Scoring

Once satisfied with a model, you want to score customers to identify the individuals most likely to churn within the next year, by quarter.

Figure 1. Coxreg nugget: Settings tab
Coxreg nugget: Settings tab
  1. Attach a third model nugget to the Source node and open the model nugget.
  2. Make sure Regular Intervals is selected, and specify 3.0 as the time interval and 4 as the number of periods to score. This specifies that each record will be scored for the following four quarters.
  3. Select tenure as the field to specify the past survival time. The scoring algorithm will take into account the length of each customer's time as a customer of the company.
  4. Select Append all probabilities. These extra fields will make it easier to sort the records for viewing in a table.
    Figure 2. Select node: Settings tab
    Select node: Settings tab
  5. Attach a Select node to the model nugget; on the Settings tab, type churn=0 as the condition. This removes customers who have already churned from the results table.
    Figure 3. Derive node: Settings tab
    Derive node: Settings tab
  6. Attach a Derive node to the Select node; on the Settings tab, select Multiple as the mode.
  7. Choose to derive from $CP-1-1 through $CP-1-4, the fields of form $CP-1-n, and type _churn as the suffix to add. This is easiest if, on the Select Fields dialog, you sort the fields by Name (that is, alphabetical order).
  8. Choose to derive the field as a Conditional.
  9. Select Flag as the measurement level.
  10. Type @FIELD>0.248 as the If condition. Recall that this was the classification cutoff identified during Evaluation.
  11. Type 1 as the Then expression.
  12. Type 0 as the Else expression.
  13. Click OK.
    Figure 4. Sort node: Settings tab
    Sort node: Settings tab
  14. Attach a Sort node to the Derive node; on the Settings tab, choose to sort by $CP-1-1_churn through $CP-1-4-churn and then $CP-1-1 through $CP-1-4, all in descending order. Customers who are predicted to churn will appear at the top.
    Figure 5. Field Reorder node: Reorder tab
    Field Reorder node: Reorder tab
  15. Attach a Field Reorder node to the Sort node; on the Reorder tab, choose to place $CP-1-1_churn through $CP-1-4 in front of the other fields. This simply makes the results table easier to read, and so is optional. You will need to use the buttons to move the fields into the position shown in the figure.
    Figure 6. Table showing customer scores
    Table showing customer scores
  16. Attach a Table node to the Field Reorder node and execute it.

264 customers are expected to churn by the end of the year, 184 by the end of the third quarter, 103 by the second, and 31 in the first. Note that given two customers, the one with a higher propensity to churn in the first quarter does not necessarily have a higher propensity to churn in later quarters; for example, see records 256 and 260. This is likely due to the shape of the hazard function for the months following the customer's current tenure; for example, customers who joined because of a promotion might be more likely to switch early on than customers who joined because of a personal recommendation, but if they do not then they may actually be more loyal for their remaining tenure. You may want to re-sort the customers to obtain different views of the customers most likely to churn.

Figure 7. Table showing customers with null values
Table showing customers with null values

At the bottom of the table are customers with predicted null values. These are customers whose total tenure (future time + tenure) falls beyond the range of survival times in the data used to train the model.

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