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

Initial Configuration: U+ Bank has implemented Pega Customer Decision Hub for the contact center and now wants to extend it to the web channel.

Understand Channel Configuration: Pega CDH requires the configuration of channels in the Next-Best-Action Designer to ensure that treatments are correctly recommended.

Modify the Strategy Framework:

Next-Best-Action Framework: This strategy framework is used to determine the best actions for customers across various channels. It needs to be modified to include configurations specific to the web channel.

Steps to Modify:

Access Next-Best-Action Designer: Navigate to the Next-Best-Action Designer.

Channels Tab: Configure the new web channel in the Channels tab.

Modify the Strategy: Update the Next-Best-Action Framework strategy to incorporate the new web channel. This involves ensuring the new actions and treatments are included and prioritized correctly for the web channel.

According to the Pega Customer Decision Hub User Guide, modifying the framework strategy to cater to new channels is necessary for extending customer engagement (Reference: Pega-Customer-Decision-Hub-User-Guide-85.pdf, Chapter on "Understanding Next-Best-Action Designer channels").

Testing and Validation:

Test the new configurations to ensure treatments for the web channel are correctly recommended in the next-best-action suggestions.

Conclusion: To configure the new treatments for the web channel in the next-best-action recommendations, the Next-Best-Action Framework strategy must be modified to cater to the new web channel.

 Understanding the Requirement:

The bank wants to suppress a group of mortgage offers if a customer ignores (does not click on) three offers within 15 days.

The suppression rule needs to be defined for the web channel.

 Configuring Suppression Rule:

The suppression rule must track the number of impressions (views) and check if there are no clicks.

It needs to be configured to act after three such impressions over a specific time period (15 days).

 Steps to Configure:

Define the group of mortgage offers as a single entity.

Set up the suppression criteria to trigger after three impressions without clicks for the web channel.

Configure the suppression period to be 15 days.

 Verification from Pega Documentation:

According to Pega's guidelines on adding tracking periods and suppression rules, this configuration accurately reflects the bank's requirements.

 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​​.

To ensure that Silver credit cards are not offered to customers under 27 years of age and that Platinum cards are offered only to customers who had a positive balance in the last year, you need to configure "Engagement policies" in the Next-Best-Action Designer. Engagement policies allow you to set criteria such as eligibility, applicability, and suitability for actions. For this scenario, you would set the eligibility criteria to ensure the age and balance conditions are met before the offers are presented.

 Understanding Refreshable Segment Option:

This option ensures that the segment is updated automatically whenever it is referenced.

 Functionality:

When enabled, all segments that reference the refreshable segment are automatically updated, ensuring that the latest data is used.

 Purpose:

This helps maintain data accuracy and relevance across all segments and ensures that the decisioning process is based on current data.

 Verification from Pega Documentation:

Pega documentation on using and configuring refreshable segments in outbound schedules.

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.

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".

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".

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.

 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 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.

To present offers based on the responses of a test group that already received the offer, you should configure the propensity factor in Pega Customer Decision Hub. Propensity models predict the likelihood of a customer accepting an offer based on historical data and responses from similar customers. By leveraging propensity, you can ensure that offers are presented to customers who are most likely to respond positively, based on the behavior of the test group.

In Pega Customer Decision Hub, the Next-Best-Action is re-evaluated in a call-center context when:

The service representative completes a task: Completing a task can change the context or status of the interaction, triggering a re-evaluation of the next best action to ensure the recommendations are still relevant.

A customer responds to an offer: Customer responses provide new information that can influence subsequent recommendations. A positive or negative response to an offer can change the customer's engagement status and preferences.

A customer changes the call context: Changes in the context of the call, such as switching topics or expressing new concerns, can prompt a re-evaluation to provide the most appropriate next actions based on the updated context.

 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.

To present offers from both groups and arbitrate across the two groups to select the best offer based on customer behavior, you need to map a real-time container to the Top-level or Issue-level in the Next-Best-Action Designer. This mapping allows the system to evaluate and arbitrate offers from different groups based on the defined criteria and customer behavior, ensuring the most relevant offers are presented.

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.

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.

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 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.

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.

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.

Goal of Next-Best-Action: The primary goal of Next-Best-Action is to balance customer needs with business objectives. This approach ensures that actions taken are beneficial for both the customer and the business, leading to better engagement and outcomes.

