PEGAPCDC87V1 Sample Questions Answers

A solid arrow from a "Set Property" component to a "Filter" component in a Pega decision strategy indicates that a property set by the "Set Property" component is being referenced or used by the "Filter" component. This relationship ensures that the data set in the "Set Property" component influences the filtering criteria in the "Filter" component, allowing for more dynamic and contextually relevant decisions.

 Requirement:

The bank wants to avoid sending promotional emails related to credit card offers to a customer if they have already received one within a limited time period.

 Configuring Suppression Policy:

A suppression policy needs to be defined to prevent over-contacting customers.

Suppression policies automatically put an action on hold after specific criteria are met, such as a customer having already received a promotional email.

This ensures that customers are not bombarded with repeated offers, improving customer experience and adhering to communication limits.

When a customer is offered an action they have already accepted, it is typically because the decision strategy has not been configured to exclude such offers:

Strategy Customization:

Step 1: Ensure that the decision strategy includes logic to filter out actions that the customer has already accepted.

Step 2: Use customer interaction history to identify previously accepted offers and exclude them from the list of eligible actions.

Implementation Steps:

Step 1: Add a filter component in the strategy to check the customer’s interaction history for accepted offers.

Step 2: Configure the filter to exclude any actions that match the accepted offers.

Step 3: Connect this filter to the rest of the strategy to ensure that only new or unaccepted actions are considered.

Benefits:

Customizing the strategy to exclude previously accepted offers improves customer experience by preventing redundant or irrelevant offers.

It ensures that customers are always presented with new and relevant opportunities, enhancing engagement and satisfaction.

Example:

If a customer has already accepted a credit card offer, the strategy should filter out this offer in future interactions to avoid presenting the same offer again.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Configuring Next-Best-Action Designer for Pega Customer Service" section.

Pega documentation on "Creating and understanding decision strategies".

 Requirement for Total Revenue Calculation:

MyCo needs to calculate the total revenue of three new actions over a specified period.

The goal is to aggregate the revenue generated by these actions.

 Use of Group By Component:

The Group By component is used to aggregate data based on specified properties.

In this scenario, it can be configured to sum the revenue of the three new actions.

 Configuration Steps:

Configure a single Group By component to group the actions by their identifiers.

Use the aggregation function within the Group By component to calculate the sum of the revenue for the grouped actions.

 Efficiency and Simplicity:

Using a single Group By component simplifies the strategy by avoiding redundancy.

It ensures that all actions are considered in a single aggregation process, providing an accurate total revenue calculation.

 Verification from Pega Documentation:

According to Pega's guidelines, a single Group By component can efficiently compute total values such as revenue by grouping and aggregating data.

To ensure that there is no gender bias when presenting mobile data packages for students, you should configure the ethical bias policy to allow a 1 Rate Ratio. This setting ensures that the system will show a warning if there is any deviation from an equal distribution between genders, thereby preventing any gender-based discrimination in offer presentation.

Pega Scenario Planner is the best tool for estimating the business value of a next-best-action run with the selected configuration:

Purpose of Scenario Planner:

Step 1: Pega Scenario Planner is designed to simulate and analyze the potential impact of business changes before they are implemented.

Step 2: It provides insights into how changes in decision strategies, engagement policies, or other configurations affect business outcomes.

Simulation Setup:

Step 1: Define the scenarios you want to test, including changes to the decision strategies or engagement policies.

Step 2: Configure the simulation to reflect the real-world conditions and customer data relevant to the business case.

Analyzing Results:

Step 1: Run the simulation and review the results to understand the estimated business value generated by the next-best-action run.

Step 2: Analyze key performance indicators (KPIs) such as acceptance rates, response rates, and financial metrics to evaluate the impact of the changes.

Example:

U+ Bank can use Pega Scenario Planner to simulate the presentation of Credit Card offers on their self-service portals, allowing them to estimate the business value generated by these offers based on the selected configuration.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Increasing your understanding of the effect of your business changes with the Scenario Planner" section.

 Ethical Bias Policy Configuration:

To allow minimal bias on age while ensuring eligibility (Age >= 18), an appropriate Gini coefficient threshold needs to be set.

The Gini coefficient measures statistical inequality, with 0 representing perfect equality and higher values indicating more bias.

