Getting Started - Pega · Decision Strategy Manager delivers two rule sets. The Pega-DecisionEngine...

Getting Started

© 2012 by Pegasystems, Inc. All rights reserved.


This document describes products and services of Pegasystems Inc. It may contain trade secretsand proprietary information. The document and product are protected by copyright and distributedunder licenses restricting their use, copying distribution, or transmittal in any form without prior writtenauthorization of Pegasystems Inc. This document is current as of the date of publication only. Changes inthe document may be made from time to time at the discretion of Pegasystems. This document remainsthe property of Pegasystems and must be returned to it upon request. This document does not imply anycommitment to offer or deliver the products or services described. This document may include referencesto Pegasystems product features that have not been licensed by your company. If you have questionsabout whether a particular capability is included in your installation, please consult your Pegasystemsservice consultant.Although Pegasystems Inc. strives for accuracy in its publications, any publication may containinaccuracies or typographical errors. This document or Help System could contain technical inaccuraciesor typographical errors. Changes are periodically added to the information herein. Pegasystems Inc. maymake improvements and/or changes in the information described herein at any time.

This document is the property of Pegasystems Inc.

Publishing Date: August 24, 2012Contact Product SupportVisit the Pega Developer Network


mailto:[email protected]

http://pdn.pega.com

Getting Started

© 2012 by Pegasystems, Inc. All rights reserved. 1

Introduction 6

Decision Management 7

Understanding Decision Management 8

Rule Sets 8

Methods & Functions 8

Predictive Model 8

Scorecard 9

Adaptive Model 9

Strategy 9

Interaction 9

Service Layer 9

Decision Execution 10

Decision Invocation and Execution 10

Interaction Data 11

Strategy Result 12

Classes 12

Properties 13

Data Instances 13

Interaction Management 13

ISClient Interface 14

Capturing Responses 14

Revoking Responses 14

Proposition Cache 14

Dimensions 15

Adaptive Decision Management 15

Adaptive Modeling 16

Predicting Behavior 17

Model Learning 17

Model Learning Explained 17

Local Learning 17

Model Updates 18

Strategies 18

Strategy Design 18

Planning Strategy Design 19

Adaptive Components 20

Strategy Chaining 23

Strategy Execution 23

Strategy Execution in Batch 24

Work Items 24

Large Scale Batch Execution 25

Getting Started


Multilevel Decisioning 26

Sub Strategies & Aggregation 26

Sub Level Interaction Rules 28

DSM Enabled Applications 29

Rule Set Dependency 29

Organizational Structure 29

Configure Application 30

Work Pool 30

Access Group & Operators 30

Product 30

Decision Management Landing Pages 31

Strategies 31

Issues 31

Propositions 31

Strategies 32

Adaptive Models 32

Predictor Overview 33

Behavior Reports 34

Active Predictors Report 35

All Predictors Report 35

Performance Overview 36

Delete Model 37

Clear Model 37

View Model Parameters 37

Upload Responses 38

Batch Data 38

Batch Runs 39

Input 41

Output 42

Output Tab 43

Batch Output Wizard 43

Create Output Configuration 43

VBD Configuration 44

Report Configuration 45

Confirm 46

Topology 46

Visual Business Director 47

Dimension Filter 49

Data Modes 49

Timeline 50

Forecasting 51

X/Y Axis 51

Multiple Grids 52

Export Grid Data 53

Services 53

Getting Started


Tools 54

Upload Data 55

Review PMML 55

Preview Data Transformation 56

Review Results 57

Decision Management Rule Types 58

Predictive Model 58

Create Rule Instance 58

Upload Predictive Model 59

Input Mapping 60

Statistics 60

Predictive Model Results 62

Classification & Strategies 63

Define Results 63

Scorecard 64


Score Calculation 65

Define Results 66

Adaptive Model 67

Methods 67

Predictor Information 67

Response Upload 68


Configure Models 69

Define Settings 70

Responsiveness 71

Data Analysis 71

Advanced Configuration 71

Interaction 71

Methods 72

Run Strategy 72

Capture Response 72


Interaction History 73

Run Strategy 73

Capture Response 74

Multi-Level 76

Strategy 76

Methods 77

Return List of Propositions 77

Return List of Properties 77

Compute Segment Logic 77

Execute Strategy 78


Design Strategy 79

Toolbar & Context Menu 79

Getting Started


Defining Components 80

General Settings 81

Data Import 81

Data Import 81

Sub Strategy 82

Proposition 83

Segmentation 83

Predictive Model 84

Scorecard 84

Adaptive Model 85

Decision Table 86

Decision Tree 86

Data Enrichment 87

Strategy Set 87

Data Join 88

Aggregation 89

Aggregation 89

Financial 90

Arbitration 91

Filter 91

Segment Filter 92

Prioritization 92

Selection 93

Champion Challenger 93

Switch 94

Connecting Components 94

Defining Expressions 95

Expressions in Strategies 95

Financial Functions 96

Cumulative Interest 96

Cumulative Principal 97

Depreciation Using Fixed-Declining Balance 97

Depreciation using Double Declining Balance 97

Future Value 97

Interest Payment 97

Number of Periods 97

Payment 97

Principal Payment 98

Present Value 98

Rate 98

Straight-Line Depreciation 98

Sum-of-Years' Depreciation 98

Variable Depreciation 98

Strategy Properties 98

Auto-Run Results 99

Overview 100

Audit Notes 100

Getting Started


Finalizing Rules 100

Testing Rules 101

Flow Shapes 103

Run Strategy 103

Capture Response 103

Tutorials 105

Predictive Models and Scorecards in Process Flows 105

Predictive Models 105

Predictive Model Decision Rule 105

Process Flow 107

Scorecards 107

Scorecard Rule 107

Process Flow 109

Strategy Driven Processes 110

Class Structure & Data Models 110

Class Structure 110

Data Model 110

Propositions 112

Define Top Level Class 112

Define Hierarchy 112

Define Propositions 114

Proposition Attributes 114

Propositions 116

Segmentation Rules 117

Decision Table 117

Scorecard 118

Adaptive Model 119

Strategies 120

Loans Strategy 120

Sales Strategy 125

NBA Strategy 130

Interaction 132

Process and User Interface 134

Process Flow 134

Flow Actions 140

Collect Customer Information 140

Display Offers 141

Training Adaptive Models 143

Upload Responses 143

Activities 144

Create Report Definition 144

Call Method in Activity 146

Glossary 147

Getting Started


Introduction

Product Release DSM and Process Commander 6.3

Contents This document describes Pega DecisionManagement, and how to use the functionalityprovided with Decision Strategy Manager in PRPCapplications.

Getting Started


Decision Management

Decision Management functionality allows you to use sophisticated mechanisms that empower and enrichyour application so that, using the Next Best Action (page 149) principle, you can develop applicationsthat determine which processes to run, and which products should be offered to customers. The NextBest Action principle is geared toward increasing customer loyalty with the ability to address multipleissues in the decision making process. Decision Management functionality is delivered with DecisionStrategy Manager (DSM). Decision Management functionality includes:

• Proposition management• Strategy development• Driving process flows using interaction, scorecard, and predictive model rules• Using third party models• Multilevel decisioning• Single and distributed batch execution of strategies• Capturing interaction results using Interaction Services (IS), and associating interaction records with

work objects• Visualization, monitoring, and forecasting using Visual Business Director (VBD)• Advanced adaptive analytics using Adaptive Decision Manager (ADM)

Getting Started


Understanding Decision Management

To understand what functionality and mechanisms your PRPC application is working with when usingDecision Management, you should have a good understanding of the DSM architecture, implementation,and technical concepts that underpin how DSM works. The purpose of the topics in this section isto provide the technical information you may require when planning, designing, implementing, andtroubleshooting your application.

Related Topics

• Rule Sets (page 8)• Methods & Functions (page 8)• Service Layer (page 9)• Decision Execution (page 10)• Strategy Result (page 12)• Interaction Management (page 13)• Adaptive Decision Management (page 15)• Strategies (page 18)• Strategy Execution in Batch (page 24)• Multilevel Decisioning (page 26)

Rule SetsDecision Strategy Manager delivers two rule sets. The Pega-DecisionEngine rule set provides theexecution data model and runtime implementations supporting Decision Management rule types, andlanding pages. The Pega-DecisionArchitect rule set provides the user interface, data model, and formssupporting editing of Decision Management rule types.

Related Topics

• Decision Management Rule Types (page 58)• Decision Management Landing Pages (page 31)

Methods & FunctionsThe sections below provides the overview of Decision Management methods and functions categorizedby rule type. Additionally, Decision Management functionality delivers the financial functions library thatcan be used in expressions.

• Predictive model (page 8)• Scorecard (page 9)• Adaptive model (page 9)• Strategy (page 9)• Interaction (page 9)

Predictive ModelLib(Pega-DecisionEngine:PredictiveModel).ObtainValue(this, myStepPage, "preditivemodelrulename")allows for obtaining the result of a predictive model rule (segment).

Getting Started


ScorecardLib(Pega-DecisionEngine:Scorecard).ObtainValue(this, myStepPage, "scorecardrulename") allows forobtaining the result of a scorecard rule (score).

Adaptive Model• Call DSMPublicAPI-ADM.pxUploadResponsesFromReport

Uploads responses to an adaptive model through a report definition.• Call DSMPublicAPI-ADM.pxLoadPredictorInfo

Returns the predictor information (active and inactive) for a given adaptive model rule.

Strategy• Call pxRunStrategy

Executes a strategy rule.• Call Rule-Decision-Strategy.pyGetStrategyPropositions

Obtains the propositions that can be returned by the public component of a strategy rule.• Call Rule-Decision-Strategy.pyGetStrategyProperties

Obtains the properties that are used by the components in a strategy rule.• Call Rule-Decision-Strategy.pyComputeSegmentLogic

Computes the segment logic that can be returned by the public component of a strategy rule.• Call Pega-DM-Batch-Work.pxCreateDecisionExecutionConfig

Creates a batch strategy execution configuration.• Call Pega-DM-Batch-Work.pxInvokeDecisionExecution

Executes a strategy in batch.

Interaction• Call Rule-Decision-Interaction.pxRunCaptureResponse

Executes an interaction rule in response capture mode.• Call Rule-Decision-Interaction.pxRunStrategy

Executes an interaction rule in strategy execution mode.

Related Topics

• Predictive Model (page 58)• Scorecard (page 64)• Strategy (page 76)• Interaction (page 71)• Financial Functions (page 96)• Batch Data (page 38)• Adaptive Model (page 67)

Service LayerInteraction between Process Commander and the service layer (Interaction Services, Adaptive DecisionManager, and Visual Business Director) is triggered by:

• Strategy execution.• Flow execution when using the run strategy or capture response shapes.• Adaptive model configuration through adaptive model rules.• Actions performed in the adaptive models landing page (deleting models, training models).• Scoring model updates by running the agent in the Pega-DecisionEngine rule set.

This interaction consists of gathering the required information for scoring, and capturing data resultingfrom interactions (responses). When IS receives the data, it replicates it to VBD. If adaptive modelsare used in the decision execution process, models are executed, and model data updated. Sendingthe necessary information to ADM is triggered by changes in adaptive model rules, deleting models in

Getting Started


the adaptive models landing page, and the model update mechanism (page 18). IS communicationto ADM, and PRPC communication to IS and ADM in the service layer is asynchronous. PRPCcommunication to IS and ADM in the service layer is also process driven.

The diagram below provides an overview of the communication between PRPC, IS, ADM, and VBD.PRPC needs to be aware of the (necessary) Decision Management service layer end points so that theprocess of passing and retrieving information can be performed. At a minimum, PRPC needs to be awareof IS, but this also means that such an environment does not use the capabilities added by ADM, or VBD.

Decision Execution• Decision invocation and execution (page 10)• Interaction data (page 11)

Decision Invocation and ExecutionExecuting decisions is triggered by one of the following methods:

1. Flow execution when the flow contains a run strategy shape.2. Activity execution when the activity contains a step that results in executing a strategy by invoking an

interaction rule.3. Activity execution when the activity contains a step that results in executing a strategy by invoking the

strategy rule itself.

In the first two cases, invoking and executing a strategy relies on referencing the interaction rule thatdefines the strategy rule to run. In the third, you invoke the strategy rule.

After executing the strategy, the interaction context is taken to perform the last steps, which consist ofsaving the clipboard pages used when executing adaptive models, and mapping public components toproperties. The latter is implemented by mapping pxResults from Code-Pega-List to page or page listproperties.

Getting Started


Interaction DataInteraction data can be accessed by strategies through proposition components. Saving and recordingdata resulting from the interaction is triggered by the capture response shape in a flow, or activityexecution when the activity contains a step that results in capturing response data. Data resulting from aninteraction consists of:

• Data used when issuing the decision.• Recommendation (proposition).• Behavior (customer response).

Getting Started


Strategy ResultThe proposition hierarchy is defined in the SR (Strategy Result) top class under the top levelorganizational class.

• Classes (page 12)• Properties (page 13)• Data instances (page 13)

ClassesThe Data-pxStrategyResult class is the base class for Decision Management data. Classes supportingthe proposition hierarchy are concrete classes. The hierarchy consists of issue, and group. It containsas many issues as required by the business (for example, Churn, Collections, and Sales). An issue canhave one or more groups, each group basically providing a label for a series of related propositions (forexample, Bundles, Credit Cards, Loans, and Mortgages).

• The SR class is a concrete class using pattern and directed inheritance from the Data-pxStrategyResult class.

• Classes defining issues have no key defined. They are defined with pattern and directed inheritancefrom the SR class.

• Classes defining groups have the pyName key. They are defined with pattern and directedinheritance from the class defining the issue.

Strategies in the proposition hierarchy use directed inheritance from the issue class if the strategy appliesto a given issue, group class if the strategy also applies to a given group, or the SR class if the strategy isnot associated with any given issue, or group.

Getting Started


If not present, the SR class is automatically created under the top level abstract class ofyour organization when you define the proposition hierarchy and, even if this hierarchy is notpresent, when you create strategies.

PropertiesThe Data-pxStrategyResult class contains properties that define the basic output of a decision(pxInteractionID, pxPriority, pxRank, pxSegment, pxIdentifier, pyChannel, pyDirection, pyGroup, pyIssue,pyName, pyPropensity, pyTreatment, and pyWeight).

Properties in the classes defining the proposition hierarchy belong to the data model in the class thatrepresents its scope in the proposition hierarchy.

Scope Description

Top level class Directed inheritance from Data-pxStrategyResult. This class supportsproperties for which issue has not been defined. By default, the pattern<OrgClass>-<ApplicationName>-SR is assumed. The top level classis defined in the application's pxDecisioningClass field value rule, anddetermines the proposition hierarchy your application can access.

Issue class Directed inheritance from the top level class.This class supports propertieswhose scope is issue, but not group. By default, the pattern <OrgClass>-<ApplicationName>-SR-<Issue> is assumed.

Group class Directed inheritance from the issue class. This class supportsproperties whose scope is group. By default, the pattern <OrgClass>-<ApplicationName>-SR-<Issue>-<Group> is assumed.

Properties in the classes defining the proposition hierarchy are defined at the applicable level dependingon its scope in the proposition hierarchy .

Purpose Description

Proposition Proposition attributes are automatically configured with the pyDecisioningItemcustom field set to PropositionProperty if added through the Manage Attributesdialog in the Strategies landing page.

Strategy Strategy properties are automatically configured with the pyDecisioningItemcustom field set to StrategyProperty if added through the Strategy Propertiestab of strategy rules.

Data InstancesThe propositions are data instances of the data class that represents the group scope. Propositionsinherit the properties of the issue class they belong to.

Interaction ManagementIntelligent decisioning is not a static exercise. Customer behavior is constantly shifting, actions by boththe enterprise and competitors impact customer behavior, changing business objectives, and priorities.Feedback on decisions made by consumers in response to propositions is vital if the enterprise is tolearn what works, and what does not. Interaction data management, which consists of retrieving theinteraction data that is used during strategy execution and response capture, is implemented throughthe combination of customer ID information provided in the interaction rule, and proposition componentsthat are configured for interaction history management. IS provides the interaction management servicesthat persist the interaction result. IS also provides routing to ADM and VBD in the service layer, includingupdating the state of adaptive models, and saving interaction result information for monitoring andreporting purposes. Interaction data can be queried and analyzed for reports on decision performance,thus allowing for identifying where changes should be made, and new opportunities arise.

Related Topics

• ISClient Interface (page 14)

Getting Started


• Proposition Cache (page 14)• Dimensions (page 15)

ISClient InterfaceThe IS Client API provides a number of methods for capturing and manipulating responses that measurecustomer behavior in response to a proposition. Data records resulting from capturing the response inan interaction are added to the IS_FACT_RESPONSE table in the IS database. If the propagation toVBD is enabled, the data records are published to VBD for monitoring, visualization, and analysis. If theproposition offered is modeled in ADM, the response is also sent to ADM for learning, and can be viewedin the Adaptive Models landing page in PRPC.

The IS Client API is documented in the JavaDoc located in the DSM deliverable (Products/InteractionServices/docs/API).

The response APIs can be broken into two logical groupings:

• Capturing responses (page 14)• Revoking responses (page 14)

Capturing ResponsesThese methods accept one or more ResponseContainer objects that contain the data for capturingthe dimensions (page 148), measurements (page 149), and customer behavior in the interactionresult. Responses contain dimensions, customer response to the proposition in the behavior dimension,CASE_ID, customer driven measurements, custom dimensions, and work object ID (EXTERNAL_ID).There are twenty flexible measurements that can be recorded for any response. The out-of-the-box seeddata defines some of these measurements to illustrate examples such as Average Handling Time, andhow these measurements can be visualized in VBD.

Typically, the setReponse methods result in a new record in the IS_FACT_RESPONSE table. ThesetResponse methods return the CASE_ID, EXTERNAL_ID, and HISTORY_ID, which can be usedlater to revoke a previous response. This record also contains dimension and measurement informationprovided through the interaction rule. Response capturing can be handled differently depending on theresponse history:

• The response can result in updating an existing response to modify the flexible customer drivenmeasurements.

• The response can result in updating response data to support consecutive behavior. In this case,although a new record is created in the IS_FACT_RESPONSE table, it is not considered a newresponse (that is, the count of responses is not increased).

Revoking ResponsesTo cover the use case in which a customer initially accepts a proposition, and later rejects it, IS allowsyou to revoke a previous response.

Proposition CacheWhen PRPC is running on multiple system nodes connected to the same database, DSM uses thesystem pulse to ensure the consistency of propositions across all nodes. The proposition cache isinvalidated when a proposition is saved (triggered by adding a proposition, or changing it), or deleted.Adding records that result in the proposition cache to become invalid is done through two declare triggerrules (pyPropositionSaved, and pyPropositionRemoved) in the Data-pxStrategyResult class which run thepyRefreshPropositions activity.

Consistent handling of the proposition cache in a multinode PRPC environment requires extraconfiguration. The configuration is typically performed in the process of installing and configuringPRPC, and described in the topics contained in the "Customization" section of the DSM Installationdocumentation.

Getting Started


DimensionsInteraction information is based on dimensions (page 148). Dimension information is a hierarchicalrepresentation of the interaction, and consists of information about:

• Customer dimension: who was subject to this interaction.• Application dimension: where in the business this interaction took place.• Time dimension: when did this interaction take place.• Proposition dimension: what was offered to the customer.• Channel dimension: how this proposition was presented to the customer.• Response context dimension: why was this proposition presented to the customer.• Behavior dimension: the reaction of the customer to this proposition.

The table below provides an overview of the default structure of the hierarchy for each dimension (page148). List numbering represents the position of each level in the hierarchy.

Dimension Levels

Application 1. RTP(This level can not be translated to any concept in DSM.)

2. Organization3. Division4. Unit5. Operator

Behavior 1. Behavior2. Response

Channel 1. Direction2. Channel3. Treatment

Customer 1. Segment2. Sub Segment

Proposition 1. Issue2. Group3. Proposition4. Deployment

(This level can not be translated to any concept in DSM.)

