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Decision Strategy Manager Version 6.2

Getting Started

Copyright 2011 Pegasystems Inc., Cambridge, MA

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Getting Started

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

Introduction 5

Decision Management 6

Decision Strategy Manager 6

Adaptive Decision Manager 6

Understanding Decision Management 7

Rule Sets 7

Methods & Functions 7

Service Layer 8

Decision Execution 9

Decision Invocation and Execution 9

Interaction Data 9

Strategy Result 10

Classes 10

Properties 11

Data Instances 12

Interaction Management 12

ISClient Interface 12

Capturing Responses 12

Revoking Responses 13

Proposition Cache 13

Dimensions 13

Adaptive Decision Management 14

Adaptive Modeling 14

Predicting Behavior 15

Model Learning 15

Model Learning Explained 15

Local Learning 16

Model Updates 16

Strategies 16

Strategy Design 17

Planning Strategy Design 17

Adaptive Components 18

Strategy Chaining 20

Strategy Execution 20

DSM Enabled Applications 22

Rule Set Dependency 22

Organizational Structure 23

Configure Application 23

Work Pool 24

Access Group & Operators 24

Product 25

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Decision Management Landing Pages 26

Strategies 26

Issues 26

Propositions 26

Strategies 27

Adaptive Models 27

Batch Data 28

Batch Runs 29

Input 30

Output 31

Visual Business Director 32

Dimension Filter 33

Data Modes 35

Timeline 35

X/Y Axis 36

Multiple Grids 37

Export Grid Data 38

Services 38

Decision Management Rule Types 40

Predictive Model 40

Create Rule Instance 40

Upload Predictive Model 41

Input Mapping 42

Statistics 42

Predictive Model Results 44

Classification & Strategies 45

Define Results 45

Scorecard 46


Score Calculation 47

Define Results 48

Adaptive Model 49


Configure Models 50

Define Settings 51

Responsiveness 52

Data Analysis 52

Advanced Configuration 53

Interaction 53


Interaction History 54

Run Strategy 54

Capture Response 55

Strategy 57


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Design Strategy 58

Toolbar & Context Menu 58

Defining Components 59

General Settings 60

Data Import 61

Segmentation 63

Data Enrichment 65

Aggregation 67

Arbitration 68

Selection 70

Connecting Components 71

Defining Expressions 72

Expressions in Strategies 72

Financial Functions 73

Strategy Properties 75

Auto-Run Results 76

Overview 77

Audit Notes 78

Finalizing Rules 78

Testing Rules 78

Flow Shapes 81

Run Strategy 81

Capture Response 81

Tutorials 83

Predictive Models and Scorecards in Process Flows 83

Predictive Models 83

Predictive Model Decision Rule 83

Process Flow 85

Scorecards 85

Scorecard Rule 85

Process Flow 87

Strategy Driven Processes 88

Class Structure & Data Models 88

Class Structure 88

Data Model 88

Propositions 89

Define Top Level Class 89

Define Hierarchy 90

Define Propositions 91

Proposition Attributes 91

Propositions 93

Segmentation Rules 94

Decision Table 95

Scorecard 95

Adaptive Model 96

Predictive Model 98

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Strategies 99

Loans Strategy 100

Sales Strategy 104

NBA Strategy 108

Interaction 111

Process and User Interface 113

Process Flow 113

Flow Actions 120

Collect Customer Information 120

Display Offers 120

Managing Adaptive Models 122

Training Models 122

Predictor Overview 123

Behavior Reports 124

Active Predictors Report 125

All Predictors Report 126

Performance Overview 126

Clearing & Deleting Models 127

Clearing Models 127

Deleting Models 127

Model Parameters 128

Glossary 129

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Introduction

Product Release DSM/ADM and Process Commander 6.2 SP2

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

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Decision Management

Decision Management functionality in your Process Commander application allows you to usesophisticated mechanisms that empower and enrich your application so that, using the Next Best Action(page 131) principle, you can develop application that easily determine the right processes to run, andthe appropriate products to offer to customers. The Next Best Action principle is geared toward increasingcustomer loyalty with the ability to address multiple issues in the decision making process. DecisionManagement functionality is delivered with Decision Strategy Manager (DSM) and Adaptive DecisionManager (ADM).

• Decision Strategy Manager (page 6)• Adaptive Decision Manager (page 6)

Decision Strategy ManagerDSM delivers proposition management, the use of strategy, scorecard, and predictive model rules todrive process flows, strategy development, capturing interaction results using Interaction Services (IS),visualization and monitoring using Visual Business Director (VBD), advanced adaptive analytics usingADM, and batch execution of strategies.

