Free and Premium Pegasystems PEGAPCDC87V1 Dumps Questions Answers

Compute SUM of PROFIT = Group BY

Determine target budget to retain a customer = Set Property

Determine customer credit score = Scorecard

Divide customers into risk segment = Decision Table

If the volume constraint shown in the image (Maximum 75 Daily with Channel: Action: StandardCard, Maximum 50 Daily with Channel: Email, Maximum 50 Daily with Channel: SMS) is applied, the outbound run will deliver 75 actions in total. This includes actions across different channels, adhering to the specified limits.

Pega Customer Decision Hub can be applied to various types of decisions, including determining how to retain a customer. It leverages decision management and AI technologies to analyze customer data, predict behaviors, and recommend personalized actions that can help improve customer retention and satisfaction.

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.

 Understand the strategy objective: The decision strategy is designed to output the most profitable shoe. The profit is calculated as Profit = Price - Cost.

 Evaluate the provided data:

Nike: Profit = $69 - $59 = $10

Adidas: Profit = $65 - $45 = $20

Puma: Profit = $85 - $65 = $20

Reebok: Profit = $75 - $50 = $25

 Determine the most profitable shoe: According to the calculations, Reebok has the highest profit ($25).

 Fill in the blank: Therefore, the output of the component in the blank space highlighted in red should be Reebok, which has the highest profit.

 Reference: The strategy design and output decision rules are outlined in the sections on "Understanding Next-Best-Action Designer arbitration" and "Next-Best-Action strategy framework components" in the Pega Customer Decision Hub User Guide​​.

The best way to organize customer actions applicable to various business issues in Pega Customer Decision Hub is into a three-level hierarchy: Business issue > Group > Actions. This structure allows for clear categorization and management of actions based on their business purpose and relevance. The "Business issue" represents the high-level goal (e.g., Retention, Acquisition), "Group" categorizes the actions under each issue (e.g., Credit Cards, Mortgages), and "Actions" are the specific offers or recommendations.

 Ensuring the Email Channel is Enabled:

Navigate to the Channels tab in the Next-Best-Action Designer.

Ensure that the toggle for the Email channel is set to ON.

This action enables the email channel, making it available for use in Next-Best-Action strategies.

To estimate the business value created by the subsequent next-best-action run using the selected configuration, you should run a simulation using Pega Scenario Planner. This tool allows you to project the potential business impact and value of different next-best-action scenarios, providing insights into the expected outcomes based on the configured strategies.

To limit the number of emails sent to customers irrespective of past outcomes with a particular offer and customer, you can implement customer contact limits. Customer contact limits are used to control the number of interactions a customer can receive over a specific period on a particular channel, ensuring that customers are not overwhelmed with too many messages.

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.

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 Strategy Application: Pega's Next-Best-Action strategy can be applied to various business issues to optimize decision-making and enhance customer interactions.

Collections: Used to manage and optimize debt collection processes by determining the best actions to take with customers who have outstanding debts.

Service: Enhances customer service interactions by suggesting the most relevant actions or responses to customer inquiries and issues.

Retention: Focuses on retaining customers by identifying and executing actions that are most likely to keep them engaged and loyal.

 Requirement:

U+ wants to show a retention offer to customers likely to leave the bank based on historical interaction data.

 Model Types:

Entity Model: Used for recognizing entities within data.

Text Analytics Model: Used for analyzing text data.

Predictive Model: Used for making predictions based on historical data.

Adaptive Model: Used for real-time adaptation based on new data.

 Suitable Model:

For predicting customer churn (likelihood of leaving), a predictive model is best suited because it leverages historical data to make future predictions.

 Verification from Pega Documentation:

Pega documentation on leveraging predictive models for customer retention and churn analysis.

The condition that possibly prevents Anna from seeing the new Rewards card offer is "Eligibility." Since Anna already holds a higher value card, Premier Rewards, the eligibility criteria might exclude her from receiving offers for the Rewards card. This ensures that customers are not offered products that are less relevant or valuable compared to what they already have.

