PEGAPCDC87V1 Certified Pega Decisioning Consultant (PCDC) 87V1 Questions and Answers

 Arbitration Factors: To prioritize customer needs in the decision strategy, adjusting the context weighting factor is essential. This factor takes into account the situational context of each action, thereby prioritizing actions that align with customer needs.

 Implementation: By boosting context weighting, you ensure that the decision strategy considers the specific context of customer interactions, leading to more relevant and satisfactory offer presentations.

To use the same action flow template with the desired flow pattern for all credit card actions, you should configure a dynamic template.

Understanding Dynamic Templates:

Dynamic Templates: These templates allow you to define a reusable flow pattern that can be applied to multiple actions. They support dynamic configuration of treatments and other flow elements.

Configuration Steps:

Step 1: Create a dynamic template in Pega Customer Decision Hub.

Step 2: Define the flow pattern within the dynamic template, including all necessary steps and treatments.

Step 3: Apply the dynamic template to each credit card action, ensuring that the flow pattern is consistent across all actions.

Benefits:

Using dynamic templates ensures consistency in the flow patterns and simplifies the maintenance process.

Any updates to the flow pattern can be made in the template and will automatically apply to all actions using that template.

Example:

U+ Bank configures a dynamic template for their promotional email flows. This template includes steps for sending the email, waiting for a response, and following up as needed. Each credit card action uses this dynamic template to ensure a consistent customer experience.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Creating and using dynamic templates" section.

Pega documentation on "Action flow templates and dynamic configuration".

To configure an ethical bias policy that allows no bias based on gender, use the 0.9-1.11 Rate Ratio:

Understanding Rate Ratios:

Step 1: Rate ratios are used to detect and measure bias in decision-making processes.

Step 2: A rate ratio of 1 indicates perfect parity, meaning there is no bias between groups.

Configuring Bias Detection:

Step 1: In Pega Customer Decision Hub, you can configure bias detection by setting an acceptable rate ratio range.

Step 2: To ensure no bias based on gender, set the rate ratio to a narrow range close to 1, such as 0.9-1.11. This range ensures that the decisioning process treats different genders fairly, with minimal deviation.

Implementation:

Step 1: Navigate to the Bias Detection settings within the Decision Strategy framework.

Step 2: Set the rate ratio to 0.9-1.11 for gender to monitor and enforce non-discriminatory practices in presenting offers.

Example:

MyCo can use this configuration to ensure that their mobile data package offers for students do not exhibit gender bias, providing equal opportunities for both male and female students.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Complying with policies or regulations by detecting unwanted bias" section.

 Understanding the Decision Table:

The decision table provided returns a label based on a customer's credit score and average balance.

The rules are evaluated in order, and the first matching rule determines the result.

 Evaluating the Customer Data:

The customer has a credit score of 115 and an average balance of 15000.

We will evaluate each row of the decision table to see where these values fit.

 Rule Evaluation:

First Rule: Credit Score >= 130 and Average Balance >= 30000

Customer's Credit Score: 115 (not >= 130)

This rule does not match.

Second Rule: Credit Score >= 110 and Average Balance >= 20000

Customer's Credit Score: 115 (>= 110) but Average Balance: 15000 (not >= 20000)

This rule does not match.

Third Rule: Credit Score >= 90 and Average Balance >= 10000

Customer's Credit Score: 115 (>= 90) and Average Balance: 15000 (>= 10000)

Both conditions match, so the result is "C".

Fourth Rule: Credit Score >= 70 and Average Balance >= 1000

This rule is not evaluated because the previous rule matched.

 Conclusion:

According to the decision table, the result for a customer with a credit score of 115 and an average balance of 15000 is "B".

In a Prioritize component in Pega Customer Decision Hub, the top action is determined based on the propensity value. Propensity refers to the likelihood that a customer will respond positively to a given action. Here’s a detailed explanation:

Definition of Propensity:

Propensity is a score that indicates the probability of a customer taking a desired action, such as accepting an offer or responding to a campaign. It is calculated using predictive models that analyze historical data and customer behavior.

Role in Prioritize Component:

The Prioritize component in a decision strategy uses the propensity values to rank actions. Actions with higher propensity values are given higher priority, ensuring that the most relevant and likely-to-succeed actions are selected for the customer.

Configuration Steps:

Step 1: In the strategy canvas, add a Prioritize component.

