Exporting historical data

2 Tasks

15 mins

Pega Customer Decision Hub '23

Pega Customer Decision Hub '24.1 Pega Customer Decision Hub 8.8 Pega Customer Decision Hub 8.7 Pega Customer Decision Hub 8.6 Pega Customer Decision Hub 8.5

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Beginner Pega Customer Decision Hub '23 English

Scenario

U+ Bank has implemented Pega Customer Decision Hub™ to display a personalized credit card offer to eligible customers on their website. As a data scientist, you want to export the raw data used by the Web Click Through Rate adaptive model to optimize the click through rate of the web banners that contain the credit card offers.

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

Role	User name	Password
Data scientist	DataScientist	rules
System administrator	SystemAdmin	rules

Your assignment consists of the following tasks:

Task 1: Configure the recording of historical data

Change the settings of the Web Click Through Rate Customer adaptive model to enable the recording of historical data generated by the model. Set the sample percentage for both positive and negative responses to 100%.

Task 2: Trigger the creation of customer interaction records

On the U+ Bank website, log in multiple times and generate a negative response by ignoring the banner, and a positive response by clicking the offer.

Task 3: Examine the JSON file

Examine the first record in the file, and determine who is the customer, what is the treatment the customer received, and what is the outcome of the interaction.

 

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

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

• Log In

Challenge Walkthrough

Detailed Tasks

1 Configure the recording of historical data

1. On the exercise system landing page, click Launch Pega Infinity™ to log in 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 on the left, click Intelligence > Prediction Studio.
4. On the Predict Web Propensity tile, click Open prediction to open the prediction.
5. On the Models tab, in the Propensity to Click section, click Web_Click_through_Rate_Customer to open the model.
6. On the Setting tab, in the Recording historical data section, select the Recording historical data checkbox to set the data recording as active.
7. Set the sample percentage for NoResponse to 100% to record all responses.
   
   Note: A web banner typically has many negative responses, so a low sampling percentage is appropriate for these items. For demonstration purposes, the sample percentage for NoResponse is set to 100%.

8. In the upper-right corner, click Save.

2 Trigger the creation of customer interaction records

1. On the exercise system landing page, in the upper-left corner, open the Application Switcher and click the U+ Bank icon to open the website.
2. In the upper-right corner, log in as customer Troy.
3. Ignore the offer, and then log out.
4. Log in as customer Barbara.
5. On the banner, click Learn more to register a positive response.

Confirm your work

1. On the Exercise System landing page, in the upper-left corner, open the Application Switcher and then click the Filebrowser icon to open the repository.
2. Log in to the repository:
   1. In the User name field, enter pega-filerepo.
   2. In the Password field, enter pega-filerepo.
3. Navigate to the ADM > Rule-Decision-AdaptiveModel > Data-Decision-Request-Customer-CDH > Web_Click_Through_Rate_Customer folder, and then open one of the json files.
   Tip: The export of the data occurs every 15 minutes. Therefore, it may take some time before the files are created in the repository.

4. Question – In the first record, who is the customer, what is the treatment the customer received, and what is the outcome of the interaction?
   1. The customer ID is recorded as the Decision_SubjectID property, the treatment is recorded as the Context_Treatment, and the outcome is recorded as the Decision_Outcome property.

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

• Exporting adaptive model data v1
• Exporting adaptive model data v2

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Available in the following mission:

• AI for 1:1 Customer Engagement v3