The Action Distribution Report is designed to test how new offers will be distributed by running a distribution test that only considers eligibility without arbitration. This report helps in understanding how different actions or offers are being allocated to customers based on the eligibility criteria defined in the engagement policies, without taking into account the arbitration logic.

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".

 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.

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.

 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 use a customer property in an expression within a decision strategy in Pega, follow these steps:

Identify the Customer Property:

Determine the specific customer property you want to use in your strategy.

Prefix with 'Customer':

In Pega, customer properties are accessed by prefixing them with the keyword Customer. This ensures that the property is correctly referenced from the customer data.

Use the Property in Expression:

When defining an expression in a decision strategy, include the Customer prefix. For example, if the property is Age, use Customer.Age.

Verify Property Definition:

Ensure that the property is correctly defined in the data model and accessible within the strategy context.

References:

Pega Customer Decision Hub User Guide 8.6: Understanding the Context Dictionary, Configuring the Context Dictionary​​.

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.

Contact Policy Conditions - U+ Bank has added all existing contact policy conditions, ensuring that offers comply with set business rules.

Unit Tests with Persona Testing - Before going live, it is essential to validate the implementation through persona-based unit tests. This helps verify that the strategy gives the expected results for different customer personas.

Process:

Design test personas that represent various customer segments.

Run tests to ensure that each persona receives appropriate offers.

Validate the expected outcomes, ensuring no regressions or policy breaches.

When MyCo updates their scorecard to include a new property, such as customer annual income, in the calculation of the postpaid credit limit, the decision strategy itself does not need to be changed to reflect the updated scorecard. This is because the scorecard rule already encapsulates the logic for calculating the credit limit, including any new properties added.

Scorecard Rule Update:

The scorecard rule in Pega is responsible for computing scores based on various properties.

If the scorecard rule is updated to include a new property, the decision strategy referencing this scorecard automatically uses the updated scorecard without requiring any additional changes to the strategy itself.

Decision Strategy:

The decision strategy references the scorecard rule, and as long as the reference remains unchanged, any updates to the scorecard are automatically applied.

There is no need to remap properties, add proposition filters, or set new properties within the strategy.

Implementation:

Ensure that the scorecard rule is correctly updated and tested to include the new property.

The decision strategy continues to use the updated scorecard without any modifications.

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 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.

 Introduction to Prioritize Component:

The Prioritize component in Pega Decisioning is used to order actions based on specific criteria.

This component evaluates the importance or priority of different actions to determine which should be executed first.

 Understanding Propensity:

Propensity is a prediction of the likelihood that a customer will respond positively to a particular action.

It is a key factor in decision-making processes as it reflects the customer's predicted behavior based on past interactions and other data.

 Role of Propensity in Prioritize Component:

The primary function of the Prioritize component is to rank actions.

This ranking is typically based on the propensity value, which is calculated using predictive models.

The action with the highest propensity is considered the top action because it has the highest predicted likelihood of a positive customer response.

 Verification from Pega Documentation:

According to Pega's guidelines on creating and understanding decision strategies, propensity is a standard and recommended metric for prioritizing actions within the Prioritize component.

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.

Score Mapping Tab - This tab in the scorecard rule is used to map the raw score to a property that can be used in decision strategies.

Steps to Map a New Property:

Open the scorecard rule in Dev Studio.

Navigate to the Score Mapping tab.

Add a new mapping for the raw score to a property (e.g., pyRawScore).

Save the scorecard rule.

Using the Mapped Property:

In the decision strategy, reference the mapped property to utilize the raw score value.

Apply this property in conditions, filters, or other decision components as needed.

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.

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".

 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.

 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.

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.

Next-Best-Action Communication: Effective Next-Best-Action communication is both contextual and timely.

Contextual: Ensures that the actions or offers presented are relevant to the customer's current situation and preferences.

Timely: Ensures that the actions or offers are presented at the most appropriate time for maximum impact and engagement.

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.

Business Issues and Groups in Pega CDH - Business issues (like Sales, Service, Retention) categorize customer engagement strategies. Each issue can have multiple groups representing specific goals (e.g., credit cards, loans under Sales).

Retention Issue - Since the objective is to retain customers who are at high risk of leaving, modifying the Retention issue aligns the strategy with the goal of reducing churn.

Steps to Modify Retention Issue:

Navigate to the Next-Best-Action Designer.

Select the Retention issue.

Update or create new applicability settings to identify high churn risk customers.

Configure actions (proactive retention offers) to be presented to these customers.

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 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.

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 .