Always retry when an update fails | Domo retries to update the DataSet. After retrying the specified number of times, Domo sends a notification if the update attempt is unsuccessful. |\n| Do not retry when update fails | Domo sends a notification if the update attempt is unsuccessful, and no retries are made.|\u003C/p\u003E\n","\u003Ch3\u003EConnecting to Snowflake\u003C/h3\u003E\n","\u003Cp\u003EDomo's native Snowflake integration enables direct, real-time connectivity to your Snowflake data warehouse without the need to extract, copy, or move data into Domo's infrastructure. Using Cloud Integration to Snowflake, Domo pushes query execution directly to Snowflake, leveraging its compute power while keeping your data securely in place. This approach eliminates data duplication, reduces storage costs, and ensures you're always working with the most current data available in your Snowflake environment\u003C/p\u003E\n","\u003Ch3\u003ECreate a Magic ETL DataFlow\u003C/h3\u003E\n","\u003Cp\u003EFollow these steps to create a \u003Cem\u003EMagic ETL\u003C/em\u003E DataFlow:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003ENavigate to the Domo \u003Cem\u003EData Center\u003C/em\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIn the ribbon at the top of the Data Center, select \u003Cem\u003ETransform Data\u003C/em\u003E > \u003Cem\u003EMagic ETL\u003C/em\u003E to open the Magic ETL canvas.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/magic_etl.jpg\" alt=\"assets/magic_etl.jpg\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIn the left panel, expand DataSets and drag an Input DataSet tile to the canvas.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/canvas_magic_etl.png\" alt=\"assets/canvas_magic_etl.png\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EThe tile editor expands below the canvas.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/dataset_magic_etl.jpg\" alt=\"assets/dataset_magic_etl.jpg\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIn the tile editor, select Choose DataSet to choose the DataSet you want to transform.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EDrag other tiles to the canvas, depending on what transformations you want to make, and make sure they are all connected by dragging the nodes on the sides of each tile to the node on the next tile.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EConfigure the Output DataSet tile:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EConnect a tile to the Output DataSet tile.\u003C/li\u003E\u003Cli\u003ESelect the Output DataSet tile, then enter a name for the new output DataSet.\u003C/li\u003E\u003Cli\u003E(Optional) Schedule the DataFlow. By default, you must run the DataFlow manually. You can schedule it to run when a trigger activates. See \u003Ca href=\"https://domo-support.domo.com/s/article/000005216\"\u003EAdvanced DataFlow Triggering\u003C/a\u003E to learn more.\u003C/li\u003E\u003Cli\u003EEnter a name and description for the DataFlow.\u003C/li\u003E\u003Cli\u003ESelect Save to keep your changes, adding an optional version description before saving again.\u003C/li\u003E\u003Cli\u003EWhen you save a DataFlow, an entry for this version is added to the Versions tab in the Details view for the DataFlow. If you add a version description, it appears in the version entry. Learn about Viewing the Version History for a DataFlow.\u003C/li\u003E\u003C/ol\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ETips and Notes\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EYou must configure each tile in the editor before you can configure the following tile. If a tile is not configured, the connector to the next tile appears as a dashed line.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/tiles_magic_etl.png\" alt=\"assets/tiles_magic_etl.png\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EUse the search tool in the left panel to find the tile you need.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/search_magic_etl.png\" alt=\"assets/search_magic_etl.png\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EThe Mini Map displays in the corner of the screen and helps you see the layout and navigate around complex and detailed DataFlows. Click and drag the white square in the mini map to move to a certain view of the DataFlow on the canvas.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/minimap_magic_etl.jpg\" alt=\"assets/minimap_magic_etl.jpg\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIf you close the mini map, reopen it by selecting the map pointer icon.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/show_minimap_magic_etl.png\" alt=\"assets/show_minimap_magic_etl.png\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EYou can get help on a specific tile in the canvas by clicking the tile, then clicking \u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/help_icon.png\" alt=\"assets/help_icon.png\"\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EYou can select a number of tiles at once by clicking on the canvas then dragging the mouse pointer over them. When multiple tiles are selected, you can drag all of the selected tiles as a group to where you want them. You can also delete the selected tiles by selecting Delete in the left panel.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EWhy are output DataSets not marked as Updated when the DataFlow completes successfully?\u003C/p\u003E\n","\u003Cp\u003EThis is usually because the data has not actually changed—no update has occurred. The DataSets show as updated if the data has changed during a successful DataFlow execution.\u003C/p\u003E\n","\u003Ch4\u003EBest Practices for Magic ETL DataFlows\u003C/h4\u003E\n","\u003Cp\u003EWe recommend the following for your DataFlow:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EOnly include the DataSets that are necessary to create the output DataSet.\u003C/li\u003E\u003Cli\u003EFilter out rows that you don't need at the beginning of the DataFlow. Learn about Filter tiles.\u003C/li\u003E\u003Cli\u003EReduce the number of columns to only those you need.\u003C/li\u003E\u003Cli\u003EUse descriptive names for each tile in your DataFlow.\u003C/li\u003E\u003Cli\u003EList the following in your DataFlow description:\n\u003Cul\u003E\u003Cli\u003EThe input DataSets being transformed and their owner's names.\u003C/li\u003E\u003Cli\u003EThe output DataSet created.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EGive your DataFlow the same name as the output DataSet.\n\u003Cul\u003E\u003Cli\u003EThis is because the outputs of a DataFlow become their own DataSet in the Data Center, and this allows you to more easily identify which DataFlows contribute to which output DataSets.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EBe aware that some tiles take longer to execute than others, including:\n\u003Cul\u003E\u003Cli\u003EGroup By\u003C/li\u003E\u003Cli\u003ERank & Window\u003C/li\u003E\u003Cli\u003EJoin Data\u003C/li\u003E\u003Cli\u003ERemove Duplicates\u003C/li\u003E\u003Cli\u003EPivot\u003C/li\u003E\u003Cli\u003EScripting tiles\u003C/li\u003E\u003Cli\u003EData Science tiles\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EData Structure Requirements\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERequired Fields\u003C/strong\u003E\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EField Type\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDescription\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EExample\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ERevenue\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENumeric sales/revenue metric\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Etotal_revenue\u003C/code\u003E, \u003Ccode\u003Esales_amount\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EDate\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETime dimension (weekly recommended)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eweek_start_date\u003C/code\u003E, \u003Ccode\u003Ereport_date\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EChannel Spend\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMarketing spend per channel (2-10 channels)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Efacebook_spend\u003C/code\u003E, \u003Ccode\u003Egoogle_ads_spend\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOptional Fields\u003C/strong\u003E\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EField Type\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDescription\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EExample\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EControl Variables\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExternal factors affecting revenue\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eseasonality_index\u003C/code\u003E, \u003Ccode\u003Ecompetitor_activity\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ECustom Variables\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBinary or numeric indicators\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Epromotion_flag\u003C/code\u003E, \u003Ccode\u003Eholiday_indicator\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch4\u003EExample Data Schema\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Example: Marketing Performance Dataset\nSELECT\n    week_start_date,           -- Date field\n    total_revenue,             -- Revenue field\n    facebook_ads_spend,        -- Channel 1\n    google_search_spend,       -- Channel 2\n    google_display_spend,      -- Channel 3\n    tiktok_spend,              -- Channel 4\n    email_marketing_spend,     -- Channel 5\n    tv_advertising_spend,      -- Channel 6\n    seasonality_index,         -- Control variable\n    is_holiday_week            -- Custom variable\nFROM marketing_performance\nWHERE week_start_date BETWEEN '2022-01-01' AND '2024-12-31'\nORDER BY week_start_date;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EData Requirements\u003C/h4\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ERequirement\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESpecification\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMinimum Channels\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2 marketing channels\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMaximum Channels\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E10 marketing channels\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMinimum Date Range\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E52 weeks\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMaximum Date Range\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E104 weeks (~2 years)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECurrency\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAll channels should use same currency\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGranularity\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWeekly  granularity (weekly recommended)\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConfigure the Model\u003C/h2\u003E\n","\u003Cp\u003EDomo MMM uses your prepared dataset to build a Bayesian Marketing Mix Model. The configuration wizard guides you through three key decisions: which revenue metric to optimise, which marketing channels to include in the analysis, and any external factors (like seasonality or promotions) that affect revenue independently of marketing spend.