Adaptive Decision ManagerADM is an integrated method establishing customer preferences without previously collected historicaldata. ADM extends predictive analytics with an adaptive mechanism for establishing customerpreferences with customer responses in real time. Due to its adaptive nature, no initial collection of data isnecessary.

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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 7)• Methods & Functions (page 7)• Service Layer (page 8)• Decision Execution (page 9)• Strategy Result (page 10)• Interaction Management (page 12)• Adaptive Decision Management (page 14)• Strategies (page 16)

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

Related Topics

• Decision Management Rule Types (page 40)• Decision Management Landing Pages (page 26)

Methods & FunctionsThe table below provides an overview of the Decision Management methods and functions. Additionally,Decision Management includes the Financial functions library which can be used in expressions.

• Lib(Pega-DecisionEngine:PredictiveModel).ObtainValue(this, myStepPage,"preditivemodelrulename")Obtain the result of a predictive model rule.

• Lib(Pega-DecisionEngine:Scorecard).ObtainValue(this, myStepPage, "scorecardrulename")Obtain the result of a scorecard rule.

• Call pxRunStrategyExecute a strategy rule.

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

• Call Rule-Decision-Interaction.pxRunStrategyExecute an interaction rule in strategy execution mode.

Related Topics

• Predictive Model (page 40)• Scorecard (page 46)• Strategy (page 57)

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• Interaction (page 53)• Financial Functions (page 73)

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 inthe adaptive models landing page, and the model update mechanism (page 16). 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 now use the capabilities added by ADM, orVBD.

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Decision Execution• Decision invocation and execution (page 9)• Interaction data (page 9)

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 the 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.

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).

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

• Classes (page 10)• Properties (page 11)• Data instances (page 12)

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 contains asmany issues as required by the business (for example, Churn, Collections, Sales). An issue can have oneor more groups, each group basically providing a label for a series of related propositions (for example,Bundles, Credit Cards, Loans, Mortgages).

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

• Classes defining issues have no key defined. They are defined with pattern inheritance, and directedinheritance from the SR class.

• Classes defining groups have the pyName key. They are defined with pattern inheritance, anddirected inheritance 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.

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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, pyChannel, pyDirection, pyGroup, pyIssue, pyLabel,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.

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Properties in the classes defining the proposition hierarchy are defined at the applicable level dependingon its scope in the proposition hierarchy, and they can be generic, or made specific to DecisionManagement.

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.

Generic Standard or generic properties that are not configured with thepyDecisioningItem custom field but belong in the proposition hierarchy datamodel.

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. Interaction Services provides the interactionmanagement services that persist the interaction result. IS also provides routing to ADM and VBD in theservice layer, including updating the state of adaptive models, and saving interaction result information formonitoring and reporting purposes. Interaction data can be queried and analyzed for reports on decisionperformance, thus allowing for identifying where changes should be made, and new opportunities arise.

Related Topics

• ISClient Interface (page 12)• Proposition Cache (page 13)• Dimensions (page 13)

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 IS database. If the propagation to VBD isenabled, the data records are published to VBD for monitoring and visualization. If the proposition offeredis modeled in ADM, the response is also sent to ADM for learning, and can be viewed in the AdaptiveModels landing page in PRPC.

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

The response APIs can be broken into two logical groupings:

• Capturing responses (page 12)• Revoking responses (page 13)

Capturing ResponsesThese methods accept one or more ResponseContainer objects that contain the data for capturingthe dimensions (page 130), measurements (page 131), and customer behavior in the interactionresult. Responses contain dimensions, customer response to the proposition in the behavior dimension,

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CASE_ID, customer driven measurements, and custom dimensions. There are twenty flexiblemeasurements that can be recorded for any response. The out-of-the-box seed data defines someof these measurements to illustrate examples such as Average Handling Time, and how thesemeasurements 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 and HISTORY_ID, which can be used later to revoke aprevious response. This record also contains dimension and measurement information provided throughthe interaction rule. Response capturing can be handled differently depending on the response 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 nodes connected to the same database, DSM uses the system pulseto ensure the consistency of propositions across all nodes. The proposition cache is invalidated whena proposition is saved (triggered by adding or changing proposition), or deleted. Adding records thatresult in the proposition cache to become invalid is done through two declare trigger rules in the Data-pxStrategyResult class which trigger the pyRefreshPropositions activity:

• pyPropositionSaved• pyPropositionRemoved

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.