 Choosing the Gini Threshold:

A 0.1 Gini coefficient is a low threshold that allows minimal bias.

It ensures that the actions are distributed fairly among customers while still respecting the eligibility condition.

 Detailed Explanation:

Setting a 0.1 Gini coefficient allows for slight variations in distribution, which is sufficient to accommodate the Age >= 18 requirement without introducing significant bias.

 Verification from Pega Documentation:

The Pega Customer Decision Hub User Guide explains the use of Gini coefficients for measuring and setting thresholds to control bias in decisioning strategies​​.

Real-Time Channels for Next-Best-Actions: Pega Customer Decision Hub supports presenting Next-Best-Actions through various real-time channels.

Retail Store: Actions can be presented to customers directly in the store through POS systems or store associates.

Call Center: Customer Service Representatives can receive real-time Next-Best-Action recommendations to present to customers during interactions.

SMS: Automated messages can be sent to customers with personalized Next-Best-Action offers via SMS.

 Next-Best-Action Offers Ranking: When a Customer Service Representative (CSR) is presented with a set of sales offers, the Next-Best-Action feature ranks these offers based on their relevance and priority.

 Decision Making: This ranking helps the CSR to decide which offer to present to the customer, ensuring that the most appropriate and effective offer is chosen.

To prevent offer overexposure, the appropriate suppression rule is to suppress a group of actions for 10 days if there are three clicks on the web channel in the last 10 days. This ensures that once a customer interacts with any mortgage offer three times within a 10-day period, they will not see any mortgage offers for the next 10 days. This rule helps in maintaining a good customer experience by avoiding overexposure.

To access a property from an unconnected component in Pega Customer Decision Hub, you use the component-dot-property construct. This allows you to reference properties from other components within your strategy by specifying the component name followed by the property name. This construct helps in accessing and using data across different components without directly connecting them.

 Pega allows for the personalization of email subjects using customer data and expressions.

 This feature enhances the relevance and engagement of the email content.

 For example, using "Dear " in the subject line can increase open rates and customer engagement.

 Reference: Pega-Customer-Decision-Hub-User-Guide-87.pdf, section on configuring email treatments and personalizing content .

When implementing the Next-Best-Action paradigm to offer the new data plan, the mobile company should focus on "Customer relevancy and business profitability." This means that the company needs to ensure that the offers are relevant to individual customers based on their needs, preferences, and behavior, while also aligning these offers with the company's business objectives to ensure profitability. This customer-centric approach leverages Pega's predictive and adaptive analytics to personalize offers and maximize both customer satisfaction and business outcomes.

Customer Lifetime Value (CLV) - The bank wants to offer a discount based on the customer's CLV.

Filter Component - The Filter component is used in decision strategies to include or exclude actions based on specific criteria, such as CLV.

Implementation:

Add a Filter component in the decision strategy.

Configure the filter to check if the CLV value is greater than 400.

If true, proceed with the 10% discount offer.

In Pega Customer Decision Hub, the characteristics of an action are defined by using properties. Properties are used to capture specific details about an action, such as the type of offer, the conditions under which it should be presented, and other relevant data that influence decision-making. These properties ensure that the actions are personalized and relevant to the customer.

Pega Customer Decision Hub supports writing action details directly to cloud storage solutions such as Microsoft Azure and Amazon S3. This capability ensures that the offer messages selected by the Decision Hub can be seamlessly transferred to the preferred cloud storage location of the email service provider, facilitating efficient and secure delivery of customer communications.

When introducing a new group of offers called Credit cards, valid actions should be specific credit card offers that provide clear value propositions to customers.

Creating Specific Offers:

No Annual Fee Credit Card: This offer targets customers looking to avoid annual fees, making it an attractive option for budget-conscious individuals.

1% Cashback Credit Card: This offer appeals to customers interested in earning cashback rewards on their purchases, adding an incentive for using the card.

Steps to Create Offers:

Step 1: Navigate to the Next-Best-Action Designer in Pega Customer Decision Hub.

Step 2: Define the new group "Credit Cards" under the appropriate business issue.

Step 3: Create individual actions for "No Annual Fee Credit Card" and "1% Cashback Credit Card," specifying the details and conditions for each offer.

Step 4: Configure the engagement policies, eligibility criteria, and treatments for these actions to ensure they are presented to the right customers.

