Pegasystems PEGACPDS88V1 - Certified Pega Data Scientist 8.8

To predict if a customer is likely to churn, you use a model of type decision tree. A decision tree is a type of predictive model that uses a set of rules to classify customers into different categories based on their attributes and behavior. A decision tree can predict a binary outcome (such as churn or not churn) or a multi-class outcome (such as low risk, medium risk, or high risk). References: https://academy.pega.com/module/predictive-analytics/topic/using-decision-tree-models

A prediction helps in proactive retention by predicting the customer’s churn risk. A prediction is an estimate of the likelihood of a future outcome based on historical data and statistical models. A prediction can help identify customers who are at risk of leaving and target them with appropriate actions to retain them. References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#decisioning-/decisioning-strategies-/decisioning-strategies-proactive-retention/main.htm

A solid arrow from a “Set Property” component to a “Filter” component means that a property from the “Set Property” component is referenced by the “Filter” component. For example, you can use a “Set Property” component to calculate a customer’s age and then use a “Filter” component to remove actions that are not suitable for that age group. References: https://academy.pega.com/module/creating-and-understanding-decision-strategies-archived/topic/setting-properties

Customer Decision Hub Reference:

To optimize the click-through rate of a web banner, you need to create a Customer Decision Hub prediction.

When developing a predictive model, the outcome value of a continuous model type can represent the purchase value of an offer.

This is because shadow mode allows you to test a new model in parallel with an existing model without affecting the decision outcomes. You can compare the performance of both models and decide whether to replace or keep the existing model. 

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

Evidence is the number of customers who exhibited statistically similar behavior. It indicates how much data the model has collected for a given predictor profile. The higher the evidence, the more reliable the model is. References: https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule-decision-/rule-decision-adaptivemodel/main.htm

The rank property is automatically recomputed for each decision component. It indicates the order in which the actions are presented to the customer, based on their priority and propensity. References: https://academy.pega.com/module/creating-and-understanding-decision-strategies-archived/topic/ranking-actions

In an adaptive model, you can use Symbolic predictors.