\u003C/p\u003E\n","\u003Cp\u003EThe model will then run 4,000 Bayesian simulations to calculate the incremental contribution of each channel, accounting for adstock (the carryover effect of marketing spend) and saturation (diminishing returns at higher spend levels). This typically takes 5 to 15 minutes depending on your data volume.\u003C/p\u003E\n","\u003Cp\u003EOnce complete, you'll have access to channel performance dashboards, statistical validation metrics, budget optimisation recommendations, and AI-powered natural language insights through Snowflake Cortex AI.\u003C/p\u003E\n","\u003Ch3\u003EStep 1: Launch the Application\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EApp Studio\u003C/strong\u003E in your Domo instance\u003C/li\u003E\u003Cli\u003ESearch for "Domo MMM"\u003C/li\u003E\u003Cli\u003EClick to launch the application\u003C/li\u003E\u003Cli\u003EYou'll see the \u003Cstrong\u003EWelcome\u003C/strong\u003E screen\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EStep 2: Field Mapping\u003C/h3\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003E"Get Started"\u003C/strong\u003E to begin the configuration wizard.\u003C/p\u003E\n","\u003Ch4\u003ESelect Revenue Dataset\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EClick \u003Cstrong\u003E"Select Dataset"\u003C/strong\u003E dropdown\u003C/li\u003E\u003Cli\u003ESearch for your marketing performance dataset\u003C/li\u003E\u003Cli\u003ESelect the dataset containing revenue and channel data\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch4\u003EMap Revenue and Date Fields\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EIn \u003Cstrong\u003E"Revenue Amount"\u003C/strong\u003E dropdown, select your revenue metric\u003C/li\u003E\u003Cli\u003EIn \u003Cstrong\u003E"Date Field"\u003C/strong\u003E dropdown, select your time dimension\u003C/li\u003E\u003Cli\u003ESelect the date range using the date pickers\u003C/li\u003E\u003Cli\u003EEnsure you have at least 8 weeks of data\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch4\u003EAdd Marketing Channels\u003C/h4\u003E\n","\u003Cp\u003EFor each marketing channel:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EMap the spend field (e.g., \u003Ccode\u003Efacebook_ads_spend\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003E\n\u003Col start=\"6\"\u003E\u003Cli\u003ERepeat for all channels (minimum 2, maximum 10)\u003C/li\u003E\u003C/ol\u003E\n\u003C/li\u003E\u003Cli\u003EIf there is more than 10 marketing Channel, click \u003Cstrong\u003E"+ Add Channel"\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EMap the aditional spend field (e.g., \u003Ccode\u003Efacebook_ads_spend\u003C/code\u003E)\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch4\u003EAdd Control Variables (Optional)\u003C/h4\u003E\n","\u003Cp\u003EControl variables help account for external factors:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick \u003Cstrong\u003E"+ Add Control Variable"\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESelect the control variable field\u003C/li\u003E\u003Cli\u003EExamples: \u003Ccode\u003Eseasonality_index\u003C/code\u003E, \u003Ccode\u003Ecompetitor_price_index\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EStep 3: Set iROAS Priors (Optional)\u003C/h3\u003E\n","\u003Cp\u003EiROAS priors incorporate your domain knowledge into the model:\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat are Priors?\u003C/strong\u003E In Bayesian statistics, priors represent your existing knowledge before seeing the data. Setting iROAS priors helps the model converge faster and can improve accuracy when you have reliable historical benchmarks.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Col\u003E\u003Cli\u003EClick \u003Cstrong\u003E"Set iROAS Priors"\u003C/strong\u003E (optional section)\u003C/li\u003E\u003Cli\u003EFor each channel, enter expected iROAS value\u003C/li\u003E\u003Cli\u003ELeave blank to let the model determine values from data\u003C/li\u003E\u003C/ol\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EChannel\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EExample Prior\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ERationale\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFacebook Ads\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2.5\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHistorical platform benchmarks\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGoogle Search\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3.5\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHigh-intent traffic typically performs well\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDisplay Ads\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1.2\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAwareness channels often have lower direct iROAS\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEmail Marketing\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E5.0\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELow cost channel with high returns\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/welcome_screen.png\" alt=\"Channel Mapping\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 4: Save Configuration\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EReview all mappings in the summary panel\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003E"Save Mapping"\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESystem validates configuration:\n\u003Cul\u003E\u003Cli\u003EMinimum 2 channels\u003C/li\u003E\u003Cli\u003E52-104 weeks of data\u003C/li\u003E\u003Cli\u003ENo duplicate column mappings\u003C/li\u003E\u003Cli\u003ERequired fields present\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ERun the Analysis\u003C/h2\u003E\n","\u003Ch3\u003ELaunching Model Execution\u003C/h3\u003E\n","\u003Cp\u003EAfter saving your configuration:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick \u003Cstrong\u003E"Run Stella MMM Analysis"\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/run_analysis.png\" alt=\"Run Analysys\"\u003E\u003C/p\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EThe workflow execution modal appears\u003C/li\u003E\u003Cli\u003EMonitor progress through 6 phases:\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EExecution Phases\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPhase\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDescription\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EData Validation\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EVerifying data quality and completeness\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFeature Engineering\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECreating adstock transformations and normalizations\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBayesian Model Setup\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EInitializing PyMC model with priors\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMCMC Sampling\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERunning Markov Chain Monte Carlo sampling\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPosterior Analysis\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EComputing statistics on posterior distributions\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGenerating Insights\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECalculating metrics and preparing visualizations\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/model_execution.png\" alt=\"Execution Progress\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EWhat Happens Behind the Scenes\u003C/h3\u003E\n","\u003Cp\u003EThe Domo Code Engine executes a PyMC-based Bayesian model that:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EApplies Adstock Transformation\u003C/strong\u003E - Models carryover effects of marketing spend\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EApplies Saturation Curves\u003C/strong\u003E - Uses Hill function to model diminishing returns\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERuns MCMC Sampling\u003C/strong\u003E - Generates thousands of samples from posterior distribution\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECalculates Key Metrics\u003C/strong\u003E - iROAS with confidence intervals, channel contribution, revenue decomposition\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E: Model execution typically takes 5-15 minutes depending on data volume and number of channels. Do not close the browser during execution.