 Understanding the Context: Myco, a telco, wants to introduce new plans without handsets. Currently, their business structure under the sales issue/plans group contains actions that are handsets.

 Business Structure: In Pega Customer Decision Hub, the business structure is organized into issues and groups.

 Action Naming: To reflect the new situation where plans do not include handsets, the term "handsets" needs to be changed.

 Level of Change: The appropriate level for renaming the plans to reflect this new structure is at the Issue level. At this level, the business context for actions is defined, and changes here will apply to all actions under that issue.

 Reference: As per the documentation, changes to business issues and groups should be done at the issue level to ensure that the new context is accurately represented across all relevant actions​​.

 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.

Aggregation components in Pega Customer Decision Hub provide the ability to make calculations based upon a list of actions. These components are used to perform operations such as summing values, averaging scores, or other statistical calculations that aggregate data from multiple actions. This is crucial for generating insights and making informed decisions based on the collective data of several actions.

 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.

 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.

Correct Action Hierarchy Levels:

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Action Hierarchy Level: Action

Retention

Action Hierarchy Level: Business Issue

Tablets

Action Hierarchy Level: Group

Service

Action Hierarchy Level: Business Issue

Change address

Action Hierarchy Level: Action

Proactive retention

Action Hierarchy Level: Group

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Action: This is a specific offering or initiative that can be presented to customers. It is a specific action within the broader strategy.

Set-Property Component: This component performs the calculation of profit for each of the four brands (Nike, Adidas, Puma, Reebok). Since the Set-Property component is configured to calculate profit for each brand individually, it processes four inputs, resulting in four outputs.

Action Flow Template: To implement the requirement of using the same action flow template for all credit card actions, configure the email treatment. This allows you to define the desired flow pattern and reuse it across different credit card actions.

Configuration: Create an email treatment template that includes the specific flow pattern, and then apply this template to all the relevant credit card actions.

To target customers living in a specific geographic area for advertising the new fiber optic service, you need to configure a "Customer segment" in the Next-Best-Action Designer. This involves defining the segment of customers based on their geographic location, ensuring that only those customers in the northern region are included in this segment. By doing so, you can ensure that the banner advertisement for the fiber optic service is displayed only to those customers.

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.

 Placement Type in Treatment Design: To present the Rewards Card offer on the top right of the customer’s account page, you should use the "Tile" placement type. This type is specifically designed to position content in a compact, visually distinct block that can be placed in specific areas of a webpage, such as the top right corner.

 Implementation: In the treatment configuration within Pega, you can specify the placement type as "Tile" to ensure the offer is displayed correctly on the account page.

Debugging Arbitration - To verify if the result of the arbitration is correct, the Prioritize component in the strategy shows the final ranking of actions.

Prioritize Component - This component ranks actions based on their calculated priority scores.

Steps:

Review the Prioritize component in the strategy.

Check the priority scores assigned to each action.

Ensure that the actions with the highest propensities are ranked at the top.

Paul cannot see the Reward card offer due to the contact policies defined by U+ Bank:

Contact Policies Defined:

Policy 1: Suppress a group of credit card offers for 30 days if any credit card offer is rejected three times in any channel in the past 15 days.

Policy 2: Suppress the Reward card offer for 7 days if it is rejected twice in any channel in the last 7 days.

Paul’s Rejection History:

Paul has rejected other credit card offers twice on the web channel and once in the contact center within the last 15 days.

Reason for Suppression:

Due to Policy 1, since Paul has rejected any credit card offer (not specifically the Reward card) three times in any channel in the past 15 days, all credit card offers, including the Reward card offer, are suppressed for 30 days.

Understanding the Suppression Logic:

Step 1: The decision strategy evaluates the rejection history of offers.

Step 2: It applies the contact policies to determine which offers should be suppressed.

Step 3: In this case, the policy conditions are met due to multiple rejections of credit card offers, leading to the suppression of the Reward card offer.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Setting constraints, contact policy limits, and controls" section.