Response Context 1. Call Mode2. Category3. Reason4. Display Category5. Top Rank

Time 1. Year2. Quarter3. Month4. Week5. Day

Adaptive Decision ManagementAdaptive Decision Management is about learning behavior in real time. Increasingly accurate decisionsare made by automatically adapting models after each behavior change. For instance, if a customer isoffered a product and accepts, the likelihood of customers with a similar profile slightly increases. Thereare mathematical ways to express these probabilities, and the way they adapt after each change.

The Adaptive Decision Manager extends existing adaptive propensity techniques. ADM is an integratedmethod establishing customer preferences without previously collected historical data. ADM extendspredictive analytics with an adaptive mechanism for establishing customer preferences with customerresponses in real time. Due to its adaptive nature, no initial collection of data is necessary.

Getting Started


Besides keeping count of the number of times specific behavior is observed, ADM also takes into accountpredictive data to forecast behavior. In contrast with predictive analytics, which requires historic dataand human resources to develop a reliable predictive model (page 149), ADM can calculate behaviorwithout historical data. ADM captures and analyzes data to deliver predictions where no history isavailable to develop offline models, and in situations where the behavior is volatile. If data and timeare available for offline modeling (page 149), predictive models can be used as an alternative or inconjunction with adaptive models (page 147). Adaptive models become more accurate with time,requiring monitoring not to become less sensitive after a sustained period of use. The advantage of usingADM is considerable in business areas where mistakes are not critical, such as marketing.

Related Topics

• Adaptive Modeling (page 16)• Predicting Behavior (page 17)• Model Learning (page 17)• Model Updates (page 18)

Adaptive ModelingThe Adaptive Decision Manager is part of the Decision Management service layer PRPC connectsto. It is fully integrated to work together with predictive models (page 149) created to address morecritical issues (for example, detecting more complex patterns for fraud, or customer attrition), and otherstrategy components. Adaptive models are created based on the adaptive model rule they have beenconfigured with by running a strategy that contains adaptive models. When adaptive models (page 147)are created, ADM is initialized and starts capturing the data relevant to the modeling process, maintainingstatistics with high granularity. The data forms the backbone for the creation of adaptive MDAP (page17) models that are used to assess propensities.

Without any data, the scoring models (page 150) are empty, and only track overall propensity (page150). The prioritization scheme ensures all propositions are considered but focusing on the observedbest propensity proposition, thus ensuring early data collection for all propositions while maximizinginteraction results. Interaction results (page 148) are processed by the adaptive analytics engine (page147), and stored in a set of adaptive statistics (page 147) from which the engine continuously createsnew scoring models. Statistics and models are stored in the adaptive data store (page 147). Scoringmodels drive the decision process, and statistics ensure persistence. Once a data set has been captured,new scoring models are created. In this second stage, the data is used to identify propositions with higheror lower average propensity.

The adaptive modeling cycle is very similar to the predictive analytics process in Predictive AnalyticsDirector. However, due to the Adaptive Decision Manager's analytical nature, no preset intervals orgroups need to be identified beforehand, and extensive selection of predictors does not need to takeplace. The full adaptive modeling cycle consists of:

1. Capturing historical data with fine granularity.2. Regularly:

• Using sophisticated auto-grouping to create coarse-grained, statistically reliable numeric intervalsor sets of symbols.

• Using predictor grouping (page 150) to assess inter-correlations in the data.• Selecting predictors to establish an uncorrelated view that contains all aspects relevant to the

proposition.3. Using the resulting statistically robust adaptive scoring model for scoring.4. Whenever new data is available, updating the scoring model.

New models are published automatically when the strategy containing adaptive models is executed forthe first time, when (for existing scoring models) the Memory setting of the corresponding adaptive modelrule is changed, right after data analysis, or by recalculating the predictor binning. Any other change inadaptive model settings results in changing the scoring model, overriding the previous settings.

Model changes are not tracked in the ADM server. At any point in time, there is only oneversion of a particular scoring model.

Getting Started


Predicting BehaviorADM employs a Multidimensional Analytical Profiler (MDAP) as its main method of predicting behavior.The technique maintains a set of sufficient statistics in order to create models particularly intended tofunction in collaboration with predictive models, and other strategy components. However it does notrequire either. To increase the scope and reliability of this basic technique, the following is applied:

• Sophisticated auto-grouping.• Correlation detection and feature selection.• Adaptive prioritization for selecting a proposition in the presence of increasing reliability.• An integration and warning system to signal the opportunity to analyze and fix the data collection in a

robust and non-linear model.

Model LearningAdaptive models are executed in Process Commander. ADM performs data analysis depending onthe run data analysis after (page 71) setting of the adaptive model rule, and also the model updatefrequency set in the UpdateAdaptiveModels (page 18) agent. The combination of these settings guardthe speed at which newly learned information is seen in Process Commander. An alternative learningmethod (local learning in PRPC) can be used when learning based on the settings that trigger dataanalysis is not producing models that output useful predictions.

• Model learning in the ADM system (page 17)• Local learning (page 17)

Model Learning ExplainedThe run data analysis setting defines the number of new responses that, when reached, trigger dataanalysis. There is a general system setting for running data analysis, which is 50. Data analysis is aprocessing intensive operation. For this reason, an additional parameter can be configured to controlmodel refresh, a light weight analysis process where predictor binning is recalculated, but predictorgrouping is left unchanged. The setting that controls model refresh is refresh after (page 71). If thevalues of both settings are the same, the light weight analysis process is never triggered.

When the model update agent runs, the current number of responses processed since running thelast data analysis count or model refresh is considered in order to compare to data analysis and modelrefresh rates. ADM runs data analysis in the following circumstances:

• If no initial data analysis has been done, and the number of responses is above the general systemsetting. Initial models are created in three stages:

a. If the number of responses is below the initial data analysis count, a model with a propensity of0.5 is created.

b. If the number of responses is above the initial data analysis count for the first time, a model witha base propensity (number of positive responses divided by the sum of positive and negativeresponses) is created. Additionally, grouped predictors are created to allow gathering responsesfor outcome profile purposes.

c. If the data analysis count is reached after the previous stage for the first time, the first model withgrouped predictors and outcome profile is returned.

• If the difference between the number of responses at which the model was last created and thenumber of responses stored since then is more than the number of responses triggering dataanalysis. If the difference between the number of responses stored and the delta obtained in thiscontext is more than the number of responses triggering model refresh, the model refresh mechanismis triggered.

Local LearningLocal learning can be enabled when the number of responses is not sufficient to evolve the model. Locallearning is enabled through the enable local updates (page 71) setting in the adaptive model rule, andconsists of configuring models to adapt with every response. This feature is designed to allow learning totake place when model update takes too long for the model to be considered useful, not as a replacementof learning in the ADM system (page 17) since models produced through learning in the ADM systemare superior in predictive quality than models produced through local learning.

Getting Started


Local learning always remains local even in an environment running multiple system nodes.

Model UpdatesPRPC keeps a local cache of scoring models. Scoring model updates are regularly retrieved from theadaptive data store (page 147). The model update frequency is implemented by periodically triggeringthe UpdateAdaptiveModels agent (Pega-DecisionEngine rule set, PegaDM:Administrators access group).The agent periodically runs the pxUpdateModels activity to retrieve model updates. By default, the agentis scheduled to run every 30 seconds. The configuration of the model update frequency is done via theservices landing page (page 53). The agent does not retrieve all scoring models in ADM because itdiscards updating any models that are not required for strategy execution, and models that are the sameas the models in the local cache.

StrategiesTypically, a strategy (page 151) is developed to deliver a personalized recommendation for a singledecision (page 148). For example, a strategy can be developed to recommend the most important issueto be dealt with for a particular customer, via a channel or system, and at a given point in time. Combinedwith the current objectives and priorities of the company, the predicted customer’s risks and interests arepart of the strategy.

A recommendation can be part of a sequence. After determining the most important issue to address,the decision chain may need to address which credit strategy to use, which retention strategy, or whichproduct to offer first. Every decision employs a combination of strategy components (page 80) thatdefine the underlying logic that is required to deliver a recommendation. Components allow you tocreate personalized customer interactions consistently across contact channels. The advantage ofbuilding decision strategies from these smaller components is that each one can be readily understood,developed, edited and tracked on an ongoing basis. You can use components to model sophisticatedcustomer behavior, and there are some common design patterns that you may end up reusing frequently.

In the context of using strategies in combination with propositions, a strategy is created to deliver thedecision for one issue, or group. The scope in the proposition hierarchy corresponds to the issue or grouplevel of the proposition dimension definition in IS. The level at which the strategy is created determinesthe properties it can access, and the properties the strategy can access in the class hierarchy define theoutput structure of components in the strategy.

Decision strategies can be developed as a self-contained single strategy, or multiple strategies that arecombined by using sub strategy components. Combining strategies allows for concurrent developmentof large scale strategies by creating smaller strategies that can be developed in a relatively independentmanner. The other use case of multiple strategies is reusing a strategy pattern across your application.

Execution of a strategy results in a page containing (at least) the results of the components that make upthe output definition of the strategy rule.

Related Topics

• Strategy Design (page 18)• Planning Strategy Design (page 19)• Adaptive Components (page 20)• Strategy Chaining (page 23)• Strategy Execution (page 23)

Strategy DesignThe visual orientation of the strategy (page 151) is a logical translation of the output orientation (page19), working backwards from the Next Best Action (page 149) end point. Structurally, this can beexplained by using a top-down tree model. For example, assume that you need to build a strategy thataddresses the following:

Getting Started


• A number of segmentation components are available that classify customers based on product andrisk of customer attrition. Different issues need to be addressed, such as sales, recruitment, andretention.

• Arbitration between the different propositions is done with NBO (page 149) prioritization.• In the sales context, the offer that has the highest cross sell score.• In the risk of customer attrition context, the offer that addresses cases falling in segments with the

highest customer attrition risk.• Depending on the issue to be addressed, a final recommendation needs to be issued.

The diagram below visualizes the concepts used when planning the strategy. A strategy implementingthe logical structure abstraction is the final result. The design starts from the final decision point. Thefundamental NBA pattern starts from the final decision point and has a right-to-left orientation, but thethe flow of the arrows starts with data import components (page 81), then segmentation components(page 83) for which possible actions are defined, next the data enrichment components (page 87),proceeding with arbitration components (page 91). Finally, the end selection component (page 93)delivers the best action in the interaction.

Planning Strategy DesignYou can approach planning the design of your strategy (page 151) in two ways:

Approach Description

Top-down From simple to more complex. This method consists of gradually adding conceptualcomplexity to a more simplified layout of the strategy. Its advantage is flexibility.

Bottom-up Address all issues at the same time. This method can be used when all refinements(issues to be addressed) are clear from the start. The risk with this method is thatyou may need to revise some of the existing concepts when complexity needs to beadded.

The standard approach for finding the NBA (page 149) for each customer consists of segmentingcustomers, assessing the propensity, selecting the action for each customer segment and, finally,selecting the best decision path. The following list describes a sequence that can be used as a startingpoint when planning your strategy (page 151).

1. Plan the final decision, and work backwards.Starting point that allows you to define the strategy plan(s), such as the most important issue toaddress, what drives the decision, the most appropriate proposition (and how to determine it), theprobability factors, characteristics, and preferences to take into account in the decision.

a. What do you want to deliver?b. What action to take in order to achieve this?c. What data is required?

2. Build from customer, product, environment and other required information to deliver the decision.a. Define propositions.b. Import propositions.c. Prioritize between propositions.d. Balance issues.e. Finalize decision.

Getting Started


Adaptive ComponentsStrategies can introduce adaptive models to model customer responses for a set of propositions. Thestrategy can contain a mix of predictive models, adaptive models, and prioritization components. Theexample below illustrates a strategy that proposes the Next Best Offer (page 149). Prioritizationcomponents can be employed to offer the customer the best action based on predicted propensity (page150), and amount of data that is used in the prediction (page 149). Predictive models could be used topredict customer attrition, fraud, and customer lifetime value.

In order to develop your strategy for trend detection (page 151), you will need to add a component thatselects the adaptive models in the decision execution process. For example, in a strategy containingthree adaptive models, we can add a prioritization component to arbitrate which adaptive model selectionto select based on performance. The performance output field is typically used to dynamically selectbetween multiple adaptive models, and/or predictive models. In the following example, adaptive modelcomponents in the strategy use adaptive model rules differentiated on the basis of performance windowsize. When the characteristics of customers change, the fast model (1000 window size) detects thechange in behavior fastest and, consequently, has the higher performance; this model is used to decideon the predicted propensity. When the other models (500 window size, and 0 window size) start tocapture this change in behavior, and earlier behavior has been discarded, they are again selectedbecause they can make more accurate predictions as they use more data. Positives and negatives canbe used to calculate the expected or base level propensity and, together with the propensity output field,calculate the lift (page 148) of individual predictions.

Getting Started


The models are selected by an aggregation component that groups by the applicable level(s) in theproposition hierarchy. The adaptive components model all propositions in the same issue (Sales), sothe aggregation component needs to group by the additional levels in the hierarchy (group, and name).Additionally, it also needs to set how to propagate data; in the example below, propagation of data ishandled by copying the first value.

Getting Started


The selection between which adaptive model to use is performed by a prioritization component thatselects the highest performing model.

Getting Started


Strategy ChainingA strategy (page 151) can use other strategies through sub strategy components (page 81) thatdefine which strategy to import, and which of the public components in the strategy should be selectedin the decision execution path. Including strategies allows for using specialized group or issue levelstrategies that address a specific business case, and combining them in a more generic strategy thatis typically at the top level class in the proposition hierarchy. The strategy design pattern used whenincluding sub strategies can be seen as always including more specialized cases to address all issues inan NBA strategy. Sub strategies can also be used to define common pieces of functionality that can bereused in different strategies.

When using sub strategies, and including a strategy that is not in the same class as thestrategy that is referencing it, consider the implications of class hierarchy and inheritance.

Strategy ExecutionStrategy execution is performed in the opposite direction of the dependency chain represented bythe black arrows in the strategy rule, taking the last component, recursively executing the dependentcomponents, and calling out the components whose configuration is tied to other decision rules, datareferences reading data records, and named pages or properties from a page depending on theAppliesTo class of the strategy. Every component that references a rule or a named page is subjectto auto-mapping, which means that properties with the same name in the referenced rule/page and inthe data class defined for the strategy are automatically mapped even if not explicitly mapped throughcomponents. The data class can be the strategy result class defined for the strategy, or the classcorresponding to the scope of the strategy in the proposition hierarchy.

Getting Started


Typically, the last component is a selection component that is also a public component. Componentswhose configuration is tied to other rules are components in the prediction/segmentation category, anddata import components. Each component creates its own page list from which the embedded pages areof the class the strategy properties belong to. This mechanism allows you to acquire and enrich data.The result of executing a strategy can be a single result, or a list. List processing can be implemented byimporting a set of propositions by group, or by combining data. Combining data is an operation performedby all components, except for selection components.

Strategy Execution in BatchThe execution of a strategy in batch is done through work objects that are stored in a worktable (pc_work_dsm_batch, of which the Rule-Obj-Class is Pega-DM-Batch-Work), and runs viaQueueForAgent (asynchronous execution). You can create these work objects:

• Using the Batch Data (page 38) landing page facilities.• In strategy rules, by running the rule in batch context mode (page 79).• In activities, by using the Call Pega-DM-Batch-Work.pxCreateDecisionExecutionConfig method.

Related Topics

• Work Items (page 24)• Large Scale Batch Execution (page 25)

Work ItemsThe diagram below shows the possible status of work items created in the context of batch runs. Theexecution of a batch run can be automatically triggered at the scheduled time, or by explicit user requestwhen you click Submit & Execute. Deleted and closed work items do not show in the Batch Data landingpage.

Getting Started


Progress checks are applied to work items in Open-Running and Pending-Scheduled status. The defaultsystem behavior can be configured to values other than default by defining the corresponding dynamicsystem settings (autoRecoveryTimeForRunningStatus, and autoRecoveryTimeForPendingStatus); referto the Customization section of the DSM Installation Guide.

• If the status is Open-Running without any progress (that is, number of records processed remainsunchanged) for five minutes, the work item's status is set to Resolved-Failed.

• A similar action is applied to work items in Pending-Scheduled status. If the status has not changed toOpen-Running for 30 minutes, the work item's status is also set to Resolved-Failed.

Large Scale Batch ExecutionLarge scale batch execution is enabled by performing strategy execution in batch across system nodes.The assignment, queuing, and management of this process is governed by the ProcessBatchJob agentconfiguration. The agent is scheduled to run with a given regularity (in seconds) to trigger checkingassignments in the [email protected] workbasket. If there are assignments, they will be queuedto create threads based on the thread configuration for each node. How many threads can be run in agiven node is something that you define in the topology tab (page 46) of the Batch Data landing page.The status of the work object (page 24) is updated accordingly as it progresses in this process, andyou monitor the assignment by viewing the instances in the workbasket.

Getting Started


Multilevel DecisioningMultilevel decisioning allows for aggregating customers into a single group so that this group may betreated as an entity. Action is taken based on the information gathered about the group as a whole. Inthe decision making context, multilevel decisioning is commonly referred to as a business-to-business(B2B) scenario, or household scenario. Because action needs to be taken based on information aboutevery member of the group, and not just the member for which the conversation is targeted, the followingfunctionality is provided:

• Sub strategies & aggregation (page 26)• Using sub level interaction rules (page 28)

The data necessary to iterate over strategies and interaction rules is assumed to be present inthe clipboard.

Sub Strategies & AggregationA specialized case of strategy chaining is strategies designed for multilevel decisioning. In this case, aspecific pattern is applied (sub strategy, and aggregation components). A sub level strategy is created torun a decision strategy over a variable number of customers, and the results are combined through anaggregation component to provide a proposition for the entire group.

By setting the sub strategy's Strategy Page to a page list or page group property, the strategy isiterated over the members defined in that property. The result of each individual iteration is appended.In the example below, considering that the Members page list represents two customers, and theAccountLevelStrategy results in four propositions, the pxResults list of the sub strategy componentcontains eight items.

Getting Started


Next, we add an aggregation component to use the results of a list of elements grouped using a certaincriteria (in the example below, pyPropensity). The pxResults list of the aggregation consists of asmany rows as the grouped view of the sub strategy's results. In the example below, by grouping bypyPropensity, and considering we only have two values for this property, the pxResults of the aggregationcomponent consists of a list with two items.

Getting Started


Sub Level Interaction RulesMultilevel decisioning is enabled in interactions by using the Multi-Level tab (page 76) of interactionrules. When the interaction rule runs, the interaction history is retrieved. The ID of each member of theaccount retrieved by the sub level interaction rule defines the ID for which the interaction history needs tobe retrieved. In the capture response process, the interaction rule runs for each member defined in thepage group, or page list. If a case was not eligible for an offer, the response is recorded as neutral.

Getting Started


DSM Enabled Applications

So that your application can use Decision Management functionality, you need to perform a fewconfiguration steps. Once you complete the configuration steps, your application has access to theDecision Management functionality, which consists of:

• Decision Management landing pages• Decision Management decision rules• In flows:

• Decision Management shapes• Predictive model and scorecard rule type selection in decision shapes

• Decision Management methods and functions

The steps described in this topic assume that you have an initial application that is not built on anotherapplication. The easiest way to create the necessary rules with a standard configuration is through theapplication accelerator. In this process, the necessary rules are created.

The steps listed below provide the additional guidelines required for a Decision Management enabledapplication.

1. Configure rule set dependency (page 29)2. Check organizational structure (page 29)3. Configure application (page 30)4. Configure work pool (page 30)5. Check access group and operators (page 30)6. Create product (page 30)

If you are working with more than one application, and the applications need to access thesame proposition hierarchy, make sure you set the same top level class in the Propositionslanding page, and that the applications have access to the same rule set containing theclasses supporting the proposition hierarchy.

If you can not define PegaDM as Built on Application in the application configuration step(page 30), add the Pega-DecisionArchitect and Pega-DecisionEngine rule sets in theApplication RuleSets section of your application rule instead.