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EOutput Datasets\u003C/h3\u003E\n","\u003Cp\u003EWhen the model execution completes, the following datasets are created in Snowflake to support the visualizations:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDataset Name\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPurpose\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EVisualization\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStella MMM metrics summary data\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECore model metrics (R², MAPE, incremental revenue)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMetric Cards\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStella MMM metrics Channel Performance\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EChannel-level iROAS and attribution\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EiROAS Chart\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStella MMM metrics Waterfall Decomposition Data\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERevenue decomposition by component\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWaterfall Chart\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStella MMM metrics Contribution Breakdown over time\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWeekly contribution trends\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EContribution Over Time Chart\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStella MMM metrics VIF Results\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EVariance Inflation Factor (multicollinearity)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EVIF Analysis\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStella MMM Correlation Matrix\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EVariable correlation values\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECorrelation Matrix Heatmap\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStella MMM metrics Out of Sample Data\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EModel validation on test data\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOut of Sample Chart\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStella MMM metrics Budget Optimization Data\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECurrent budget allocation\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBudget Allocation\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStella MMM metrics Budget Optimization Summary Data\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOptimization summary metrics\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBudget Summary Cards\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStella MMM Optimization Details\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBudget optimizer results\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOptimization Results\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStella MMM Allocation Summary\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EChannel allocation recommendations\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAllocation Comparison\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EThese datasets can be accessed in Domo's Data Center for further analysis or custom reporting.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EInterpret Results\u003C/h2\u003E\n","\u003Cp\u003EOnce the model completes, you'll be taken to the \u003Cstrong\u003EInsights Dashboard\u003C/strong\u003E with four main tabs.\u003C/p\u003E\n","\u003Ch3\u003ETab 1: Channel Performance\u003C/h3\u003E\n","\u003Cp\u003EContains two view toggles: \u003Cstrong\u003EPerformance View\u003C/strong\u003E and \u003Cstrong\u003EContribution View\u003C/strong\u003E.\u003C/p\u003E\n","\u003Ch4\u003EiROAS Chart\u003C/h4\u003E\n","\u003Cp\u003EDisplays the Incremental Return on Ad Spend for each marketing channel with 95% confidence intervals.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/iroas_results.png\" alt=\"iROAS Results\"\u003E\u003C/p\u003E\n","\u003Ch4\u003ERevenue Waterfall Analysis\u003C/h4\u003E\n","\u003Cp\u003EShows the incremental revenue contribution by marketing channel.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/contribution_trend.png\" alt=\"Contribution Trend\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ETab 2: Statistical Analysis\u003C/h3\u003E\n","\u003Cp\u003EThis tab provides statistical validation metrics and diagnostics to assess model quality and reliability.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EModel R²\u003C/strong\u003E: Measures how well the model explains revenue variance (higher is better, target >0.70)\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMAPE (Mean Absolute Percentage Error)\u003C/strong\u003E: Average prediction error as a percentage (lower is better, target <15%)\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETotal Incremental Revenue\u003C/strong\u003E: Sum of all marketing-driven revenue across channels\u003C/p\u003E\n","\u003Ch4\u003EVIF Analysis\u003C/h4\u003E\n","\u003Cp\u003EVariance Inflation Factor (VIF) measures multicollinearity between marketing variables. VIF values above 10 indicate high correlation between channels, which may affect model reliability.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/vif_chart.png\" alt=\"VIF Chart\"\u003E\u003C/p\u003E\n","\u003Ch4\u003ECorrelation Matrix\u003C/h4\u003E\n","\u003Cp\u003EHeatmap displaying pairwise correlations between all model variables.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/correlation_matrix.png\" alt=\"Correlation Matrix\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EOut of Sample Analysis\u003C/h4\u003E\n","\u003Cp\u003EModel performance metrics on unseen test data with prediction intervals to validate accuracy.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/out_of_sample.png\" alt=\"Out of Sample Analysis\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ETab 3: Budget Allocation\u003C/h3\u003E\n","\u003Cp\u003EDisplays KPI cards (Total Budget, Recommended Allocation, Optimized Revenue, Revenue Lift %) and model-recommended budget allocation by channel based on iROAS rankings.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/budget_allocation.png\" alt=\"Budget Allocation\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ETab 4: Budget Optimizer\u003C/h3\u003E\n","\u003Cp\u003EThe Budget Optimizer recommends optimal spend allocation across channels, maximizing total revenue within your budget constraints.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFeatures:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ECurrent Allocation\u003C/strong\u003E: Your existing budget distribution\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERecommended Allocation\u003C/strong\u003E: AI-optimized budget distribution\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EProjected Lift\u003C/strong\u003E: Expected revenue increase from optimization\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EConstraints\u003C/strong\u003E: Set min/max budgets per channel to reflect business requirements\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EHow to Use:\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EReview current vs. recommended allocations\u003C/li\u003E\u003Cli\u003EAdjust constraints if needed (e.g., minimum brand spend)\u003C/li\u003E\u003Cli\u003EClick "Apply Scenario" to see projected impact\u003C/li\u003E\u003Cli\u003EExport recommendations for planning\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/budget_optimizer_results.png\" alt=\"Budget Optimizer Results\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAI-Powered Insights with Snowflake Cortex\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESnowflake Cortex Integration\u003C/strong\u003E: This feature provides AI-powered natural language insights on your MMM results.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EEnabling Cortex Analysis\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003ECortex Analysis\u003C/strong\u003E button is available in the \u003Cstrong\u003EChannel Performance\u003C/strong\u003E and \u003Cstrong\u003EStatistical Analysis\u003C/strong\u003E tabs:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to Channel Performance or Statistical Analysis tab\u003C/li\u003E\u003Cli\u003EClick the \u003Cstrong\u003E"Cortex Analysis"\u003C/strong\u003E button\u003C/li\u003E\u003Cli\u003EAI analyzes the current view and generates insights\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/cortex_button.png\" alt=\"Cortex Button\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESnowflake CoWork Integration\u003C/h2\u003E\n","\u003Cp\u003EAI-powered chat interface for natural language insights on your MMM results. This feature enables you to ask questions in natural language about your model execution results and receive contextually relevant answers powered by Snowflake Cortex AI.\u003C/p\u003E\n","\u003Ch3\u003EHow It Works\u003C/h3\u003E\n","\u003Cp\u003EThe Snowflake CoWork integration uses a \u003Cstrong\u003ESemantic View\u003C/strong\u003E to define relationships between the MMM output datasets. The semantic view is built around a central concept: the \u003Cstrong\u003EDocument ID\u003C/strong\u003E, which serves as the execution identifier that links all output tables from a single model run.\u003C/p\u003E\n","\u003Cp\u003EWhen you run the Domo MMM analysis, each execution generates a unique \u003Ccode\u003EdocumentID\u003C/code\u003E. This identifier is written to every output dataset, creating a relational structure that allows the Cortex Agent to understand how data across different tables relates to a specific model execution.\u003C/p\u003E\n","\u003Ch3\u003ESetting Up Snowflake CoWork\u003C/h3\u003E\n","\u003Cp\u003ETo enable this feature, you need to configure two components in your Snowflake environment:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E1. Create the Semantic View\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EIn Snowflake, create a Semantic View that includes the 10 output datasets generated by Domo MMM:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003ESTELLA_MMM_VARIABLES\u003C/code\u003E (base table - model configuration and inputs)\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ESTELLA_MMM_METRICS_SUMMARY_DATA\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ESTELLA_MMM_METRICS_CHANNEL_PERFORMANCE\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ESTELLA_MMM_CORRELATION_MATRIX\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ESTELLA_MMM_ACTUAL_VS_PREDICTED_DATA\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ESTELLA_MMM_METRICS_OUT_OF_SAMPLE_DATA\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ESTELLA_MMM_METRICS_CONTRIBUTION_BREAKDOWN_OVER_TIME\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ESTELLA_MMM_METRICS_WATERFALL_DECOMPOSITION_DATA\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ESTELLA_MMM_METRICS_BUDGET_OPTIMIZATION_DATA\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EAdditional optimization tables as needed\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EDefine relationships between these tables using \u003Ccode\u003EDOCUMENTID\u003C/code\u003E as the join key. This ensures that when the Cortex Agent answers questions, it retrieves coherent results from a single model execution. Each table should include business definitions for all columns and metrics to help the AI understand the semantic meaning of the data.