Step 2: Configure the component to use the propensity value for ranking actions. This can be done by selecting the appropriate property that holds the propensity score, typically provided by a prediction model.

Step 3: Connect the Prioritize component to other components in the strategy to ensure the highest priority actions are selected and offered to the customer.

Benefits:

Using propensity to determine the top action helps in making data-driven decisions, increasing the chances of customer engagement and improving overall campaign effectiveness.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Understanding the Next-Best-Action strategy framework" section.

Pega documentation on "Creating and understanding decision strategies".

 Requirement:

U+ Bank has added five new actions to the Credit Cards group and wants to enable these actions in the email channel.

 Configuring Each Action with an Email Treatment:

Each action must be configured with an appropriate treatment for the email channel.

Treatments define how the actions are presented in the channel, including the content and format of the email.

This step ensures that the actions are prepared to be sent via email with the correct content.

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.

To ensure that the Gold credit card offer is presented only to customers who have an annual income of more than USD 150,000, you need to configure an engagement policy condition in the Next-Best-Action Designer. Here’s why:

Engagement Policy Condition:

Engagement policies in the Next-Best-Action Designer are used to define conditions that determine whether a particular action (such as offering a Gold credit card) is suitable or eligible for a customer.

By setting an engagement policy condition based on the customer's annual income, you ensure that only those customers who meet the specified income threshold are eligible to receive the offer.

This condition acts as a filter that evaluates each customer’s income and includes only those who earn more than USD 150,000.

Correct Engagement Policy Level Configuration:

Criterion 1: Owns a smart mobile

Engagement policy level: Group level

Criterion 2: Under graduation student / Post graduation student

Engagement policy level: Issue level

Criterion 3: New customer / Existing customer

Engagement policy level: Action level

To configure engagement policies correctly in Pega, it is important to choose the appropriate level of engagement policy based on the criteria. The levels are:

Issue level: Applies to all actions within a business issue.

Group level: Applies to all actions within a specific group.

Action level: Applies to individual actions.

1. Criterion 1: Owns a smart mobile

This criterion is relevant for all data plans under a specific group of data plans.

Therefore, it is best suited at the Group level, ensuring that all plans within the group check for this criterion.

2. Criterion 2: Under graduation student / Post graduation student

These educational statuses may be relevant across various groups and actions under a broader business issue like "Education" or "Student Plans".

Therefore, this should be configured at the Issue level to apply across all relevant actions within the issue.

3. Criterion 3: New customer / Existing customer

This criterion is specific to individual actions and can vary greatly between different offers.

Hence, it should be configured at the Action level to ensure precise control over which actions are offered to new versus existing customers.

 Understanding Volume Constraints:

Volume constraints can be applied to limit the number of actions offered to customers.

Constraints can be set to apply to actions as a group or individually.

 Scenario Analysis:

In the given scenario, a customer qualifies for three actions.

The top-ranked action has not reached its volume limit, but the lower-ranked actions have.

Since the constraints are applied as a group, if the top-ranked action is available, it will be selected.

The lower-ranked actions, having reached their limits, will not be selected.

 Conclusion:

Only the top-ranked action will be selected for the customer in the outbound run.

 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.

Best practices for designing an action flow in Pega Customer Decision Hub emphasize reusability and efficient configuration:

Reusable Flows:

Step 1: When creating a flow that will be reused across multiple actions, it is recommended to use the "Specify Treatment" option.

Step 2: This approach allows you to configure the flow with a specific treatment, making it easier to apply the same flow logic to different actions without having to redesign the flow each time.

Flow Configuration:

Using the "Specify Treatment" option ensures that the treatment details are dynamically inserted when the action is executed, providing flexibility and consistency across various actions.

This method helps maintain a modular design, where flows can be reused and updated independently of the actions they are associated with.

Avoid Redundancy:

Step 1: Reusing flows across multiple actions reduces redundancy and simplifies maintenance.

Step 2: Any changes to the flow logic need to be made only once, and these changes will be reflected across all actions using that flow.

Example:

If you have a standard email flow for promotional offers, you can configure this flow with the "Specify Treatment" option and reuse it for different promotional actions such as discounts, new product launches, or seasonal offers.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Configuring the delivery of an email treatment with the Send Email shape" section.

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.

 Context: A bank wants to use the Next-Best-Action capability of Pega Customer Decision Hub to promote offers on their website.

 Real-time Container Request: When a real-time container request is made, Pega Customer Decision Hub processes this request.

