Reference module: Testing engagement policy conditions using audience simulation
U+ Bank, a retail bank, recently implemented a project in which mortgage offers are presented to qualified customers when the customers log in to the web self-service portal. As one of the offers is not performing well, the business wants to understand how many customers qualify for the offer. As a Decisioning Consultant, which simulation do you run to check how many customers qualify for an action?
To check how many customers qualify for a mortgage offer, you should run an audience simulation. Audience simulations help in testing engagement policies and understanding the eligibility of different customer segments for specific actions, allowing the business to gauge the reach and effectiveness of their offers.
Audience Simulation: Testing engagement policies and qualifying customers for actions (Page 137-139)
Using audience simulations to analyze offer performance (Page 140-141)
U+ Bank, a retail bank, wants to send promotional emails related to credit card offers to their qualified customers. You have already created an action flow template with the desired flow pattern and reused it for all the credit card actions.
What must you do to ensure that this action is not selected for any customers?
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.
A bank has been running traditional marketing campaigns for many years. One such campaign sends an offer email to qualified customers on day one. On day five, the bank presents a similar offer if the first email is ignored.
If you re-implement this requirement by using the always-on outbound customer engagement paradigm, how do you approach this scenario?
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.
Pega-Customer-Decision-Hub-User-Guide-85.pdf: 'Configuring a recurring schedule for Next-Best-Action' section.
Pega documentation on 'Using AI in decisioning'.
Reference module: Avoiding overexposure of actions in outbound.
U+ Bank's marketing department currently promotes various credit card offers by sending emails to qualified customers. Now the bank wants to limit the number of emails sent to their customers irrespective of past outcomes with a particular offer and customer. Which of the following options allows you to implement this business requirement?
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.
Setting constraints and customer contact policy limits (Page 34-35)
Creating and managing contact policies (Page 66-67)
As a Decisioning consultant, you are tasked with running an audience simulation to test the engagement policy conditions. Which statement is true when the simulation scope is: Audience simulation with engagement policy and arbitration?
Understanding Audience Simulation with Engagement Policy and Arbitration:
When the simulation scope includes both engagement policy and arbitration, it evaluates which actions are top actions for customers.
Arbitration involves prioritizing actions based on factors like business value, customer propensity, and other criteria.
Interpreting Simulation Results:
The results show the number of customers who receive each action as their top action after considering engagement policies and arbitration.
Detailed Explanation:
Engagement policies filter out actions that do not meet eligibility, applicability, or suitability criteria.
Arbitration ranks the remaining actions, and the simulation identifies the top-ranked action for each customer.
Verification from Pega Documentation:
Pega documentation states that audience simulations with engagement policy and arbitration scope display the top actions received by customers.