Rule Set DependencyThe rule set needs to have a dependency on the Pega-DecisionArchitect.

1. Go to the rule set rule.2. In Required RuleSets And Versions, add Pega-DecisionArchitect:06-03-01.

Organizational StructureThe organizational structure is required in Decision Management enabled applications. The organizationrecord provides the dedicated class which becomes the class containing the application's propositionhierarchy. Make sure the organization hierarchy you want to use in your application is fully defined(organization, division, unit), and available to all operators working with or using your DecisionManagement enabled application.

Getting Started


Configure Application1. Go to the application rule.2. In the General tab:

a. Select PegaDM in the Built On Application field. b. Select 06.03 in the Version field.

Work PoolThe work class is necessary for strategies, as well as for work objects.

1. Go to the concrete work class.2. In the Class Inheritance section, make sure Parent class (Directed) is set to the appropriate work

class (Work-, Work-Cover, etc.).

Access Group & OperatorsThe final stage consists of checking access groups operators.

1. Go to the access group, and check the following:a. The minimum required roles in the access group (PegaRULES:SysAdm4,

PegaRULES:SysArch4, PegaRULES:ProArch4, or PegaRULES:WorkMgr4), and the necessaryportal layouts (for example, Developer, WorkManager, and Manager). PegaRULES:SysAdm4needs to be present for users changing the top level class in the Issues tab of the Strategieslanding page.

b. The local customization points to the application's rule set and rule set version.2. Go the operator(s) of the access group, and check the following:

• The Allow Rule Checkout setting is enabled in the Advanced tab.• The operator ID record has the appropriate associated rule set.

Product1. Create a product rule.2. In the applications to include section, add your application.3. In the rulesets to include section, add the necessary rule set(s).4. So that your product may include the proposition hierarchy, you need to include the proposition

hierarchy classes and group data instances in the class instances to include section (typically,<OrgClass>-<ApplicationName>-SR). Make sure you check the Include Descendants option.

5. In the individual instances to include section, use the SmartPrompt and the Query button to insert thenecessary instances:• Access group (Data-Admin-Operator-AccessGroup)• Operator ID (Data-Admin-Operator-ID)• Work pool (Data-Admin-DB-ClassGroup)• Organization (Data-Admin-Organization)• Division (Data-Admin-OrgDivision)• Unit (Data-Admin-OrgUnit)• Work group (Data-Admin-WorkGroup)• Workbasket (Data-Admin-WorkBasket)

Getting Started


Decision Management Landing Pages

Decision Management landing pages are accessed via the Decisioning item in the Pega menu.

• Strategies (page 31)• Adaptive Models (page 32)• Batch Data (page 38)• Visual Business Director (page 47)• Services (page 53)• Tools (page 54)

StrategiesThe tabs that are accessed via Decisioning | Strategies in the Pega menu provide the facilities to defineand manage your application's decision hierarchy. Depending on the type of action you want to perform,use the corresponding tab.

• Issues (page 31)• Propositions (page 31)• Strategies (page 32)

IssuesThe Issues tab allows you to manage your application's issues and groups. The top level class of theclasses representing the proposition hierarchy is displayed above the issues/groups grid, as well as therule set and rule set version they belong to. By default, the top level class is assumed to be <OrgClass>-<ApplicationName>-SR, but you can change it by click the top level class link to change your application'spxDecisioningClass field value rule.

• Add Issue: open the Class: New rule form to add a new issue.• Add Group: open the Class: New rule form to add a new group.• Export to Excel: export the overview of issues and groups to Excel.• Export to PDF: export the overview of issues and groups to PDF.

PropositionsThe Propositions tab provides the overview of the propositions in your application, also allowing you tomanage propositions, and proposition attributes. The overview can be exported to Excel, or PDF. Use theIssue and Group drop down lists to focus on specific parts of the proposition hierarchy.

Getting Started


• Export to Excel: export the overview of propositions to Excel.• Export to PDF: export the overview of propositions to PDF.• Manage Attributes: open the Manage Attributes dialog to create and delete attributes.• Manage Propositions: open the facilities to create and delete propositions using PRPC, or Excel. This

button is only displayed after selecting the hierarchy down to the group level.• Export to IS: open the Deploy Propositions dialog to reflect the proposition data instances and

defined proposition attribute values in the Decision Management service layer (Interaction Servicesdatabase).

• Refresh: refresh the information displayed in the propositions grid.

StrategiesThe Strategies tab provides the overview of strategy rules in your application, which can be exported toExcel or PDF. Strategy rule details are displayed in tabular view. Use the New button to create a newstrategy rule (page 78). Use the Add/Remove Columns column selection to control the amount ofdetails in the overview.

Adaptive ModelsThe Adaptive Models landing page provides access to the adaptive models in the ADM system that arespecific to your application. Models in the ADM system are the result of executing a strategy that containsadaptive model components. Adaptive model components in a strategy reference an adaptive model rule,which is the rule configuring how the model is created, how it learns, and how it predicts behavior. Theperformance of models over time is reported in this landing page.

Getting Started


• Use the Export To Excel button to export the current overview of adaptive models to Excel.• Use the Export To PDF button to perform the same action as described above, but then to PDF.• Use the Add/Remove Columns column selection to control the amount of details in the overview.

The overview of adaptive models shows the information pertaining to every adaptive model. Theinformation displayed in the overview depends on how each adaptive model component is defined in thestrategy. The list below describes all possible columns in the overview of adaptive models.

• Issue: displays the issue in the decision hierarchy set in the model definition.• Group: displays the group in the decision hierarchy set in the model definition.• Name: displays the name of the proposition the adaptive model is modeling.• Direction: displays the direction defined in the model definition.• Channel: displays the channel defined in the model definition.• Treatment: displays the treatment of predictors defined in the model definition.• Rule: displays the name of the adaptive model rule that configures the adaptive model.• Applies To: displays the AppliesTo class of the adaptive model rule.• Responses: displays the number of responses.• Performance: displays the model performance.• Active Predictors: displays the number of active predictors.• Positives: displays the number of positive responses.• Negatives: displays the number of negative responses.• Last Update Count: displays the number of responses present when the model was last updated.• Last Data Analysis Count: displays the number of responses present when the data analysis

operation was performed.

For each model, the Actions menu allows you to:

• Obtain the predictor overview (page 33)• Two types of actions allow you to obtain behavior reports (page 34)

• Active Predictors Report.• All Predictors Report.

• Obtain the performance overview (page 36)• Two types of actions allow you to remove adaptive statistics

• Clear model (page 37)• Delete model (page 37)

• View model parameters (page 37)• Upload responses (page 38)

Predictor OverviewThe predictor overview shows which predictors (page 150) are currently used in the scoring model.Information is provided for the number of positives and negatives used in each predictor, the rangeof values for numeric predictors, the number of symbols for symbolic predictors, and the predictiveperformance of each predictor.

1. In the Pega menu, go to Decisioning | Adaptive Models.2. Locate the model for which you want to analyze the usage of predictors.3. In the Actions menu, select Predictor Overview.4. The Predictor Overview dialog displays the overview of predictors used in the adaptive model.

Getting Started


5. The Active Predictors grid table shows the predictors used for modeling, and the Inactive Predictorstable shows the predictors that are not used for modeling. For each predictor, information is addedabout the number of positives and negatives used in each predictor, the range of values that havebeen encountered for numeric predictors, or the number of symbols that has been encountered forsymbolic predictors. Predictors are inactive if their performance is below the threshold for the selectedcomponent. If the performance is above the threshold, the predictor is correlated with a predictor thatis active, and outperforms it.

Behavior ReportsBehavior reports contain the model's behavior analysis, and allow you to observe the treatment ofpredictors (page 151) in a given model. The behavior analysis is centered around:

• Predictive performance (page 149) of the model, and its classification.• Predictor grouping (page 150)

• Using the Active Predictors Report (page 35), the grouping performed on active predictors,which is also referred to as filtered behavior report.

• Using the All Predictors Report (page 35), the grouping performed on all predictors for whichstatistics are kept, which is also referred to as unfiltered behavior report.

Together with the classification output, these reports fully describe the predictions of the adaptivemodel. The predictors are displayed as grouped intervals, or categories. The grouping is automaticallydetermined by the Adaptive Decision Manager. The behavior report generates a profile that describes thedifferences between positive and negative cases.

Analysis Description

Count and Percentage Analysis of the count and percentage of each type of case in each interval orcategory.

Distribution Chart The proportions of each type of case in each interval or category. The greenbar indicates the positive cases, and the red bar the negative cases.

Behavior The probability of having a positive response.Z-ratio Z-ratio (page 152) values greater than +/-1.96 indicate a 95% probability that

the difference is real. These Z-ratio values are displayed in orange. Valuesgreater than +/-3.0 are displayed in red, and indicate a 98% probability.

Behavior Chart Combination of the behavior and Z-ratio information. The dots indicate theprobability of positive behavior. Orange and red dots indicate probabilitieswhich are material and reliable. Whereas a large green bar may be material

Getting Started


but the difference may be due to chance, smaller red bars may also indicateinteresting differences.

Lift Lift (page 148) comparison.Interpretation Look for intervals or categories with larger red and orange dots as an

indication of intervals or categories that have distinctive behavior. Look forfields with few missing or residual values as an indication of fields that havemore reliable relationship with the behavior to be predicted.

Active Predictors ReportThis type of report generates and displays the filtered model behavior report based on active (used)predictors only. Follow the steps described below to generate a filtered behavior report.

1. In the Pega menu, go to Decisioning | Adaptive Models.2. Locate the model for which you want to analyze the behavior.3. In the Actions menu, select Active Predictors Report. The generated report contains the analysis of

the adaptive model in terms of predictive performance (page 149) and predictor grouping (page150) based on the predictors that are currently active in the model.

All Predictors ReportThis type of report generates and displays the unfiltered model behavior report. Unfiltered model behaviorconsiders all predictors regardless of whether they are active, or inactive. Follow the steps describedbelow to generate an unfiltered behavior report.

1. In the Pega menu, go to Decisioning | Adaptive Models.2. Locate the model for which you want to analyze the behavior.

Getting Started


3. In the Actions menu, select All Predictors Report.4. The generated report contains the same analysis as generated by the active predictors report (page

35), but then based on all predictors for which statistics are kept (predictors that are currently usedin predictions, and predictors that are not).

Performance OverviewPerfomance overview charts allow you to monitor the model's predictive performance (page 149)

1. In the Pega menu, go to Decisioning | Adaptive Models.2. Locate the model for which you want to analyze the predictive performance.3. In the Actions menu, select Performance Overview.4. The Performance Overview dialog displays the chart visualizing the performance in number of cases

for a particular model. Use the sliders above the chart to focus on number of cases. The button

allows you to display the dialog in full screen, and the button to view the data that determines thechart visualization.

Getting Started


Delete ModelThe Delete Model action in the Adaptive Models landing page allows you to delete the current model. Ifused by a strategy, the model is recreated again when the strategy is executed. Deleting models impliesthe loss of adaptive statistics (page 147) associated with that model.

Follow the steps described below to delete models.

1. In the Pega menu, go to Decisioning | Adaptive Models.2. Locate the model you want to delete.3. In the Actions menu, select Delete Model.4. Confirm removal of the model by clicking the Delete button in the Delete Adaptive Model dialog.

Clear ModelThe Clear Model action in the Adaptive Models landing page allows you to remove all adaptive statistics(page 147) associated with the adaptive model. In this process, everything is cleared except for numericpredictors boundaries. Used in conjunction with historical data upload (page 38), it allows you to havecomplete control over the contents of the adaptive statistics. If the model is used in the strategy, learningwill take place using the existing predictor binning.

Follow the steps described below to clear models.

1. In the Pega menu, go to Decisioning | Adaptive Models.2. Locate the model for which you want remove the adaptive statistics.3. In the Actions menu, select Clear Model.4. Confirm removal of the adaptive statistics by clicking the Clear button in the Clear Adaptive Model

dialog.

View Model ParametersModels are created based on the settings configured in the adaptive model rule (page 70) they areassociated with in the strategy rule's adaptive model component.

Getting Started


1. In the Pega menu, go to Decisioning | Adaptive Models.2. Locate the model for which you want to view the model configuration settings.3. In the Actions menu, select View Model Parameters.4. The Adaptive Model Parameters dialog displays the modeling and configuration settings that were

used when creating the scoring model in ADM.

Upload ResponsesThe Upload Responses wizard allows you to train the model by uploading existing customer datarepresenting previous behavior, or sample data. The use of previous results allows for the AdaptiveDecision Manager to create models that are able to predict behavior. Only positive and negative casesare considered by ADM. Positive and negative cases correspond to the behavior that will be taken intoaccount by the settings defined in the adaptive model rule. You can also train models through activities(page 68). The process of uploading responses consists of:

1. Upload data: import the CSV file containing the input data for each case.2. Select outcome: select the column that provides the outcome for each case.3. Map Behavior: map the outcome in the data to the response.

Batch DataThe tabs that are accessed via Decisioning | Batch Data in the Pega menu provide the facilities todefine and manage batch execution of the strategies that are accessed by your application. Additionally,for large scale batch decisioning purposes, you can access the Topology configuration throughthe Decisioning | Batch Data menu. Depending on the type of action you want to perform, use thecorresponding tab. Batch data functionality assumes the availability of customer data, data classes, andreport definition rules.

• Batch Runs (page 39)• Input (page 41)• Output (page 42)• Topology (page 46)

Getting Started


The definition of input configurations, output configurations, and topology is typically performedby system architects. The definition and triggering of strategy execution is typically performedby strategy designers.

Batch RunsThe Batch Runs tab allows you to manage the configurations that trigger batch execution of the strategiesdefined in your application.

In activities, you can define the execution of a strategy in batch by using the Call Pega-DM-Batch-Work.pxCreateDecisionExecutionConfig method, and providing the customer data class, the reportdefinition name (input), the strategy Applies To class, the strategy rule, the public component, and theoutput data class. Once defined, you can trigger the execution of the strategy in batch by using the CallPega-DM-Batch-Work.pxInvokeDecisionExecution method, and providing the work object's ID.

Use the Refresh button to make sure you are looking at the latest set of configurations. Use the button atthe end of the corresponding row to delete a configuration.

The overview shows the configurations available in your application.

• Name: the name of the configuration.• Strategy Name: the name of the strategy rule executed by the batch run.• Input: the name of the configuration that defines the input data for the batch run.• Output: the name of the configuration that defines where and how to store the results of the batch

run.• Records Processed: the number of records processed during strategy execution.• Finished: the time since the batch run was last executed, and completed.• Status: the status of the strategy execution in batch.• Updated By: the user that last created, updated, or executed the configuration.• ID: the identifier of the batch run.

Use the New button to open the Strategy Execution Configuration form. The Strategy ExecutionConfiguration form allows you to create a new batch run configuration, something you can also do whentesting strategy rules. Once the form is completed, click Submit to save the configuration, or Submit andExecute to save the configuration, and trigger batch execution.

Getting Started


The Strategy Execution Configuration form provides the fields that allow you to define the batch run.

• Name: the name of the configuration. A default name identical to the ID is assigned to a new batchconfiguration, but you can change it by entering a new value.

• Input: provide the configuration that defines the input data for the batch run.• Strategy: provide the strategy rule to execute in the batch run.• Component: select the decision component in the strategy. Only public components are displayed.• Output: provide the configuration that defines how to store the output of the batch run.

The progress of executing the batch run is displayed after clicking Submit and Execute. Click the links inStrategy, Input, and Output to go to the corresponding rule, or configuration. Click Refresh to update theinformation displayed under Batch Progress. Click Stop to stop the process or running the batch. ClickDelete to delete the batch run. Click Re-Execute to execute the batch run again.

Getting Started


When the status is Resolved-Completed, go to the Reports tab to view reports for the batch run. If theoutput configuration type used in the batch run included VBD, you can perform analysis in VBD (page47) by clicking the Visual Business Director link. Proposition, segment and channel centric reports areavailable.

• Distribution of the top ranked proposition selected by the strategy.• Distribution of the second highest ranked proposition selected by the strategy.• Distribution of the third highest ranked proposition selected by the strategy.• Average priority of propositions selected by the strategy.• Recurrence of propositions considered by the strategy, and corresponding rank.• Average priority in the proposition rank report.• Distribution of segments selected by the strategy.• Distribution of all propositions without considering rank selected by the strategy.• Distribution of channels selected by the strategy.

InputThe Input tab allows you to manage the configurations that define the input data used in the batchrun. Use the Refresh button to make sure you are looking at the latest information about input dataconfigurations. Use the button at the end of the corresponding row to delete a configuration.

Getting Started


The overview shows the input configurations available in your application.

• Name: the name that identifies the configuration. You can open the configuration by clicking its name.• Strategy Applies To: the data class on which the strategy is defined.• Report Definition: the name of the report definition that provides the data for strategy execution.• Case ID: the case ID.

Use the New button to open the Create/Edit Input Configuration form. An input configuration maps data ina database table to data instances.

The Create/Edit Input Configuration form provides the fields that allow you to define the inputconfiguration.

• Name: provide the name that identifies the configuration.• Strategy Applies To: select the data class on which the strategy is defined.• Report Definition: select the name of the report definition that provides the input data for strategy

execution. A specific filter pattern is required in the report definition so that batch execution can bedistributed across PRPC nodes. This pattern consists of a filter condition that maps the appropriatecolumn to the param.PartitionKey value. Distributed batch execution depends on this filter pattern, aswell as how system nodes are configured for that purpose in the Topology (page 46) landing page.

• Case ID: this field displays the primary key in the database table providing the input data.

OutputThe Output tab allows you to manage the configurations that define how and where to store the results ofthe batch run.

• Output tab (page 43)• Batch output wizard (page 43)

Getting Started


Output TabUse the Refresh button to make sure you are looking at the latest information about output configurations.Use the button at the end of the corresponding row to delete a configuration. Use the New button to startthe Batch Output Wizard (page 43).

The overview of output configurations shows the information pertaining to every output data configuration. This information consists of:

• Name: the unique name of this configuration.• Type: the type of output configuration (VBD, VBD and Reporting, or Reporting).• Strategy Result Class: the name of the SR class configured in the output configuration.• Database: the name of the database where the output of the batch run should be stored.• Database Table: the name of the database table where the results should be stored.• Updated By: the name of the user that last changed the output configuration.

Database and Database Table only display values if the output configuration includesReporting.

Batch Output WizardAn output configuration defines how data instances (strategy results) map back to data in a databasetable. The Batch Output Wizard consists of four steps:

1. Create output configuration (page 43)2. VBD configuration (page 44)3. Report configuration (page 45)4. Confirm (page 46)

Create Output ConfigurationIn this step, define the name of the output configuration, select the strategy result class, and select thetype of output configuration.

Getting Started


• Name: the name that identifies the configuration.• Strategy Result Class: the strategy result class that provides the initial set of properties of the data

class generated by the configuration.• Purpose: check the output configuration type (VBD and Reporting, VBD, or Reporting).

Click Next to proceed. Depending on the purpose, the next step(s) consists of:

• VBD Configuration (page 44) for VBD only configurations.• Report Configuration (page 45) for reporting only configurations.• VBD Configuration (page 44), and Report Configuration (page 45) for VBD and reporting

configurations.

VBD ConfigurationDefine the VBD configuration, customer segmentation, organization hierarchy, and measurements. Theconfiguration in this step is very similar to the Capture Response tab (page 74) of the interaction rule,with an additional field (Interaction Time) provided to select the property that defines the date and timethe interaction would take place.

Getting Started


After defining the necessary fields, click Next to proceed. Depending on the purpose, the next step(s)consists of:

• Report Configuration (page 45) (VBD and reporting configurations), or• Confirm (page 46)

Report ConfigurationDefine the report class, the database, the database table, and specify the input data, custom andpredefined properties.

Getting Started


• New Report Class: the name of the data class created by the configuration. The data class isconfigured based on the Name field, but you can change it if required.

• Database: the name of the database where to store the output of the strategy execution.• New Database Table: the name of the database table where the results of strategy execution are

written to. If a database table with the name configured in this field does not exist, it is created inthe batch run process. If a database table with the same name already exists, it is dropped, anddata stored in that table is lost. The actual creation of the database table takes place in the final step(Confirm).

