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Mission

AI for 1:1 Customer Engagement

10 Modules

20 Challenges

13 hrs 25 mins

Visible to: All users
Beginner Pega Customer Decision Hub 8.8 English

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.

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Data Scientist
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Customer Decision Hub overview

  • Module

    Customer Decision Hub overview

    3 Topics

    30 mins

  • Familiarize yourself with the one-to-one customer engagement paradigm and discover how Pega's omni-channel AI delivers the right action during every...

Exploring decisions in Customer Decision Hub

  • Challenge

    Exploring decisions in Customer Decision Hub

    4 Tasks

    10 mins

  • U+ Bank uses Pega Customer Decision Hub™ to decide which one of four credit card offers to show in a web banner when a customer logs in to the U+ Bank...

Customer Decision Hub predictions

  • Module

    Customer Decision Hub predictions

    2 Topics

    35 mins

  • Prediction Studio is the dedicated workspace for data scientists to control the life cycles of predictions and the predictive models that drive them...

Exploring Prediction Studio

  • Challenge

    Exploring Prediction Studio

    4 Tasks

    10 mins

  • U+ Bank implements Pega Customer Decision Hub™ to optimize customer interactions on their web channel by showing a personalized web banner when...

Adaptive models

  • Module

    Adaptive models

    5 Topics

    55 mins

  • Online, adaptive models play a crucial part in Pega Customer Decision Hub™ next-best-action decision strategies. These models drive the predictions...

Adding predictors to an adaptive model

  • Challenge

    Adding predictors to an adaptive model

    6 Tasks

    10 mins

  • 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...

Monitoring adaptive models

  • Module

    Monitoring adaptive models

    3 Topics

    40 mins

  • Data scientists must regularly inspect the health of the out-of-the-box Pega Customer Decision Hub™ predictions and the adaptive models that drive...

Monitoring predictions

  • Challenge

    Monitoring predictions

    6 Tasks

    15 mins

  • U+ Bank uses Pega Customer Decision Hub™ to display a personalized credit card offer to eligible customers on their website. The bank now wants a...

Monitoring adaptive models

  • Challenge

    Monitoring adaptive models

    3 Tasks

    10 mins

  • The models for the U+Bank implementation of cross-selling on the web of their credit cards have been learning for some time. Your task in this...

Exporting adaptive model data

  • Module

    Exporting adaptive model data

    3 Topics

    35 mins

  • The reporting datamart of Pega Adaptive Decision Manager (ADM) is an open data model. As a result, data scientists that work on Pega Customer Decision...

Exporting historical data

  • Challenge

    Exporting historical data

    5 Tasks

    15 mins

  • U+ Bank implements Pega Customer Decision Hub™ to display a personalized credit card offer to eligible customers on their website. As a data scientist...

Exporting adaptive model data for external analysis

  • Challenge

    Exporting adaptive model data for external analysis

    4 Tasks

    25 mins

  • U+ Bank implements cross-selling of their credit cards on the web by using Pega Customer Decision Hub™. Self-learning, adaptive models drive the...

Creating predictions and predictive models

  • Module

    Creating predictions and predictive models

    6 Topics

    1 hr 5 mins

  • Predicting customer churn is one of many business use cases that involve predictive models. Pega Customer Decision Hub™ provides predictions that use...

Creating a churn prediction using a scorecard

  • Challenge

    Creating a churn prediction using a scorecard

    3 Tasks

    20 mins

  • U+ Bank wants to predict and avoid potential customer churn before it happens. When customers leave a bank, the result is costly in terms of lost...

Building models with Pega machine learning

  • Challenge

    Building models with Pega machine learning

    6 Tasks

    15 mins

  • U+ Bank implements Customer Decision Hub™ to determine which credit card offer to show a customer on the bank's website. To reduce the number of...

Importing predictive models

  • Challenge

    Importing predictive models

    1 Task

    10 mins

  • U+ Bank has recently implemented Pega Decision Management, but already uses predictive models that an external bureau created. You are asked to make...

Creating a churn prediction using an ML model

  • Challenge

    Creating a churn prediction using an ML model

    2 Tasks

    15 mins

  • U+ Bank implements Pega Customer Decision Hub™ to personalize the credit card offer a customer is presented on their website. If a customer is...

MLOps

  • Module

    MLOps

    6 Topics

    1 hr

  • Machine Learning Operations (MLOps) is an approach that streamlines the process of building, testing, and deploying machine learning models. As a data...

Adding predictors to an adaptive model in BOE

  • Challenge

    Adding predictors to an adaptive model in BOE

    5 Tasks

    10 mins

  • U+ Bank uses Pega Customer Decision Hub™ to personalize the credit card and mortgage offers that customers receive on its website. To optimize...

Replacing a predictive model

  • Challenge

    Replacing a predictive model

    3 Tasks

    10 mins

  • 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...

Promoting a shadow model to active status

  • Challenge

    Promoting a shadow model to active status

    5 Tasks

    10 mins

  • U+ Bank uses Pega Customer Decision Hub™ to personalize the credit card offers that customers receive on its website. The bank makes a proactive...

Creating and understanding decision strategies

  • Module

    Creating and understanding decision strategies

    4 Topics

    1 hr 10 mins

  • 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

  • Challenge

    Creating a decision strategy

    3 Tasks

    25 mins

  • 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

  • Challenge

    Testing a decision strategy

    3 Tasks

    25 mins

  • 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...

Defining prediction patterns

  • Module

    Defining prediction patterns

    2 Topics

    20 mins

  • Learn how to improve the predictive power of your adaptive models by configuring additional potential predictors in Pega Customer Decision Hub™. For...

Using behavioral data as predictors

  • Challenge

    Using behavioral data as predictors

    3 Tasks

    10 mins

  • 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

  • Challenge

    Using model scores as predictors

    4 Tasks

    15 mins

  • 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...

Leveraging a churn prediction

  • Challenge

    Leveraging a churn prediction

    3 Tasks

    25 mins

  • U+ Bank implements Customer Decision Hub™ to determine which credit card offers to show to customers on its website. As part of the implementation...

Model governance

  • Module

    Model governance

    4 Topics

    50 mins

  • AI has the potential to deliver significant benefits, but improper controls can result in regulatory issues, public relations problems, and liability...

Detecting unwanted bias in engagement policy conditions

  • Challenge

    Detecting unwanted bias in engagement policy conditions

    7 Tasks

    15 mins

  • U+ Bank is currently cross-selling on the web by showing various credit cards to its customers.

    The bank wants to run an ethical bias simulation in...

mission badge: AI for 1:1 Customer Engagement

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