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/semantic_view_model.png\" alt=\"Semantic View Model\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E2. Create a Cortex Agent\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ECreate a Snowflake Cortex Agent that uses the Semantic View you configured. The agent interprets natural language queries and generates SQL to retrieve answers from the semantic view.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/cortex_agent.png\" alt=\"Cortex Agent\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E3. Configure Agent Behavior\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EDefine the agent's behavior to ensure responses are relevant to marketing mix modeling:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EProvide context about MMM concepts (iROAS, incrementality, Bayesian modeling)\u003C/li\u003E\u003Cli\u003EInstruct the agent to always filter by \u003Ccode\u003EdocumentID\u003C/code\u003E when answering questions\u003C/li\u003E\u003Cli\u003EConfigure response style (concise, data-driven, with statistical context)\u003C/li\u003E\u003Cli\u003ESet parameters for response length and specificity\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003E4. Connect to Domo MMM\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce the Cortex Agent is deployed:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ENote the agent's endpoint and authentication credentials\u003C/li\u003E\u003Cli\u003EIn Domo MMM configuration, provide the Snowflake connection details\u003C/li\u003E\u003Cli\u003EGrant appropriate permissions for the agent to access MMM datasets\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EUsing Snowflake CoWork in Domo MMM\u003C/h3\u003E\n","\u003Cp\u003EOnce configured, the Snowflake CoWork tab provides:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ENatural Language Queries\u003C/strong\u003E: Ask questions about model results in plain English\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EContextual Responses\u003C/strong\u003E: Answers are automatically filtered to the current execution (documentID)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EData-Driven Insights\u003C/strong\u003E: Agent retrieves actual values from output datasets, not generic recommendations\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EFollow-Up Questions\u003C/strong\u003E: Maintain conversation context for deeper analysis\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExample Queries:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E"Which channel should I invest more in based on iROAS?"\u003C/li\u003E\u003Cli\u003E"What's the confidence interval for Facebook's incremental contribution?"\u003C/li\u003E\u003Cli\u003E"How does my model fit compare to best practices?"\u003C/li\u003E\u003Cli\u003E"What budget reallocation would maximize revenue?"\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/snowflake_intelligence.png\" alt=\"Snowflake CoWork\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ERequirements Summary\u003C/h3\u003E\n","\u003Cp\u003ETo enable Snowflake CoWork, ensure:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake account with \u003Cstrong\u003ECortex Agents\u003C/strong\u003E feature enabled\u003C/li\u003E\u003Cli\u003EMMM output datasets written to Snowflake (via Domo connector or federated dataset)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESemantic View\u003C/strong\u003E created with proper \u003Ccode\u003EdocumentID\u003C/code\u003E relationships and business definitions\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Agent\u003C/strong\u003E deployed and connected to the semantic view\u003C/li\u003E\u003Cli\u003EDomo MMM configured with Snowflake connection details\u003C/li\u003E\u003Cli\u003EProper database and schema permissions for the agent service account\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBest Practices\u003C/h2\u003E\n","\u003Ch3\u003EData Quality\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPractice\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhy It Matters\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUse weekly aggregation\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOptimal balance of granularity and statistical power\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EInclude 1-2 years of data\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECaptures seasonality and long-term patterns\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EValidate spend data with finance\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEnsures accuracy of channel attribution\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERemove test/invalid data\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPrevents model contamination\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ETroubleshooting\u003C/h2\u003E\n","\u003Ch3\u003ECommon Issues and Solutions\u003C/h3\u003E\n","\u003Ch4\u003EModel Won't Run\u003C/h4\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESymptom\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECause\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESolution\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Minimum 2 channels required"\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELess than 2 channels mapped\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAdd more marketing channels\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Insufficient data"\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELess than 8 weeks\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExtend date range or add more data\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Duplicate column mapping"\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESame field used twice\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EReview and remove duplicate mappings\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch4\u003EPoor Model Quality\u003C/h4\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESymptom\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECause\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESolution\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELow R² (< 0.70)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMissing important variables\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAdd control variables\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHigh MAPE (> 25%)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EData quality issues\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EReview and clean input data\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHigh VIF (> 10)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMulticollinearity\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECombine correlated channels\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch4\u003EUnexpected Results\u003C/h4\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESymptom\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECause\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESolution\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENegative iROAS\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EData issues or wrong mapping\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EVerify spend/revenue mapping\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAll channels similar iROAS\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EInsufficient variation\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENeed more diverse spending patterns\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBaseline dominates\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMarketing has low impact\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENormal for some businesses, or check data\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EGetting Help\u003C/h3\u003E\n","\u003Cp\u003EIf you encounter issues not covered here:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EDomo Support\u003C/strong\u003E: Contact through the Domo Help Center\u003C/li\u003E\u003C/ol\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Ch3\u003EConclusion\u003C/h3\u003E\n","\u003Cp\u003EWith Domo MMM, you're now equipped to measure true marketing incrementality, optimize campaign performance, and maximize ROI through data-driven budget allocation. The Bayesian approach provides statistical confidence in your results, while Snowflake Cortex AI integration enables natural language insights for faster decision-making.