• Input Data Properties: section displaying properties pertaining to the input data. Partition Key must bedefined for distributed batch execution to take place according to how system nodes are configuredfor that purpose in the Topology (page 46) landing page.

• Custom Defined Properties: section displaying properties defined in the proposition hierarchy class.• Predefined Properties: section displaying properties inherited from the base class. The base class for

strategy execution is Data-pxStrategyResult.

After defining the necessary fields, click Next to go to the final step (page 46).

ConfirmThe final step displays information about the result of completing the output configuration process,including name of the data class that is going to be created, and database table name.

TopologyThe topology landing page allows you to define the participation and load of PRPC system nodes in batchexecutions. Click Refresh to display an actualized list of system nodes. In the grid displaying the systemnodes, use the Enable Batch and Threads columns to configure a system node's participation in strategyexecution in batch. Click Save to submit your changes.

Getting Started


The grid showing the system nodes displays the details listed below.

• Host Name: the name of the system node.• Description: displays the description as defined in the system node instance. Changing the

description of the system node may be necessary when multiple system nodes run on the sameserver.

• Agent Status: the status (Disabled, or Enabled) of the ProcessBatchJob agent.• Enable Batch: check to add a given system node to the pool of nodes used to distribute batch

execution.• Threads: select the number of threads that can run in a given node.• Updated Time, and Updated By: the time stamp and the user name corresponding to the last time the

configuration for a given node was saved.• Node ID: the PRPC node ID.

Related Topics

• Input (page 41)• Output (page 42)• Topology (page 46)

Visual Business DirectorVisual Business Director (VBD) allows you to perform historical analysis, and forecasting. VBD enablesbusiness users to visualize the business based on different views (proposition, channel, customer, etc.),and examine the success levels down to any level. Through the VBD applet, the Visual Business Directorlanding page provides the facilities to visualize decision results, and monitor business metrics with a 3Dgraphical view of the different dimensions and measurements (such as accept rate, conversion rate,average price, volume, number accepted, and number of processed responses). The Visual BusinessDirector landing page can be accessed through the Pega menu by going to Decisioning | Visual BusinessDirector.

Getting Started


• The button on the top left corner of the VBD applet allows for refreshing the data.• On the top right corner, buttons are available for zooming in/out, changing the position of the VBD

applet, and setting it to default view.

• The context menu allows you to capture the current state of the VBD applet to clipboard or file, exportgrid data, save a view, and load a predefined view.

• Bar charts on the VBD applet's walls show the calculation for a single dimension, and target values.• Each line chart shows a different metric over the current time period selected in the timeline. Clicking

a chart makes the grid use that metric, and double clicking allows you to change the metric the chartdisplays.

• Bars show the performance for a combination of two dimensions. Color saturation is applied based onquantity below/above target, and each bar provides a view of the statistics.

• The VBD applet supports changing the dimensions displayed in the X (right) Y (left) axis, usingdifferent data modes, displaying multiple grids, filtering on different dimensions, and exporting griddata.

Related Topics

• Dimension Filter (page 49)• Data Modes (page 49)• Timeline (page 50)• Forecasting (page 51)

Getting Started


• X/Y Axis (page 51)• Multiple Grids (page 52)• Export Grid Data (page 53)

Dimension FilterThe dimension filter allows for dynamically defining customer interactions using multiple dimensions.The filter works as a slider displaying the hierarchy of the different dimensions. The tree view shows thedifferent levels up to the selected item. Use single (left) click selection to select/deselect levels. If the itemis already part of the defined filter settings, it will be deselected. Each dimension is displayed in a differentcolor. The color saturation shows the proportion of selected items in the dimension's hierarchy. Whennothing is selected, the item in the tree displays with white background.

Data ModesThe top part of the panel on the far right of the back wall of the VBD applet provides the facilities forsetting different data modes. The VBD applet can operate in regular, reference data, or delta mode.

Getting Started


The regular mode shows the source versusreference data sources. Switch to this data mode byclicking the icon.

The reference mode shows the interaction historyrecords corresponding to the time period selected inthe Reference drop down. Switch to this data modeby clicking the icon.

The delta mode provides the comparison betweenthe Reference and Source data sources, allowingyou to analyze their relative effects. Switch to thisdata mode by clicking the icon.

TimelineThe timeline is the interface allowing for browsing recorded historical performance, and predicted futureperformance. This interface consists of timeline console and timeline display. The and buttons allowyou to hide/show the display and the console.

The console allows you to select the date settings for displaying data in the VBD applet, and select thedata sources.

Selection Description

From Selects the start date for showing the data. The earliest date to be displayedis determined by the Start Date of Reference Data setting (page 53) in theServices landing page.

To Selects the end date for showing the data.

Duration Displays the duration between start and end date. Duration is displayed indays, hours, minutes, and seconds.

Source Drop down for selecting which data source to use as source.

Reference Drop down for selecting which data source to use as reference.

Getting Started


Unless the VBD database has been configured otherwise, Reference and Source display the datasources listed below. Additionally, Source and Reference also list any data source created in the processof batch execution configured to perform forecasting (page 43).

• Last Month: compare to last month aligned on days. For example, compare September 15 withAugust 15.

• Last Quarter: compare to last quarter aligned on days. For example, compare September 15 withJune 15.

• Last Year: compare to last year aligned on days. For example, compare September 15, 2012 withSeptember 15, 2011.

The selected data sources are listed on the left side. Each data view has the corresponding line showingwhen data is available.

Two time sliders allow you to change the start and end date. The recommended method for changingthe time span is by using the From/To selection in the timeline console (page 50), but you can alsochange it by dragging the vertical sliders, or using the scroll buttons. When doing this, the timelineconsole reflects the changes.

ForecastingVBD supports making future projections based on simulation (page 151) data generated from anexisting data (forecasting) through two types of batch output configurations (page 43). In order to usethis functionality, the customer sample used in forecasting must be based on customer data. The dataalso needs to provide a table/view with the case ID column.

X/Y AxisBy default, the Y axis in the VBD applet is set to proposition group, and the X axis to customer subsegment. You can change this by double clicking the selected level, and using the set axis dialog toselect a different dimension, or level within the dimension. The VBD applet reflects the changes whenyou confirm the new dimension/level selection in the set axis dialog. For example, you can perform timebased analysis for behavior by setting the Y axis to behavior, and setting the X Axis to the time period forshowing customer behavior.

• Y axisa. Double click the axis label on the left. By default, it displays Group.b. Use the Set Y Axis dialog to change it to show Behavior.

Getting Started


c. Click OK.• X axis

a. Double click the axis label on the right. By default, it displays Sub Segment.b. Use the Set X Axis dialog to change it to show Day.

c. Click OK.

Multiple GridsYou can add multiple grids in the VBD applet, allowing you to focus on different aspects of the businessstrategy. Enabling multiple grids is done by using the + icon at the far right of the X axis. After addingmultiple grids, you can define filters for each individual grid using the facilities provided in the back wall.

Getting Started


You can remove a grid by clicking the close button on the top right corner of the back wall. The timelineselection remains common to all grids. If the + button is not visible, click the control (zoom out) until the +button is visible in the grid. In the example below, two grids are added to compare the number of responsesprocessed versus number of positive responses.

Export Grid DataYou can export grid data by using the icon on the right side of the VBD applet's back wall. You need toprovide the name of the CSV file, select the location on disk for this file, and save the file. This file can beused for offline analysis using a suitable tool, such as Microsoft Excel.

ServicesThe Services landing page that is accessed through Decisioning | Services in the Pega menu allowsyou to configure the connection to the servers running Interaction Services, Adaptive Decision Manager,and Visual Business Director. The settings defined in the Services configuration are system generic.Connecting to the DSM services is necessary so that you can perform the runtime actions (run interactionand strategy rules, perform adaptive model management activities, execute flows or activities that resultin running strategies or capturing response data, and use the VBD applet). However, it is not requiredwhen creating and defining Decision Management rules.

Changes to the configuration can be done any time. When changing or defining new service end points,make sure the IS service is running. If you do not have ADM or VBD, leave the host and port fields empty.

1. Login as the PRPC system architect user defined for your DSM enabled application.2. Go to the System Management Application, and check if the UpdateAdaptiveModels agent in the

Pega-DecisionEngine rule set is running. If not, start it.3. In the Pega menu, go to Decisioning | Services.4. In the Interaction Services, Adaptive Decision Manager, and Visual Business Director sections, enter

the name of the server in the Host field, and the port number in the Port field.5. In the Visual Business Director section, define the monitoring data start date in the Start Date of

Reference Data field.Changing this setting requires restarting the VBD Planner.

6. In the Agents section:

Getting Started


• If required, change the time interval (in seconds) to trigger the UpdateAdaptiveModels agent inthe Pega-DecisionEngine rule set.

• Check the Entitled to Use Adaptive Decision Manager if your software agreement includes ADM'scapability to use predictive data (adding predictors in adaptive model rules).

7. Two buttons allow you to save your changes:• Click Save & Ping to save the settings, and test the connection to the DSM services. The

version of the client library is displayed at the top, and the corresponding server library displayedin the Status message for each service. The version follows a <Major Version>.<MinorVersion>.<Revision>.<Build Number> pattern.

• Click Save to save the settings.

Saving settings makes the changes effective in the Decision Management service layer, and PRPC. Ifyou have not configured ADM or VBD, the applicable propagation of data resulting from interactions tothe corresponding service(s) is turned off.

ToolsThe PMML Import wizard can be accessed by going to Decisioning | Tools in the Pega menu. This wizardallows you to import third party regression and mining models that are represented in PMML format (page149) version 4.0. The result of running the PMML Import wizard is a set of rules that translate the thirdparty regression or data mining model in PRPC:

• Property rules• Scorecard rule (page 64)

Getting Started


The PMML Import process is done in fours stages.

1. Upload data (page 55)2. Review PMML (page 55)3. Review data transformation (page 56)4. Review results (page 57)

Upload DataIn this step, you upload the PMML file, and define where the rules resulting of the translating processshould be created.

1. In the Upload Data section, use the Browse button to select the third party model. The validity of thethird party model in terms of being handled by the PMML Import wizard is based on content, and notfile extension. Typically, third party models are stored with XML or PMML file extension.

2. In the Select RuleSet Information section, enter the details necessary to define where to create therules that translate the third party model in PRPC. Select the rule set name, version, and base class.

3. Click Cancel to exit the wizard, or Next to proceed to the Review PMML step (page 55).

Review PMMLIn this step, you review the information about the data dictionary, and model.

Getting Started


1. The Data Dictionary section shows the data fields in the third party model's data dictionary. Thisinformation is based on the DataField child elements of the DataDictionary element.

2. The Transformation Dictionary section shows the derived fields in the third party model'stransformation dictionary. This information is based on the DerivedField child elements of theTransformationDictionary element.

3. Depending on the type of model represented by the corresponding PMML element, the last sectiondisplays model information: name of the model (spaces are removed), type of algorithm, function, andtarget field name.• If the PMML element is MiningModel, this information is displayed in the Mining Model section.• If the PMML element is RegressionModel, this information is displayed in the Regression Model

section.4. Click back to go to the previous step (page 55), or Next to proceed to the Preview Data

Transformation step (page 56).

Preview Data TransformationIn this step, you are provided with information about the rules that are generated by running the datatransformation process.

1. The Scorecard Model section displays details about the scorecard rule to be generated.2. The Properties section displays details about the property rules to be generated.3. The Map Values display details about the map value rules to be generated.4. Rules that already are present in the defined base class are deselected by default, and you should

inspect the usage of these rules before overwriting them. You can check/uncheck the rules that aregoing to be created in the PMML import process through the check box in the Generate column.

Getting Started


5. Click back to go to the previous step (page 55), or Next to proceed to the Review Results step(page 57).

Review ResultsIn the final step, you review the rules generated by running the data transformation process. It is also inthis step that, if applicable, you can click the Remove Generated Rules button to delete the set of PRPCrules generated by running the data transformation process. Click Back to go to the previous step (page56), or click Finish.

Getting Started


Decision Management Rule Types

• Predictive Model (page 58)• Scorecard (page 64)• Adaptive Model (page 67)• Interaction (page 71)• Strategy (page 76)

Related Topics

• Audit Notes (page 100)• Finalizing Rules (page 100)• Testing Rules (page 101)

Predictive ModelCreate a predictive model rule to use a predictive model (page 149) developed and generated usingPredictive Analytics Director. Predictive models predict behavior for one or more segments (classes)using customer data. For example, you can create a predictive model to predict the likelihood ofcustomers defaulting on payments. The output of a predictive model rule is statistics generated by thePAD model that provides the prediction.

Predictive model rules are referenced in strategies through the predictive model component (page83). In flows, predictive model rules are referenced through the decision shape by selecting thepredictive model type. In expressions, you can obtain the segments calculated by the predictivemodel rule by using the Lib(Pega-DecisionEngine:PredictiveModel).ObtainValue(this, myStepPage,"preditivemodelrulename") syntax.

1. Create instance (page 58)2. Upload predictive model (page 59)3. Define input mapping (page 60)4. Review model statistics (page 60)5. Define results (page 62)6. Finalize rule (page 100)7. Test rule (page 101)

Create Rule Instance1. In the Application Explorer for your PRPC application, right click and select New | Decision |

Predictive Model.2. In the Predictive Model: New rule form, enter the name of the rule instance in the Purpose Field, and

make sure the appropriate class is set in the Applies To field.

Getting Started


3. Click Create.4. Upload PAD model (page 59).

Upload Predictive ModelAfter creating the rule instance (page 58), you need to upload the predictive model created,developed, and exported using PAD. Spaces are not supported in the OXL file name. Make sure yourename the OXL file to avoid naming conflicts in PRPC. Another limitation to using PAD models is in theexported model itself: make sure the model was not exported with additional model output fields (alsoknown as crosstab fields). Additionally, you should use models generated by PAD V6.1.2 or lower, ashigher versions generate OXL files that do not contain aggregation information.

1. In the Predictive Model tab, click Browse to navigate to the location of an exported PAD model, andselect the appropriate OXL file (page 149).

2. Information about the predictive model is displayed when the upload is successful, including fileand model details, and model performance measured in terms of Coefficient of Concordance (page148). It is using this section that you can also use the Import Different Model to upload a newversion of the OXL file. You can also click the link in the File field to download the OXL file.

Getting Started


3. Proceed with mapping inputs (page 60).

Input MappingThe Input Mapping tab allows you to map fields used by the model to properties.

Map the fields to properties in the Property column by using the SmartPrompt to select existingproperties, or the button to create a new property.

StatisticsThe Statistics tab displays statistical information for each class the model can generate to define thesegmentation.

In the case of the predictive model we are using in this example, the Statistics section showsClassification, Percentage, churn rate, and Lift. It is based on the number of classes that you can defineresults (page 62) to apply for cases falling in a given segment.

Getting Started


The Attributes section displays information about the model attributes (page 149). Model attributes arepart of the information generated when exporting the model using Predictive Analytics Director.

Getting Started


Predictive Model ResultsThe segmentation provided by the PAD model needs to be assigned to actions defining what resultapplies to a given class, or class range.

• Classification and business strategies (page 63)• Defining results (page 63)

Getting Started


Classification & StrategiesPredictive models are often constructed to generate the largest possible number of classes (segments)that exhibit predicted behavior, steadily increasing as the class number increases. However, businessstrategies are often translated to two or three alternative business strategies, typically associated withprobability of predicted behavior (high, medium, and low). Remapping the classification defined inthe predictive model to the typically smaller number of business strategies allows you to increase thequality of business. For example, if a lower propensity (page 150) class is reassigned to the mediumpropensity class where fewer customers are presented with a product offer but a greater proportionresponds, although the volume of business decreases, the quality increases.

Define ResultsMap the segments output by the PAD model to decision results. You can also examine the groupedstatistics as defined based on the PAD model statistics, allowing you to understand the effect ofcombining the different classes to create predictive based segmentation. If the original PAD model doesnot contain aggregation/grouping statistics information, N/A is displayed.

1. Go to the Results tab of the predictive model rule instance.2. The total number of classes corresponding to the segmentation output by the predictive model is

displayed by default.

3. Click Edit to group classes, mapping them to class ranges that are assigned to the same action thatshould be taken for cases falling in the corresponding classification. Use the Map with next segmentcheck boxes to map the current class to the next, and click Save.

Getting Started


4. The Results tab displays a reduced number of classes according to the class range mapping definedin the previous step. In the Result column, define the actions.

5. Save the rule instance.

ScorecardCreate a scorecard rule to create segmentation based on one or more conditions, and a combiningmethod. Scorecard rules are also the rules that represent third party models in PRPC as a result ofrunning the the PMML Import wizard (page 54). The score based segmentation can be mapped to resultsby defining cutoff values used to map a given score range to a result. For example, you can create ascorecard rule to calculate customer segmentation based on age and income, and then map particularscore ranges to defined results. The output of a scorecard rule is a score, and a segment defined by theresults.

Scorecard rules are referenced in strategies through the scorecard component (page 83). In flows,scorecard rules are referenced through the decision shape by selecting the scorecard model type. Inexpressions, you can obtain the segments calculated by the scorecard rule by using the Lib(Pega-DecisionEngine:Scorecard).ObtainValue(this, myStepPage, "scorecardrulename") syntax.

Getting Started


1. Create instance (page 65)2. Define score calculation (page 65)3. Map score ranges to results (page 66)4. Finalize rule (page 100)5. Test rule (page 101)


Scorecard.2. In the Scorecard: New rule form, enter the name of the rule instance in the Purpose Field, and make

sure the appropriate class is set in the Applies To field.

3. Click Create.4. Define scorecard calculation (page 65).

Score CalculationAfter creating the rule instance, define the predictors by adding properties, defining how the score shouldbe calculated, and assigning the weight of each predictor in the score calculation.

1. In the Scorecard tab of the Scorecard rule instance, use the Combiner Function drop down to selectthe method for combining the score.• Select SUM to combine based on the score sum.• Select AVERAGE to combine based on the score average.• Select MIN to combine based on the minimum score.• Select MAX to combine based on the maximum score.

2. The grid under the Combiner Function drop down allows you to define the properties, conditions,score, and weight attributed to cases matching the conditions.• In the Property column, use the down arrow to select existing properties using the Smart Prompt,

or use the button to create a new property.• In the Condition column, define the operator and condition values. Use the button to add as

many conditions as necessary.

Getting Started


• In the Score column, enter the score that should be applied for cases falling in the definedcondition. The score can be defined explicitly in the scorecard (for example, 20), it can bedefined through a property (for example, .Score), or it can involve a computation expressed by anexpression (for example, .Score*.PenaltyMargin, or @divide(.Score,100)).

• Optionally, define a fallback score for any case that does not match the defined conditions in theOTHERWISE row.

• In the Weight column, define the coefficient of the predictor. By default, every predictor isassigned the same weight (1). Changing the default value results in calculating the final scoreas weight multiplied by score (for example, 0.5*30). Maintaining the default value implies that,effectively, only score is considered because the coefficient is 1 (for example, 1*30).

• Use the button under the first property to add as many properties as necessary to segmentyour customer base, and repeat the process specified in the previous steps.

3. Map scores to results (page 66).

Define ResultsAfter defining the way scores should be calculated (page 65), and the conditions for cases falling in agiven score, map score ranges to results by defining the cutoff value.

1. Go to the Results tab of the scorecard rule instance. 2. Information is provided about the score ranges. The maximum and minimum scores depend on

the combiner function selected in the Scorecard tab. If you use expressions to calculate the score,minimum and maximum scores display as 'unknown' because they can not be calculated.

3. In the Result column, enter the decision result corresponding for the score range specified in theCutoff Value column.

Getting Started


4. In the Cutoff Value column, define the score range according to the minimum and maximum score

the scorecard calculates. Use the button to add as many results as necessary. Score ranges aredefined top-down, and they are automatically calculated based on the cutoff value defined in theprevious result.