Benefits:

Creating specific and clear offers helps in better targeting and engagement with customers, leading to higher acceptance rates and customer satisfaction.

These offers align with common customer preferences and needs, making them more likely to be accepted.

Example:

U+ Bank creates the "No Annual Fee Credit Card" and "1% Cashback Credit Card" offers in the Pega Customer Decision Hub. Each offer is configured with specific eligibility criteria and personalized email treatments to ensure relevant customers receive these offers.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Creating and managing actions" section.

Pega documentation on "Next-Best-Action Designer and offer creation".

 Understanding Refresh the Audience Option:

This option ensures the decision process uses the most current data available.

 Functionality:

Before each run of the schedule, the audience data is refreshed to reflect any changes or updates in customer information.

 Purpose:

This ensures decisions are made based on the latest customer profiles, improving the relevance and accuracy of recommendations.

 Verification from Pega Documentation:

Pega documentation on scheduling and audience refresh options for ensuring up-to-date customer data is used.

Decision Table Evaluation - Decision tables evaluate conditions in a specified order and return a result based on the first matching condition.

Conditions and Labels - Based on the decision table provided, the CLV score of 550 and LOW propensity to leave match the condition for label B.

Process:

Evaluate the CLV score condition.

Evaluate the propensity to leave condition.

The matching row in the decision table specifies the label to return.

Identifying the Requirement:

The bank wants to leverage Pega CDH’s Next-Best-Action capability to promote new offers on their website.

The task is to determine what information Pega CDH sends back to the website in response to a real-time container request.

Evaluating Pega CDH Capabilities:

Pega CDH’s Next-Best-Action Designer is designed to handle real-time interactions and provides personalized recommendations.

The system returns comprehensive details to ensure the website can properly display and execute the recommended actions.

Process Overview:

Real-Time Container Request:

When the website sends a real-time container request, Pega CDH processes the request and identifies the most relevant actions based on customer data and engagement policies.

Response Details:

The response includes treatment details such as the action’s visual elements (image URLs, descriptions) and relevant action attributes (e.g., offer type, benefits, eligibility criteria).

References and Supporting Documentation:

The Pega Customer Decision Hub User Guide clearly outlines that in response to a real-time container request, the system sends back the treatment details and relevant action attributes to ensure the offer can be presented accurately on the website.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf

Pega-Customer-Decision-Hub-User-Guide-86.pdf

Pega-Customer-Decision-Hub-User-Guide-87.pdf​

 Propensity Increase: In Pega Customer Decision Hub, propensity indicates the likelihood of a customer responding positively to an offer. A propensity value of 0.83 signifies a high likelihood of customers showing interest in the Gold credit card offer.

 Customer Behavior Analysis: The increase to 0.83 suggests that similar customers have demonstrated interest, such as clicking on the offer or engaging with it in some way.

Business Levers: These are used to apply weights to different actions or groups of actions to influence their prioritization based on business objectives.

Context Weighting: This factor is used to adjust the priority of actions based on the specific context of the customer interaction, ensuring that both business objectives and customer needs are balanced.

References: The configuration and utilization of business levers and context weighting within the Next-Best-Action Designer are key components in aligning the decision strategy with both business and customer goals .

Email treatments in Pega Customer Decision Hub allow for personalization, including customizing the email subject to address the customer by name or other personalized data.

Definition and Purpose:

Email treatments are used to define the content and presentation of email communications sent to customers as part of outbound campaigns.

Personalization enhances the relevance and engagement of these emails by including customer-specific information.

Capabilities of Email Treatments:

Personalization Tags: Pega supports the inclusion of dynamic tags in the email subject and body, allowing for personalized greetings, offers, and other content. For example, "Dear " where "" is dynamically replaced with the actual name of the customer.

HTML Content: Design rich email content using HTML, which can include images, links, and other multimedia elements.

Steps to Personalize Email Subjects:

Step 1: Create or edit an email treatment in Pega.

Step 2: In the subject line field, use the syntax for personalization tags. For example, "Dear , check out our new offers!".

Step 3: Map the personalization tags to the appropriate customer data fields to ensure the correct information is inserted when the email is sent.

Testing Personalization:

Use seed lists to test personalized emails. Seed lists are groups of test email addresses used to preview how the email will look when sent to actual customers.

Ensure that personalization attributes appear correctly in the received test emails.

