1. Add a Data Asset node that points to pm_customer_train1.csv.

   

2. Attach a Filler node to the Data Asset node. Double-click the node to open its properties and, under Fill in fields, select campaign.
3. Select a Replace type of Always.
4. In the Replace with text box, enter to_string(campaign) and click Save.

   

5. Add a Type node and set the Role to None for the following fields:
   • customer_id
   • response_date
   • purchase_date
   • product_id
   • Rowid
   • X_random
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 click Save. Because the campaign field data shows 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 after the Type node and open its properties.
9. Under Reclassify Into, select Existing field.
10. Under Reclassify Field, select campaign.
11. Click Get values. 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 Save.

    

14. Attach an SLRM modeling node to the Reclassify node. Select campaign for the Target field, and response for the Target response field.
15. Under MODEL OPTIONS, for Maximum number of predictions per record, 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. Make sure Take account of model reliability is selected, then click Save and run the flow.