5. Check the audit notes (page 100) option if you want scorecard details captured in the work object'shistory.


Adaptive ModelAdaptive model rules configure the adaptive scoring models (page 147) in the ADM system. The outputof an adaptive model rule is a partial list of adaptive statistics (evidence, propensity, and performance).

Adaptive model rules are used in strategy rules for a given proposition issue or group through theadaptive model component (page 83). Methods (page 67) are available to support using adaptivemodel rules in activities.

• Create rule instance (page 68)• Define model configuration (page 69)• Define model settings (page 70)• Finalize rule (page 100)• Test rule (page 101)

MethodsThe methods listed below support the use of adaptive model rules in activities.

• Obtain predictor information (page 67)• Train models using report definitions (page 68)

Predictor InformationYou can obtain the predictor information for an adaptive model rule trough the Call DSMPublicAPI-ADM.pxLoadPredictorInfo method with the following parameters:

• Option to include active, or active and inactive predictors.• Set it to true if you want to retrieve only active predictors.• Set it to false if you want to retrieve active and inactive predictors.

• Result page where to store the predictor information.• Adaptive model key

The page (pxObjClass: Embed-Decision-AdaptiveModel-Key) containing the adaptive modelparameters. The Embed-Decision-AdaptiveModel-Key class is used to uniquely identify an adaptive

Getting Started


model. The properties of data type text in this class provide the proposition dimension (pyIssue,pyGroup, and pyName), channel dimension (pyDirection, pyChannel, and pyTreatment), the AppliesTo class of the adaptive model rule (pyConfigurationAppliesTo), and the name of the adaptive modelrule (pyConfigurationName).

Response UploadYou can train models using a report definition representing response history trough the CallDSMPublicAPI-ADM.pxUploadResponsesFromReport (recommended) or the Call Rule-Decision-AdaptiveModel.pyUploadResponsesFromReport methods with the following parameters:

• Report definition ruleOnly properties that are optimized for reporting when they have have been created should be used inthe report definition.

• Class of the report definition rule• Outcome column information

The page (pxObjClass: Embed-Decision-OutcomeColumnInfo) mapping the possible response valuesto defined behavior values.

• Adaptive model keyThe page (pxObjClass: Embed-Decision-AdaptiveModel-Key) containing the adaptive modelparameters. See usage of the adaptive model key to obtain predictor information (page 67).

Create Rule Instance1. In the Application Explorer for your PRPC application, right click and select New | Decision | Adaptive

Model.2. In the Adaptive Model: New rule form, enter the name of the rule instance in the Model Name field,

and make sure the appropriate class is set in the Applies To field.

3. Click Create.4. Configure predictors (page 69).

Getting Started


Configure ModelsProperties in the data model can be used as predictors (page 150). If an adaptive model does not haveexplicitly defined predictors, ADM dynamically adjusts to keep all data encountered within its internalrepresentation of the data. You should select all properties that have potential predictive performance(page 149). The adaptive analytics engine (page 147) automatically detects the most importantpredictive fields to use in an adaptive scoring model (page 150).

The capability of using predictors depends on the the Entitled to Use Adaptive DecisionManager setting in the Services landing page (page 54). If this setting is disabled, you candefine and use adaptive models in your application, but these models can not operate basedon predictive data.

1. Go to the Configuration tab of the new adaptive model rule instance.2. In the Predictors section, use the button to add as many rows as the predictors you want to use in

the adaptive scoring models. Use the SmartPrompt to select existing properties, or use the buttonto create a new property. The Property Type column displays the data type of the property. Predictorscan be treated as numeric or symbolic. In the Predictor Type column, select the predictor data type.

3. Two sections are provided to select values using the SmartPrompt. The models in ADM that areconfigured by this rule learn from the behavior defined in these sections. The values that aredisplayed correspond to the possible behavior defined in the system for the behavior dimension,which consists of the combination provided for type of behavior (typically, positive, or negative), andresponse (for example, Accept, and Reject). In the Positive Behavior and Negative Behavior sections,select the possible values in the behavior dimension to associate with positive and negative behavior.ADM can also learn from response values in the interaction result that are not defined in thesesections. In this case, you simply define the behavior type in the corresponding behavior section (forexample, Positive in the Positive Behavior sections, and Negative in the Negative Behavior section),and ADM learns from responses set as, for example, Positive-Accept, Positive-Yes, Negative-Reject,Negative-No, etc.

Getting Started


4. Define settings (page 70).

Define SettingsAdaptive model settings configure how ADM operates by controlling the runtime throughput of ADM, andthe creation and update of the individual scoring models. The settings should be configured to appropriatevalues to prevent high loads on the database. The settings are grouped by category.

1. Go to the Settings tab of the new adaptive model rule instance.

2. The following topics provide information on how to configure the settings used to create and retrievethe scoring models.• Responsiveness (page 71)• Data analysis (page 71)• Advanced configuration (page 71)


Getting Started


ResponsivenessIn the Responsiveness section of the Settings tab, configure the Memory setting. This settingcorresponds to the value that specifies the amount of interaction results history, which are translated innumber of cases (page 147), the scoring models maintain during predictions (page 149). By default,it is set to never discard information (0). The memory configuration allows you to discard the oldestcases, and it allows you to implement trend detection (page 151) by creating multiple adaptive models,all triggered by the same proposition (page 150). This setting influences the binning of predictors asbehavior changes with new cases being recorded.

• Low memory values allow the identification of new trends.• High memory values provide robust and long-term predictive power (page 150).

Data AnalysisIn the Data Analysis section of the Settings tab, configure the settings that influence data analysis.

• Run Data Analysis After: a value that determines the number of interaction results that trigger runningdata analysis for a model. Data analysis is triggered after the number of interaction results configuredin this setting is reached. Default setting is 500.

• Grouping Granularity: a value between 0 to 1 that determines the granularity of predictor groups.Default setting is 0.25.

• Grouping Minimum Cases: a value between 0 to 1 that determines the minimum percentage of casesper interval. Default setting is 0.05.

• Performance Threshold: a value between 0 to 1 that determines the threshold for excluding poorlyperforming predictors. Default setting is 0.52.

• Correlation Threshold: a value between 0 and 1 that determines the threshold for excluding correlatedpredictors. Default setting is 0.8.

Advanced ConfigurationIn the Advanced Configuration section of the Settings tab, configure the settings that control otheroperations performed in the ADM database.

• Performance Memory: a value that determines the number of cases of moving window size perproposition. The number of cases of moving window size per proposition influences the calculationof the CoC (page 148), and it is implemented so that equal comparison between models can beperformed. Default setting is 0.

• Refresh After: a value that determines the number of interaction results that trigger refreshing thescoring models in the ADM database. Model refresh is performed when the number of interactionresults in this settings is reached. You should set this value to a value lower than the value forrunning data analysis (page 71). Default setting is 500.

• Enable Local Updates: check box to enable or disable updating the model's local profile after everyresponse. This setting allows you to enable local (PRPC) learning for the adaptive models configuredby the adaptive model rule. Default setting is enabled.

• Check the audit notes (page 100) option if you want adaptive model details captured in the workobject's history. Default setting is disabled.

InteractionInteraction rules define the parameters for running the strategy, how to prepare the interaction history,and how to save the interaction results.

Interaction rules are used in flows (page 103) through the run strategy, and capture response shapes.Interaction rules can also be used for process monitoring through the capture response shape alone.Methods (page 72) are available to support using interaction rules in activities.

1. Create rule instance (page 72)2. Define interaction history (page 73)3. Define strategy execution settings (page 73)4. Define capture response settings (page 74)

Getting Started


5. In a multi-level decisioning scenario, define the sub-level pages and interaction rules (page 76)6. Finalize rule (page 100)7. Test rule (page 101)

MethodsThe methods listed below support the use of interaction rules in activities.

• Run strategy (page 72)• Capture response (page 72)

Run StrategyInteraction rules can be executed in run strategy mode by using the Call Rule-Decision-Interaction.pxRunStrategy method with the following parameters:

• Name of the interaction rule• Applies To class

Capture ResponseInteraction rules can be executed in capture response mode by using the Call Rule-Decision-Interaction.pxRunCaptureResponse method with the following parameters:

• Name of the interaction rule• Applies To class


Interaction.2. In the Interaction: New rule form, enter the name of the rule instance in the Purpose Field, and make


Getting Started


3. Click Create.4. Define interaction history (page 73).

Interaction HistoryDefine the settings the interaction rule needs to provide for recording the interaction history in the ISdatabase tables.

1. Go to the Interaction History interaction rule instance2. Define the customer context by providing the customer ID in the Customer ID field.3. Use the Work ID field to associate interaction records with work object. The work object ID can be

used to retrieve extended information about a case from the interaction history.

4. Define the settings for running the strategy (page 73).

Run StrategyDefine the settings for running the strategy. The configuration in this particular tab is used in flowsthrough the run strategy shape (page 103).

1. Go to the Run Strategy tab of the interaction rule instance.2. Provide the strategy rule in the Strategy Name field.3. The Components Mapping section displays the public components defined in the strategy rule, and

also whether each component delivers one or multiple results. Typically, the public components inthe strategy provide the possible evaluation paths in the decision making chain. Use the Property

Getting Started


column to map to the page (one result) or page list (multiple results) property that holds the output ofthe corresponding strategy component.

4. Define settings for capturing the interaction data (page 74).

Capture ResponseDefine the settings for capturing the interaction data (response). IS dimensions and measurements fieldsare automatically retrieved from the IS database tables in the Decision Management service layer. Theconfiguration in this particular tab is used in flows through the capture response shape (page 103).

1. Go to the Capture Response tab of the interaction rule instance.

Getting Started


2. In the Behavior and Response fields, map to the properties providing the behavior dimensioninformation. In the example above, the behavior dimension requires the Behavior, and Responselevels. The interaction rule does not support combining dimension levels in a composite value (for

Getting Started


example, Positive-Accept), so you need to limit the application to provide (or pass through properties)the value corresponding to the appropriate level, as combining dimension levels in a composite valueis only supported in adaptive model rules (page 69).

3. Check the audit notes (page 100) option if you want interaction details captured in the work object'shistory.

4. In the Customer Segmentation section, map to the properties providing the customer dimensioninformation. In the example above, the customer dimension requires the Segment, and Sub Segmentlevels.

5. In the Organization Hierarchy section, the Use Default Organization Hierarchy option allows you touse your application's default organization to provide the application dimension. Deselect this option ifyou want to use another organization.

6. In the Contact section, map to the properties providing the response context dimension information.In the example above, the response context dimension requires the Category, and Reason levels.

7. Before proceeding with configuring the Customer Response field(s), define whether the propositionwas offered as a single proposition (in which case you need to map to the property providing theproposition in the Selected Proposition field), or as part of a proposition bundle (page 150). Inproposition bundles, map the properties defining the bundle parent in the Bundle Parent field, and theproperties defining the bundle members in Bundle Members field.

8. In the Measurements section, select the property that provides the value for each desiredmeasurement. What measurements to provide when storing the interaction data depends on the typeof interaction the rule is configuring. In the example above, we are not recording measurement data.

Multi-LevelThe Multi-Level tab allows you to provide the page group or page list property that contains customerinformation, and then defining the interaction rule that provides the ID for which the interaction historyneeds to be retrieved so that the interaction can be iterated as many times as necessary. You can call as

many sub levels as defined in your strategy design by using the button to add the sub level specificinteraction rules.

• Sub level pages are properties of type page group, or page list. The property that you select in thisfield corresponds to the property that is defined as Strategy Page in the sub strategy component thatimports the sub level strategy. This also means the page class of the page group or page list propertyis the same as the data class the sub level interaction (and strategy) are in.

• Sub level interaction rules define the interaction rule that runs in capture response mode. The captureresponse configuration of the sub level interaction rule should be the same as the one of the strategycalling it as a sub level interaction rule.

StrategyStrategy (page 151) rules define the decision that is delivered to an application. The decision ispersonalized and managed by the strategy to reflect the interest, risk, and eligibility of an individualcustomer in the context of the current business priorities, and objectives.

Getting Started


Strategy rules are used in other strategy rules through the sub strategy component (page 81), andin interaction rules (page 71). Methods (page 77) are available to support the use of strategies inactivities.

1. Create rule instance (page 78)2. Design strategy (page 79)3. Manage strategy properties (page 98)4. Analyze strategy component results (page 99)5. Segmentation overview (page 100)6. Test rule (page 101)

MethodsThe methods listed below support the use of strategy rules in activities.

• Return list of propositions (page 77)• Return list of properties (page 77)• Compute segmentation logic (page 77)• Execute strategy (page 78)• Execute strategy in batch (page 39)

Return List of PropositionsThe list of propositions that can be returned by the public component of a strategy can be obtainedthrough the Call Rule-Decision-Strategy.pyGetStrategyPropositions method. This method takes thefollowing parameters:

• Name of the strategy rule• Name of the public component• Name of the page for the list of propositions• Applies To class of the strategy

Return List of PropertiesThe list of properties that are used by components in a strategy can be obtained through the Call Rule-Decision-Strategy.pyGetStrategyProperties method. If found, duplicate values are ignored. This methodtakes the following parameters:

• Name of the strategy rule• Name of the public component

If the name of the public component is not provided, the method returns all properties used in strategycomponents. If the name of the public component is provided, the method returns properties of thiscomponent, including properties of other components in its execution chain.

• Name of the page for the list of properties• Applies To class of the strategy• Option to exclude referenced strategies

By default, all strategies in the execution chain are considered.

Compute Segment LogicThe list of segments that can be returned by the public component of a strategy can be obtained throughthe Call Rule-Decision-Strategy.pyComputeSegmentLogic method. The segment logic computation goesthrough the chain of component connections, gathering information about segment components, andlogical connections between them. If there is a sub strategy component involved, also segments of thesub strategy are gathered. The results are represented in a tree structure that contains the resultingclasses: Embed-AST (base class), Embed-AST-Operator-Boolean (logical operator and operands),Embed-AST-Constant-String (segment rule name). AND-nodes are generated for segment componentsin a sequence (for example, SegmentA component connects to SegmentB component). OR-nodes aregenerated for segment components that do not connect to each other, but instead connect to the samecomponent (for example, SegmentA and SegmentB components connect to a strategy set) generated.The activity can be executed in the strategy results page, or you can provide the name of the strategy andApplies To class.

Getting Started


This method takes the following parameters:

• Name of the strategy rule• Name of the public component• Name of the page for the result of computing the segmentation logic• Applies To class of the strategy

Execute StrategyA strategy can be executed through the Call pxRunStrategy method. This method takes the followingparameters:

• Name of the strategy rule• Name of the strategy component• (Optional) Customer ID• Name of the page that holds the result of running the strategy


Strategy. 2. In the Strategy: New rule form, enter the name of the rule instance in the Purpose Field, and make


3. There are two typical use patterns when defining a strategy.• Strategy rules using propositions. Use the Issue and Group drop down lists to select the

applicability of the strategy rule instance in the context of the proposition hierarchy. If yourstrategy should apply to all issues and groups, leave the Issue and Group fields undefined;

Getting Started


otherwise, select the issue and, if applicable, the group. The level at which the strategy is created(top level, issue, or group) determines the properties it can access.

• Strategy rules without propositions. Enable the Choose Strategy Result Class option to select adata class that is indirectly derived from Data-pxStrategyResult. If left empty, the strategy resultclass is automatically considered to be the top level class of your application.

4. Click Create.5. Design strategy (page 79).

Design Strategy• Toolbar and context menu (page 79)• Defining components (page 80)• Connecting components (page 94)• Defining expressions (page 95)

Toolbar & Context MenuThe strategy toolbar in the Strategy tab displays buttons that correspond to the same functionality asprovided through the editing of flows with the Modeler. Two buttons are specific to the strategy rule:the button allows you to turn off and turn on strategy auto-run mode (page 99), and the buttonallows you to run the strategy rule in the context of a batch run (page 39).

The context menu in the Strategy tab is accessed by right clicking the working area.

Getting Started


The context menu allows you to perform a number of actions:

• Add components (page 80).• Annotate your strategy in the same way as you would do in a flow rule.• Change the layout of the strategy.• Select all components.• Use the zoom options.

Defining ComponentsA strategy is defined by the relationships of the components that are used in the interaction (page 148)that delivers the decision (page 148).

Editing components is done via the Properties dialog of the selected component. This dialog is displayedwhen you double click the component, or when you right click the component, and select Properties fromthe context menu. The context menu also allows you to delete the component, but you can also deleteit by selecting the component, and pressing Delete on your keyboard. The Properties dialog consists ofelements common to all components, and tabs that are specific to the type of component.

Getting Started


• General settings (page 81)• Data import (page 81)• Segmentation (page 83)• Data enrichment (page 87)• Aggregation (page 89)• Arbitration (page 91)• Selection (page 93)

General SettingsEvery component is assigned a default generated name when added to the strategy.

• The Name field allows you to change the default generated name to a meaningful name in thecontext of the strategy you are designing. Although this field defines the component's Java Identifier,and as such you should follow PRPC naming conventions, you can define names containing spacecharacters.

• Above the Name field, Component ID displays the actual name of the component in the clipboard,and the name that can be used in expressions, which is the name of the component excluding spacecharacters.

• The Make Public option allows you to define components that can be accessed by the rules using thestrategy (interaction rule, and other strategy rules).

• The Description options allow you to define how to handle the description of the component. If youselect Use generated, the component's summary displays information based on the component'sconfiguration. If you select Use custom, you can enter a user-defined description for the component,and have this description showing in the component's summary instead.

The Source tab applies to most components. It displays the components that connect to a givencomponent. In the Source tab, the order of connected components can be changed by dragging the rowup, or down. The exception to this pattern are components that belong to the data import, segmentation,and selection categories.

The Properties tab is generic to components that are not selection components. This tab allows you tomap the properties brought to the strategy by the component to properties that are strategy properties.

Data ImportComponents in this category acquire data or other strategies into the current strategy. The page typedetermines the type of data import.

• Data import (page 81)• Sub strategy (page 82)• Proposition (page 83)

Data ImportData import components import data that is in an embedded or named page.

In the Source tab, provide the name of the named/embedded page in the Page Name field. If the PageName imports data from a named page, the Page Class field allows you to provide the class context forthe named page. Data import components that refer to named or embedded pages map the page's singlevalue properties to strategy properties through the Properties tab. If the page name is defined with a dotfor example, .SelectedProposition), the property is an embedded page, and if defined without the dot (for

Getting Started


example, CustomerPage), the property is a named page. The Properties tab is automatically populatedwith the page's single value properties.

Sub StrategySub strategy components reference other strategy rules. They define the way two strategies are relatedto each other, access the public components in the strategy they refer to, and define how to run thestrategy if the strategy is in another class.

In the Source tab of the sub strategy component, select the strategy rule in the Strategy field, select thecomponent in the reference strategy from the Component drop down that displays the public components(page 81) in the referenced strategy, define the page for the referenced strategy to run on (if notdefined, the strategy runs on the AppliesTo class), define the class of the strategy to be imported in theStrategy Class field if the strategy is in another class than the strategy importing it.

When the Strategy Page field is set to a page group or list, the decision making process is iterated overas many times as defined in the page group or list. For example, if a strategy runs through a sub strategycomponent over a list containing two customers, and assuming the strategy outputs three offers, the substrategy component results in a list containing six offers.

Getting Started


PropositionProposition components import propositions (page 150) defined in the proposition hierarchy.

In the Source tab, select the propositions in the proposition hierarchy. Use the Issue drop down to selectthe issue, and group. In the Group/Proposition drop down lists, you can either use the Import All option, orspecify a group/proposition. You can import all propositions in an issue by selecting the Issue, and usingImport All in the Group and Proposition fields.

In the Interaction History tab, check the Enable Interaction History option to automatically bring interactiondata to the strategy, and map the interaction history properties to strategy properties.

SegmentationComponents in this category typically use customer data to segment cases based on characteristics andpredicted behavior, and place each case in a segment (page 151), or score (page 150).

• Predictive model (page 84)• Scorecard (page 84)• Adaptive model (page 85)• Decision table (page 86)• Decision tree (page 86)

Getting Started


Some common configuration applies to these components.

