--- title: "Creating a Targeted Mailing Mining Model Structure (Basic Data Mining Tutorial) | Microsoft Docs" ms.custom: "" ms.date: "03/06/2017" ms.prod: "sql-server-2014" ms.reviewer: "" ms.suite: "" ms.technology: - "analysis-services" ms.tgt_pltfrm: "" ms.topic: "article" ms.assetid: a9c67f29-0c47-4a5a-862b-db0f5213c2c9 caps.latest.revision: 54 author: "jeannt" ms.author: "jeannt" manager: "jhubbard" --- # Creating a Targeted Mailing Mining Model Structure (Basic Data Mining Tutorial) The first step in creating a targeted mailing scenario is to use the Data Mining Wizard in [!INCLUDE[ssBIDevStudioFull](../includes/ssbidevstudiofull-md.md)] to create a new mining structure and decision tree mining model. In this task you will set up a new mining structure, and add an initial mining model based on the [!INCLUDE[msCoName](../includes/msconame-md.md)] Decision Trees algorithm. To create the structure, you will first select tables and views and then identify which columns will be used for training and which for testing. ### To create a mining structure for the targeted mailing scenario 1. In Solution Explorer, right-click **Mining Structures** and select **New Mining Structure** to start the Data Mining Wizard. 2. On the **Welcome to the Data Mining Wizard** page, click **Next**. 3. On the **Select the Definition Method** page, verify that **From existing relational database or data warehouse** is selected, and then click **Next**. 4. On the **Create the Data Mining Structure** page, under **Which data mining technique do you want to use?**, select **Microsoft Decision Trees**. > [!NOTE] > If you get a warning that no data mining algorithms can be found, the project properties might not be configured correctly. This warning occurs when the project attempts to retrieve a list of data mining algorithms from the [!INCLUDE[ssASnoversion](../includes/ssasnoversion-md.md)] server and cannot find the server. By default, [!INCLUDE[ssBIDevStudio](../includes/ssbidevstudio-md.md)] will use **localhost** as the server. If you are using a different instance, or a named instance, you must change the project properties. For more information, see [Creating an Analysis Services Project (Basic Data Mining Tutorial)](../../2014/tutorials/creating-an-analysis-services-project-basic-data-mining-tutorial.md). 5. Click **Next**. 6. On the **Select Data Source View** page, in the **Available data source views** pane, select **Targeted Mailing**. You can click **Browse** to view the tables in the data source view and then click **Close** to return to the wizard. 7. Click **Next**. 8. On the **Specify Table Types** page, select the check box in the **Case** column for vTargetMail to use it as the case table, and then click **Next**. You will use the ProspectiveBuyer table later for testing; ignore it for now. 9. On the **Specify the Training Data** page, you will identify at least one predictable column, one key column, and one input column for your model. Select the check box in the **Predictable** column in the **BikeBuyer** row. > [!NOTE] > Notice the warning at the bottom of the window. You will not be able to navigate to the next page until you select at least one **Input** and one **Predictable** column. 10. Click **Suggest** to open the **Suggest Related Columns** dialog box. The **Suggest** button is enabled whenever at least one predictable attribute has been selected. The **Suggest Related Columns** dialog box lists the columns that are most closely related to the predictable column, and orders the attributes by their correlation with the predictable attribute. Columns with a significant correlation (confidence greater than 95%) are automatically selected to be included in the model. Review the suggestions, and then click **Cancel** toignore the suggestions. > [!NOTE] > If you click **OK**, all listed suggestions will be marked as input columns in the wizard. If you agree with only some of the suggestions, you must change the values manually. 11. Verify that the check box in the **Key** column is selected in the **CustomerKey** row. > [!NOTE] > If the source table from the data source view indicates a key, the Data Mining Wizard automatically chooses that column as a key for the model. 12. Select the check boxes in the **Input** column in the following rows. You can check multiple columns by highlighting a range of cells and pressing CTRL while selecting a check box. - **Age** - **CommuteDistance** - **EnglishEducation** - **EnglishOccupation** - **Gender** - **GeographyKey** - **HouseOwnerFlag** - **MaritalStatus** - **NumberCarsOwned** - **NumberChildrenAtHome** - **Region** - **TotalChildren** - **YearlyIncome** 13. On the far left column of the page, select the check boxes in the following rows. - **AddressLine1** - **AddressLine2** - **DateFirstPurchase** - **EmailAddress** - **FirstName** - **LastName** Ensure that these rows have checks only in the left column. These columns will be added to your structure but will not be included in the model. However, after the model is built, they will be available for drillthrough and testing. For more information about drillthrough, see [Drillthrough Queries (Data Mining)](../../2014/analysis-services/drillthrough-queries-data-mining.md) 14. Click **Next**. ## Next Task in Lesson [Specifying the Data Type and Content Type (Basic Data Mining Tutorial)](../../2014/tutorials/specifying-the-data-type-and-content-type-basic-data-mining-tutorial.md) ## See Also [Specify Table Types (Data Mining Wizard)](../../2014/analysis-services/specify-table-types-data-mining-wizard.md) [Data Mining Designer](../../2014/analysis-services/data-mining-designer.md) [Microsoft Decision Trees Algorithm](../../2014/analysis-services/microsoft-decision-trees-algorithm.md)