 Response Information: The response sent back to the website includes comprehensive details about the actions.

 Details Provided: According to Pega documentation, the response to a real-time container request includes treatment details (like images and descriptions) and relevant action attributes that personalize the offer for the customer​​.

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.

 Triggering Segment Runs with Next-Best-Action Outbound Schedule:

Segments can be configured to run in alignment with the Next-Best-Action outbound schedule.

This setup ensures that the segment is refreshed and populated with the latest customer data before each outbound run.

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.

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 update the underlying decision strategy of an engagement policy condition, the change request should be created in the 1:1 Operations Manager Portal. This portal is designed for business users to manage changes and revisions in a controlled environment.

Understanding the Portals:

1:1 Operations Manager Portal: This portal allows business users to manage and implement business changes, such as updating decision strategies and engagement policies.

Pega Customer Decision Hub Portal: This portal is used for overall customer decision management but not specifically for creating change requests.

Dev Studio Portal: This portal is more technical and used for developing and configuring applications.

Development Manager Portal: This is generally used for deployment and managing development pipelines.

Steps to Create a Change Request:

Step 1: Log into the 1:1 Operations Manager Portal.

Step 2: Navigate to the section where you can create a new change request.

Step 3: Specify the details of the change request, including the engagement policy and the decision strategy that needs to be updated.

Step 4: Submit the change request for approval and implementation.

Benefits:

Using the 1:1 Operations Manager Portal ensures that changes are tracked, reviewed, and approved through a formal process, maintaining governance and control over business operations.

Example:

U+ Bank wants to update the decision strategy for credit card offers. A change request is created in the 1:1 Operations Manager Portal, detailing the required updates to the engagement policy conditions.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Managing business-as-usual changes with Pega 1:1 Operations Manager" section.

Pega documentation on "Revision management and change request process".

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.

 Marketing and Sales Actions: Creating actions like "No annual fee credit card" and "1% cash back credit card" address different customer needs and stages in the customer lifecycle, including acquisition and engagement.

 Full Customer Lifecycle: These actions involve marketing and sales efforts aimed at attracting and retaining customers by offering attractive benefits.

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.

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.

Enrichment Components - These are used to add additional information or modify properties within a decision strategy.

Setting Text Values:

Enrichment components can be configured to set or modify strategy properties.

This includes setting text values, numerical values, or other types of data.

Steps:

In the decision strategy, add an Enrichment component.

Define the target property and the value to be set.

This value can be static text or dynamically generated based on other strategy components.

Understanding Aggregation Components: Aggregation components in Pega decision strategies are used to perform calculations based on a list of actions. This can include summing values, calculating averages, or other statistical measures.

Use Case of Aggregation Components:

Set a Text Value: This is typically handled by Set Property or Data Transform components.

Filter Actions: Filters are used for filtering actions based on priority and relevance.

Choose Between Actions: This is typically handled by a Decision or Filter component.

Aggregation is specifically used to perform calculations based on a group of actions. It can be used to sum the total value of actions, calculate the average propensity, etc.

Implementation in Decision Strategy:

Add Aggregation Component: In the decision strategy, add an Aggregation component.

Define Calculation: Specify the type of calculation (sum, average, count, etc.) and the actions to be included in the calculation.

The Pega Customer Decision Hub User Guide mentions the use of aggregation components to perform calculations on action lists, confirming their primary function is for making calculations based on a list of actions (Reference: Pega-Customer-Decision-Hub-User-Guide-85.pdf, Chapter on "Using aggregation components in decision strategies").

Conclusion: In a decision strategy, aggregation components are used to make calculations based upon a list of actions, enabling the strategy to derive meaningful metrics from groups of actions.

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 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.

 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​​.

Scorecard Component - This component in Pega CDH is designed to evaluate attributes and produce a score based on those attributes. It is commonly used for calculating credit scores, risk scores, etc.

Steps to Use Scorecard:

In the decision strategy, add a Scorecard component.

Configure the scorecard to include relevant customer attributes (e.g., payment history, credit utilization).

Set the rules and weights for each attribute to calculate the final credit score.

Use the score in further decision logic (e.g., filtering out customers with a score below 500).

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.

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.