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n","\u003Cp\u003EBy following this guide, you've learned how to:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERequest and deploy Domo MMM for your organization\u003C/li\u003E\u003Cli\u003EPrepare marketing data with proper structure and requirements\u003C/li\u003E\u003Cli\u003EConfigure field mappings and set iROAS priors\u003C/li\u003E\u003Cli\u003ERun Bayesian Marketing Mix Model analysis\u003C/li\u003E\u003Cli\u003EInterpret iROAS, contribution, and diagnostic metrics\u003C/li\u003E\u003Cli\u003EUse AI-powered budget optimization recommendations\u003C/li\u003E\u003Cli\u003ELeverage Snowflake Cortex for natural language insights\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n","\u003Cp\u003EFor further learning, explore the following resources:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://domo-support.domo.com/s/knowledge-base?language=en_US\"\u003EDomo Knowledge Base\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/data-cloud/marketplace/\"\u003ESnowflake Marketplace\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://en.wikipedia.org/wiki/Marketing_mix_modeling\"\u003EMarketing Mix Modeling Overview\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.stellaheystella.com/\"\u003EStella\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/overview\"\u003ESnowflake Cortex AI\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.domo.com/domo-central/community\"\u003EDomo Community\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://community-forums.domo.com/main\"\u003EDomo Community Forum\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.domo.com/login/customer-community\"\u003EDomo Support\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EArchitecture\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/architecture.png\" alt=\"Architecture\"\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"Build Marketing Mix Models with Domo MMM and Snowflake Cortex AI to measure channel incrementality, optimize budgets, and maximize marketing ROI.","title":"Getting Started with Domo Marketing Mix Modelling",":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n\n## Overview\n\nIn this quickstart, you'll learn how to use Domo to consolidate your marketing performance data from multiple sources and run Marketing Mix Modelling analysis using Domo MMM. By the end of this guide, you'll have a unified marketing dataset connected to an AI-powered analysis that shows which channels drive true incremental revenue, where budget is being wasted, and how to reallocate spend for maximum impact. You'll also be able to interrogate your model results using natural language through Snowflake CoWork, asking questions like \"What happens if I cut social spend by 20%?\" and receiving evidence-based answers in plain English.\n\n\n### What is Domo MMM?\n\nEvery marketing team faces the same question from finance: which channels actually work? Platform metrics show clicks and conversions, but they cannot separate correlation from causation. They cannot tell you which sales would have happened anyway, or explain the impact of channels that don't have direct tracking.\n\nDomo MMM answers these questions using Bayesian statistical modelling. It analyses your historical marketing spend alongside revenue data to isolate the incremental impact of each channel, accounting for seasonality, carryover effects, and diminishing returns. The result is a confidence interval for each channel's true return on investment, not a single point estimate that hides uncertainty.\n\nWhat makes Domo MMM different is how you interact with these results. Traditional MMM outputs require a statistician to interpret. Domo MMM integrates with Snowflake CoWork to create a conversational layer over your model. Ask \"Which channel should I prioritise next quarter?\" or \"Why is the model recommending I reduce TV spend?\" and receive answers drawn directly from your analysis. The system understands marketing concepts like saturation, diminishing returns, and incremental contribution, translating statistical findings into decisions you can act on immediately. \n\n### Prerequisites\n\n- Basic understanding of Snowflake\n- A Snowflake account. If you do not have a Snowflake account, you can register for a [free trial account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides).\n- A Domo account and a basic understanding of Domo\n- Access to data\n- Familiarity with marketing metrics and KPIs\n\n### What You'll Learn\n\n- How to request and deploy Domo MMM from Domo\n- How to initialise a dataset using Domo Connectors\n- How to prepare and structure your marketing data for MMM analysis\n- How to configure field mappings in Domo MMM\n- How to run and interpret Bayesian Marketing Mix Model results\n- How to use budget optimisation recommendations\n- How to set up and use Snowflake CoWork for natural language queries\n- How to generate AI-powered insights using Cortex Analysis\n\n### What You'll Need\n\n- A [Snowflake](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides) account\n- A [Domo](https://www.domo.com/) account with admin privileges\n- Marketing performance data with revenue and channel spend (minimum 8 weeks, recommended 2 years)\n\n### What You'll Build\n\n- A Marketing Mix Modeling solution to analyze channel performance, view iROAS metrics with confidence intervals, decompose revenue by channel, and optimize budget allocation using AI-powered recommendations.\n\n\u003C!-- ------------------------ --\u003E\n## Install\n### Request Access\n\nRequest the Domo MMM app:\n\n- Open the Domo provider page in the **Snowflake Marketplace**.\n- Locate and click the Domo MMM app listing.\n- Click **Request**.\n- Fill out then submit the request form.\n\nThe Domo team will review the request and contact you with more information.\n\n**Important**: Domo MMM cannot be self-installed from the Snowflake Marketplace. You must contact Domo to provision the application for your instance.\n\n### What Domo Configures For You\n\n| Component | Description |\n|-----------|-------------|\n| **Custom App** | Domo MMM application deployed to your instance |\n| **Snowflake Connection** | Cortex AI integration (if requested) |\n\n\u003C!-- ------------------------ --\u003E\n\n## Prepare Your Data\n\nBefore running your MMM analysis, you need to consolidate your marketing performance data into a single dataset. Most organisations have this data scattered across multiple platforms: revenue in their CRM or ERP, digital spend in Google and Meta, offline spend in separate tracking systems. Domo's connectors and Magic ETL bring these sources together into the unified structure that Domo MMM requires.\n\nOver the next few sections, you'll supercharge your marketing data by leveraging SQL in Snowflake alongside Domo's Magic ETL. Connect key sources such as Adobe Analytics, Google Analytics, Marketo, NetSuite, Salesforce, Facebook, and Instagram. Use customizable join logic and preparatory steps tailored to your data environment to build a cohesive, centralized data foundation. These preparatory steps allow for advanced attribution models and media mix analysis, ensuring you have the insights needed to optimize your marketing strategy.\n\nLeverage the Domo Data Warehouse to access your data wherever it sits to transform & visualize.\n![assets/warehouse.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/warehouse.png)\n\n### Add a DataSet using a Connector\n\nWhen you add a DataSet, you are automatically assigned as the DataSet owner. For information about changing the owner of a DataSet, see Changing the Owner of a DataSet.\n\nYou can access the interface for adding Connector DataSets via the Appstore, the Data Center, or the menu.\n\n**To add a DataSet using a Connector**\n\n1. Choose one of the following:\n\n   - (Conditional) If you want to connect to data via the **APPSTORE**, do the following:\n\n     - Select _Appstore_ in the toolbar at the Features Section of the left menu\n     - Use the search bar to locate the Connector you want to connect to, then click it to open its details view.\n     - Click _Get the Data_.\n\n   - (Conditional) If you want to connect to data via the **DATA CENTER**, do the following:\n\n     - Go to  _Datasets_ in the Features Section at the left menu.\n     - Select Connect Data at the top menu\n     - In the Connect Data submenu at the top of the screen, select _Connectors_, _Upload a spreadsheet_, depending on the connection type.\n\n     - You can use the following table to learn more about these Connector types:\n\n       | Connector Type | Description                                                                                                                                                                                   |\n       | -------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n       | Connectors     | A Connector for a third-party app in which data is stored in the cloud. Most of Domo's Connectors fall into this category. Examples include Facebook, Salesforce, Adobe Analytics, and so on. |\n       | Upload a Spreadsheet           | A Connector used to pull files into Domo. Examples include Excel, Google Sheets, and Box.                                                                                                     |\n       \n\n     - The other  icons in this area denote other non-Connector methods for bringing data into Domo. Federated refers to federated DataSets, and Cloud Integration refers to native integration with different CDW (i.e. Snowflake).\n     - Select the Connector type you want.\n     - Click the desired Connector tile.\n\n     _Note: Popular Connectors are marked with a Preferred tag. This is also used when there are several different Connectors to the same data, such as Facebook. The most commonly used option will display the Preferred tag._\n\n1. Specify the settings in each section. - Refer to the general information included in this topic and to the help in the specific data Connector.\n   _For more information about configuring specific data Connectors, see Configuring Each Connector.\n   As applicable, click Connect, Next, or Save and open the next section._\n1. When finished, click _Save_.\n   - You are taken to the details view for the DataSet in the Data Center. For more information about this view, see Data Center Layout.\n\n### Connector Settings\n\nAll [Connector](https://domo-support.domo.com/s/topic/0TO5w000000ZammGAC/connect-data-to-domo?language=en_US) types in Domo have different options for setting up a DataSet.\n\nMost Connectors require you to enter login credentials, an API key, a server URL, or a combination of these to access the Connector. If you cannot connect after entering your credentials, you have most likely entered incorrect credentials.\n\nAfter you connect, you are usually asked for information about the data you want to pull and the desired format. Most Connectors have two or more associated report types. In addition, many Connectors request a timeframe for the data to be retrieved. You may also be asked to submit a query for retrieving data. For example, when connecting to JIRA you can enter a JQL query to retrieve data for a specified search filter.\n\nFor most Connectors, you are also asked to schedule data updates. You can use basic scheduling, in which you select a single, specific unit of time (such as \"Every hour\") and enter the time of day when the update is to occur, if required. Or you can use advanced scheduling, in which you can select multiple update times.