Pega documentation on "Managing contact policies".

Referencing Customer Properties: In Pega decision strategies, customer properties are accessed by prefixing the property name with "Customer." This convention helps in clearly identifying the context of the properties being used.

In Pega Customer Decision Hub, the property that is automatically recomputed for each decision component is "Priority." This property determines the order in which actions are evaluated and presented to the customer. The Priority property ensures that the most relevant and valuable actions are prioritized based on the defined criteria, including customer relevance, business rules, and real-time contextual data.

To address the issue where customers have ignored a particular mortgage offer and the bank wants to offer the action 30% more frequently, we need to configure the appropriate arbitration factor in Pega Customer Decision Hub.

Arbitration Overview: Arbitration in Pega CDH determines how actions are prioritized and selected for presentation to the customer. It involves factors like business value, propensity, and action weighting.

Action Weighting: Action weighting is used to adjust the frequency and priority of specific actions. By increasing the action weighting, you effectively make the action more likely to be selected during the arbitration process.

Implementation:

Navigate to the Next-Best-Action Designer in Pega CDH.

Locate the action for the mortgage offer that needs increased frequency.

Adjust the action weighting to increase by 30%. This adjustment modifies the priority calculation, making the action more likely to be selected.

In a call-center application that receives Next-Best-Actions from Pega Customer Decision Hub, the customer service representative (CSR) is guided on the next important conversation to have with the customer. The Next-Best-Action recommendations help CSRs make informed decisions and provide relevant, personalized service to customers by suggesting the most appropriate actions based on the customer's profile, interaction history, and current context.

 Understanding the Requirement:

Myco wants to understand the impact on revenue if a new data plan offer is introduced.

This involves simulating the potential effects of adding a new offer to the existing strategy.

 Pega Scenario Planner:

Scenario Planner is used to simulate "what-if" scenarios to forecast results and understand the impact of changes to the Next-Best-Action Designer configuration.

It allows businesses to compare the effects of different strategies and adjust levers to align with business objectives.

 Running the Simulation:

Create and run an audience simulation with the new data plan offer included.

Use Scenario Planner to compare the projected reach, accepts, and value capture with and without the new offer.

 Steps to Use Scenario Planner:

Log in to the Pega Customer Decision Hub portal.

Navigate to Discovery -> Scenario Planner.

Create a new simulation, including the new data plan offer.

Run the simulation and compare the results to understand the impact on revenue.

 Verification from Pega Documentation:

The Pega Customer Decision Hub User Guide explains how to use Scenario Planner to simulate business changes and understand their impact​​​​.

To enable access to customer properties such as age, income, etc., in a decision strategy, the Data Import component is required. This component imports the relevant customer data into the strategy so it can be utilized for decision-making processes.

To enable the website to communicate with the Pega Customer Decision Hub and show the Reward Card offer to qualified customers, you need to:

Enable the web channel in the Next-Best-Action Designer's Channel tab. This step activates the web channel for inbound interactions, allowing the system to deliver personalized actions to customers visiting the website.

Map the real-time container to a business structure level. This ensures that the container is correctly linked to the business context, allowing the system to deliver the right offers based on the customer’s interactions with the website.

Correct Engagement Policy Configuration:

The customer must not be flagged as a defaulted borrower

Engagement policy: Eligibility

Customer already owns a higher-value offer

Engagement policy: Applicability

Must be a resident of New York City

Engagement policy: Eligibility

The savings product offers are relevant only if the customer has not explicitly opted out of direct marketing

Engagement policy: Applicability

Inappropriate for customers with a credit score < 300 as they are likely to default

Engagement policy: Suitability

The customer must not be flagged as a defaulted borrower

Eligibility: Eligibility conditions determine whether a customer qualifies for an offer at a basic level. Being flagged as a defaulted borrower is a fundamental criterion to disqualify a customer from receiving certain offers.

With the volume constraints applied (75 maximum daily actions for StandardCard, 50 maximum daily actions for Email, and 50 maximum daily actions for SMS), the outbound run will deliver a total of 75 actions. This includes actions across different channels while adhering to the specified constraints.