Best Practices:

Always test personalized emails thoroughly before launching a campaign to ensure that the tags are working correctly and the content renders as expected.

Use personalization to enhance customer engagement by making emails feel more relevant and tailored to individual recipients.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Configuring email treatments" section.

Pega documentation on "Defining an action for outbound".

Next-Best-Action leverages customer lifecycle analysis to anticipate retention issues. By analyzing patterns and trends in customer behavior, Next-Best-Action strategies can proactively address potential retention problems and enhance customer engagement to prevent churn.

To configure the arbitration factor for nudging the Platinum Plus credit card to customers who visit the home page, you need to focus on action value. Action value takes into account the business value or importance of an action, which aligns with the goal of promoting a specific credit card to customers.

Arbitration in Pega CDH - Arbitration is the process of ranking actions based on their priority. Factors like propensity, context weighting, action value, and business levers are considered during arbitration.

Action Value - This factor assesses the intrinsic value of an action, which includes its business value. It is crucial when a business objective, like promoting a specific credit card, needs to be prioritized.

Implementation - By configuring action value, you ensure that the Platinum Plus credit card offer has a higher priority over other actions for customers visiting the homepage.

 Configuring Ethical Bias Policy:

To configure bias fields, the context of the bias must be selected, which usually pertains to customer-related properties.

 Selecting Bias Fields:

Bias fields such as age, gender, and ethnicity are typically customer properties.

These properties are analyzed to detect and measure bias in decision strategies.

 Detailed Explanation:

By selecting the Customer context, you can add and configure relevant customer properties to the bias policy.

This allows for comprehensive bias testing and ensures fairness in action distribution.

 Verification from Pega Documentation:

Pega documentation confirms that customer-related properties are the primary context for configuring bias fields in ethical bias policies​​​​.

To update the cashback offer to be presented through a new channel (SMS) and to update the suitability condition, you need to perform two main tasks: adding a new treatment and editing the engagement policy.

Add a New Treatment:

Step 1: Navigate to the "Actions" section in the Pega Customer Decision Hub.

Step 2: Locate the existing cashback offer action.

Step 3: Add a new treatment specifically for the SMS channel. This involves creating a new SMS treatment that defines the message content, format, and any other relevant details.

Step 4: Ensure the new treatment is linked to the cashback offer action.

Edit the Engagement Policy:

Step 1: In the Pega Customer Decision Hub, go to the "Engagement Policies" section.

Step 2: Locate the engagement policy associated with the cashback offer.

Step 3: Update the suitability condition by lowering the debt-to-income ratio threshold from 48 to 45. This ensures that the new condition is applied when evaluating customer eligibility for the cashback offer.

Step 4: Save and publish the updated engagement policy to ensure it takes effect.

Verify the Changes:

Step 1: Test the new SMS treatment by sending test messages to a seed list to verify the content and formatting.

Step 2: Ensure that the updated engagement policy correctly filters customers based on the new debt-to-income ratio threshold.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Defining treatments for actions" and "Configuring engagement policies" sections.

Pega documentation on "Creating and managing actions" and "Editing engagement policies".

Value Finder in Pega Customer Decision Hub identifies under-served customer segments and provides recommendations to optimize engagement policies. It helps in understanding how different customer groups are being served and suggests adjustments to engagement conditions to ensure a more inclusive and effective customer outreach.

To promote offers in email using Next-Best-Action Designer, U+ Bank needs to ensure that specific configurations are in place.

Enable the Email Channel:

Step 1: Go to Next-Best-Action Designer in Pega Customer Decision Hub.

Step 2: Navigate to the Channels tab and ensure that the Email channel is toggled on.

Step 3: Configure the email channel settings, including the email server and any necessary authentication details.

Define the Primary Schedule:

Step 1: In Next-Best-Action Designer, navigate to the Engagement Policy tab.

Step 2: Define the primary schedule for when email communications should be sent. This includes setting the frequency and timing of emails.

Step 3: Ensure that the schedule aligns with the bank’s overall marketing calendar and customer engagement strategy.

Additional Considerations:

Arbitration Configuration: While not mandatory, configuring arbitration to boost prioritization of actions meant for email can help ensure that email offers are given appropriate priority.

