Applying the model

1. Open the data file dmdata3.sav. This data file contains demographic and other information for all the contacts that were not included in the test mailing. See the topic for more information.
2. Open the Scoring Wizard. To open the Scoring Wizard, from the menus choose:

   Utilities > Scoring Wizard

   

3. Click Browse to navigate to the location where you saved the model XML file and click Select in the Browse dialog.

   All files with an .xml or .zip extension are displayed in the Scoring Wizard. If the selected file is recognized as a valid model file, a description of the model is displayed.

4. Select the model XML file you created and then click Next.

   

   In order to score the active dataset, the dataset must contain fields (variables) that correspond to all the predictors in the model. If the model also contains split fields, then the dataset must also contain fields that correspond to all the split fields in the model.

   • By default, any fields in the active dataset that have the same name and type as fields in the model are automatically matched.
   • Use the drop-down list to match dataset fields to model fields. The data type for each field must be the same in both the model and the dataset in order to match fields.
   • You cannot continue with the wizard or score the active dataset unless all predictors (and split fields if present) in the model are matched with fields in the active dataset.

   The active dataset does not contain a field named Income. So the cell in the Dataset Fields column that corresponds to the model field Income is initially blank. You need to select a field in the active dataset that is equivalent to that model field.

5. From the drop-down list in the Dataset Fields column in the blank cell in the row for the Income model field, select IncomeCategory.

   Note: In addition to field name and type, you should make sure that the actual data values in the dataset being scored are recorded in the same fashion as the data values in the dataset used to build the model. For example, if the model was built with an Income field that has income divided into four categories, and IncomeCategory in the active dataset has income divided into six categories or four different categories, those fields don't really match each other and the resulting scores will not be reliable.

   Click Next to continue to the next step of the Scoring Wizard.

   

   The scoring functions are the types of "scores" available for the selected model. The scoring functions available are dependent on the model. For the binary logistic model used in this example, the available functions are predicted value, probability of the predicted value, probability of a selected value, and confidence. See the topic Selecting scoring functions for more information.

   In this example, we are interested in the predicted probability of a positive response to the mailing; so we want the probability of a selected value.

6. Select (check) Probability of Selected Category.
7. In the Value column, select 1 from the drop-down list. The list of possible values for the target is defined in the model, based on the target values in the data file used to build the model.

   Note: When you use the Propensity to Purchase feature to build a model, the value associated with a positive response will always be 1, since Propensity to Purchase automatically recodes the target to a binary field where 1 represents a positive response, and 0 represents any other valid value encountered in the data file used to build the model.

8. Deselect (clear) all the other scoring functions.
9. Optionally, you can assign a more descriptive name to the new field that will contain the score values in the active dataset. For example, Probability_of_responding. For information on field (variable) naming rules, see Variable names.
10. Click Finish to apply the model to the active dataset.

    The new field that contains the probability of a positive response is appended to the end of the dataset.

    You can then use that field to select the subset of contacts that are likely to yield a positive response rate at or above a certain level. For example, you could create a new dataset that contains the subset of cases likely to yield a positive response rate of at least 5%.

11. From the menus choose:

    Data > Select Cases

12. In the Select Cases dialog, select If condition is satisfied and click If.
13. In the Select Cases: If dialog enter the following expression:

    Probability_of_responding >=.05

    Note: If you used a different name for the field that contains the probability values, enter that name instead of Probability_of_responding. The default name is SelectedProbability.

14. Click Continue.
15. In the Select Cases dialog, select Copy selected cases to a new dataset and enter a name for the new dataset. Dataset names must conform to field (variable) naming rules. See the topic Variable names for more information.
16. Click OK to create the dataset with the selected contacts.

The new dataset contains only those contacts with a predicted probability of a positive response of at least 5%.