DimensionsInteraction information is based on dimensions (page 130). 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 (page130). 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

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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 (page 6) extends existing adaptive propensity techniques. Besideskeeping 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 131), 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 131), predictive models can be used as an alternative or inconjunction adaptive models (page 129). Adaptive models become more accurate with time, requiringmonitoring not to become less sensitive after a sustained period of use. The advantage of using ADM isconsiderable in business areas where mistakes are not critical, such as marketing.

Related Topics

• Adaptive Modeling (page 14)• Predicting Behavior (page 15)• Model Learning (page 15)• Model Updates (page 16)

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 131) 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 129)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 (page15) models that are used to assess propensities.

Without any data, the scoring models (page 133) are empty, and only track overall propensity (page132). The prioritization scheme ensures all propositions are considered but focusing on the observed

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best propensity proposition, thus ensuring early data collection for all propositions while maximizinginteraction results. Interaction results (page 130) are processed by the adaptive analytics engine (page129), and stored in a set of adaptive statistics (page 129) from which the engine continuously createsup-to-date scoring models. Statistics and models are stored in the adaptive data store (page 129).Scoring models drive the decision process, and statistics ensure persistence. Once a data set has beencaptured, new scoring models are created. In this second stage, the data is used to identify propositionswith higher or 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 132) to assess inter-correlations in the data.• Selecting predictors to establish an uncorrelated view that contains all relevant aspects 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.

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 52) setting of the adaptive model rule, and also the model updatefrequency set in the UpdateAdaptiveModels (page 16) 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 15)• Local learning (page 16)

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 control

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model refresh, a light weight analysis process where predictor binning is recalculated, but predictorgrouping is left unchanged. The setting that controls model refresh is the refresh after (page 53)setting. If the values 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 will be created, allowing for gatheringresponses for 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 this contextis more than the number of responses triggering model refresh, the model refresh mechanism istriggered.

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 53) 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 15). The models produced through learning in the ADM systemare superior in predictive quality than models produced through local learning.

Model UpdatesPRPC keeps a local cache of scoring models. Scoring model updates are regularly retrieved from theadaptive data store (page 129). The model update frequency is implemented by periodically triggeringthe UpdateAdaptiveModels agent in the Pega-DecisionEngine rule set. The agent periodically runs thepxUpdateModels activity to retrieve model updates. By default, the agent is scheduled to run every 30seconds. The configuration of the model update frequency is done via the services landing page (page38). The agent does not retrieve all scoring models in ADM because it discards updating any modelsthat are not required for strategy execution, and models that are the same as the models in the localcache.

StrategiesTypically, a strategy (page 133) is developed to deliver a personalized recommendation for a singledecision (page 130). 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(risk or product/service offer), the decision chain may need to address which credit strategy to use,which retention strategy, or which product to offer first. Every decision employs a combination of strategycomponents (page 59) that define the underlying logic that is required to deliver a recommendation.Components allow you to create personalized customer interactions consistently across contactchannels. The advantage of building decision strategies from these smaller components is that each canbe readily understood, developed, edited and tracked on an ongoing basis. You can use components to

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model sophisticated customer behavior, and there are some common design patterns that you may endup reusing frequently.

In the context of using strategies in combination with propositions, a strategy is created to deliverthe decision for one issue or group. The issue or group corresponds to the issue or group level ofthe proposition dimension definition in IS. The level at which the strategy is created determines theaccessibility to properties, or proposition attributes. The properties a strategy can access in the classhierarchy define the output structure of each component 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 for multiple strategies is reuse of a strategy pattern in other strategies acrossyour application.

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

Related Topics

• Strategy Design (page 17)• Planning Strategy Design (page 17)• Adaptive Components (page 18)• Strategy Chaining (page 20)• Strategy Execution (page 20)

Strategy DesignThe visual orientation of the strategy (page 133) is a logical translation of the output orientation (page17), working backwards from the Next Best Action (page 131) 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:

• 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 131) 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 61), then segmentation components(page 63) for which possible actions are defined, next the data enrichment components (page 65),proceeding with arbitration components (page 68). Finally, the end selection component (page 70)delivers the best action in the interaction.

Planning Strategy DesignYou can approach planning the design of your strategy (page 133) in two ways:

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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 131) 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 133).

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.

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 models the decision process, and proposes the Next Best Offer(page 131).

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Prioritization components are employed to offer the customer the best action based on predictedpropensity (page 132) and amount of data that is used in the prediction (page 131). A predictivemodel is used to predict customer attrition, fraud, and customer lifetime value.

