Building the Stream

1. Add a Statistics File source node pointing to pm_customer_train1.sav, located in the Demos folder of your IBM® SPSS® Modeler installation.

   

2. Add a Filler node and select campaign as the Fill in field.
3. Select a Replace type of Always.
4. In the Replace with text box, enter to_string(campaign) and click OK.

   

5. Add a Type node, and set the Role to None for the customer_id, response_date, purchase_date, product_id, Rowid, and X_random fields.

   

6. Set the Role to Target for the campaign and response fields. These are the fields on which you want to base your predictions.

   Set the Measurement to Flag for the response field.

7. Click Read Values, then OK.

   Because the campaign field data show as a list of numbers (1, 2, 3, and 4), you can reclassify the fields to have more meaningful titles.

8. Add a Reclassify node to the Type node.
9. In the Reclassify into field, select Existing field.
10. In the Reclassify field list, select campaign.
11. Click the Get button; the campaign values are added to the Original value column.
12. In the New value column, enter the following campaign names in the first four rows:
    • Mortgage
    • Car loan
    • Savings
    • Pension
13. Click OK.

    

14. Attach an SLRM modeling node to the Reclassify node. On the Fields tab, select campaign for the Target field, and response for the Target response field.

    

15. On the Settings tab, in the Maximum number of predictions per record field, reduce the number to 2.

    This means that for each customer, there will be two offers identified that have the highest probability of being accepted.

16. Ensure that Take account of model reliability is selected, and click Run.

Next