When an adaptive model has not collected any evidence, its performance is 0.5, which means that it

has no predictive power and is equivalent to a random guess. As more evidence is collected, the

performance can increase or decrease depending on how well the model predicts customer behavior.

Reference:

https://academy.pega.com/module/predicting-customer-behavior-using-real-time-dataarchived/topic/adaptive-models-overview

Customer Decision Hub Reference:

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

Hub prediction.

Pega AI is primarily based on self-learning models Reference:

Pega AI is primarily based on self-learning models.

The Proposition Distribution report is used to compare the results of two simulations.

The most important aspects taken into consideration when determining the Next-Best-Action are

business objectives and customer needs. Business objectives reflect the goals and priorities of the

organization, such as increasing revenue, reducing costs, or managing risk. Customer needs reflect

the p

and expectations of the customers, such as their interests, intents, or life events.

Reference:

https://academy.pega.com/module/one-one-customer-engagement/topic/next-bestaction-designer

Pega adaptive models learn about customer behavior in real time by analyzing the responses to each offer and updating their predictions accordingly. They do not require historical data, human effort, or inbound channels to function. Reference: https://academy.pega.com/module/predicting-customerbehavior-using-real-time-data-archived/topic/adaptive-models-overview

The outcome of a scoring model indicates the likely response to an offer that is presented to a

customer. For example, a scoring model can predict if a customer will accept, reject, or defer an offer

for

a

credit

card

upgrade.

Reference:

https://academy.pega.com/module/predictiveanalytics/topic/using-scoring-models

Up to 100 fields to limit the impact on model speed Reference:

When configuring an adaptive model, it is recommended to select up to 100 potential predictors to

limit the impact on model speed.

Adaptive model predictors are selected from the customer profile, which contains information about

the customer’s attributes and behavior. Predictors can be either scalar or aggregate properties that

capture

customer

context,

such

as

channel,

location,

time,

etc.

Reference:

https://academy.pega.com/module/predicting-customer-behavior-using-real-time-dataarchived/topic/configuring-adaptive-models

Dates with absolute time/date values, such as birthdays and Numeric data such as customer age and

income Reference:

Dates with absolute time/date values, such as birthdays and Numeric data such as customer age and

income require no preprocessing before being used as potential predictors in adaptive analytics.