In order to develop your strategy for trend detection (page 133), 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 model components in the strategy use adaptive model rulesdifferentiated on the basis of performance window size. The selection between which adaptive model isused is performed via a prioritization component. The prioritization rule selects the highest performingmodel.

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• When the characteristics of the customers change, the fast model (limited window size) detects thechange in behavior fastest and, consequently, has the higher performance of the models. It will beused to decide on the predicted propensity.

• When the other models start to capture this change in behavior, and earlier behavior has beendisregarded, they will again be selected because they can make more accurate predictions as theyuse more data.

• Positives and negatives can be used to calculate the expected or base level propensity and, togetherwith the propensity output field, calculate the lift (page 131) of individual predictions.

Strategy ChainingA strategy (page 133) can use other strategies. A strategy uses another strategy through the substrategy component (page 61) defining which strategy to import, and which of the public componentsin the strategy should be selected in the decision execution path. Including strategies allows for usingspecialized group or issue level strategies that address a specific business case, and combining themin a more generic strategy that is typically at the organizational level in the class hierarchy. The strategydesign pattern used when including sub strategies can be seen as always including more specializedcases to address all issues in an NBA strategy. Sub strategies can also be used to define common piecesof functionality that can be reused 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.

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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 whose purpose is to select the decision execution.

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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 22)2. Check organizational structure (page 23)3. Configure application (page 23)4. Configure work pool (page 24)5. Check access group and operators (page 24)6. Create product (page 25)

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 23), 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 the Pega-DecisionArchitect.

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3. Go to the Security tab, and enable the Use check-out option.This option must be enabled to ensure a functional proposition hierarchy.

Organizational StructureThe organizational structure is necessary for the proposition hierarchy. The organization record providesthe dedicated class which becomes the class containing the application's proposition hierarchy. Makesure 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 DSM enabled application.

Configure Application1. Go to the application rule.2. In the General tab, select PegaDM in the Built On Application field, and use the version applicable to

your application.

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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 Work-.

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 (at least, PegaRULES:SysAdm4), and the

necessary portal layouts (for example, Developer, WorkManager, and Manager).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.

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Product1. Create a product rule that includes your application, and the application's rule set.

2. In the individual instances section, use the SmartPrompt and the Query button to insert the instancescorresponding to access group, operator ID, work pool, organization, division, unit, work group, andworkbasket.

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Decision Management Landing Pages

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

• Strategies (page 26)• Adaptive Models (page 27)• Batch Data (page 28)• Visual Business Director (page 32)• Services (page 38)

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 26)• Propositions (page 26)• Strategies (page 27)

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.

• 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 all 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.

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• 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 write the proposition hierarchy and propositions

to the Interaction Services database tables in the Decision Management service layer. Exportingpropositions to IS allows you to, for example, make the transition of a development system to aproduction system by pointing the Services configuration to the production server, and then using theExport to IS functionality. The process of exporting propositions to the Interaction Services databaseis basically a synchronization between the decision hierarchy and group data instances in PRPC andthe database tables in the IS database.

• 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 Add/Remove Columns columnselection to control the amount of details in the overview. Use the New button to create a new strategyrule (page 57).

Adaptive ModelsThe Adaptive Models landing page provides the facilities to track the performance of adaptive models,train models, and manage the models accessed by your application in the ADM system.

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• 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 Upload Responses button to train models.

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.

• 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.• Negative: 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 provides the following options:

• Predictor Overview: displays the overview of predictors used in the model.• Active Predictors Report: generates and displays the filtered model behavior report based on active

(used) predictors only.• All Predictors Report: generates and displays the unfiltered model behavior report. Unfiltered model

behavior considers all predictors regardless of whether they are active or inactive predictors.• Performance Overview: generates and displays the model's performance overview.• Delete Model: deletes the adaptive model.• View Model Parameters: displays the modeling and configuration settings as defined in the adaptive

model rule.• Upload Responses: train the model through the use of historical data.

Batch DataThe tabs that are accessed via Decisioning | Batch Data landing page in the Pega menu provide thefacilities to define and manage batch execution of the strategies accessed by your application. Dependingon the type of action you want to perform, use the corresponding tab. Batch data functionality assumesthe availability of customer data, data classes, and report definition rules.

• Batch Runs (page 29)• Input (page 30)• Output (page 31)

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The definition of input and output configurations is typically performed by system architects,and the definition and triggering of strategy execution by strategy designers.

