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When you increase the performance threshold setting of an adaptive model rule, the number of active predictors may decrease. The performance threshold is the minimum performance that a predictor must have to be included in the model. If you increase this value, some predictors may not meet the criteria and be excluded from the model. References:

 Through analysis of customer lifecycles, Next-Best-Action anticipates retention issues and takes proactive actions to prevent customer churn. It uses predictive analytics to identify customers who are at risk of leaving and offers them incentives or solutions to retain them. References:

Pega Customer Decision Hub™ uses the PCV*L arbitration formula to select the next best action for each customer. The purpose of the formula is to balance customer needs with business objectives.

Predictions combine predictive analytics and best practices in data science. Two best practices included in Pega Customer Decision Hub™ predictions are using a control group and selecting the model with the highest performance.

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:

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:

To assess the general health of the adaptive models in Prediction Studio, you can inspect the performance of the models and the number of decisions. The performance of the models shows how well they predict customer behavior over time. The number of decisions shows how much data is available for each model to learn from. References:

When building a predictive model, a valid predictor data type is Boolean, which can have only two values: true or false. Other valid predictor data types are numeric, date, and symbolic (categorical). References:

The Switch component allows you to use a third-party Credit Risk Model 80% of the time and a Pega Credit Risk Model 20%.

 The value that is output by an adaptive model is behavior, which indicates the likelihood that the customer will accept or respond to an offer. Behavior is also known as propensity or probability in decision strategies. References:

The lift that the prediction generates Reference:

Using a control group is a best practice in data science that supports the business metric of the lift that the prediction generates.

If a strategy designer has created 10 actions in the Sales/Credit Cards group and 10 actions in the Sales/Mortgages group and would like to import all 10 actions from the Credit Cards group and only two actions from the Mortgage group into one decision strategy, the minimum number of Proposition Data components he needs to use in his strategy is three.

Predictive models need historical data to be created. Predictive models are statistical models that use historical data to learn patterns and trends and make predictions for future outcomes. Predictive models can be built with Pega machine learning or imported from third-party tools such as PMML or H2O. References:

One of the types of analysis that occurs during natural language processing is Semantic analysis.

In Prediction Studio, the key metrics of adaptive models are visualized in a bubble chart. The three key metrics displayed in this chart are number of responses, propensity of the model, and performance of the model.

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:

The expected performance of a binary model must be set before the model can be deployed.

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:

Text prediction is a type of natural language processing that uses AI and statistics to predict the next word in a sentence based on the preceding words1. Text analysis is a process of extracting information from text data using various methods and tools. One of the types of text analysis that a text prediction performs is entity extraction23, which is the identification and classification of named entities such as people, places, organizations, dates, etc. in a text.

To confirm the continuing accuracy of your adaptive models, two tasks that are part of a regular inspection are check the performance and success rate of the models and check the performance of individual predictors.

When building a predictive model, you compare the performance of predictive models at the Model Comparison stage. This stage allows you to select the best model based on various metrics, such as accuracy, lift, or area under curve (AUC). References: