Exploring Prediction Studio

2 Tasks

10 mins

Pega Customer Decision Hub '23

Pega Customer Decision Hub '24.1 Pega Customer Decision Hub 8.8

Visible to: All users

Beginner Pega Customer Decision Hub '23 English

Scenario

U+ Bank implements Pega Customer Decision Hub™ to optimize customer interactions on their web channel by showing a personalized web banner when customers log in to the bank's website. The data science team at U+ Bank uses Prediction Studio to manage the AI that supports the decisions in this process. Some sample customer data is available for testing purposes.

In the production phase of the project, data scientists usually do not access Customer Decision Hub on a daily basis. Prediction Studio delivers notifications as a daily email digest to alert the data science team when the performance of predictions, the predictive models that drive them, or predictors need attention. Setup of the content of the email digest occurs in the implementation phase.

Browse the predictions that are included with Customer Decision Hub. Customize the email digest notifications that trigger an investigation to match the requirements of the data science team, and then configure the model transparency settings to comply with the business requirements.

Use the following credentials to log in to the exercise system:

Role	User name	Password
Data Scientist	DataScientist	rules

Your assignment consists of the following tasks:

Task 1: Inspect the out-of-the-box Customer Decision Hub predictions

Confirm that Customer Decision Hub provides a prediction for each relevant combination of action, treatment, and channel.

Task 2: Customize the Prediction Studio notifications

Configure the Prediction Studio daily email digest to focus the data science team on the decline of the predictor performance in the production phase of the project.

 

You must initiate your own Pega instance to complete this Challenge.

Initialization may take up to 5 minutes so please be patient.

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Challenge Walkthrough

Detailed Tasks

1 Inspect the out-of-the-box Customer Decision Hub predictions

1. On the exercise system landing page, click Pega Infinity™ to access Customer Decision Hub.
2. Log in as a data scientist:
   1. In the User name field, enter DataScientist
   2. In the Password field, enter rules.
3. In the navigation pane of Customer Decision Hub, click Intelligence > Prediction Studio to open the Prediction Studio portal.
4. In the Predictions workspace, in the All predictions section, verify that a prediction configuration is available for each relevant combination of direction and channel.
   Note: For example, the Predict Inbound CallCenter Propensity prediction calculates the likelihood that a customer who contacts the call center accepts an action, and the Predict Outbound Email Propensity prediction calculates the likelihood that a customer clicks a link in an email. The prediction that calculates the propensity that a customer clicks on a web banner is named Predict Web Propensity.

5. At the top of the page, click the Predict Web Propensity tile to open the prediction.
   
6. On the Models tab, in the Propesity to Click section, click Web_Click_Through_Rate_Customer to open the adaptive model configuration that drives the prediction at the customer level.
7. On the Predictors tab of the model configuration, inspect the fields that are currently available to the models as potential predictors.
8. Click the Parameter tab, and then inspect the available parameterized predictors.
9. Click the IH Summaries (Enabled) tab, and then inspect the interaction history summaries that are enabled by default.
   
   Note: Interaction history is the decision management data layer that stores all customer interactions. Aggregated fields based on the interaction history of U+ Bank and its customers are available as potential predictors by default, for example, the name of the group in the business hierarchy of the most recently accepted action in the web channel.

2 Customize the Prediction Studio notifications

1. In the navigation pane of Prediction Studio, click Settings > Monitor & notification settings to open the General tab of the settings page.
2. In the Notifications section, ensure that Receive email digest is active.
3. On the Performance tab, click Deselect all.
4. Select and expand the Lift in comparison to the previous week notification:
   
   1. Set the condition to a drop of more than 15%.
      
      Tip: This setting ensures that the system sends a notification whenever the lift that a model generates drops 15% or more compared to the previous week.

5. Select and expand the Model performance compared to the previous day notification, and then add it to the daily email digest when the drop is more than 25%.
   Tip: This setting ensures that the system sends a notification whenever a drop in the performance of a model is 25% or more compared to the previous day.

6. Select and expand the Model performance is low for adaptive model notification, and then add it to the daily email digest.:
   Tip: This setting ensures that the system sends a notification when the adaptive model performance is unusually low.

7. Click Save to finish the setup of the email digest.

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This Challenge is to practice what you learned in the following Module:

• Customer Decision Hub predictions v5

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Available in the following mission:

• AI for 1:1 Customer Engagement v3