Batch RunsThe Batch Runs tab allows you to manage the configurations that trigger batch execution of the strategiesdefined in your application. Use the Refresh button to make sure you are looking at the latest set ofconfigurations. Use the button at the end of the corresponding row to delete a configuration.

The overview shows the configurations available in your application.

• ID: the unique identifier of the batch run.• Strategy: the name of the strategy rule executed by the batch run.• Public Component: the name of the public component providing the decision path for 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.

Use the New button to open the Strategy Execution Configuration form. The Strategy ExecutionConfiguration form allows you to create a new batch run configuration. Once the form is completed,click Submit to save the configuration, or Submit and Execute to save the configuration and trigger theexecution of the batch run.

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

• 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 hot to store the output of the batch run.

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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.

The overview shows the input configurations available in your application.

• Name: the name that identifies the configuration.• 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.

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.

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• 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.• Case ID: this field displays the primary key in the database table providing the input data.• Values Fields: section that allows you to add fields (other than the field providing the case ID) from

the report definition as values fields (page 133). Defining values fields is optional.

OutputThe Output tab allows you to manage the configurations that define how and where to store the results ofthe batch run. Use the Refresh button to make sure you are looking at the latest information about outputconfigurations. Use the button at the end of the corresponding row to delete a configuration.

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

• Name: the unique name of this configuration.• Input: the name of input 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.

Use the New button to open the Create/Edit Output Configuration form. An output configuration basicallydefines how data instances (strategy results) map back to data in a database table.

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The Create/Edit Output Configuration form provides the fields that allow you to define the outputconfiguration.

• Name: the name that identifies the configuration.• New Report Class: the name of the data class created by the configuration. The data class is

configured 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 in thebatch run process. If a database table with the same name already exists, it is recreated, and datastored in that table is lost.

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

class generated by the configuration.• Outputs

• Input Data Properties: section displaying properties pertaining to the input data.• 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.

Output configurations should be checked for the column size they generate in DB2 databases.The maximum VARCHAR of a column in the output database table is 500 characters.

Visual Business DirectorVisual Business Director (VBD) allows you to perform historical analysis. VBD enables business users tovisualize the business based on different views (proposition, channel, customer, etc.), and examine thesuccess levels down to any level. Through the VBD applet, the Visual Business Director landing page

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provides the facilities to visualize decision results, and monitor business metrics with a 3D graphical viewof the different dimensions and measurements, such as accept rate, conversion rate, average price,volume, number accepted, and number of processed responses. The Visual Business Director landingpage can be accessed through the Pega menu by going to Decisioning | Visual Business Director.

• 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 orientation 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 the targetvalues.

• Each line chart shows a different metric over the current time period selected in the timeline. Clickinga chart makes the grid use that metric.

• 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.

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.

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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.

The regular mode displays all records in theinteraction history versus the records in theinteraction history for the time period selected in theReference drop down. 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 betweenall records in the interaction history and the recordsin the interaction history for the time period selectedin the Reference drop down, allowing you toanalyze their relative effects. Switch to this datamode by clicking the icon.

TimelineThe timeline is the interface allowing for browsing recorded historical performance. This interface consistsof timeline console and timeline display. The and buttons allow you to hide/show the display and theconsole.

The console allows you to select the date settings for displaying data in the VBD applet, and the historicaldata to be used as reference.

Selection Description

From Selects the start date for showing the data.

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.

Reference Drop down for selecting which data source to use as reference. Unless theVBD database has been configured otherwise, the Reference drop downdisplays the following options:

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

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

• Last Year: compare to last year aligned on days. For example, compareSeptember 15, 2011 with September 15, 2010.

The selected historical data views are listed on the left side. Each data view has the corresponding lineshowing when 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 35), but you can alsochange it by dragging the vertical sliders, or using the scroll buttons. When doing this, the timelineconsole reflects the changes.

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 youare interested in seeing customer behavior for.

• 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.

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.

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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.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.

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Export Grid DataYou can export grid data by using the icon in the right side of the VBD applet's back wall. You need toprovide a name for 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 allows you to define the connection to the servers running InteractionServices, Adaptive Decision Manager, and Visual Business Director. Additionally, it allows you to controlagents. The services configuration is necessary so that you can perform the actions listed below, but it isnot required when simply creating and defining Decision Management rules.

• Run interaction and strategy rules.• Perform adaptive model management activities.• Execute flows or activities that result in running strategies or capturing response data.• Use the VBD applet.

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. In the Pega menu, go to Decisioning | Services.3. 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.4. In the Agents section:

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

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• 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).

