Pegasystems PEGACPDS88V1 - Certified Pega Data Scientist 8.8

 The Monte Carlo method enables the company to generate data that simulates customer behavior and can be used as input for adaptive decisioning. The generated data is based on predefined probabilities and distributions that reflect realistic scenarios. References: https://academy.pega.com/module/demonstrating-adaptive-learning-archived/topic/creating-monte-carlo-data-set

As a data scientist, you create a prediction that calculates the probability that a client is likely to cancel a subscription. The type of prediction you create is Customer Decision Hub.

Evidence is the number of customers who exhibited statistically similar behavior to the current customer and responded to the modeled offer. It indicates how reliable the propensity score is based on the available data. References: https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-archived/topic/adaptive-models-overview

This is because a customer’s propensity to accept an offer depends on their past behavior and preferences. If a customer has accepted similar offers in the past, they are more likely to accept a new offer that matches their interests

https://academy.pega.com/sites/default/files/media/documents/2020-12/Mission20301-2-EN-StudentGuide.pdf

A scoring model allows you to differentiate between Good, Better, and Best outcomes for a given proposition or action. A scoring model assigns a numerical value to each outcome based on its desirability or profitability for the business. References: https://academy.pega.com/module/predictive-analytics/topic/using-scoring-models

To associate an adaptive model with each action in a decision strategy created from scratch, you must define the adaptive model instances in the Adaptive Decision Manager.

The adaptive model output that is automatically mapped to a strategy property is propensity, which indicates the likelihood that the customer will accept or respond to an offer. Propensity is also known as behavior or probability in decision strategies. References: https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-archived/topic/using-adaptive-models-decision

In Customer Decision Hub™, the factor in the arbitration formula that is calculated by using AI is Propensity.

Two results of an adaptive model are propensity and performance. Propensity is the probability that the customer will accept or respond to an offer. Performance is a measure of how well the adaptive model predicts customer behavior over time. References: https://academy.pega.com/module/predicting-customer-behavior-using-real-time-data-archived/topic/adaptive-models-overview