Real-Time Container Triggers: Setting up real-time container triggers to communicate with the email server is essential if real-time email communication is required, though it is not specifically needed for scheduled email campaigns.

Example Implementation:

U+ Bank can start by enabling the email channel and configuring the primary schedule for their email campaigns. Once these steps are completed, they can create email treatments for their offers and test them using seed lists to ensure personalization and content accuracy.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Understanding Next-Best-Action Designer channels" and "Configuring email treatments" sections.

Pega documentation on "Sending offer emails" and "Creating treatments for additional channels".

In designing the next-best-action prioritization expression that balances customer needs with business objectives, you need to consider "Predicted customer behavior." This factor involves using predictive models to forecast how customers are likely to respond to different actions, ensuring that the recommended actions are both relevant to the customer and aligned with business goals.

To re-implement the requirement using the always-on outbound customer engagement paradigm, configure the primary schedule to run daily and let the AI choose the best action from all the actions that a customer qualifies for based on engagement policies. This approach leverages AI to dynamically select the most relevant next best actions, ensuring timely and personalized customer interactions.

To analyze the distribution of offers to well-engaged, under-engaged, or not-engaged customers, you would run the Pega Value Finder simulation. The Value Finder tool is designed to help identify and understand the engagement levels of different customer segments.

Pega Value Finder:

Pega Value Finder is used to identify under-served or over-served customers by analyzing historical interaction data.

It provides insights into how well different customer segments are being served and helps in understanding engagement levels.

This tool can categorize customers into well-engaged, under-engaged, or not-engaged based on their interactions and responses to offers.

 Business Structure: Setting up the business structure to Sales/CreditCards ensures that the actions and offers are correctly categorized and managed within the appropriate business context.

 Real-Time Container: Creating and configuring a real-time container is necessary for presenting the credit card offer to customers in real-time as they log in to the website.

 Action and Web Treatment: Defining the action and configuring its web treatment ensures that the offer is ready to be displayed to the customers, complete with the necessary details and presentation format.

To promote various auto loan offers to qualified customers on its website using Pega Customer Decision Hub, you need to:

Create a decision strategy: This involves defining the logic that determines which auto loan offers are most relevant to each customer based on their profile, behavior, and eligibility criteria.

Set up the business structure: This involves organizing the offers into a hierarchical structure of business issues and groups, such as categorizing them under a "Loans" issue and an "Auto Loans" group. This structure helps in managing and prioritizing the offers effectively.

To define a contact policy that suppresses an action for 20 days if it was rejected twice in any channel in the last 30 days, you need to specify the suppression rule as follows: "Suppress an action for 20 days if there are 2 rejects for any channel in the last 30 days." This rule ensures that the action is paused after it has been rejected a specified number of times across any channel within the given timeframe.

 Introduction to Group By and Filter Components:

The Group By component groups data based on specified properties, allowing aggregation of data points.

The Filter component is used to include or exclude actions based on specific criteria.

 Dotted Line Relationship:

In Pega's visual strategy canvas, a dotted line between two components indicates a reference relationship rather than a direct data flow.

When a dotted line connects a Group By component to a Filter component, it signifies that the Filter component is using a property defined or aggregated in the Group By component.

 Functionality Explanation:

For example, if the Group By component aggregates customer data by region, the Filter component can then use this aggregated data to filter actions for specific regions.

This relationship ensures that the filtering process considers the aggregated data, making the decision strategy more dynamic and contextually aware.

 Verification from Pega Documentation:

As outlined in Pega's documentation, a dotted line signifies that the Filter component references a property from the Group By component, enabling more complex and accurate decision-making processes.

 Arbitration Factors: In Pega, arbitration is the process of prioritizing actions based on multiple factors.

 Action Value: This is a specific arbitration factor that allows assigning financial values to actions.

 Purpose: Action value helps in determining the prioritization of actions based on their financial impact or benefit to the business.

 Reference: According to Pega Customer Decision Hub documentation, the action value is indeed the term used for this specific arbitration factor​​.

Correct Engagement Policy Configuration:

Age

Engagement policy: Group-level eligibility

Opt-out

Engagement policy: Group-level applicability

Debt-to-income ratio

Engagement policy: Action-level suitability

Age

Group-level eligibility: This criterion determines whether the customer is eligible to receive any credit card offers. Eligibility conditions ensure that only customers meeting the basic criteria (e.g., being above the age of 18) are considered for the offers. This condition applies to all credit card offers within the group.

