Creating a churn prediction using an ML model
2 Tasks
15 mins
Beginner
Pega Customer Decision Hub 8.8
English
Scenario
U+ Bank implements Pega Customer Decision Hub™ to personalize the credit card offer a customer is presented on their website. If a customer is eligible for multiple offers, artificial intelligence (AI) decides which offer to show.
To customers that are likely to leave the bank soon, the bank wants to make a proactive retention offer instead of a credit card offer. The bank has recorded historical churn data for its customer base, which a data scientist used to create a churn model. You create a prediction that is driven by the churn model. This prediction can then be used by a decisioning architect in an engagement strategy.
Use the following credentials to log in to the exercise system:
Role | User name | Password |
---|---|---|
Data scientist | DataScientist | rules |
Caution: This challenge requires specific artifacts. Ensure that you click Initialize (Launch) Pega instance for this challenge to get the correct exercise system.
Your assignment consists of the following tasks:
Task 1: Create a new prediction
As a data scientist, create a new prediction to calculate churn risk.
Task 2: Replace the scorecard with the churn model in the new prediction
Replace the placeholder scorecard with the ChurnPML model from the Model list in the new prediction.
Note: The prediction is created in a branch in the development environment. A system architect needs to merge the branch to the application to ensure that the prediction is part of the CDH-Artifacts ruleset. Only then can the changes be deployed to the other environments using the enterprise change pipeline.
Challenge Walkthrough
Detailed Tasks
1 Create a new prediction
- On the exercise system landing page, click Pega CRM suite to log in to Customer Decision Hub.
- Log in as a data scientist with user name DataScientist and password rules.
- In the navigation pane on the left, click Intelligence > Prediction Studio.
- In the upper-right corner, click New to create a prediction.
- Ensure that Customer Decision Hub is selected, and then click Next.
- In the Prediction name field, enter Predict Churn Propensity.
- In the Outcome field, select Churn.
- In the Subject field, select Customer.
- Click Create.
- In the upper-right corner, click Save.
2 Replace the scorecard with the churn model in the new prediction
- On the Models tab, in the Churn section, click the More icon for the Predict Churn Propensity prediction.
- Click Replace model.
- Ensure that Model is selected, and then click Next.
- Clear the Compare the models checkbox.
- In the Model list tab, select the ChurnPML model.
- Click Next.
- Click Replace.
- When the status of the Churn model changes to Ready for review, click ChurnPML (M-1).
- In the upper-right corner, click Evaluate.
- Ensure that Approve candidate model and replace current active model is selected.
- In the Reason field, enter the appropriate information.
- Click Save.
- Confirm that the Churn model has replaced the placeholder scorecard as Active in the prediction.
Confirm your work
- In the upper-right corner, click Run.
- Select Troy as the data source.
Note: Customer Troy is likely to churn in the near future.
- Click Run.
- Select Barbara as the data source.
Note: Customer Barbara is likely to remain loyal to the company.
- Click Run.
Note: The prediction is created in a branch in the development environment. A system architect needs to merge the branch to the application to ensure that the prediction is part of the CDH-Artifacts ruleset. Only then can the changes be deployed to the other environments using the enterprise change pipeline.
This Challenge is to practice what you learned in the following Module:
Available in the following mission:
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