Pegasystems PEGAPCDC87V1 Dumps

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.

 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).

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.

Traditionally, segments were used to identify the target audience for a campaign. In the always-on approach, segments translate into engagement policies and AI. Engagement policies define when specific actions or groups of actions are appropriate for customers, while AI-driven decisions help determine the most relevant next best actions for individual customers based on predictive and adaptive analytics. This approach ensures that each customer interaction is highly personalized and contextually relevant.

 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.

Updating Scorecard in Decision Strategy: When a scorecard rule is updated to include a new property such as customer income, it is essential to reflect this change in the decision strategy. The scorecard properties must be correctly mapped to ensure the new calculations are utilized.

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.

 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".

 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​​​​.

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.

To define a suppression rule in Pega for the scenario where a group of mortgage offers should be suppressed for 15 days if a customer ignores three offers from the mortgage group, follow these steps:

Define the Suppression Rule:

Create a contact policy in Pega Customer Decision Hub to suppress the group of mortgage actions.

Specify the Condition:

Set the condition to suppress the group of actions if there are 3 rejects (ignored offers) for any channel. This means the suppression applies regardless of whether the offers were displayed on the web, mobile, email, etc.

Set the Suppression Duration:

Configure the suppression period to be 15 days. This ensures that the group of actions will not be shown to the customer for 15 days after they have ignored three offers.

Apply the Contact Policy:

Ensure the contact policy is associated with the relevant group of actions (mortgage offers) and that it is enforced by the strategy framework.

Test and Validate:

Test the suppression rule to verify it works as expected and that the offers are suppressed correctly after three rejects.

References:

Pega Customer Decision Hub User Guide 8.7: Adding more tracking time periods for contact policies, Creating a contact policy, Contact policy library​​.

 Finalization of Action Details:

Finalization ensures that all data from a specific run is written to a file or database.

This step is crucial for preparing the data to be shared with third parties.

 Single Run Data:

Finalizing at the end of each run means the file will only contain data from that particular run.

This approach ensures accuracy and relevance of the data being shared.

 Verification:

The documentation confirms that finalization at the end of each run results in the data being specific to that run.

To select the best offer from both groups based on customer behavior, you should ensure that the propensity is enabled in the Arbitration tab. Propensity scores predict the likelihood of a customer accepting a particular offer, and by enabling propensity, you can prioritize offers that have a higher likelihood of acceptance, thus aligning offers with customer behavior.

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.

Customer Properties - These are attributes related to the customer, such as demographics, behavior, and engagement history.

Expression Syntax:

When defining expressions in decision strategies, customer properties must be prefixed with "Customer."

For example, to use the customer's age, you write Customer.Age.

Steps:

In the decision strategy, select the component where you need to use the customer property.

Write the expression using the "Customer" prefix.

To present customer-centric mobile internet offers on Facebook, MyCo should place a paid ad. This action ensures that the offers are targeted and displayed to the relevant audience on the social media platform, leveraging Pega Customer Decision Hub’s integration with paid media channels like Facebook Ads.

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".

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

To ensure that customers do not receive more than four promotional emails per quarter, you can use customer contact limits. This setting allows you to define the maximum number of contacts a customer can receive over a specific period, regardless of their responses to previous actions. By setting this limit, you can prevent overcommunication and enhance customer experience.

 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.

To prioritize mortgages over credit cards for the fourth quarter, you should configure the business levers in Pega Customer Decision Hub. Business levers allow you to adjust the weight or priority of different actions based on business goals. By increasing the weight for mortgage offers, you can ensure that these offers are prioritized over credit card offers, even if a credit card may create more value.

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.

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.

Understand the Scenario: A Customer Service Representative (CSR) presents an offer to a customer, and the customer rejects the offer.

Next Step in Pega CDH:

In Pega Customer Decision Hub, when an offer is rejected, the system typically reevaluates the Next-Best-Action. This means it reassesses the available actions to determine the most appropriate next step for the customer based on current context and customer data.

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".

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.

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

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

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

To determine which of the new credit card offers (Diamond, Platinum, and Gold) will be the best performing action, the Action Distribution Report is used. This report helps in running a distribution test to analyze how the different offers are being distributed among the customer base, providing insights into which offer might perform the best based on initial distribution and eligibility criteria.

 Understanding Refresh the Audience Option:

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

 Functionality:

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

 Purpose:

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

 Verification from Pega Documentation:

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

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.

 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.

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.

To ensure that a customer does not see offers on their account page if they have already received three home loan offers in the last two weeks, you need to define customer contact limits. These limits allow you to control the number of times a customer can be contacted with specific types of offers within a given timeframe, thus preventing overexposure and enhancing customer satisfaction.

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".

Understanding the Scenario:

There are two segments: Segment A and Segment B.

Segment A references Segment B.

The goal is to ensure that Segment B is automatically refreshed when Segment A is refreshed.

Segment Refresh Settings:

Refreshable Segment: This setting determines if a segment can be refreshed automatically.

Refresh Child Segments: This setting ensures that when a parent segment is refreshed, all its child segments are also refreshed.

Configuration Requirement:

To achieve the automatic refresh of Segment B when Segment A is refreshed, you need to enable both the Refreshable option for Segment B and the Refresh Child Segments option for Segment A.

Implementation:

Segment A: Enable the Refresh Child Segments option.

Segment B: Enable the Refreshable option.

Correct Configuration:

The correct configuration is to set Segment A with Refresh Child Segments enabled and Segment B with Refreshable enabled.

 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.

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.

 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.

 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.

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.

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

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

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

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

To 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.

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.

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​​.

 Addressing Test Failures: When persona tests fail after adding new engagement policy conditions, the appropriate step is to analyze the test case failures, update the tests as necessary, and re-execute them until all tests pass. This ensures that the changes made are correctly reflected in the tests and the system functions as expected.

 Implementation: Review the failing tests to understand why they failed, adjust the conditions or the expected results, and run the tests again to validate the changes.

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".

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

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

Process:

Evaluate the CLV score condition.

Evaluate the propensity to leave condition.

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

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.

Decision Table Processing: The decision table is configured to evaluate the customer's credit score and average balance to determine the risk segmentation.

Conditions Evaluation:

For a credit score of 240 and an average balance of 35,000:

The first condition checks if the credit score is >= 400 and < 600, and average balance is >= 30,000, which does not match.

The second condition checks if the credit score is >= 200 and < 400, and average balance is >= 20,000, which matches both the credit score (240) and the average balance (35,000).

Therefore, the correct result for these conditions is "Fair".

References: This is derived from typical Pega Decision Table configuration and logical evaluation as described in Pega documentation on using decision tables for such scenarios .

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.

 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​​.