Mission
AI for 1:1 Customer Engagement
10 Modules
13 Défis
10 heures 20 mins
Familiarize yourself with the one-to-one customer engagement paradigm and discover how Pega omni-channel AI delivers the right action during every customer interaction. Learn how to optimize the adaptive models that drive Pega Customer Decision Hub™ predictions. Learn how to use predictive models to improve the decisions that Customer Decision Hub makes and how to update predictions with the MLOps process.
Disponible dans la mission suivante :
Customer Decision Hub predictions
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Module
Customer Decision Hub predictions
2 Rubriques
35 mins
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Prediction Studio is the dedicated workspace for data scientists to control the life cycles of predictions and the predictive models that drive them...
Customer Decision Hub overview
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Module
Customer Decision Hub overview
4 Rubriques
40 mins
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Familiarize yourself with the one-to-one customer engagement paradigm and discover how Pega's omni-channel AI delivers the right action during every...
Creating and understanding decision strategies
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Module
Creating and understanding decision strategies
4 Rubriques
1h 10 mins
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Next-Best-Action Designer provides a guided and intuitive UI to bootstrap your application development with proven best practices that generate the...
Creating a decision strategy
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Défi
Creating a decision strategy
3 Tâches
25 mins
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As a Decisioning Architect, you are tasked with designing a basic decision strategy that outputs a Label action with the lowest printing cost. A set...
Testing a decision strategy
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Défi
Testing a decision strategy
3 Tâches
25 mins
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A decision strategy that produces the next-best-label action is set up in the application. The purpose of the decision strategy is to select the label...
Creating predictions
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Module
Creating predictions
2 Rubriques
20 mins
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Predicting customer churn is one among many business use cases involving predictive models. Pega Customer Decision Hub™ uses predictions that use...
Creating a churn prediction
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Défi
Creating a churn prediction
2 Tâches
15 mins
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U+ Bank uses Pega Customer Decision Hub™ to personalize the credit card offer a customer is presented on their website. If a customer is eligible for...
Leveraging a churn prediction
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Défi
Leveraging a churn prediction
2 Tâches
25 mins
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U+ Bank uses AI to determine which credit card offers to show to customers on its website. The bank wants to reduce the number of clients that leave...
Creating predictive models
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Module
Creating predictive models
5 Rubriques
50 mins
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In Prediction Studio, three option to leverage historical data are available: creating models using Pega machine learning, importing models created in...
Building models with Pega machine learning
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Défi
Building models with Pega machine learning
6 Tâches
15 mins
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U+ Bank uses AI to determine which credit card offer to show a customer on the bank's website. To reduce the number of clients that leave the bank...
Importing predictive models
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Défi
Importing predictive models
1 Tâche
10 mins
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U+ Bank has recently implemented Pega Decision Management but already uses predictive models that an external bureau created. You are asked to make an...
MLOps
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Module
MLOps
2 Rubriques
25 mins
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Learn how to use Machine Learning Operations (MLOps) to replace the predictive model that drives a prediction with a new model. You can import a...
Replacing a predictive model
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Défi
Replacing a predictive model
3 Tâches
10 mins
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U+ Bank uses Pega Customer Decision Hub™ to personalize the credit card offers that a customer receives on its website. The bank makes a proactive...
Adaptive analytics overview
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Module
Adaptive analytics overview
4 Rubriques
35 mins
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Pega Adaptive Decision Manager (ADM) is a component that allows you to build self-learning adaptive models that continuously improve predictions. ADM...
Optimizing AI in the NBA Framework
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Module
Optimizing AI in the NBA Framework
2 Rubriques
30 mins
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Learn how to improve the predictive power of your adaptive models by configuring additional potential predictors. Input fields that are not directly...
Adding predictors to an adaptive model
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Défi
Adding predictors to an adaptive model
3 Tâches
10 mins
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U+ Bank is implementing cross-selling of their credit cards on the web by using Pega Customer Decision Hub™. The implementation team has set up the...
Using behavioral data as predictors
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Défi
Using behavioral data as predictors
3 Tâches
10 mins
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U+ Bank is implementing cross-sell of their credit cards on the web by using Pega Customer Decision Hub™. All available customer data, including...
Using model scores as predictors
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Défi
Using model scores as predictors
4 Tâches
10 mins
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U+ Bank is implementing cross-selling of its credit cards on the web by using Pega Customer Decision Hub™. To further enhance the predictive power of...
Monitoring adaptive models
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Module
Monitoring adaptive models
5 Rubriques
1h
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It is a regular data scientist task to inspect the health of the adaptive models and share the findings with the business. The predictive performance...
Monitoring adaptive models
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Défi
Monitoring adaptive models
3 Tâches
10 mins
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The models for the U+Bank implementation of cross-sell on the web of their credit cards have been learning for some time. Your task in this challenge...
Exporting historical data
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Défi
Exporting historical data
5 Tâches
15 mins
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U+ Bank has implemented Pega Customer Decision Hub™ to display a personalized credit card offer to eligible customers on their website. As a data...
The impact of machine learning
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Module
The impact of machine learning
1 Rubrique
15 mins
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The boost in the success rate, also known as lift, that artificial intelligence (AI) achieves is an important business metric. To report on this...
Monitoring predictions
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Défi
Monitoring predictions
7 Tâches
15 mins
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U+ Bank has recently used Pega Customer Decision Hub™ to display a personalized credit card offer to eligible customers on their website. The bank now...
