You are a company with a new and unique product, and you want to offer it to the right customer.Give the scenario, which rule type should you use?
Answer(s): A
You are a company with a new and unique product, and you want to offer it to the right customer. Given the scenario, you should use an adaptive model rule type. An adaptive model rule type allows you to define the predictors and the outcome of the model and associate it with an action. An adaptive model learns from customer responses in real time and predicts the propensity of each customer to accept the action. An adaptive model is suitable for new products or markets where there is no historical data available.
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#rule-/rule- decision-/rule-decision-adaptivemodel/main.htm
An online store is interested in increasing its revenues from cross-selling and wants to predict the acceptance rate of the offers presented on their website. A customer's propensity to accept an offer increases when_________.
Answer(s): C
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
The Predictive Model Markup Language (PMML) allows for predictive models to
Answer(s): B
The Predictive Model Markup Language (PMML) allows for predictive models to be easily shared between applications. PMML is a standard XML format that describes the input parameters, output score, and mathematical formulas of predictive models. PMML enables interoperability between different tools and platforms that support PMML, such as Pega Customer Decision Hub.
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#data-/data- predictivemodel-/data-predictivemodel-pmml/main.htm
How does a prediction help in proactive retention?
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.
https://community.pega.com/sites/default/files/help_v82/procomhelpmain.htm#decisioning- /decisioning-strategies-/decisioning-strategies-proactive-retention/main.htm
In a predictive model rule, the predictors must be mapped to
Customer properties are used to map predictors to customer data that is available in the system. They can be either scalar or aggregate properties.
https://academy.pega.com/module/creating-and-understanding-decision-strategies- archived/topic/mapping-predictors-customer
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