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

Saving settings makes the changes effective in the Decision Management service layer. If you havenot configured ADM or VBD, the applicable propagation of data resulting from interactions to thecorresponding service(s) is turned off.

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Decision Management Rule Types

• Predictive Model (page 40)• Scorecard (page 46)• Adaptive Model (page 49)• Interaction (page 53)• Strategy (page 57)

Related Topics

• Audit Notes (page 78)• Finalizing Rules (page 78)• Testing Rules (page 78)

Predictive ModelCreate a predictive model rule to use a predictive model (page 131) developed and generatedusing Predictive 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 thelikelihood of customers defaulting on payments. Predictive model rules are referenced in strategiesthrough the predictive model component (page 63). In flows, predictive model rules arereferenced through the decision shape by selecting the predictive model type. In expressions,you can obtain the segment calculated by the predictive model rule by using the Lib(Pega-DecisionEngine:PredictiveModel).ObtainValue(this, myStepPage, "preditivemodelrulename") syntax. Theoutput of a predictive model rule is statistics generated by the PAD model that provides the prediction.

1. Create instance (page 40)2. Upload predictive model (page 41)3. Define input mapping (page 42)4. Review model statistics (page 42)5. Define results (page 44)6. Finalize rule (page 78)7. Test rule (page 78)

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.

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3. Click Create.4. Upload PAD model (page 41).

Upload Predictive ModelAfter creating the rule instance (page 40), 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 V6.4 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 131).

2. Information about the predictive model is displayed when the upload is successful, including the fileand model details, and model performance measured in terms of Coefficient of Concordance (page130). It is using this section that you can also use the Download button to create a copy of the fileon disk, or use the Upload button to upload a new version of the OXL file.

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3. Proceed with mapping inputs (page 42).

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 44) to apply for cases falling in a given segment.

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The Attributes section displays information about the model attributes (page 131). Model attributes arepart of the information generated when exporting the model using Predictive Analytics Director.

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

• Classification and business strategies (page 45)• Defining results (page 45)

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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 strategies, typically associated with probability ofpredicted behavior (high, medium, and low). Remapping the classification defined in the predictive modelto the typically smaller number of business strategies allows you to increase the quality of business. Forexample, if a lower propensity (page 132) class is reassigned to the medium propensity class wherefewer customers are presented with a product offer but a greater proportion responds, the volume ofbusiness decreases but 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.

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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. The score based segmentation can be mapped to results by defining cutoff values used to mapa given score range to a result. For example, you can create a scorecard rule to calculate customersegmentation based on age and income, and then map particular score ranges to defined results.Scorecard rules are referenced in strategies through the scorecard component (page 63). In flows,scorecard rules are referenced through the decision shape by selecting the scorecard model type.In expressions, you can obtain the segment calculated by the scorecard rule by using the Lib(Pega-DecisionEngine:Scorecard).ObtainValue(this, myStepPage, "scorecardrulename") syntax. The output of ascorecard rule is a score.

1. Create instance (page 47)2. Define score calculation (page 47)

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3. Map score ranges to results (page 48)4. Finalize rule (page 78)5. Test rule (page 78)


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 47).

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.

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• In the Score column, enter the score that should be applied for cases falling in the definedcondition. Optionally, define a fallback score for any case that does not match the definedconditions in the OTHERWISE 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 48).

Define ResultsAfter defining the way scores should be calculated (page 47), 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.3. In the Result column, enter the decision result corresponding for the score range specified in the

Cutoff Value column.

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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 automatically calculated based on the cutoff value defined in the previousresult.

5. Check the audit notes (page 78) option if you want scorecard details captured in the work object'shistory.


Adaptive ModelAdaptive model rules configure the adaptive scoring models (page 129) in the ADM system. Adaptivemodel rules are used in strategy rules for a given proposition issue or group through the adaptive modelcomponent (page 63). The output of an adaptive model rule is a partial list of adaptive statistics(evidence, propensity, and performance).

• Create rule instance (page 49)• Define model configuration (page 50)• Define model settings (page 51)• Finalize rule (page 78)• Test rule (page 78)

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.

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3. Click Create.4. Configure predictors (page 50).

Configure ModelsProperties in the data model can be used as predictors (page 132). 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 131). The adaptive analytics engine (page 129) automatically detects the most importantpredictive fields to use in an adaptive scoring model (page 133).

The capability of using predictors depends is added by the Entitled to Use Adaptive DecisionManager setting in the Services landing page (page 39). 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.

Getting Started


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.

4. Define settings (page 51).

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.

