Building a predictive model

1. Open the data file dmdata2.sav.

   This file contains various demographic characteristics of the people who received the test mailing, and it also contains information on whether or not they responded to the mailing. This information is recorded in the field (variable) Responded. A value of 1 indicates that the contact responded to the mailing, and a value of 0 indicates that the contact did not respond.

2. From the menus choose:

   Direct Marketing > Choose Technique

3. Select Select contacts most likely to purchase and click Continue.

   

4. For Response Field, select Responded to test offer.
5. For Positive response value, select Yes from the drop-down list. A value of 1 is displayed in the text field because "Yes" is actually a value label associated with a recorded value of 1. (If the positive response value doesn't have a defined value label, you can just enter the value in the text field.)
6. For Predict Propensity with, select Age, Income category, Education, Years at current residence, Gender, Married, Region, and Children.
7. Select (check) Export model information to XML file.
8. Click Browse to navigate to where you want to save the file and enter a name for the file.
9. In the Propensity to Purchase dialog, click the Settings tab.

   

10. In the Model Validation Group, select (check) Validate model and Set seed to replicate results.
11. Use the default training sample partition size of 50% and the default seed value of 2000000.
12. In the Diagnostic Output group, select (check) Overall model quality and Classification table.
13. For Minimum probability, enter 0.05. As a general rule, you should specify a value close to your minimum target response rate, expressed as a proportion. A value of 0.05 represents a response rate of 5%.
14. Click Run to run the procedure and generate the model.

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