• You can select if the component should be defined on the Applies To class or the Strategy Resultclass for predictive model, scorecard, decision tree, and decision tree components.

• Scorecard, predictive model, and adaptive model components map the output of the correspondingdecision rule to strategy properties through the Properties tab.

• You select the rule the component brings to the strategy in the Rule Name field. Use the

SmartPrompt to select an existing rule, or click the button to create a new rule of the same type asselected in the Rule Type field.

Predictive ModelPredictive model components reference predictive model rules (page 58).

ScorecardScorecard components reference scorecard rules (page 64).

Getting Started


Adaptive ModelAdaptive model components provide segmentation based on adaptive models in ADM. Thesecomponents reference adaptive model rules (page 67). The Adaptive Model tab is displayed for thistype of segmentation components.

• Configuration: select the adaptive model rule. Since the scope in the proposition hierarchy ispropagated through proposition components, if proposition components connect to the adaptivemodel component, the configuration field is the only setting available in the Model Definition tab.

• If proposition components do not connect to the adaptive model, the remaining fields should be setaccording to what the scoring model created in ADM is going to model.• Use the Issue, Group, and Name fields to select the hierarchy and proposition defined when

managing propositions (page 31). Depending on the scope the strategy was added to, Issue andGroup fields can be predefined, and can not be changed.

• Use the Direction, Channel, and Treatment fields to select values defined in the IS channeldimension.

Getting Started


Decision TableDecision table components reference decision table rules, and can be used to implement characteristicbased segmentation by referencing a decision table using customer data to segment on a given trait (forexample, salary, age, and mortgage).

Decision TreeDecision tree components reference decision tree decision rules, and can often be used for the samepurposes as decision tables.

Getting Started


Data EnrichmentComponents in this category add information and value to strategies.

• Strategy set (page 87)• Data join (page 88)

Strategy SetStrategy set components enrich data by adding information to the components they are connectedto. Using strategy set components, you can define personalized data to be delivered when issuing adecision. Personalized data often depends on segmentation components (page 83), and includesdefinitions used in the process of creating and controlling a personalized interaction, such as:

• Instructions for the channel system or product/service propositions (page 150) to be offeredincluding customized scripts, incentives, bonus, channel, revenue, and cost information.

• Probabilities of subsequent behavior, or other variable element.

Strategy set components enrich data through the Properties and Overrides tabs.

• Use the Properties tab to add strategy properties for which you want to define default values.

Getting Started


• Use the Overrides tab to define property values for each segment available in the strategy. In the

Segment drop down, select the appropriate segment. Use the Properties and Value columns todefine properties for the segment selected in the Segment drop down. Segments are driven by thesegmentation components used in the strategy rule, and can be viewed in the overview tab of thestrategy (page 100).

Data JoinData join components import data from an embedded or named page using a key to match data, and mapits contents to properties from the imported data to strategy properties.

Data join components enrich data through the Data and Properties tabs. In the Data tab of the data joincomponent:

• Select the type of page in the Join With field.• In the Name field:

• When joining data with a named page, select or enter the name of the page.• When joining data with an embedded page, select or enter the name of the page group.

• The Class field provides the page's class context.• You can reduce the amount of data to include by checking the Exclude entries not matching data

check box. This options effectively results in performing an inner join operation.

Getting Started


• In the Join Conditions, enter the expression to match data in the page.

AggregationTwo components fall in this category (aggregation, and financial).

• Aggregation (page 89)• Financial (page 90)

AggregationAggregation components set strategy properties using an aggregation method applied to propertiesfrom the source components. The Properties tab of the aggregation component allows you to define theaggregation operations.

• So that you can use the results of a list of elements, the Group Output Rows By option is available inthe aggregation component. The properties that can be used to group the results are the propertieslisted in the Strategy Properties tab; that is, properties of Data-pxStrategyResult class, and propertiesavailable to the strategy depending on its applicability in the context of the proposition hierarchy. Forexample, selecting grouping by pyName allows you to obtain the list of results for each propositionname.

• In the Aggregation section, select the strategy properties in the Property column, the method forsetting the property value based on an expression (SUM, COUNT, FIRST, MIN, MAX, AVERAGE,TRUE IF ALL, TRUE IF NONE, TRUE IF ALL, or STDEV), and type the expression in the Sourcecolumn.

Getting Started


• The properties that can be used in the Property fields are properties listed in the StrategyProperties tab, excluding px properties.

• The properties that can be used in the Source fields are properties of Data-pxStrategyResultclass, and properties available to the strategy depending on its applicability in the context of theproposition hierarchy.

• Properties that are not mapped in the component are automatically copied. In the For RemainingProperties Select setting, select how to handle the remaining properties.• None: empty.• First: copy with first value.• With Highest: copy with highest value, specifying which property of the SR class corresponding to

the level of the strategy in the proposition hierarchy provides the value.• With Lowest: copy with lowest value, specifying which property of the SR class corresponding to

the level of the strategy in the proposition hierarchy provides the value.

FinancialFinancial components perform financial calculations using the following functions:

• Internal rate of return calculates the internal rate of return for a series of cash flows.• Modified internal rate of return calculates the modified internal rate of return for a series of periodic

cash flows.• Net present value calculates the net present value of an investment.

The Properties tab of the financial component allows you to define the financial calculation, and selectthe properties that provide the arguments for each financial function. The arguments that can be selectedin the Target and Payments drop down lists are strategy properties of type decimal, double, or integer.

Getting Started


If the value for the arguments is set through source components, the order of the components in theSource tab is important because it is directly related to the order of arguments considered by the functionto perform the financial calculation. Typically, the Payments argument should be a list of values, and nota single value. So that you can use a list of values to provide the Payments argument, use a data importcomponent to set the properties to be used by the financial component.

ArbitrationComponents in this category filter, rank, or sort the information input by the source components. Enricheddata representing equivalent alternatives are selected by prioritization components.

• Filter (page 91)• Segment filter (page 92)• Prioritization (page 92)

FilterFilter components apply a filter condition to the outputs of the source components. Filter componentsexpress the arbitration through the Filter Condition tab. The Filter Condition field allows you to enter theexpression used when filtering the results of the source components.

Getting Started


Segment Filter

Segment filter components are only available in a Pega Unified Marketing (PUMa) application.

Segment filter components reference a segment rule (page 151), allowing for determining whethera case falls in a given segment, or not. The arbitration itself is expressed through the segment rule.The segment rule is executed on customer data (the primary page of the strategy), and returns true ifthe case is part of the segment it represents. Segment components set the pxSegment property to thename of the referenced segment rule. Additionally, segment components also set the pxRank property. Ifother components do not connect to the segment component, the component returns a list with a singlerow (the case is part of the segment), or an empty list (the case is not part of the segment). If there arecomponents that connect to it, the component returns all or no strategy results.

PrioritizationPrioritization components rank the components that connect to it based on the value of a strategyproperty, or based on a combination of strategy properties. These components can be used to determinethe service/product offer predicted to have the highest level of interest, or profit. Prioritization componentsexpress the arbitration through the Prioritization tab.

• Two modes can be used to order the results: the mode in which the results are ordered according topriority values, or the mode in which results are ordered alphabetically. Each mode toggles its own

Getting Started


specific settings. If Prioritize Values is selected, Order By settings are displayed. If Sort Alphabeticalis selected, Sort settings are displayed instead.

• The Expression field is used to define the property or properties providing prioritization criteriathrough an expression.

• The Output settings (Top, and All) define how many results should be considered in the arbitration.The Top option considers the first results as specified in the field next to it, and All considers allresults.

SelectionStrategies must be balanced to determine the most important issue when interacting with a customer.The first step in creating this flow is to use prioritization components (page 91) in the strategy tofilter the possible alternatives (for example, determining the most interesting proposition for a givencustomer). The second step is to balance your company’s different objectives by defining the conditionswhen one strategy should take precedence over another. This optimization can be accomplished byselection component that can select the decision path based on a condition, and can also be used to testalternative strategies. Using selection components, the assignment of a particular customer to a possiblealternative can be random.

Champion challenger components introduce alternative behavior by testing different strategies. Enricheddata and prioritized decisions can be selected by a switch rule that decides the strategy for the next step.

• Champion challenger (page 93)• Switch (page 94)

Champion ChallengerChampion challenger components randomly allocate customers between two or more alternativecomponents, thus allowing for testing the effectiveness of various alternatives. For example, you canspecify that 70% of customers are offered product X, and 30% are offered product Y.

Getting Started


Champion challenger components express component selection through the Champion Challenger tab.Add as many rows as alternative paths for the decision as necessary, and define the percentage of casesfor each decision path. All alternative decision paths need to add up to 100%.

SwitchSwitch components select between components on the basis of specified conditions. These componentsare typically used to select different issues (such as, interest, and risk), or they select a component basedon customer characteristics, or the current situation. For example, a case can be allocated to a sub-network dealing with recent customers with little history, or to a sub-network dealing with long standingcustomers.

Switch components express component selection through the Switch tab. Add as many rows asalternative paths for the decision as necessary, use the Select drop down to select the component, andenter the selection criteria as an expression in the If field. The component selected through the Otherwisedrop down is always selected when the conditions expressed in the If fields are not met.

Connecting ComponentsYou can connect components by selecting a component, and dragging the arrow to another component.Alternatively, you can use a strategy component's Source tab.

Getting Started


Selecting one component is performed by mouse over on the center area of the component until the icon is displayed. For components that select other components, the connections established this

way determine the entries in the component's Source tab (the connected to component) or, for selectioncomponents, the Switch or Champion Challenger tabs.

Note that this method of connecting components does not fully define the relationship betweenthe components. In the example above, this method would have connected the components,but you would still need to define the percentage of cases when each component should beselected.

Another type of connection represented by dotted blue arrows is displayed when a component is used inanother through expressions. If the component is also referenced in the source tab of the component itconnects to, a thicker grey arrow is displayed.

Defining Expressions• Expressions in strategy components (page 95)• Expressions using financial functions (page 96)

Expressions in StrategiesWorking with strategies means working with the strategy result data classes, and the AppliesTo classof the strategy rule. These classes can be combined in expressions, or by introducing segmentationcomponents (page 83) that work on the strategy result data class, and not the AppliesTo class.

Getting Started


The context of an expression is always the strategy result class (using the dot notation in theSmartPrompt accesses this context). For example, .pyPropensity.

To use properties of the AppliesTo context, you must declare the primary page. For example,Primary.Price.

To use properties of one strategy component in another, you must declare the name of the component.For example, ChurnModel.churnrate. If the component used in the expression outputs a list (multipleresults), only the first element in the result list is considered when computing the expression.

Financial FunctionsYou can use the financial functions available in the Financial library to perform financial calculations. Thefollowing functions are provided in the Financial library:

• cumipmt (page 96)• cumprinc (page 97)• db (page 97)• dbb (page 97)• fv (page 97)• ipmt (page 97)• nper (page 97)• pmt (page 97)• ppmt (page 98)• pv (page 98)• rate (page 98)• sln (page 98)• syd (page 98)• vdb (page 98)

General remarks when using providing the arguments:

• Rate and number of periods must be calculated using the same period unit. For example, if the rate iscalculated in months, the number of periods should also be expressed in months.

• Payments should be expressed as an array of negative numeric values.• Incomes/loans should be expressed as an array of positive numeric values.

Cumulative InterestCalculates the cumulative interest paid on a loan for a given period of time taking the followingarguments:

• Interest rate: the interest rate (page 98) applied to the loan.

Getting Started


• Total number of periods: the total number of periods (page 97) for the loan.• Present value: the present value (page 98) of the loan.• Starting period: the starting period for measuring the cumulative interest paid. Periods are index one

based.• Ending period: the ending period for measuring the cumulative interest paid.

Cumulative PrincipalCalculates the cumulative principal paid on a loan for a given period of time taking the same argumentsas described in the cumulative interest paid function (page 96).

Depreciation Using Fixed-Declining BalanceCalculates the depreciation of an asset using the fixed-declining balance method, a method thatcomputes the depreciation at a fixed rate. This function takes the following arguments:

• Cost: the original cost of the asset.• Salvage: the salvage value at the end of the depreciation.• Number of periods: the number of periods (page 97) over which the asset is being depreciated,

also known as the useful life of the asset.• Period: using the same unit measure as provided for the number of periods, the period to calculate

asset depreciation. • Number of months in the first year: optional argument used to provide a value other than 12 for the

first year of asset depreciation.

Depreciation using Double Declining BalanceCalculates the depreciation of an asset using the double-declining balance method, or some userspecified method. The four initial arguments are similar to the ones used with the fixed-declining balancefunction (page 97). The fifth factor argument is applied to provide the rate at which the balancedeclines (default is assumed to be 2).

Future ValueCalculates the future value of an investment taking the following arguments:

• Interest rate: the constant interest rate (page 98).• Number of periods: number of periods (page 97) for the payments.• Payments: the payment (page 97) (negative value) to be paid each period.• Present value: the present value (page 98) of the investment.• True/false: condition indicating if the payments are due at the end of each period (false, which is also

the default value) or beginning of each period (true).

Interest PaymentCalculates the interest payment for a given period for an investment taking the interest rate (page 98),period, number of periods (page 97), and present value (page 98) arguments, optionally using thefuture value (page 97) and type arguments.

Number of PeriodsCalculates the number of periods for an investment, optionally using the future value (page 97), andstarting the calculation at the beginning of the period (use true in this case). The function assumes thatperiodic and constant payments are made, and that the interest rate is constant.

PaymentCalculates the payment of a loan based on constant interest rate and constant payments taking the samearguments as described in the interest payment function (page 97). Typically, the payment containsprincipal and interest, and no other fees, or taxes.

Getting Started


Principal PaymentCalculates the payment on the principal for a given period of an investment based on periodic, constantpayments, and constant interest rate. This function takes the same arguments as described in the interestpayment function (page 97). This calculation can also be expressed by payment (page 97) minusinterest payment (page 97).

Present ValueCalculates the net present value, optionally using the future value (page 97), and calculating thefunction at the beginning of the period (use type false in this case). The function assumes that periodicand constant payments are made, and that the interest rate is constant.

RateCalculates the interest rate per period of an annuity, optionally using the future value (page 97), andcalculating the function at the beginning of the period. This function takes the number of periods (page97), payment (page 97), and present value (page 98) arguments, optionally taking into accountthe future value and type arguments.

Straight-Line DepreciationCalculates the straight-line depreciation of an asset for one period in the life of an asset. Cost, salvage,and life arguments are explained in the depreciation function (page 97).

Sum-of-Years' DepreciationCalculates the sum-of-years' digits depreciation of an asset after a specified period taking the samearguments as described in the depreciation function (page 97).

Variable DepreciationCalculates the depreciation of an asset for any specified period. The depreciation calculation is variableand uses the double-declining balance method, or a user-specified method. The arguments arequite similar to the ones used in the double-declining depreciation function (page 97). Three extraarguments apply:

• Start period: the start period for which you want to calculate the depreciation.• End period: the end period for which you want to calculate the depreciation.• True/false: a condition specifying to switch to straight-line depreciation if depreciation is greater than

the declining balance calculation (true, the default setting when omitted), or not (false).

Strategy PropertiesThe Strategy Properties tab displays the list of properties available to the strategy. Click the Refreshbutton to refresh the list of strategy properties. Provided the strategy is used in the more specializedused of strategies with propositions, this tab also allows you to add and remove properties at the classlevel determined by the applicability of the strategy in the scope of the proposition hierarchy. Propertiesadded through the Strategy Parameters tab have a specific configuration, which consists of having thepyDecisioningItem custom field set to StrategyProperty.

Getting Started


A newly created strategy rule lists the properties from Data-pxStrategyResult. It also lists everyproposition attribute explicitly defined as applicable to all strategies. If the issue level applicability of thestrategy has been selected in the process of creating the new rule, properties in the data model of theissue class are also listed, and the same applies to group. The lower the granularity level of the strategy,the more properties it accesses.

With the exception of predictive model outputs, the output of segmentation rules is generally availablein Strategy Properties. If an output of the predictive model that is not available in the strategy propertiesshould be used in expressions, you need to add the property at the appropriate class in the propositionhierarchy, where the class corresponds to the applicability of the strategy rule in the proposition hierarchy.

Auto-Run ResultsThe Auto-Run Results tab allows you to view existing clipboard data for every strategy component.Clipboard data, if available, is displayed for the selected component. Use the Select Component dropdown to select the strategy component. The arrows that are displayed when data is available overmultiple pages allow you to navigate through the pages displayed for the selected component.

Getting Started


OverviewThe Overview tab displays the segments (page 151) introduced in the strategy through scorecard,predictive model, decision tree, an decision table components.

Audit NotesSome Decision Management rules provide the option to view the details captured in the work object'shistory (audit notes). The rule's specific details captured in the work object's history consist of valuesprovided by each property, and subsequent execution (decision) result. Generating audit notes isavailable for scorecard, adaptive model, and interaction rules.

Finalizing RulesFinalizing a rule instance is a common step to all rule types, except strategy rules.

1. On the Pages & Classes tab, specify the names and classes of any pages this rule will reference. Theuse of named pages is particularly important when the rule instance is used by multiple clients.

2. On the History tab, enter a description in the Usage and Full Description fields.

Getting Started


3. Click to save the rule instance.

Testing RulesIn general, you can test rules by simply running the rule instance. In the test page, provide the inputs inthe Inputs section, click Execute, and verify the results in the Outputs section. Integrated application leveltesting can be done by testing flows or activities that use the rule.

The example described in this topic illustrates testing a predictive model rule. The process of testing otherDecision Management rules using this method is similar with the following differences:

• When testing an interaction rule, you are prompted to select how the interaction rule should be tested(run strategy mode, or capture response mode). In capture response mode, running the rule results insending results to the IS database.

• When testing a strategy rule that is used in a batch run, you can also run the strategy in the batch runcontext.

Follow the steps described below to test rules.

1. Click to run the rule.2. The Test Page is displayed.

3. In the Inputs section, provide values for the model inputs, and click Execute.

Getting Started


4. The result of evaluating the model against the input values is displayed in the Outputs section.

5. The Errors section reports data errors, such as missing input values, and input values that have beenprovided but fall in the Wide of Scheme category.

Getting Started


Flow Shapes

The Decision Management rule sets add the capability of designing strategy driven processes in yourapplication through the DSM specific shapes in the process flow editor. Both shapes reference aninteraction rule (page 71), but they use different parts of the rule definition to operate.

• Run strategy (page 103)• Capture response (page 103)

Run StrategyThe Run Strategy shape uses the configuration in the interaction rule's run strategy tab (page 73) toexecute the strategy. Go to the Utility tab to select the interaction rule that configures the operation of thisshape. Complete the remaining fields according to your application.

Capture ResponseThe Capture Response shape uses the configuration in the interaction rule's capture response tab (page74) to write the interaction results (data records) in the Decision Management service layer. Go to theUtility tab to select the interaction rule that configures the operation of this shape. Complete the remainingfields according to your application. Capture Response shapes can also be used to implement processmonitoring independently of using the Run Strategy shape in your flow.

Getting Started


Getting Started


Tutorials

• Using predictive model and scorecard rules to guide processes (page 105)• Using strategy rules to guide processes (page 110)• Training adaptive models (page 143)

Predictive Models and Scorecards in ProcessFlows• Using predictive models to guide processes (page 105)• Using scorecards to guide processes (page 107)

Related Topics

• Predictive Model (page 58)• Scorecard (page 64)

Predictive ModelsUsing predictive model rules in flows allows you to leverage the predictive capability of this rule to guideprocesses. For example, you can introduce a predictive model to predict risk, and decide the course ofaction when customers apply for a loan (accept, reject, or refer). So that you can implement this pattern,you need to create the predictive model rule (page 105), and then reference the predictive model rule inthe process flow (page 107).

Predictive Model Decision RuleSo that we can predict risk when customers apply for a loan, we create a predictive model rule (page 58)that uses information known about the customer to predict the probability of defaulting on payments.

Getting Started


The risk predictive model places customers in 13 classes (segments), which we map to the possiblecourses of actions in the results tab of the predictive model rule. Classes one to five should lead torejecting the loan application, classes six to nine to referring the loan application, and the remainingclasses to accepting the loan application.