\n\n### Connector Credentials\n\nIf required, specify the credentials for connecting to the data provider. If available, you can select an account or create an account to use in connecting. For more information about accounts, see [Manage Connector Accounts](https://domo-support.domo.com/s/article/360042926054).\n\nSome Connectors, such as Google Drive, use OAuth to connect. This means that you only need to enter your credentials once for a given account. In the future, when you go to create a DataSet using this Connector account, your credentials are passed in automatically. Other Connectors do not use OAuth, so you must enter your credentials each time you create a DataSet using this Connector account.\n\n#### Connector Details\n\nMost Connectors include a Details settings category. Here you usually specify options like the report to run, the timeframe for the data, a data query for pulling specific information from a database, and so on. If a query is required, the type of query you need to use depends on the Connector type and the source data in your system.\n\nClick _Load Preview_ to verify that your data is accessible. If connection errors occur, verify the specified connection information.\n\n### Connector Scheduling\n\nIn the **Scheduling** settings category, you can specify the update schedule, retry settings, and update method you want for this DataSet.\nYou can use either basic or advanced scheduling for connectors.\n\n#### Basic scheduling\n\nIn the **Basic Scheduling** tab, you can create a basic update schedule in which you specify a predefined update interval for this DataSet (such as \"every Monday at 10:00 AM\").\n\nBy default, schedules are set from the current time. Update intervals include every hour, day, weekday, week, month, and manually. Schedule times are based on UTC and will also show what time that is for you based on your Company Time Zone setting.\nFor hour, day, and week options, you can specify the interval (every # hours/days/weekdays) and the start period.\n\n_Note: If you set a Connector schedule using the hourly method, the end time is not inclusive. For example, if the schedule is set to hourly with the active hours set to run 8 AM UTC to 7 AM UTC it will skip the 7 AM UTC run because the end hour is not treated as inclusive.\nIf you select Manually for your update interval, you can instruct Domo to send you a notification when the data has not been updated for a given period of time. Time periods range from one hour to three months._\n\n  ![assets/connector.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/connector.png)\n\n_Note: If you need your DataSet to update faster than every 15 minutes, please reach out to your account team for evaluation._\n\n#### Update Method\n\nWhen creating or editing a DataSet, you can specify whether to append or replace data when updates occur. The update options are found at the bottom of the Basic Scheduling and Advanced Scheduling tabs.\n| Option | Description |\n|----|----|\n| Replace | Replace the current version of the data with a new version of the data. Previous versions are preserved. |\n| Append | Add data to the current version of the data, increasing the size of the DataSet. |\nUpsert | Update DataSets with restated data to ensure you have the most up-to-date information. Available for selected connectors only. For a list of available connectors, see [DataSet Update Methods](https://domo-support.domo.com/s/article/360043430733). |\nPartition | Select a rolling window of data to keep, making it easier to focus on relevant data. Available for selected connectors only. For a list of available connectors, see [DataSet Update Methods](https://domo-support.domo.com/s/article/360043430733).\n\n### Advanced Scheduling\n\nIn the **Advanced Scheduling** tab, you have more control over when data is updated than you do when using basic scheduling. You can create schedules by month, day of the month, or day of the week. You can even specify which days of the week out of the month you want to update (for example, every second and fourth Sunday).\n\nYou can indicate whether updates are done on a set interval (such as \"every 15 minutes,\" \"every 8 hours,\" etc.) or at a specified time. You can also set the start time (based on the current minute). If you want, you can set the update schedule to start immediately.\n\n_Note: If you need your DataSet to update faster than every 15 minutes, please reach out to your account team for evaluation.\nSchedule times are based on UTC but can be seen in your timezone._\n\n  ![assets/advanced_connector.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/advanced_connector.png)\n  ![assets/advanced_connector2.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/advanced_connector2.png)\n\n\n### Connector Error handling\n\nRetry settings determine whether Domo should attempt to retry if updates fail for this DataSet and, if so, the frequency and maximum number of retries. These settings apply only to scheduled runs, not manual runs. You access the retry options dialog by selecting *Always retry when an update fails*.\n\n  ![assets/error_handling_connector.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/error_handling_connector.png)\n\nThe options in this dialog are as follows:\n| Option | Description |\n|----|----|\n| Always retry when an update fails | Domo retries to update the DataSet. After retrying the specified number of times, Domo sends a notification if the update attempt is unsuccessful. |\n| Do not retry when update fails | Domo sends a notification if the update attempt is unsuccessful, and no retries are made.|\n\n\n### Connecting to Snowflake\nDomo's native Snowflake integration enables direct, real-time connectivity to your Snowflake data warehouse without the need to extract, copy, or move data into Domo's infrastructure. Using Cloud Integration to Snowflake, Domo pushes query execution directly to Snowflake, leveraging its compute power while keeping your data securely in place. This approach eliminates data duplication, reduces storage costs, and ensures you're always working with the most current data available in your Snowflake environment\n\n### Create a Magic ETL DataFlow\n\nFollow these steps to create a *Magic ETL* DataFlow:\n\n1. Navigate to the Domo *Data Center*.\n\n1. In the ribbon at the top of the Data Center, select *Transform Data* \u003E *Magic ETL* to open the Magic ETL canvas.\n\n    ![assets/magic_etl.jpg](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/magic_etl.jpg)\n\n1. In the left panel, expand DataSets and drag an Input DataSet tile to the canvas.\n\n    ![assets/canvas_magic_etl.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/canvas_magic_etl.png)\n\n1. The tile editor expands below the canvas.\n\n    ![assets/dataset_magic_etl.jpg](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/dataset_magic_etl.jpg)\n\n1. In the tile editor, select Choose DataSet to choose the DataSet you want to transform.\n\n1. Drag other tiles to the canvas, depending on what transformations you want to make, and make sure they are all connected by dragging the nodes on the sides of each tile to the node on the next tile.\n\n1. Configure the Output DataSet tile:\n\n   1. Connect a tile to the Output DataSet tile.\n   1. Select the Output DataSet tile, then enter a name for the new output DataSet.\n   1. (Optional) Schedule the DataFlow. By default, you must run the DataFlow manually. You can schedule it to run when a trigger activates. See [Advanced DataFlow Triggering](https://domo-support.domo.com/s/article/000005216) to learn more.\n   1. Enter a name and description for the DataFlow.\n   1. Select Save to keep your changes, adding an optional version description before saving again.\n   1. When you save a DataFlow, an entry for this version is added to the Versions tab in the Details view for the DataFlow. If you add a version description, it appears in the version entry. Learn about Viewing the Version History for a DataFlow.\n\n### Tips and Notes\n\n- You must configure each tile in the editor before you can configure the following tile. If a tile is not configured, the connector to the next tile appears as a dashed line.  \n  ![assets/tiles_magic_etl.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/tiles_magic_etl.png)\n\n- Use the search tool in the left panel to find the tile you need.  \n  ![assets/search_magic_etl.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/search_magic_etl.png)\n\n- The Mini Map displays in the corner of the screen and helps you see the layout and navigate around complex and detailed DataFlows. Click and drag the white square in the mini map to move to a certain view of the DataFlow on the canvas.  \n  ![assets/minimap_magic_etl.jpg](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/minimap_magic_etl.jpg)\n\n- If you close the mini map, reopen it by selecting the map pointer icon.  \n  ![assets/show_minimap_magic_etl.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/show_minimap_magic_etl.png)\n\n- You can get help on a specific tile in the canvas by clicking the tile, then clicking ![assets/help_icon.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/help_icon.png).\n\n- You can select a number of tiles at once by clicking on the canvas then dragging the mouse pointer over them. When multiple tiles are selected, you can drag all of the selected tiles as a group to where you want them. You can also delete the selected tiles by selecting Delete in the left panel.\n\nWhy are output DataSets not marked as Updated when the DataFlow completes successfully?\n\nThis is usually because the data has not actually changed—no update has occurred. The DataSets show as updated if the data has changed during a successful DataFlow execution.\n\n#### Best Practices for Magic ETL DataFlows\n\nWe recommend the following for your DataFlow:\n\n- Only include the DataSets that are necessary to create the output DataSet.\n- Filter out rows that you don't need at the beginning of the DataFlow. Learn about Filter tiles.\n- Reduce the number of columns to only those you need.\n- Use descriptive names for each tile in your DataFlow.\n- List the following in your DataFlow description:\n  - The input DataSets being transformed and their owner's names.