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2. The following topics provide information on how to configure the settings used to create and retrievethe scoring models.• Responsiveness (page 52)• Data analysis (page 52)• Advanced configuration (page 53)


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 129), the scoring models maintain during predictions (page 131). By default,it is set to 0. The memory configuration allows you to discard the oldest cases, and it allows you toimplement trend detection (page 133) by creating multiple adaptive models, all triggered by the sameproposition (page 132). This setting influences the binning of predictors as behavior changes with newcases being recorded.

• Low memory values allow the identification of new trends.• High memory values provide robust and long-term predictive power (page 132).• Set the memory to 0 to never discard information.

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 0.05.

• Grouping Minimum Cases: a value between 0 to 1 that determines the minimum percentage of casesper interval. Default setting is 0.25.

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• 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 130), 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 52). 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 78) 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 81) through thestrategy and capture response shapes. Interaction rules can also be used for process monitoring throughthe capture response shape alone. In activities, interaction rules can be executed in run strategy modeby using the Call Rule-Decision-Interaction.pxRunStrategy method (providing the name of the interactionrule, and the AppliesTo class as parameters), or in capture response mode by using the Call Rule-Decision-Interaction.pxRunCaptureResponse method (providing the name of the interaction rule asparameter).

1. Create rule instance (page 53)2. Define interaction history (page 54)3. Define strategy execution settings (page 54)4. Define capture response settings (page 55)5. Finalize rule (page 78)6. Test rule (page 78)


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


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3. Click Create.4. Define interaction history (page 54).

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. Define the settings for running the strategy (page 54).

Run StrategyDefine the settings for running the strategy. The configuration in this particular tab is used in flowsthrough the run strategy shape (page 81).

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

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4. Define settings for capturing the interaction data (page 55).

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 81).

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

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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 (forexample, 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 50).

3. Check the audit notes (page 78) option if you want adaptive model details captured in the workobject's history.

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.

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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 132). Inproposition bundles, you need to map to the properties defining the bundle parent and members inthe Bundle Parent and Bundle Members fields.

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.

StrategyStrategy (page 133) 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. Strategy rules are used in otherstrategy rules through the sub strategy component (page 61), and in interaction rules (page 53). Inactivities, interaction rules can be executed by using the Call pxRunStrategy method, and providing thename of the strategy rule, the name of the strategy , customer ID (optional), and the name of the pagethat holds the result of running the strategy.

1. Create rule instance (page 57)2. Design strategy (page 58)3. Manage strategy properties (page 75)4. Analyze strategy component results (page 76)5. Segmentation overview (page 77)6. Finalize rule (page 78)7. Test rule (page 78)


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


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3. There are two typical use patterns when defining a strategy.• A generalized use of strategy rules without propositions. Enable the Choose Strategy Result

Class option to select a data class that is indirectly derived from Data-pxStrategyResult.• A specialized use of 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. Ifyour strategy should apply to all issues and groups, leave the Issue and Group fields undefined;otherwise, select the issue and, if applicable, the group. The level at which the strategy is created(Issue, and Group) determines the properties it can access.

4. Click Create.5. Design strategy (page 58).

Design Strategy• Toolbar and context menu (page 58)• Defining components (page 59)• Connecting components (page 71)• Defining expressions (page 72)

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 76), and the buttonallows you to run the strategy rule in the context of a batch run (page 29).

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

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

• Add components (page 59).• 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 130)that delivers the decision (page 130).

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.

• General settings (page 60)• Data import (page 61)

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• Segmentation (page 63)• Data enrichment (page 65)• Aggregation (page 67)• Arbitration (page 68)• Selection (page 70)

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.

• If the component name contains spaces, the Page Name field displays the actual name of thecomponent in the clipboard, and the name that can be used in expressions. The Page Name of acomponent is the name you defined excluding space characters.

• The Use Description field allows you enter a user-defined description for the component. Select thisoption to enter and show user-defined description, deselect this option to show the automaticallygenerated component summary. The component summary displays information based on thecomponent's configuration.

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

The Source tab applies to most components, and 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 following categories:

• Data import• Segmentation• Selection

The Properties tab is generic to components that are not selection components. This tab allows you tomap the properties brought into the strategy by the component to properties that are strategy properties.In the Add New Mapping section, make sure you click the button to complete the mapping. Propertiesare only mapped when displayed in the corresponding columns (Source, or Target) above the Add NewMapping section.

Getting Started


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

• Sub strategy components reference other strategy rules. They define the way two strategies arerelated to each other, access the public components in the strategy they refer to, and define how torun the strategy if the strategy is in another class.