Getting Started


Process FlowThe loan application process flow employs a standard entry point (sub process) for collecting customerdata, proceeding with updating the predictive inputs required by the predictive model, executing thepredictive model for assessing the probability of default, and deciding the course of action that connectsto the corresponding assignment shapes for rejecting, accepting, or referring the loan application.

It is through the AcceptLoan decision shape the predictive model rule named PredictiveSegmentation isreferenced.

ScorecardsUsing scorecard rules in flows allows you to leverage the segmentation capability of this rule to guideprocesses. For example, you can introduce a scorecard to define your own point system to scorecustomers, and decide the course of action when customers apply for a loan (accept, reject, or refer).So that you can implement this pattern, you need to create the scorecard rule (page 107), and thenreference the scorecard rule in the process flow (page 109).

Scorecard RuleSo that we can score each customer applying for a loan, we create a scorecard rule (page 65) to definethe score system. The score result is based on combining points according to customer characteristics(gender, age, credit history, and credit amount), and multiplying the score of each property by the weightdefined for each property.

Getting Started


Getting Started


Scores are combined using the SUM combiner function, and results define that scores below 200 shouldlead to rejecting the loan application, scores between 200 and 349 to referring the loan application, andscores equal or above 350 to accepting the loan application.

Process FlowThe loan application process flow employs a standard entry point (sub process) for collecting customerdata, proceeds with executing the scorecard for scoring the customer according to the informationcollected in the first step, and decides the course of action that connects to the corresponding assignmentshapes for rejecting, accepting, or referring the loan application.

The Capture Customer Data sub process shape needs to supply the customer characteristics comingfrom historical data to calculate the score, and connects immediately to the Score Customer decisionshape that references the ProbabilityOfDefaultScoreCard rule.

Getting Started


Strategy Driven ProcessesFlows can use strategies to drive the process they define. The steps below guide you in using the powerof strategies to drive processes.

1. Define class structure, and data model (page 110)2. Define proposition hierarchy, propositions, and proposition attributes (page 112)3. Define segmentation rules (page 117)4. Design strategy rules for each business issue, and define the top level NBA strategy rule (page

120)5. Define interaction rule (page 132)6. Define process, and user interface (page 134)

Class Structure & Data Models• Class structure (page 110)• Data model (page 110)

Class StructureThe Top Offers use case assumes every rule is created and defined in the same class (DMDoc-FW-DMDoc-Work), and the top level organizational class is DMDoc, which is also the top level class for theproposition hierarchy (DMDoc-SR).

Data ModelThe data model in your application needs to have the properties necessary to define and configure therules providing Decision Management functionality in your application. You can create these properties inthe process of defining the rules. To simplify the property definition process, and provide an overview ofthe necessary properties, we assume they have been defined upfront.

• Single value properties required by the segmentation rules, and strategy.

Property name Property Configuration

Age Integer

Getting Started


CreditHistory Text, PromptSelect, and Local List with the following values:

• Critical Account• Past Arrears• Repaid on Time

ChurnRate Decimal• Single value properties required by the interaction rule, and process flow rule.

Property Name Property Configuration

CustomerID Text, PromptSelect, and Local List with the following values:

• C111• C222• C333• C444

Behavior TextResponse TextSegment Text, PromptSelect, and Local List with the following values:

• Consumer• Corporate

SubSegment Text, PromptSelect, and Local List with the following values:

• New• Long-Standing

SelectedProposition Page, Data-pxStrategyResult page classOfferedProposition Page List, Data-pxStrategyResult page classSelect1 TrueFalse, pxCheckBoxSelect2 TrueFalse, pxCheckBox

• Data transform rule to initialize data for the selected customer.• Add a first When action for customer C111, and set the necessary properties:

• Repeat the same process for customer C222 using the following table:

Target Source Value

Segment ConsumerSub Segment NewChurn Rate 0.97Age 23

• Repeat the same process for customer C333 using the following table:

Target Source Value

Segment ConsumerSub Segment Long-StandingChurn Rate 0.2

Getting Started


Age 45• Repeat the same process for customer C444 using the following table:

Target Source Value

Segment CorporateSub Segment NewChurn Rate 0.93Age 30

Related Topics

• Decision Execution (page 11)• Defining Expressions (page 95)

PropositionsStrategies are directly related to the proposition hierarchy. For this reason, the definition of propositionstakes place before designing strategies. Since classes created as a result of the hierarchy definitionprocess inherit directly from the application's top level class, the prerequisite to proceeding with theprocess of defining propositions is that the top level class must be set for your application, which istypically the case if you use the application accelerator. Every proposition is defined according to thehierarchy of the proposition dimension (page 15) in IS. The fully qualified name (FQN) of a propositionis a combination of issue, group, and proposition identifier. Issue and group also provide the necessarystructure for reporting purposes.

• Define top level class (page 112)• Define hierarchy (page 112)• Define propositions (page 114)

Related Topics

• Decision Management Landing Pages (page 31)• Strategy Result (page 12)

Define Top Level ClassChanging the default assumed top level class is not necessary if this pattern suits your application. Followthe steps described below to change the top level class for the proposition hierarchy.

1. In the Pega menu, go to Issues tab (page 31) in the Strategies landing page, and click the top levelclass link to go to your application's pxDecisioningClass field value rule.

2. Change the value in the Localized Label field.

3. After saving the rule, the new top level class is reflected in the Issues tab.

Define Hierarchy The definition of the hierarchy is typically a step that is performed in the stage of setting up yourapplication. Creating the classes supporting the proposition hierarchy does not require using the Issuesfacilities in the Strategies landing page. You can create these data classes as you would create any otherdata class, provided that you understand the class structure and inheritance (page 12) supporting theproposition hierarchy.

1. In the Pega menu, go to Issues tab (page 31) in the Strategies landing page. 2. Use the toolbar buttons to add as many issues and groups as necessary for your application.3. Click Add Issue. In the Class: New rule form, enter Retention in the Issue Name field, and click

Create.

Getting Started


4. Repeat the previous step for the Sales issue.5. Click Add Group. In the Class: New rule form, select Sales in the Issue drop down, enter Loans in the

Group Name field, and click Create.

6. Repeat the previous step to complete the hierarchy.• Add the Savings group to the Sales issue.• Add the Promotion group to the Sales issue.• Add the Proactive group to the Retention issue.• Add the Reactive group to the Retention issue.

7. The complete hierarchy is displayed below.

Getting Started


8. Define propositions (page 114).

Define PropositionsPropositions and attributes are defined in the hierarchy using the Propositions tab (page 31) in theStrategies landing page, or directly in the strategy result data classes that support the propositionhierarchy. Once defined, they can be used in decision strategies. The process of defining propositionsand attributes is a logical step after defining the hierarchy, but it does not need to take place directly in theprocess of defining the structure.

• Define attributes (page 114)• Define propositions (page 116)

Proposition AttributesProposition attributes are parameters that can be specialized for each proposition. The data type ofproperties supporting proposition attributes are Text, Double, TrueFalse, Integer, Decimal, DateTime,Date, or TimeOfDay. The Description attribute is automatically available for every data instancerepresenting a proposition.

The following steps describe the process of adding the attributes required to define propositions. For theTop Offers use case, we use the Cost and CreditLimit attributes. Cost applies to every proposition, andCreditLimit to propositions in the Retention issue. The data type of both attributes is decimal.

1. Go to the Propositions tab (page 31) in the Strategies landing page.2. Click Manage Attributes.3. In the Manage Attributes dialog, click to display the Property: New rule form. By default, the Scope

drop down is set to the same level as in the Manage Attributes dialog before starting the process ofadding a new proposition attribute, but you can select another level. No selection of group or issueallows you create SR level proposition attributes only. Selection down to the issue level allows you tocreate SR or issue level proposition attributes. Selection down to the group level allows you to createSR, issue, or group level proposition attributes. Select the scope applicable to all strategies, enterthe Cost in the Property Name field, and select the Decimal data type in the Type field under QuickCreate.

Getting Started


4. Click Create.5. Before proceeding with creating the CreditLimit attribute, in the Manage Attributes dialog, select the

Retention issue in the Issue drop down at the top of the dialog.6. Repeat the same steps in the Property: New rule form as described for the Cost attribute to add the

CreditLimit attribute.7. Depending on the selected scope, attributes are added as properties in the appropriate class.

Getting Started


PropositionsThe Manage Propositions button on the toolbar is visible after selecting the issue and group levels. In thesteps below, we define the propositions necessary for the Top Offers use case.

1. In the Propositions tab of the Strategies landing page:• Select the Retention issue.• Select the Reactive group.• Click Manage Propositions.

2. Manage propositions using the data table instances editor in PRPC (you can also click Edit in Excelto manage the propositions from Excel). Start by adding FreeCarInsurance, and FreeEvaluation in theProposition name column.

The following buttons allow you to manage propositions, which are data instances of the group class:• Click to add an instance at the end of the data table.• Click to insert a copy of the current instance below the current row.• Click to lock and edit the data instance. This method can be cumbersome when your

propositions have a large number of attributes. An alternative is clicking at end of the row. Thisbutton opens the facilities for editing the data instance.

• Click to delete the instance.3. Close the edit data table instances dialog, and repeat the same generic steps to define the remaining

propositions.• Sales issue/Loans group

• CarLoan• HomeLoan

• Sales issue/Savings group• TermDeposit• YoungSaverDeposit

• Sales issue/Promotion group• FreeSubscription• BonusPoints

• Retention issue/Proactive group• BronzeCard• SilverCard• GoldCard• PlatinumCard• NoCard

The next step consists of defining attribute values for each proposition.

1. Select the appropriate issue and group (we start with Sales issue, and Loans group), and clickManage Propositions.

2. Using the button for each data instance representing the proposition, define the propositionattribute values for every proposition.

Getting Started


3. Click Save, and then the Back button.4. In the previous steps, we defined the cost of propositions in the Loans group, with 200 for CarLoan,

and 100 for HomeLoan. Close the edit instances dialog to proceed with the next group.5. Repeat the same process for the remaining propositions.

• Cost in the Savings group• TermDeposit: 300• YoungSaverDeposit: 400

• Cost in the Reactive group• FreeCarInsurance: 0.123• FreeEvaluation: 0.456

• CreditLimit in the Proactive group• BronzeCard: 25000• SilverCard: 50000• GoldCard: 75000• PlatinumCard: 100000• NoCard: 0

Segmentation RulesDefine the segmentation the strategy requires for ranking and eligibility purposes.

• Decision table (page 117)• Scorecard (page 118)• Adaptive model (page 119)

Related Topics

• Decision Management Rule Types (page 58)• Strategies (page 120)

Decision TableThe decision table rule described in this topic is designed to segment customers based on theCreditHistory property to calculate risk, and defines two results (Accept, and Reject). This rule is used inthe loans strategy (page 120) to assess the risk of customers defaulting on payments.

1. Create a new decision table rule named CalculateRisk.2. In the Table tab:

a. Add the CreditHistory property in the Conditions column. b. Define Reject as the first result in the Actions column, and Accept as second.

3. Define the conditions for the results. The first condition returns Reject for critical accounts, otheraccounts return Accept.

Getting Started


ScorecardThe scorecard rule described in this topic is designed to segment customers based on the Age andCreditHistory properties, and defines two results (Accept, and Reject). This rule is used in the loansstrategy (page 120) to assess the risk of the customer defaulting on payments.

1. Create a new scorecard rule named CreditScore.2. Leave the combiner function as SUM in the Combiner Function drop down.3. In the Scorecard tab, add one row for Age property, and another for CreditScore.4. For each property, define three conditions, and provide a score for each condition. The fourth

possible score is attributed when none of the conditions defined previously is met. In the case of theAge property, 23 scores 20, 24-30 scores 30, 31-50 scores 40, and the default score for missing dataor any other age not defined in the previous conditions scores 30. In the case of the CreditHistoryproperty, critical account scores 20, past arrears scores 30, repaid on time scores 40, and the defaultscore for missing data or any other account status not defined in the previous conditions scores 30.

5. Switch to the Results tab, and define the cutoff values. The result is Reject if the scorecard outputsa score below 49, otherwise it is Accept. In the case of the scorecard we are creating, we check theoption to show the audit trail when this rule is executed.

Getting Started


Related Topics

• Decision Management Rule Types (page 58)• Segmentation (page 83)

Adaptive ModelThe adaptive model rule described in this topic is designed to segment customers based on the Age andCreditHistory properties, and defines two type of behavior (Accept, and Reject). This rule is used in thesales strategy (page 125) to model sales propositions.

1. Create a new adaptive model rule named SalesModel.2. In the Configuration tab, add the Age and CreditHistory properties as predictors, and define positive

and negative behavior. You can define as many values as necessary to use as positive/negativebehavior. In this model, we define one value for positive behavior (Positive-Accept), and one fornegative behavior (Negative-Reject).

Getting Started


3. In the Settings tab:a. Change the Refresh After setting to 300. We change the default value because this threshold

should be set to a value lower than the Run Data Analysis After setting so that the light weightanalysis process (model refresh) may be triggered.

b. Check the Enable Local Updates option so that the local scoring models can learn from everyresponse. We enable this option because, initially, we do not expect to meet the number ofresponses required for the model to adapt based on the number of responses that trigger runningdata analysis.

Related Topics

• Decision Management Rule Types (page 58)• Decision Management Landing Pages (page 31)• Model Updates (page 18)• Model Learning (page 17)• Segmentation (page 83)

Strategies• Loans strategy (page 120)• Sales strategy (page 125)• Top level NBA strategy (page 130)

Related Topics

• Decision Management Rule Types (page 58)• Strategies (page 18)

Loans StrategyThe objective of this strategy is to use the segmentation provided by the decision table and scorecardrules we defined previously, import the car loans and home loans propositions, and then refine theproposition selection using the champion challenger's output after calculating or scoring the probability ofthe customer to default on payments.

Getting Started


After creating a new strategy rule named LoansStrategy applying to the Sales issue and Loans group inthe proposition hierarchy:

1. Go to the Strategy Properties tab, and add the Count property of integer data type to the SR class ofyour application's top level class by selecting the All <TopLevelClass>- Strategies option in the Scopedrop down.

2. Add segmentation components.a. Add a scorecard component named ScoreRisk, and select the rule named CreditScore in the

Rule Name field.

b. Add a decision table component named CalculateRisk, and select the rule named CalculateRiskin the Rule Name field.

Getting Started


3. The two segmentation rules output the credit risk based on a slightly different technique. Bothsegmentation rules segment customers for credit risk using account status (credit history), but thescorecard also uses customer characteristics (age). These are two alternative credit assessmentpatterns for which we need to define the selection criteria.

a. Add a champion challenger component named Challenger.b. Set ScoreRisk to be selected in 70% of the cases, and apply the remaining percentage to

CalculateRisk.

4. Add propositions.a. Add a proposition component named CarLoans, and select CarLoan in the Proposition drop

down.

Getting Started


b. In the Interaction History of the CarLoans component, check the Enable Interaction Historyoption, and map the pyCount property from the interaction history records to the Count strategyproperty.

c. Add a proposition component named HomeLoans, and select HomeLoan in the Proposition dropdown.

Getting Started


d. In the Interaction History tab of the HomeLoans component, repeat the same step described forthe Interaction History tab of the CarLoans component.

5. So that we can design the strategy to offer loans when the credit risk is not high, we add a filtercomponent named AcceptOnly.

a. Connect the two proposition components to the filter components.b. In the Filter Condition tab of AcceptsOnly, define the expression that checks for the result of the

Challenger component. Propositions are offered only when the champion challenger outputs theAccept segment. The segment is provided by the pxSegment property of the component. In thiscase, the filter condition is Challenger.pxSegment=="Accept".

c. Make sure you check the Public option to use the output of the AcceptOnly component in the

sales strategy (page 125).

Related Topics

• Decision Table (page 117)• Scorecard (page 118)• Propositions (page 112)

Getting Started


Sales StrategyThe objective of this strategy is to import the sales propositions, combine them in a strategy set thatdefines in which channel and direction should the propositions be offered, use an adaptive model thatuses adaptive learning to predict customer behavior, set the margin, and then prioritize based on acalculation using margin, cost, and propensity.

After creating a new strategy rule named SalesStrategy in the Sales scope of the proposition hierarchy:

1. Go to the Strategy Properties tab, and add properties.a. Click to add a property named Margin, select the decimal data type, and add it to the SR class

of your application's top level class by selecting the All <TopLevelClass>- Strategies option in theScope drop down.

b. Repeat the same process to add the Performance property.2. Go to the Strategy tab, and add a sub strategy component named LoansStrategy. Select the

LoansStrategy rule in Strategy field, and select the AcceptsOnly filter component in the Componentdrop down.

Getting Started


3. Add a proposition component named SavingsPropositions.a. Select Savings in the Group drop down, and import all propositions from the Savings group by

using Import All in the Proposition drop down.

b. In the Interaction History of the SavingsPropositions component, check the Enable InteractionHistory option, and map the pyCount property from the interaction history records to the Countstrategy property.

Getting Started


4. We need to add data to the propositions. Add a strategy set component named PreparePropositions.a. Connect the LoansStrategy and SavingsPropositions components to the PreparePropositions

strategy set.b. In the Properties tab, add the pyChannel, pyDirection, and Margin properties. Provide the value

SMS for pyChannel, Inbound for pyDirection, and 0.30*.Cost as the expression to calculate theMargin property based on the Cost property.

5. Add an adaptive model named SalesModel to model the propositions.a. Connect the PreparePropositions strategy set to the SalesModel adaptive model.b. In the Model Definition tab of the SalesModel component, reference the SalesModel rule.

Getting Started


c. Go to the Properties tab, click the Enable Additional Mapping option, and map the Evidence andPerformance adaptive model outputs to respectively Count, and Performance.

6. Prioritize propositions by adding a prioritization component named TopSalesOffered.a. Connect the SalesModel adaptive component to this prioritization.b. In the Prioritization tab of the TopSalesOffered component, define the priority expression. Priority

is calculated based on adding the Cost and Margin properties, and multiplying the value resultingfrom this sum by pyPropensity. In the Output settings, enter 2 in the field next to Top.

Getting Started


7. If savings or loans propositions are not eligible, the strategy will not output any offer. To avoid this, weadd a fallback proposition.

a. Add a proposition component named Promotion.b. Select Savings in the Group drop down, and import all propositions from the Promotion group by

using Import All in the Proposition drop down.

8. Add the final selection component.a. Add a switch component named SalesOffer.b. Connect the TopSalesOffered and Promotion components to the switch component.c. In the Switch tab of the SalesOffer component, add the SalesModel.Count==0 expression to

check if the proposition has not been offered. If it has not, the prioritization path is used, otherwisepromotion propositions are presented instead.

Getting Started


d. Make sure you check the Public option to use the output of the SalesOffer switch in the Next BestAction strategy (page 130).

Related Topics

• Propositions (page 112)• Loans Strategy (page 120)• Adaptive Model (page 119)

NBA StrategyThe objective of this strategy is to import the sales strategy, import the retention offers, and select thedecision path based on the likelihood of customer attrition provided by the ChurnRate property.

After creating a new strategy rule named NextBestAction, and leaving the strategy's issue and groupapplicability undefined:

1. Import sales strategy.a. Add a sub strategy component named SalesStrategy.b. In the Strategy field of this component, select the SalesStrategy rule.c. The SalesStrategy rule only has one public component, which is the switch component. Select

SalesOffer in the Component drop down.

Getting Started


d. Leave the Strategy Page and Strategy Class fields empty (the strategy we are importing is not ina different class, and should not run it on a different page).

2. Import retention propositions by adding a proposition component named RetentionOffers. In theSource tab of this component, import every proposition in the Reactive group of the Retention issue.

3. The final selection component is a switch component that selects the sales strategy if the probabilityof customer attrition is below a certain level (0.9). Add a switch component named BestAction,check the Public option to define it as a public component, add SalesStrategy in the first Select row,define the expression that sets the customer attrition risk as Primary.ChurnRate<0.9, and selectRetentionOffers in the Otherwise drop down.