\n  - The output DataSet created.\n- Give your DataFlow the same name as the output DataSet.\n  - This is because the outputs of a DataFlow become their own DataSet in the Data Center, and this allows you to more easily identify which DataFlows contribute to which output DataSets.\n- Be aware that some tiles take longer to execute than others, including:\n  - Group By\n  - Rank & Window\n  - Join Data\n  - Remove Duplicates\n  - Pivot\n  - Scripting tiles\n  - Data Science tiles\n\n### Data Structure Requirements\n\n**Required Fields**\n\n| Field Type | Description | Example |\n|------------|-------------|---------|\n| **Revenue** | Numeric sales/revenue metric | `total_revenue`, `sales_amount` |\n| **Date** | Time dimension (weekly recommended) | `week_start_date`, `report_date` |\n| **Channel Spend** | Marketing spend per channel (2-10 channels) | `facebook_spend`, `google_ads_spend` |\n\n**Optional Fields**\n\n| Field Type | Description | Example |\n|------------|-------------|---------|\n| **Control Variables** | External factors affecting revenue | `seasonality_index`, `competitor_activity` |\n| **Custom Variables** | Binary or numeric indicators | `promotion_flag`, `holiday_indicator` |\n\n#### Example Data Schema\n\n```sql\n-- Example: Marketing Performance Dataset\nSELECT\n    week_start_date,           -- Date field\n    total_revenue,             -- Revenue field\n    facebook_ads_spend,        -- Channel 1\n    google_search_spend,       -- Channel 2\n    google_display_spend,      -- Channel 3\n    tiktok_spend,              -- Channel 4\n    email_marketing_spend,     -- Channel 5\n    tv_advertising_spend,      -- Channel 6\n    seasonality_index,         -- Control variable\n    is_holiday_week            -- Custom variable\nFROM marketing_performance\nWHERE week_start_date BETWEEN '2022-01-01' AND '2024-12-31'\nORDER BY week_start_date;\n```\n\n#### Data Requirements\n\n| Requirement | Specification |\n|-------------|---------------|\n| Minimum Channels | 2 marketing channels |\n| Maximum Channels | 10 marketing channels |\n| Minimum Date Range | 52 weeks |\n| Maximum Date Range | 104 weeks (~2 years) |\n| Currency | All channels should use same currency |\n| Granularity | Weekly  granularity (weekly recommended) |\n\n\n\u003C!-- ------------------------ --\u003E\n\n## Configure the Model\n\nDomo MMM uses your prepared dataset to build a Bayesian Marketing Mix Model. The configuration wizard guides you through three key decisions: which revenue metric to optimise, which marketing channels to include in the analysis, and any external factors (like seasonality or promotions) that affect revenue independently of marketing spend.\n\nThe model will then run 4,000 Bayesian simulations to calculate the incremental contribution of each channel, accounting for adstock (the carryover effect of marketing spend) and saturation (diminishing returns at higher spend levels). This typically takes 5 to 15 minutes depending on your data volume.\n\nOnce complete, you'll have access to channel performance dashboards, statistical validation metrics, budget optimisation recommendations, and AI-powered natural language insights through Snowflake Cortex AI.\n\n### Step 1: Launch the Application\n\n1. Navigate to **App Studio** in your Domo instance\n2. Search for \"Domo MMM\"\n3. Click to launch the application\n4. You'll see the **Welcome** screen\n\n\n### Step 2: Field Mapping\n\nClick **\"Get Started\"** to begin the configuration wizard.\n\n#### Select Revenue Dataset\n\n1. Click **\"Select Dataset\"** dropdown\n2. Search for your marketing performance dataset\n3. Select the dataset containing revenue and channel data\n\n#### Map Revenue and Date Fields\n\n1. In **\"Revenue Amount\"** dropdown, select your revenue metric\n2. In **\"Date Field\"** dropdown, select your time dimension\n3. Select the date range using the date pickers\n4. Ensure you have at least 8 weeks of data\n\n#### Add Marketing Channels\n\nFor each marketing channel:\n\n1. Map the spend field (e.g., `facebook_ads_spend`)\n2. 6. Repeat for all channels (minimum 2, maximum 10)\n3. If there is more than 10 marketing Channel, click **\"+ Add Channel\"**\n4. Map the aditional spend field (e.g., `facebook_ads_spend`)\n\n\n#### Add Control Variables (Optional)\n\nControl variables help account for external factors:\n\n1. Click **\"+ Add Control Variable\"**\n2. Select the control variable field\n3. Examples: `seasonality_index`, `competitor_price_index`\n\n### Step 3: Set iROAS Priors (Optional)\n\niROAS priors incorporate your domain knowledge into the model:\n\n\u003E **What are Priors?** In Bayesian statistics, priors represent your existing knowledge before seeing the data. Setting iROAS priors helps the model converge faster and can improve accuracy when you have reliable historical benchmarks.\n\n1. Click **\"Set iROAS Priors\"** (optional section)\n2. For each channel, enter expected iROAS value\n3. Leave blank to let the model determine values from data\n\n| Channel | Example Prior | Rationale |\n|---------|--------------|-----------|\n| Facebook Ads | 2.5 | Historical platform benchmarks |\n| Google Search | 3.5 | High-intent traffic typically performs well |\n| Display Ads | 1.2 | Awareness channels often have lower direct iROAS |\n| Email Marketing | 5.0 | Low cost channel with high returns |\n\n![Channel Mapping](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/welcome_screen.png)\n\n\n### Step 4: Save Configuration\n\n1. Review all mappings in the summary panel\n2. Click **\"Save Mapping\"**\n3. System validates configuration:\n   - Minimum 2 channels\n   - 52-104 weeks of data\n   - No duplicate column mappings\n   - Required fields present\n\n     \n\u003C!-- ------------------------ --\u003E\n\n## Run the Analysis\n\n### Launching Model Execution\n\nAfter saving your configuration:\n\n1. Click **\"Run Stella MMM Analysis\"**\n\n     \n![Run Analysys](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/run_analysis.png)\n\n3. The workflow execution modal appears\n4. Monitor progress through 6 phases:\n\n### Execution Phases\n\n| Phase | Description |\n|-------|-------------|\n| Data Validation | Verifying data quality and completeness |\n| Feature Engineering |  Creating adstock transformations and normalizations |\n| Bayesian Model Setup |  Initializing PyMC model with priors |\n| MCMC Sampling |  Running Markov Chain Monte Carlo sampling |\n| Posterior Analysis | Computing statistics on posterior distributions |\n| Generating Insights | Calculating metrics and preparing visualizations |\n\n![Execution Progress](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/model_execution.png)\n\n### What Happens Behind the Scenes\n\nThe Domo Code Engine executes a PyMC-based Bayesian model that:\n\n1. **Applies Adstock Transformation** - Models carryover effects of marketing spend\n2. **Applies Saturation Curves** - Uses Hill function to model diminishing returns\n3. **Runs MCMC Sampling** - Generates thousands of samples from posterior distribution\n4. **Calculates Key Metrics** - iROAS with confidence intervals, channel contribution, revenue decomposition\n\n\u003E **Note**: Model execution typically takes 5-15 minutes depending on data volume and number of channels. Do not close the browser during execution.\n\n### Output Datasets\n\nWhen the model execution completes, the following datasets are created in Snowflake to support the visualizations:\n\n| Dataset Name | Purpose | Visualization |\n|--------------|---------|---------------|\n| Stella MMM metrics summary data | Core model metrics (R², MAPE, incremental revenue) | Metric Cards |\n| Stella MMM metrics Channel Performance | Channel-level iROAS and attribution | iROAS Chart |\n| Stella MMM metrics Waterfall Decomposition Data | Revenue decomposition by component | Waterfall Chart |\n| Stella MMM metrics Contribution Breakdown over time | Weekly contribution trends | Contribution Over Time Chart |\n| Stella MMM metrics VIF Results | Variance Inflation Factor (multicollinearity) | VIF Analysis |\n| Stella MMM Correlation Matrix | Variable correlation values | Correlation Matrix Heatmap |\n| Stella MMM metrics Out of Sample Data | Model validation on test data | Out of Sample Chart |\n| Stella MMM metrics Budget Optimization Data | Current budget allocation | Budget Allocation |\n| Stella MMM metrics Budget Optimization Summary Data | Optimization summary metrics | Budget Summary Cards |\n| Stella MMM Optimization Details | Budget optimizer results | Optimization Results |\n| Stella MMM Allocation Summary | Channel allocation recommendations | Allocation Comparison |\n\nThese datasets can be accessed in Domo's Data Center for further analysis or custom reporting.\n\n\u003C!-- ------------------------ --\u003E\n\n## Interpret Results\n\nOnce the model completes, you'll be taken to the **Insights Dashboard** with four main tabs.\n\n### Tab 1: Channel Performance\n\nContains two view toggles: **Performance View** and **Contribution View**.\n\n#### iROAS Chart\n\nDisplays the Incremental Return on Ad Spend for each marketing channel with 95% confidence intervals.\n\n![iROAS Results](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/iroas_results.png)\n\n#### Revenue Waterfall Analysis\n\nShows the incremental revenue contribution by marketing channel.\n\n![Contribution Trend](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/contribution_trend.png)\n\n\n### Tab 2: Statistical Analysis\n\nThis tab provides statistical validation metrics and diagnostics to assess model quality and reliability.\n\n**Model R²**: Measures how well the model explains revenue variance (higher is better, target \u003E0.70)\n\n**MAPE (Mean Absolute Percentage Error)**: Average prediction error as a percentage (lower is better, target \u003C15%)\n\n**Total Incremental Revenue**: Sum of all marketing-driven revenue across channels\n\n#### VIF Analysis\n\nVariance Inflation Factor (VIF) measures multicollinearity between marketing variables. VIF values above 10 indicate high correlation between channels, which may affect model reliability.\n\n![VIF Chart](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/vif_chart.png)\n\n\n#### Correlation Matrix\n\nHeatmap displaying pairwise correlations between all model variables.