Next-Best-Action Framework - This framework in Pega CDH is designed to decide the best action for each customer interaction by evaluating eligibility, suitability, and propensity.

Complex Eligibility Criteria - To define complex eligibility criteria, modifications are needed within the Next-Best-Action decision strategy to handle multiple conditions and business rules.

Steps:

Access the Next-Best-Action Designer.

Navigate to the decision strategy.

Add or modify components to incorporate complex eligibility conditions.

Test and validate the changes using persona tests and simulations.

Interaction History Summary - This rule aggregates data like customer responses, action outcomes, and interactions over a set time period.

Steps to Create the Rule:

In Dev Studio, go to Configure -> Decisioning -> Decisions -> Data Sources -> Interaction History Summaries.

Create a new summary rule.

Set the time period to 20 days.

Define the interaction data to be tracked (e.g., accepts, rejects).

Benefits - Enables precise tracking of customer engagement and responses, facilitating better decision strategies based on historical data.

Scorecard Rule - A scorecard in Pega is used to calculate a score based on various customer attributes. To include a new attribute (customer income), the scorecard rule must be updated.

Steps to Update Scorecard:

Navigate to the scorecard rule in Dev Studio.

Add a new input for customer income.

Define the scoring logic for the new input.

Save the updated scorecard.

Decision Strategy - The decision strategy that uses this scorecard must also be updated to ensure it correctly utilizes the new score calculations.

Steps to Update Decision Strategy:

Locate the decision strategy in the Next-Best-Action Designer.

Ensure the updated scorecard is referenced correctly.

Adjust any logic or thresholds that depend on the scorecard output.

Test the strategy to ensure it works as expected with the updated scorecard.

 Identify the issue: When a customer receives an offer they have already accepted, it suggests that the decision strategy did not consider previous interactions.

 Check strategy customization: Strategies in Pega CDH can be customized to include rules that prevent the same offer from being presented again.

 Confirm the exclusion of previous offers: Specifically, you need to ensure that there is a suppression rule or a logic to check the Interaction History to exclude previously accepted offers.

 Reference: Refer to the section on "Next-Best-Action Designer constraints" and "Engagement policies" in the Pega Customer Decision Hub User Guide. These sections explain how to configure rules to prevent repetitive offers​​.

 Understanding Customer Events:

Customer events are interactions triggered by specific actions or behaviors of the customer.

In this case, an incomplete transaction due to a slow internet connection is an event that prompts further action.

 Email with Link to Continue Transaction:

Sending an email with a link to continue an incomplete transaction is a response to a customer event.

This type of communication is designed to help the customer complete their intended action, improving user experience.

 Verification:

The documentation categorizes interactions like these as customer events.

Step by Step Comprehensive Detailed Explanation with References:

Defining Property Constraint:

A property constraint can be used to limit the number of VISA card offers sent in an outbound run.

This constraint is based on the card_type action property, ensuring that only a specified number of VISA card offers are included.

This method allows for precise control over the types of actions being sent, adhering to business requirements.

 Volume Constraint Reset Interval: To meet the requirement of giving away phone accessories in batches of 50 and promoting a new batch once the stock in a batch is completed, you should select the "Manual" reset interval. This setting allows manual control over when the constraint is reset, enabling the business to start a new batch promotion as needed.

 Implementation: Configure the volume constraint for the promotional emails with the manual reset interval to ensure controlled batch processing.

 Volume Constraint Application: Given the volume constraints of a maximum of 100 daily for the StandardCard action, and 75 daily for both Email and SMS channels, the total actions delivered would be limited to the smallest constraint applicable to each channel.

 Calculation:

The StandardCard constraint allows 100 actions per day.

The Email constraint allows 75 actions per day.

The SMS constraint allows 75 actions per day.

Therefore, the total actions delivered = 100 (since the StandardCard action is constrained to 100 but both email and SMS actions can only reach 75 each).

To present a free 2GB data offer when customers contact the call center for mobile number portability, you should configure context weighting in Pega Customer Decision Hub. Context weighting allows you to prioritize actions based on the specific context of the interaction, such as the reason for the call. By setting a higher weight for the free data offer in the context of mobile number portability, you can ensure this offer is prioritized when relevant.