Getting Started


Related Topics

• Sales Strategy (page 125)• Propositions (page 112)

InteractionDefine the interaction rule so that you can use the Next Best Action strategy to guide the process flow.

After creating a new interaction rule named Interaction:

1. In the Interaction History tab, provide the CustomerID property in the Customer ID field, and pyID inthe Work ID field.

2. In the Run Strategy tab, select the NextBestAction strategy rule. In the Component Mapping section,the BestAction public component delivering the decision is displayed. Map it to the OfferedPropositionpage property.

3. Go to the Capture Response tab to configure how the data resulting from the interaction should becaptured.

Getting Started


4. Configure the fields in the Capture Response tab as described below.

Getting Started


a. The Behavior and Response fields should be mapped to the properties providing behaviordimension information. Map the behavior level to the Behavior property, and the response level tothe Response property.

b. The fields in the Customer Segmentation section should be mapped to the properties providingcustomer dimension information. Map the segment level to the Segment property, and the subsegment level to the SubSegment property.

c. Make sure the option to use the default organizational hierarchy is enabled.d. Typically, the fields in the Contact section should be mapped to the properties providing the

response context dimension information. In the example of the Top Offers, we are defining astatic exercise where the call reason is always a campaign, and the reason always account. Enter"Campaign" as category level, and "Account" as reason level.

e. The fields in the Customer Segmentation section should be mapped to the properties providingcustomer dimension information. Map the segment level to the Segment property, and the subsegment level to the SubSegment property.

f. The fields in the Customer Response section deal with the proposition dimension information.The Top Offers is a single offer application, so do not check the option that enables working withproposition bundles. Provide the SelectedProposition property.

g. The Top Offers use case is not designed to record measurement data. Leave the fields in theMeasurements section blank.

Related Topics

• Understanding Decision Management (page 8)• Strategies (page 120)• Process Flow (page 134)• Class Structure & Data Models (page 110)

Process and User InterfaceThe purpose of the rules we are about to define is to support the work user entering customerinformation, based on which the strategy is executed, and the list of outputs of the strategy's publiccomponent copied to the page list named OfferedProposition. The section that displays offers showsthe top two propositions in the list. The work user signals which proposition is offered, and the customerresponse is handled as defined through the flow actions created for customers rejecting or accepting theproposition. The information to capture the customer response is passed to the capture response shape.The capture response shape is executed when the values values passed through the interaction rule areprocessed, the corresponding data record is stored in the IS database, adaptive statistics are updatedin ADM when the sales model is selected in the strategy execution path, and (if VBD is enabled in thesystem) the response data is propagated to the VBD database.

• Design process flow (page 134)• Configure flow actions (page 140)

Process FlowThe process flow starts by collecting customer information, and ends by setting the data resulting fromthe interaction after handling the application. After collecting the customer information, the process flowis driven by running the strategy. Depending on the information about the customer, propositions aredisplayed, and the interaction is handled to record the customer response.

Getting Started


After creating a new flow process rule named TopOffersFlow:

1. Start the flow by having an entry point assignment shape to collect customer information namedCollectUserInfo.

Getting Started


2. Add a run strategy shape named TopOffers to reference the interaction rule (page 132).

Getting Started


3. Connect CollectUserInfo to TopOffers, and define the CustomerInfo flow action (page 140) definedin the connector Flow Action field.

4. Add an assignment shape named PropositionsOffered to show the propositions output by thestrategy, and connect TopOffers to PropositionsOffered.

Getting Started


5. Add an assignment shape named HandleResponse.6. Connect PropositionsOffered to HandleResponse, and define the DisplayOffers flow action (page

141) in the connector's Flow Action field.

Getting Started


7. Add a capture response shape named CaptureCustomerResponse referencing the interaction rule.

8. Define two connections from HandleResponse to CaptureCustomerResponse for the two types ofoutcome. The accept path uses the Approve flow action, the reject path uses the Reject flow action.

a. The accept path uses the Approve flow action, and sets the Behavior property as Positive, andResponse as Accept.

b. The reject path uses the Reject flow action, and sets the Behavior property as Negative, andResponse as Reject.

Getting Started


9. End the flow.10. Configure flow actions (page 140).

Related Topics

• Class Structure & Data Models (page 110)• Interaction (page 132)• Flow Shapes (page 103)

Flow ActionsCreate the flow actions that provide the user interface. Accept and Reject do not require any sectionsbecause they serve the purpose of setting properties, the flow actions to collect customer information anddisplay propositions require sections, and properties mapped to the user interface.

• Collect customer information (page 140)• Display offered propositions (page 141)

Collect Customer InformationDefine the section rule.

1. Create a section rule named CustomerInfo.2. Complete the layout by adding the necessary rows, defining the label, and mapping fields.

• In the first row, define the label as Customer ID, and map the field to the CustomerID property.So that the remaining fields display demo data based on the customer ID, we define theONCHANGE behavior for the CustomerID property with On Change as event, Refresh thisSection as action, and, in the Pre-Processing section, the InitCustomerData data transform rule.

Getting Started


• In the second row, define the label as Age, and map the field to the Age property.• In the third row, define the label as Credit History, and map the field to the CreditHistory property.• In the fourth row, define the label as Segment, and map the field to the Segment property.• In the fifth row, define the label as Sub Segment, and map the field to the SubSegment property.

Define the flow action to allow the work user to select the customer ID, and view the necessaryinformation.

1. Create a flow action named CustomerInfo flow action.2. Add a section to the flow action that references the CustomerInfo section rule.

Display OffersBefore proceeding with the configuration of the flow action, create an activity that dynamically sets theSelectedProposition property with the selected offers.

1. Create an activity rule named PropositionSet.2. In the Steps tab, add a first step using the Property-Set method, defining the property name as

SelectedProposition, and its value as OfferedProposition(Param.Set).

Getting Started


3. In the Parameters tab, define the parameter Set (integer data type).

To define fields and map properties, create a section rule named DisplayOffers.

1. Go to the Pages & Classes tab of this section rule, and add the OfferedProposition class.• Page Name: OfferedProposition().• Class: Data-pxStrategyResult.

2. The First Offer field is retrieved from the OfferedProposition page byusing .OfferedProposition(1).pyName, and the Second Offer field byusing .OfferedProposition(2).pyName.

3. For both fields, control check boxes are available. The control corresponding to the first offeredproposition uses the Select1 property, the control for the second uses the Select2 property.

1. Configure the pxCheckbox control by defining the behavior for the Change event (RefreshSection targeting the current section, and running the PropositionSet activity to set the Setparameter). Select1 sets the Set property defined in the PropositionSet activity to 1, and Select2to 2.

Getting Started


Define the flow action to allow the work user to enter the necessary information.

1. Create a flow action named DisplayOffers to provide the first and second of the top offers to the workuser in the interaction.

2. Add a section to the flow action, and reference the DisplayOffers section rule.

Related Topics

• Class Structure & Data Models (page 110)• Process Flow (page 134)

Training Adaptive ModelsModels can be trained using the Upload Responses wizard in the Adaptive Models landing page. You canalso train models through activities.

• Upload Responses (page 143)• Activities (page 144)

Upload ResponsesThe CSV file should contain the input data for each case (page 147), and a set of interaction results(page 148).

Follow the steps described below to use a CSV file to train models.

1. In the Pega menu, go to Decisioning | Adaptive Models.2. Click Upload Responses in the Actions menu of the model you want to train.3. In the Upload Data step, click the Browse button to select the CSV file containing historical data, and

click Upload File.

Getting Started


4. In the Select Outcome step, select the column that provides the historical outcome for each case.

5. In the Map Behavior step, define how the outcome in the historical data should be mapped to theresponse defined in the behavior dimension.

6. Click Finish to make the historical data available to the adaptive analytics engine (page 147).7. Click Done to return to the Adaptive Models landing page.

ActivitiesYou can train models through the use of work object data, or data in a database.

• Create report definition (page 144)• Define step in activity (page 146)

Create Report DefinitionThe first step consists of creating a report definition rule that gathers the sample data. In the steps below,we define a report definition that gathers work object data. If the data is in an external data source, usethe Connector & Metadata Wizard to create the necessary classes and rules.

1. Create a report definition rule.2. In the Columns To Include section, add the necessary number of rows. Define the column's name,

and heading. If applicable, configure additional data related settings, such as sorting options. In thecase of the adaptive model in our application, we include the Age and CreditHistory predictors, aswell as the property that defines the outcome (Behavior).

Getting Started


3. Click Save & Preview to examine the data gathered by the report definition rule.

4. Save the rule.

Getting Started


5. Define step in activity (page 146).

Call Method in ActivityAfter defining the report definition rule (page 144), you can call the method that trains models based onthe report definition.

1. In your activity rule, include a step using the Call DSMPublicAPI-ADM.pxUploadResponsesFromReport method.

2. Provide the necessary parameters.

• pyReportName: the name of the report definition rule.• pyReportClass: the Applies To of the report definition rule.• outcomeColumnInfo: page of class Embed-Decision-OutcomeColumnInfo. This page needs to

define pyName as the outcome column in the report definition that defines the behavior, and mapthe values in the outcome column to the behavior the adaptive model rule learns from.

• adaptiveModelKey: page of class Embed-Decision-AdaptiveModel-Key. This page needsto define the adaptive model parameters. Adaptive model parameters consist of values thatpoint to the model in the channel (pyChannel, pyDirection, and pyTreatment), propositiondimension (pyIssue, pyGroup, and pyName), and class context (pyConfigurationAppliesTo, andpyConfigurationName).

Getting Started


Glossary

Predictive Analytics DirectorPredictive Analytics Director (PAD) is a desktop application used to develop and create predictive models(page 149). Predictive models exported in PAD can be used to create and define predictive modelrules, which can then be used directly in flows, or combined with other components in a strategy. PADdevelops the means to differentiate between cases on the basis of likely future behavior. Powerful andreliable predictive models deliver the key insights that enable opportunities and risks to be evaluated,constituting the foundation of personalized strategies. PAD reveals the relationships in your data, thecritical information, and the interactions that drive customer behavior in an intelligent data mining processthat knows what needs to be done. Your role is to define the objectives, and judge the results.

Adaptive Analytics EngineThe main process of the Adaptive Decision Manager. The engine is responsible for storing sufficientadaptive statistics (page 147), analyzing them, and producing individual scoring models (page 150)that are used in PRPC. These statistics keep the relevant values for adaptive models defined in decisionstrategies. From these statistics, the adaptive analytics engine creates scoring models that are publishedto the adaptive data store (page 147). PRPC retrieves the scoring models from the database, and usesthem to calculate the prediction.

Adaptive Data StoreThe database scoring adaptive statistics (page 147) and adaptive models (page 147).

Adaptive ModelAdaptive models are ADM scoring models (page 150) that output predictions (page 149) calculatedand adapted in real time as responses are captured after executing a strategy (page 151). Models inADM are configured through adaptive model rules. Adaptive model rules define the settings that influencethe behavior of the adaptive models in ADM. Adaptive models are created by executing strategies withadaptive model components. When adding the adaptive model component in the strategy, you configurethe proposition (page 150) the adaptive model is going to model, and the interpretation of the outputs.Adaptive models belong to the self-learning aspect of Decision Management, and typically used in theabsence of historical records to make predictions.

Adaptive StatisticsThe persistent information resulting from running a strategy (page 151) containing adaptive models(page 147).

Behavioral ProfileA behavioral profile represents a model created on the basis of univariate performance (page 151). Theprobabilities of a positive outcome for each interval/category are score bands (page 150) that can beused to predict in the same way as those of any other model.

CaseA case can be any person, company, or event that exhibits some defined behavior. For example, goodand bad outcomes.

Getting Started


CoefficientA weight that is used for each predictor (page 150) in the logistic regression formula. The coefficientis an indication of the importance of a predictor. Negative coefficients imply the presence of predictorswith very similar behavioral profile (page 147). If present, they can lead to over fitting and unreliablemodels. Consider reanalyzing the predictor grouping to ensure predictors with highly correlated behaviorare placed in the same predictor group.

Coefficient of ConcordanceThe Coefficient of Concordance (CoC) is a non-parametric coefficient (page 148) sensitive to thecomplete range of score bands (page 150) irrespective of their distribution.The CoC measures howwell the scores generated by the model separate positive from negative outcome cases using the statisticknown as coefficient of concordance. CoC can vary between 50% (a random distribution of positiveand negative cases by score band) and 100% (a perfect separation). The minimum is 50% becausethe scores are simply used in reverse if a set of scores orders negative cases before positive cases. Itsvirtue as a measure is that it encourages models to be predictive across the score range. If the desiredoperational circumstances (volume or quality of business) are unknown, CoC generates powerful andgeneralized models.

Data SourceData about customers, and their previous behavior. This data can be used for modeling, strategy design,batch execution, and forecasting. A source should contain one record per customer with the samestructure for each record. Ideally, data should be present for all fields, and customers, but some missingdata can be tolerated.

DecisionThe result of running a strategy in the interaction context. Several decisions can be involved in a singleinteraction (page 148).

DimensionsDimensions provide the hierarchical context for the facts and responses associated with an interaction(page 148). Dimension levels are stored as delimited strings called FQN (page 148). Dimensions aredefined in IS, and VBD. The following dimensions are implemented: customer, application, proposition,channel, behavior, response context, and time.

FQNFully Qualified Name.

InteractionSome contact with the customer in real time, or offline.

Interaction ResultThe reaction of a customer to a proposition (page 150). Interaction results are recorded in the ISdatabase tables, and propagated to ADM, and VBD.

IntervalTypically, the values of numeric predictors (page 150) are grouped in intervals. Each interval provides auseful building block for understanding behavior.

LiftA measure (multiplied by 100) of the improvement in behavior exhibited by cases (page 147) in oneinterval (page 148) or segment (page 151) over the average of all cases.

Getting Started


MeasurementsMeasurements are the primary storage of numeric information associated with interactions (page 148),which can be used for storing Key Performance Indicators (KPI). There are two types of measurements.In the context of the simulation process these two types can be defined as strategy driven and customerdata driven. Decision Management supports up to 20 measurements.

Model AttributesModel attributes include various descriptions and settings defined during model development, which canbe made available to the decision (page 148) making system at decision time.

ModelingThe process of generating a model as a conceptual representation to identify patterns in behavior.

Next Best ActionThe Next Best Action (NBA) strategy (page 151) allows applications to take the best decision (page148) in a multidimensional context (retention, recruitment, risk, recommendation, etc.).

Next Best OfferNext Best Offer decisions (page 148) deliver the facilities to take the best proposition (page 150)based on different product ratings, taking into account other factors, such as products already owned bythe customer.

OutcomeThe field representing the behavior to be predicted.

OXLOmega XML Language. The XML file format of predictive models (page 149) as published usingPredictive Analytics Director.

PMMLPredictive Model Markup Language. An XML-based language that provides compatibility methods forapplications to define statistical and data mining models, and further sharing these models betweenPMML compliant applications.

PopulationThe group of cases (page 147) with known behavior, which is consistent with the group of cases whosebehavior is to be predicted. In predictive analytics, it is from the population that samples (page 150) areextracted for modeling and validation.

PredictionThe outcome (page 149) to be predicted, which is specific to a form of behavior at a given point in time.

Predictive ModelAn algorithm that delivers predicted behavior and values for one or more segments (page 151) giventhe input of the required data about a case (page 147). Predictive models are developed in PredictiveAnalytics Director.

Predictive PerformanceSome measure of the scores (page 150) or segments (page 151) generated by models. Performancecan be measured in terms of predictive power (page 150), value, or rate achieved under selectedconditions.

Getting Started


Predictive PowerFor scoring models (page 150), the predictive power is the measure of the ability of a model to separatecases (page 147) with a positive outcome (page 149) from those with a negative outcome. Thesemodels use behavior defined in terms of two opposite types of outcome, either a symbol indicating whichtype of behavior, or the probability of being one of the types.

Predictor GroupingThe grouping of predictors (page 150) whose relationship with behavior are correlated at, or above, aselected level of correlation.

PredictorsPredictors are properties considered to have a predictive relationship with the outcome (page 149).Predictors contain information available about the cases (page 147) whose values may potentially showsome association with the behavior you are trying to predict. Examples include:

• DemographicFor example, age, gender, and marital status.

• Geo-demographicFor example, home address, and employment address.

• FinancialFor example, income, and expenditure.

• Activity or transaction informationFor example, the amount of loan taken out of the price of the product.

PropensityThe probability of positive behavior or membership.

PropositionA product offer. By product we mean tangible product offers (a handset, or a subscription), or lesstangible ones (benefits, compensations, or services).

Proposition BundlingProposition bundling is a method of combining and presenting a number of propositions as a coherentand justifiable set in terms of cross-product eligibility, propensity, and likelihood of interest linked to thecall reason. The proposition set is provided in a bundle, such as the cheapest proposition is offeredat a reduced price or for free, a discount is given on all propositions, and there are additional freepropositions.

SampleA sub-set of historical data extracted by applying a selection and/or sampling method on the data source(page 148). To be meaningful and reliable, it is essential that sufficient records are used and that thedistribution of values and patterns of behavior are representative of those in the population (page 149).

ScoreThe value calculated by a model. Score intervals (page 148) are aggregated under a score band (page150).

Score BandA score band is a set of score intervals (page 148).

Scoring ModelThe value calculated by the model, known as the score (page 150), places a case (page 147) ona numerical scale. High scores are associated with good performance and low scores are associated

Getting Started


with bad performance. Typically, the range of scores is broken in intervals (page 148) of increasinglikelihood of one of the two types of behavior (positive, or negative), based on the behavior of the cases inthe development sample (page 150) that fall into each interval.

SegmentA group of customers defined by predicted behavior, score, and characteristics. Segments areimplemented through segmentation components in a strategy (page 151), and they drive the decisionflow by placing a customer in a given segment for which actions/results are defined.

Segment RuleSegment rules define how to retrieve a population, and check if a customer falls in a segment, or not.Segment rules are in the Marketing category that is enabled in a Pega Unified Marketing (PUMa)application.

SimulationSimulations are executed based on changes in the strategy. The strategy decides the top propositions(page 150) to be offered to the customer.

Statistical SignificanceStatistical significance is defined as the degree to which a value is greater or smaller than it would beexpected by chance.

StrategyThe reasoning built up by a set of components that allow you to define the business strategy. A strategyprovides the decision (page 148) support to manage the interaction (page 148) in the context of thedecision hierarchy. Each component has a well defined functionality. A strategy can reference otherdecision rules (scorecards, predictive models, decision tables, decision trees, adaptive models, andstrategies), and import data and propositions.

Treatment of PredictorsSymbolic predictors (page 150) can be treated as categorical or ordinal data. Numeric predictors can betreated as categorical or continuous data. Categorical treatment bases the recording of the data on theprobabilities of a positive outcome of each interval/category. Ordinal treatment bases the recording of thedata on the sequence code of each category. Continuous treatment bases the recording of the data onthe raw data of the predictor.

Trend DetectionTrend detection is possible by comparing the performance of multiple models. To make this possible,the models triggered by the same proposition (page 150) are configured with different performancewindow sizes to determine the time frame in number of cases (page 147) over which the performanceis calculated. Implementing trend detection requires a combination of strategy design patterns, and usingcompatible adaptive model rules with different memory settings.

Univariate PerformanceUnivariate performance represents the potential performance of a predictor on its own.

Wide of SchemeWhen other samples (page 150) are analyzed, values may be detected that were not present in thedevelopment sample. From these values, Wide of Scheme cases (page 147) are formed, and thecorresponding values reported separately.

Getting Started


Z-ratioThe Z-ratio measures the reliability of expected behavior. It is a measure of predicted percentage versusactual behavior that takes into account error by allowing for statistical significance (page 151). The Z-ratio is positive when expected behavior is above the average behavior, and negative when below.

Date post:	10-Jul-2020
Category:	Documents
Upload:	others
View:	2 times
Download:	1 times

Getting Started - Pega · Decision Strategy Manager delivers two rule sets. The Pega-DecisionEngine...

Documents