Task: Create a new action

Portal: Pega Customer Decision Hub

Task: Modify a decision table

Portal: Operations Manager

Task: Create a decision strategy

Portal: Pega Customer Decision Hub

Task: Edit an engagement policy for an action

Portal: Pega Customer Decision Hub

Task: Edit a group level engagement policy

Portal: Pega Customer Decision Hub

Task: Edit an email treatment for an action

Portal: Pega Customer Decision Hub

Create a new action:

Pega Customer Decision Hub: Creating new actions is typically performed within the Pega Customer Decision Hub where users can manage and configure actions that will be part of the decisioning strategies.

Next-Best-Action in Pega Customer Decision Hub is primarily applied to customer engagement and decisioning scenarios:

Purpose of Next-Best-Action:

Step 1: Next-Best-Action aims to provide personalized recommendations and decisions that are most relevant to the customer at any given moment.

Step 2: It uses predictive and adaptive analytics to determine the most appropriate actions based on customer data and business rules.

Application in Loan Decisions:

Step 1: In the context of determining if a borrower gets a loan, Next-Best-Action evaluates various factors such as creditworthiness, loan history, and current financial status.

Step 2: It leverages AI models to assess the risk and likelihood of repayment, ensuring that loan offers are extended to eligible and suitable customers.

Implementation Steps:

Step 1: Set up decision strategies in Pega Customer Decision Hub to include criteria for loan eligibility, such as credit scores and income levels.

Step 2: Configure predictive models to analyze customer data and predict loan approval probabilities.

Step 3: Use engagement policies to ensure that the loan offers are aligned with business objectives and regulatory requirements.

Benefits:

Applying Next-Best-Action to loan decisions helps in making data-driven and objective decisions, reducing the risk of defaults and improving customer satisfaction by offering relevant financial products.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Understanding Next-Best-Action Designer basics" section.

Pega documentation on "Decision management and AI in financial services".

 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.

In the always-on outbound customer engagement paradigm, leveraging AI and continuous decisioning is crucial:

AI-Driven Decisioning:

Step 1: Configure the primary schedule to run daily, allowing the AI engine to continuously evaluate and select the best action for each customer.

Step 2: The AI considers engagement policies, customer behavior, and contextual data to determine the most relevant action.

Benefits of Always-On Engagement:

Continuous evaluation of customer interactions ensures that the actions presented are timely and contextually relevant.

It allows for dynamic adjustment of offers based on real-time data and customer responses.

Implementation Steps:

Step 1: In Next-Best-Action Designer, set up a primary schedule to run daily.

Step 2: Define engagement policies and configure AI models to select actions based on customer interactions and preferences.

Step 3: Allow the AI to decide whether to resend the original offer or present a new offer based on the customer’s response or lack thereof.

Example:

If the customer ignores the first email, the AI might decide to send a reminder email or a different offer based on updated engagement data and policy rules.

References:

Pega-Customer-Decision-Hub-User-Guide-85.pdf: "Configuring a recurring schedule for Next-Best-Action" section.

Pega documentation on "Using AI in decisioning".

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.

 Action Availability Configuration: To ensure that a specific action is not selected for any customers, you need to set its availability to "Never." This setting ensures that the action will not be available for selection in any campaign or strategy, effectively preventing its use.

 Implementation: Navigate to the action configuration settings and set the availability to "Never." This will ensure that the action is not picked up by the decision strategy or any outbound run.

To test the visualization and rendering of the email content, including the replacement of placeholders with customer information, you use a seed list from the Test Message tab. A seed list allows you to send test emails to a predefined group of recipients, ensuring that the dynamic content is correctly rendered and personalized before the actual campaign is launched.

Business Requirement: The need is to implement risk segmentation rules based on age and credit score.

Decision Table Integration: The decision table can be added to the decision strategy where it will evaluate the credit score and age, and then assign the appropriate risk segment.

Parameter Passing: The decision table within the strategy will use the credit score passed to it as a parameter to determine the segmentation.

References: Pega's methodology for incorporating decision tables into strategies for evaluating and segmenting customers based on specific criteria is well documented in the user guides .

To limit the number of emails sent to customers irrespective of past outcomes with a particular offer and customer, customer contact limits should be implemented. Customer contact limits help control the frequency of communications with customers across different channels, ensuring that they do not receive an excessive number of messages.

 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.

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".

 Translating Traditional Segments: When moving away from traditional marketing to always-on outbound capabilities in Pega Customer Decision Hub, translating traditional segments is essential to identify the target audience correctly.

 Segmentation Configuration: In Pega, segmentation rules are configured to define and group customers based on various attributes. This allows the system to identify the right target audience for outbound campaigns.

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.

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.