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Exporting historical data

3 Tasks

15 mins

Visible to: All users
Beginner Pega Customer Decision Hub 8.5 English
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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 that aims 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

Username

Password

Data scientist

DataScientist

rules

System architect

CRMDecisioningAdministrator

install

Your assignment consists of the following tasks:

Task 1: Configure the recording of historical data

Change the settings of the Web Click Through Rate 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%.

Note: These sampling percentages are for demonstration purposes only. A web banner typically has a very high number of negative responses, so a low sampling percentage is more appropriate for negative responses, while maintaining 100% for the positive reponses.

Create a new data set and set the Apply to class to PegaCRM-Data-Customer and Add to ruleset to Pega-CRM-Artifacts. Map the data set to the file in the repository that contains the historical data.

Task 2: Trigger the creation of customer interaction records

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

Tip: The first time you log in a generic offer is shown, and it may take up to several minutes to display the credit card offer. Thereafter, the credit card offer will be displayed immediately.

Task 3: Export the data set and examine the JSON file

In Dev Studio, export the data set. The data set is exported in the JSON format. Examine the file by answering the following questions:

  • In the first record, who is the customer, what is the treatment the customer received and what is the outcome of the interaction?
  • You logged into the website three times. Why does the data set contain four records?

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

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

Challenge Walkthrough

Detailed Tasks

1 Configure the recording of historical data

  1. Log in as a data scientist with user name DataScientist and password rules.
  2. In the left navigation pane, click Intelligence > Prediction Studio > Models.
  3. Click Web_Click_through_Rate to open the model.
    Model tile
  4. On the Settings tab, in the Model update frequency section, set the model to update after 1 response.
    Note: This frequency setting is for demonstration purposes only. A web banner typically has a very high number of customer interaction and should be updated after a significant number is reached.
  1. In the Recording historical data section, mark Recording historical data as active.
  2. Set the sample percentage for both Clicked and NoResponse, Impression to 100%.
    Enable recording
    Note: These sampling percentages are for demonstration purposes only. A web banner typically has a very high number of negative responses, so for these items, a low sampling percentage is appropriate.
  1. Click Save.
  2. In the left navigation pane, click Data > Data sets.
  3. In the upper right, click New.
  4. Configure the data set with the following information:
  • Name: ADMPayload
  • Type: File
  • Apply to: PegaCRM-Data-Customer
  • Add to ruleset: Pega-CRM-Artifacts
  • Ruleset version: 01-01-99
  1. Click Create.
    New data set
  2. In the Connection section, select the defaultstore repository.
  3. In the File configuration section, set the file path to ADM/Rule-Decision-AdaptiveModel/Data-Decision-Request-Customer/Web_Click_Through_Rate/records*.json.
  4. Click Save.

2 Trigger the creation of customer interaction records

  1. On the Customer Engagement & Analytics landing page, click U+ Bank.
  2. In the upper right, log in as customer Troy.
Tip: The first time that you log in, a generic offer is shown, and it may take several minute to display the credit card offer. After the first login, the credit card offer is displayed immediately when you log in again.
  1. Ignore the offer and log out.
  2. Log in as customer Barbara.
  3. Ignore the offer and log out.
  4. Log in as customer Joanna.
  5. On the banner, click Learn more.
  6. Return to Prediction Studio.
  7. In the lower left, click DS and log off.

3 Export the data set and examine the JSON file

  1. Log in as a system architect with user name CRMDecisioningAdministrator and password rules.
  2. In the upper right, search for ADMPayload.
  3. Click ADMPayload.
  4. In the upper right, click Actions > Export.
  5. In the Export data window, click Export.
  6. Click Download file.
    Export data set
  7. Save the ZIP file locally and extract the data.json file.
  8. Open the data.json file in a text editor that displays the JSON format correctly .
Tip: There are many (online) options available on the internet, such as Notepad++.
Question In the first record, who is the customer, what is the treatment the customer received, and what is the outcome of the interaction?
First record
Tip: The name of the customer is recorded as the Customer_pyFirstName property, the treatment is recorded as the Context_Treatment, and the outcome is recorded as the Decision_Outcome property.
Question You logged into the website three times. Why are there four records in the data set?
Impression and click
Tip: When an offer is shown to the customer, an Impression record is recorded. When a customer clicks a banner, a Clicked record is recorded. As a result, for customer Joanna, two records are recorded.
Question – How many predictors have a captured value, and how many predictors are configured in the adaptive model of the first record? What explains the difference?
  1. In the JSON file, count the predictors for which a value has been recorded.
  2. Return to Dev Studio.
  3. In the lower left, click CA and log off.
  4. Log in as a data scientist with user name DataScientist and password rules.
  5. In the left navigation pane, click Intelligence > Prediction Studio > Models.
  6. Click Web_Click_through_Rate to open the model.
  7. On the Predictors tab, note the number of configured fields.
Tip: The number of predictors for which a value is recorded is 25, the number of potential predictors configured in the model is 71. The model only activates predictors that perform above the threshold configured on the Settings tab.
Threshold


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