\n\n![Correlation Matrix](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/correlation_matrix.png)\n\n\n#### Out of Sample Analysis\n\nModel performance metrics on unseen test data with prediction intervals to validate accuracy.\n\n![Out of Sample Analysis](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/out_of_sample.png)\n\n### Tab 3: Budget Allocation\n\nDisplays KPI cards (Total Budget, Recommended Allocation, Optimized Revenue, Revenue Lift %) and model-recommended budget allocation by channel based on iROAS rankings.\n\n![Budget Allocation](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/budget_allocation.png)\n\n### Tab 4: Budget Optimizer\n\nThe Budget Optimizer recommends optimal spend allocation across channels, maximizing total revenue within your budget constraints.\n\n**Features:**\n- **Current Allocation**: Your existing budget distribution\n- **Recommended Allocation**: AI-optimized budget distribution\n- **Projected Lift**: Expected revenue increase from optimization\n- **Constraints**: Set min/max budgets per channel to reflect business requirements\n\n**How to Use:**\n1. Review current vs. recommended allocations\n2. Adjust constraints if needed (e.g., minimum brand spend)\n3. Click \"Apply Scenario\" to see projected impact\n4. Export recommendations for planning\n\n![Budget Optimizer Results](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/budget_optimizer_results.png)\n\n\u003C!-- ------------------------ --\u003E\n\n## AI-Powered Insights with Snowflake Cortex\n\n\u003E **Snowflake Cortex Integration**: This feature provides AI-powered natural language insights on your MMM results.\n\n### Enabling Cortex Analysis\n\nThe **Cortex Analysis** button is available in the **Channel Performance** and **Statistical Analysis** tabs:\n\n1. Navigate to Channel Performance or Statistical Analysis tab\n2. Click the **\"Cortex Analysis\"** button\n3. AI analyzes the current view and generates insights\n\n![Cortex Button](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/cortex_button.png)\n\n\u003C!-- ------------------------ --\u003E\n\n## Snowflake CoWork Integration\n\nAI-powered chat interface for natural language insights on your MMM results. This feature enables you to ask questions in natural language about your model execution results and receive contextually relevant answers powered by Snowflake Cortex AI.\n\n### How It Works\n\nThe Snowflake CoWork integration uses a **Semantic View** to define relationships between the MMM output datasets. The semantic view is built around a central concept: the **Document ID**, which serves as the execution identifier that links all output tables from a single model run.\n\nWhen you run the Domo MMM analysis, each execution generates a unique `documentID`. This identifier is written to every output dataset, creating a relational structure that allows the Cortex Agent to understand how data across different tables relates to a specific model execution.\n\n### Setting Up Snowflake CoWork\n\nTo enable this feature, you need to configure two components in your Snowflake environment:\n\n**1. Create the Semantic View**\n\nIn Snowflake, create a Semantic View that includes the 10 output datasets generated by Domo MMM:\n\n- `STELLA_MMM_VARIABLES` (base table - model configuration and inputs)\n- `STELLA_MMM_METRICS_SUMMARY_DATA`\n- `STELLA_MMM_METRICS_CHANNEL_PERFORMANCE`\n- `STELLA_MMM_CORRELATION_MATRIX`\n- `STELLA_MMM_ACTUAL_VS_PREDICTED_DATA`\n- `STELLA_MMM_METRICS_OUT_OF_SAMPLE_DATA`\n- `STELLA_MMM_METRICS_CONTRIBUTION_BREAKDOWN_OVER_TIME`\n- `STELLA_MMM_METRICS_WATERFALL_DECOMPOSITION_DATA`\n- `STELLA_MMM_METRICS_BUDGET_OPTIMIZATION_DATA`\n- Additional optimization tables as needed\n\nDefine relationships between these tables using `DOCUMENTID` as the join key. This ensures that when the Cortex Agent answers questions, it retrieves coherent results from a single model execution. Each table should include business definitions for all columns and metrics to help the AI understand the semantic meaning of the data.\n\n![Semantic View Model](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/semantic_view_model.png)\n\n**2. Create a Cortex Agent**\n\nCreate a Snowflake Cortex Agent that uses the Semantic View you configured. The agent interprets natural language queries and generates SQL to retrieve answers from the semantic view.\n\n![Cortex Agent](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/cortex_agent.png)\n\n**3. Configure Agent Behavior**\n\nDefine the agent's behavior to ensure responses are relevant to marketing mix modeling:\n\n- Provide context about MMM concepts (iROAS, incrementality, Bayesian modeling)\n- Instruct the agent to always filter by `documentID` when answering questions\n- Configure response style (concise, data-driven, with statistical context)\n- Set parameters for response length and specificity\n\n**4. Connect to Domo MMM**\n\nOnce the Cortex Agent is deployed:\n\n1. Note the agent's endpoint and authentication credentials\n2. In Domo MMM configuration, provide the Snowflake connection details\n3. Grant appropriate permissions for the agent to access MMM datasets\n\n### Using Snowflake CoWork in Domo MMM\n\nOnce configured, the Snowflake CoWork tab provides:\n\n- **Natural Language Queries**: Ask questions about model results in plain English\n- **Contextual Responses**: Answers are automatically filtered to the current execution (documentID)\n- **Data-Driven Insights**: Agent retrieves actual values from output datasets, not generic recommendations\n- **Follow-Up Questions**: Maintain conversation context for deeper analysis\n\n**Example Queries:**\n\n- \"Which channel should I invest more in based on iROAS?\"\n- \"What's the confidence interval for Facebook's incremental contribution?\"\n- \"How does my model fit compare to best practices?\"\n- \"What budget reallocation would maximize revenue?\"\n\n![Snowflake CoWork](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/snowflake_intelligence.png)\n\n### Requirements Summary\n\nTo enable Snowflake CoWork, ensure:\n\n- Snowflake account with **Cortex Agents** feature enabled\n- MMM output datasets written to Snowflake (via Domo connector or federated dataset)\n- **Semantic View** created with proper `documentID` relationships and business definitions\n- **Cortex Agent** deployed and connected to the semantic view\n- Domo MMM configured with Snowflake connection details\n- Proper database and schema permissions for the agent service account\n\n\n\u003C!-- ------------------------ --\u003E\n\n## Best Practices\n\n### Data Quality\n\n| Practice | Why It Matters |\n|----------|----------------|\n| Use weekly aggregation | Optimal balance of granularity and statistical power |\n| Include 1-2 years of data | Captures seasonality and long-term patterns |\n| Validate spend data with finance | Ensures accuracy of channel attribution |\n| Remove test/invalid data | Prevents model contamination |\n\n\u003C!-- ------------------------ --\u003E\n\n## Troubleshooting\n\n### Common Issues and Solutions\n\n#### Model Won't Run\n\n| Symptom | Cause | Solution |\n|---------|-------|----------|\n| \"Minimum 2 channels required\" | Less than 2 channels mapped | Add more marketing channels |\n| \"Insufficient data\" | Less than 8 weeks | Extend date range or add more data |\n| \"Duplicate column mapping\" | Same field used twice | Review and remove duplicate mappings |\n\n#### Poor Model Quality\n\n| Symptom | Cause | Solution |\n|---------|-------|----------|\n| Low R² (\u003C 0.70) | Missing important variables | Add control variables |\n| High MAPE (\u003E 25%) | Data quality issues | Review and clean input data |\n| High VIF (\u003E 10) | Multicollinearity | Combine correlated channels |\n\n#### Unexpected Results\n\n| Symptom | Cause | Solution |\n|---------|-------|----------|\n| Negative iROAS | Data issues or wrong mapping | Verify spend/revenue mapping |\n| All channels similar iROAS | Insufficient variation | Need more diverse spending patterns |\n| Baseline dominates | Marketing has low impact | Normal for some businesses, or check data |\n\n### Getting Help\n\nIf you encounter issues not covered here:\n\n1. **Domo Support**: Contact through the Domo Help Center\n\n\u003C!-- ------------------------ --\u003E\n\n## Conclusion and Resources\n\n### Conclusion\n\nWith Domo MMM, you're now equipped to measure true marketing incrementality, optimize campaign performance, and maximize ROI through data-driven budget allocation. The Bayesian approach provides statistical confidence in your results, while Snowflake Cortex AI integration enables natural language insights for faster decision-making.\n\n### What You Learned\n\nBy following this guide, you've learned how to:\n- Request and deploy Domo MMM for your organization\n- Prepare marketing data with proper structure and requirements\n- Configure field mappings and set iROAS priors\n- Run Bayesian Marketing Mix Model analysis\n- Interpret iROAS, contribution, and diagnostic metrics\n- Use AI-powered budget optimization recommendations\n- Leverage Snowflake Cortex for natural language insights\n\n### Related Resources\n\nFor further learning, explore the following resources:\n- [Domo Knowledge Base](https://domo-support.domo.com/s/knowledge-base?language=en_US)\n- [Snowflake Marketplace](https://www.snowflake.com/en/data-cloud/marketplace/)\n- [Marketing Mix Modeling Overview](https://en.wikipedia.org/wiki/Marketing_mix_modeling)\n- [Stella](https://www.stellaheystella.com/)\n- [Snowflake Cortex AI](https://docs.snowflake.com/en/user-guide/snowflake-cortex/overview)\n- [Domo Community](https://www.domo.com/domo-central/community)\n- [Domo Community Forum](https://community-forums.domo.com/main)\n- [Domo Support](https://www.domo.com/login/customer-community)\n- Architecture\n  ![Architecture](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/domo-mmm-guide/architecture.png)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-55edb46383","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-24ae7b6373",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-c8bef18a58","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-01-22",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-89536403be","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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