• Proposition components import propositions (page 132) defined in the proposition hierarchy.• Embedded page components import data in an embedded page.• Named page components import data in a named page.

Data import components that refer to named or embedded pages map the page's single value propertiesto strategy properties through the Properties tab. The definition of a data import component depends onthe Page Type (Proposition, Embedded Page, Named Page, and Strategy).

• Embedded/Named PageIn the Source tab, provide the name of the named/embedded page in the Page Name field. If thePage Type is selected to import data from a named page, the Page Class field allows you to providethe class context for the named page. The Properties tab is automatically populated with the page'ssingle value properties.

• Proposition• In the Source tab, select the propositions in the proposition hierarchy. Use the Issue and Group

drop down lists to select the issue, and group. In the Proposition field, you can either import allpropositions, or specify a single proposition.

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• In the Interaction History tab, check the Enable Interaction History option to automatically bringinteraction data to the strategy, and map the interaction history properties you require to strategyproperties.

• StrategyIn the Source tab, define the page for the referenced strategy to run on (if not defined, the strategyruns on the work object data), define the class of the strategy to be imported in the Strategy Classfield if the strategy is in another class than the strategy importing it, select the strategy rule in theStrategy field, and select the component in the reference strategy from the Component drop down.The Component drop down displays the public components (page 60) in the referenced strategy.

Getting Started


SegmentationComponents in this category typically use customer data to segment cases based on characteristics andpredicted behavior, placing each case in a segment (page 133), or score (page 132).

• Predictive model components reference predictive model rules (page 40).• Scorecard components reference scorecard rules (page 46).• Adaptive model components provide segmentation based on adaptive models in ADM. These

components reference adaptive model rules (page 49).• Decision table components reference decision table rules, and can be used to implement

characteristic based segmentation by referencing a decision table using customer data to segment ona given trait (for example, salary, age, and mortgage).

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

In segmentation components, the tab additional to the Source tab is displayed according to the type ofdecision rule the segmentation component references. The Decision Table, Decision Tree, PredictiveModel, and Scorecard tabs have a similar configuration. The example below illustrates the functionality inthese tabs by showing a decision table component.

Getting Started


• Rule Type: use this field if you want to change the type of rule the component references. By default,components are added with the appropriate rule type depending on what you selected in the Addcontext menu, but you can change the rule type (Decision Table, Decision Tree, Predictive Model, orScorecard) through the Rule Type drop down.

• Defined On: select if the component should be defined in the Applies To class or the StrategyProperties class.

• Rule Name: use the SmartPrompt to select an existing rule, or click the button to create a new ruleof the same type as selected in the Rule Type field.

The Model Definition tab is displayed for adaptive model components. Changing the rule type is notsupported because the nature of the configuration of adaptive model components is fundamentallydifferent when compared to other segmentation components.

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• 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 26). 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.

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

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

• Strategy set components enrich data to define personalized data to be delivered when issuing adecision. Personalized data often depends on segmentation components (page 63), 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 132) to be offered

including customized scripts, incentives, bonus, channel, revenue, and cost information.• Probabilities of subsequent behavior, or other variable element.

• Data join components import data from an embedded or named page using a key to match data, andmap its contents to properties from the imported data to strategy properties.

The Source tab allows you to select the component in the strategy you want to add information to.Strategy set components express data through the Properties and Overrides tabs, data join componentsthrough the Data and Properties tabs.

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

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• Use the Overrides tab to define property values for each segment available in the strategy. In theSegment 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 bythe segmentation components used in the strategy rule, and can be viewed in the overview tab ofthe strategy (page 77).

• Data joinUse the Data tab to provide the name of the named/embedded page in the Page Name field. ThePage Class field provides the page's class context. The Key field allows you to enter the expressionto match data in the page. You can further refine the pages to include by checking the Exclude pagesnot matching data check box.

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

• Aggregation components set strategy properties using an aggregation method applied to propertiesfrom the source components.

• Financial 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 aggregation component allows you to select the strategy properties in theProperty column, the method for setting the property value based on an expression, and type theexpression in the Source column.

Getting Started


The Properties tab allows you to define the financial calculation performed by the financial component,and select the properties that provide the arguments for each financial function. The arguments that canbe selected in the Target and Payments drop down lists are strategy properties of type decimal, double,or integer. If the value for the arguments is set through source components, the order of the componentsin the Source tab is important because it is directly related to the order of arguments considered by thefunction to perform the financial calculation. