PEGA CPDC

Class Schedule

1. Introduction PEGA Decisioning, NBA & Prediction Studio

1. Overview of NBA – Next Best Action
2. Real-Life NBA Usage
3. Decisioning Studios – CDH & Prediction Studio

2. Application Architecture

1. PEGA’s DMSample Application
2. Creating our new Decisioning Application
3. NBA Class Structures & Data Types.
4. Walkthrough of ADM Service Infrastructure
5. Configuring the DDS Node in the Cluster

3. Proposition Management

1. Overview of Proposition Management
2. Adding a new Business Issue
3. Adding a new Business Group.
4. Adding a new Property.
5. Uploading Propositions from CSV.

4. Proposition Data & Decision Data

1. Versioned Proposition Data
2. Unversioned Proposition Data
3. Getting started with Decision Data
4. Copy Proposition Groups.

5. Working with Data Sets

1. About Data Sets
2. Types of Data Sets
3. Copy Proposition Groups.

6. Configuring the Data Flows

1. About Data Flows
2. Types of Data Flows – Internal & External
3. Configuring the Incoming Data
4. Applying Data Flow Actions – Compose, Convert, Merge
5. Applying Data Flow Actions – Strategy, Data Transforms,
6. Filters & Abstracts
7. External Data Flows

7. Using the Event Strategy

1. About Event Strategy
2. Differences between Strategy & Event Strategy
3. Event Strategy Actions – Filter, Lookup, Split
4. Event Strategy Actions – Split & Join, Window, Aggregate

8. Using the Strategy

1. About Strategy
2. Strategy Shapes – Sub Strategy, Prediction, Import
3. Strategy Shapes – Business Rules, Decision Analytics
4. Strategy Shapes – Enrichment, Arbitration
5. Strategy Shapes – Selection, Aggregation

9. Modelling Decisions – Adaptive Models

1. About Adaptive Models
2. Adaptive Models – Predictors
3. Adaptive Models – Context
4. Adaptive Models – Outcomes
5. Adaptive Models – Monitoring
6. Interaction History & Table
7. Customer Responses
8. Configuring Self-Learning Models

10.Modelling Decisions – Predictive Models

1. About Predictive Model
2. PMML Schema and Modelling
3. Predictive Models – Monitor
4. Predictive Models – Model
5. Predictive Models – Input Mapping
6. Predictive Models – Parameters

11.Modelling Decisions – Text Analytics

1. About Text Analyzer
2.  Text Analytics & Extraction
3. Analyzing a Text File

12.Using the Prediction Studio

1. Analyzing Adaptive Model Performance
2.  Analyzing Predictive Model Performance
3. Analyzing Text Extraction Performance
4. Propensity Score Calculation.

13.Treatments and Actions

1. Defining and Managing Customer Actions
2. About Treatments & Types
3. Presenting Offers via Treatments.
4. Outbound & Multi-Level Campaigns

14.Engagement & Contact Policy

1. Creating an Engagement Strategy
2.  Creating a Contact Policy
3. Understanding Volume Constraints
4. Understanding Proposition Eligibility Rules
5. Simulations & Segmentations

15.Containers

1. Types of Artifacts and Containers
2. Using the Realtime Container
3. Action Arbitration and Intelligence