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.

In response to the real-time container request by Customer Decision Hub, the U+ Bank website receives only relevant action details and its treatment details. This information includes the specifics of the action to be displayed (such as the offer) and the associated treatment (such as the banner or message), enabling the website to present personalized and contextually relevant offers to customers.

Enrichment decision components in Pega Customer Decision Hub provide the ability to set customer properties. These components are used to enhance the customer data within a decision strategy by adding or updating information about the customer. This allows for more personalized and contextually relevant decisions.

Definition and Purpose:

Enrichment components are used within decision strategies to add or update customer data, ensuring that the decisioning process has the most relevant and up-to-date information.

This can include setting properties like customer preferences, behaviors, or other attributes that might influence the decision outcome.

Types of Enrichment Components:

Set Property: This is the primary enrichment component used to set a value to a property within the strategy.

Data Import: Importing data from external sources to enrich the customer profile.

Data Transform: Applying transformations to data to format or manipulate it as needed within the strategy.

Usage in Decision Strategies:

When designing a decision strategy, enrichment components are typically used in the initial stages to ensure that all necessary customer data is available and accurate before proceeding to decision logic components.

For example, before evaluating eligibility or applicability, you might use an enrichment component to update the customer's credit score or recent transaction history.

Implementation Steps:

Step 1: Drag the enrichment component into the decision strategy canvas.

Step 2: Configure the component by selecting the property to set and defining the value or expression to be used.

Step 3: Connect the enrichment component to other components within the strategy to ensure the enriched data is utilized in subsequent decision-making steps.

Example:

Consider a scenario where a bank wants to offer personalized loan options based on the customer's recent financial activities. An enrichment component can be used to update the customer's profile with their latest transaction data, ensuring that the loan offer is tailored to their current financial status.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Understanding the Next-Best-Action strategy framework" section, which describes the use of enrichment components in decision strategies.

Pega documentation on "Creating and understanding decision strategies".

 Understanding Engagement Policies: Pega Customer Decision Hub uses engagement policies like eligibility, applicability, and suitability to determine if an action should be presented to a customer.

 Eligibility Criteria: These criteria define whether a customer qualifies for an action.

 Bank's Requirement: U+ Bank wants to avoid offering a mortgage refinance option to customers who have less than 5% left on their loan.

 Best Fit: The eligibility policy best suits this requirement as it allows the bank to set specific criteria (loan balance percentage) to exclude certain customers from receiving the offer​​.

 Understanding the Requirement:

Myco wants to find new opportunities to improve marketing during the ideation stage of a project for presenting post-paid offers.

This involves identifying gaps and underserved customer segments that can be targeted with new or improved offers.

 Pega Value Finder:

Pega Value Finder is designed to analyze the distribution of actions across different customer segments.

It helps identify customers who are not receiving any actions or are receiving only low-propensity actions, thus highlighting opportunities to improve the strategy.

 Running the Simulation:

In Pega Value Finder, you can run simulations to discover underserved customers.

The tool provides insights into customer groups that are not well-engaged, allowing businesses to adjust their strategies and create more relevant actions or treatments.

 Steps to Run Value Finder:

Log in to the Pega Customer Decision Hub portal.

Navigate to Discovery -> Value Finder.

Create a new simulation and select the relevant issue and group.

Run the simulation to analyze customer distribution and identify underserved segments.

 Verification from Pega Documentation:

The Pega Customer Decision Hub User Guide details the process and benefits of using Value Finder to discover gaps and opportunities in customer engagement strategies​​​​.

To implement the change in the threshold value for the USD 2000 credit limit from “<175” to “<200”, as a Strategy Designer, you should change the cutoff value in the results tab of the scorecard decision component. This adjustment ensures that the decision logic used to automatically approve credit card limits reflects the new threshold, thereby updating the conditions under which customers qualify for the specified credit limit.

Ensuring Latest Customer Information: To ensure that the outbound run always uses the latest customer information, you need to select the "Refresh the audience" option. This setting ensures that the audience is refreshed with the most recent customer data before the campaign is executed.

Implementation: Navigate to the campaign configuration and select the option to refresh the audience before running the campaign. This guarantees that any changes or updates to the customer data are taken into account.

 Understanding the Scenario Planner Output:

The image shows the projected reach and projected responses for several credit card offers, including the Standard card.

The "Projected responses" column indicates the number of customers expected to respond positively to each offer.

 Interpreting the Data for the Standard Card:

For the Standard card, the "Projected responses" is 21, with a change indicator showing a decrease of 7.

This decrease is factored into the projected response, meaning the expected number of positive responses after considering the applied business weight.

 Step-by-Step Calculation:

The base projected responses for the Standard card is 21.

The decrease of 7 has already been factored into this projection.

Therefore, the number of customers likely to accept the Standard card remains 21, as it represents the final projected response after adjustments.

 Verification from Pega Documentation:

The Scenario Planner in Pega Customer Decision Hub provides these projections to help visualize the impact of business weight adjustments and other lever changes on customer actions.

Identify the Requirement: U+ Bank wants to offer credit cards only to customers with specific risk segments and also ensure that the age of the customer is greater than 25 years. This involves setting eligibility criteria for credit card offers.

Understand Engagement Policies: Engagement policies in Pega CDH are used to define rules that govern when specific actions are relevant for a customer. This includes eligibility, applicability, and suitability conditions.

Access Next-Best-Action Designer: To implement these criteria, navigate to the Next-Best-Action Designer within Pega Customer Decision Hub.

Engagement Policy Tab:

Eligibility Criteria: Set the criteria to include customers with an age greater than 25 years.

Define the other conditions based on Credit Score and Average Balance to segment customers into Good, Fair, Poor, and Very Poor risk categories.

According to the Pega Customer Decision Hub User Guide, the engagement policy tab allows users to define when specific actions or groups of actions are appropriate for customers (Reference: Pega-Customer-Decision-Hub-User-Guide-85.pdf, Chapter on "Understanding Next-Best-Action Designer engagement policies").

Implementing the Criteria:

Navigate to the Engagement Policy tab: This is within the Next-Best-Action Designer.

Add a New Policy: Create a new engagement policy where you define the eligibility criteria for the credit card offer.

Set Age Condition: Ensure the age condition (Age > 25) is part of the eligibility criteria.

Validation:

Ensure the conditions are tested thoroughly to verify that the credit card offers are only presented to customers meeting the set criteria.

Additional Resources:

For detailed steps on configuring engagement policies, refer to the Pega documentation on "Configuring engagement policies for actions" (Reference: Pega-Customer-Decision-Hub-User-Guide-85.pdf).

Conclusion: The best practice to ensure that credit cards are offered only to customers with age greater than 25 is to use the Engagement Policy tab in the Next-Best-Action Designer, where you can set the eligibility conditions appropriately.

To manage the communication between Pega Customer Decision Hub and external channels for promoting new offers to each customer on their website, you need to configure a Real-time Container. Real-time Containers are used to deliver personalized content and actions to customers in real-time, based on their interactions with the website and other channels. They ensure that the most relevant offers are presented to each customer in a timely and contextually appropriate manner.

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.

To prevent the same offer from being shown repeatedly to a customer, the correct suppression rule is to suppress an action for 10 days if there are five impressions for the web channel in the last 14 days. This ensures that after a customer has been presented with the same offer five times within a 14-day period, they will not see that offer for the next 10 days, thereby preventing offer fatigue.

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".

One-to-One Marketing Channels: As the company transitions to a one-to-one marketing approach, leveraging various channels is essential to effectively present new offers like a data plan.

Retail Store: Offers can be presented directly to customers through in-store interactions, allowing personalized recommendations based on their profile and behavior.

Chatbot: Automated chatbots can engage customers in real-time on digital platforms, offering personalized data plans based on customer interactions and preferences.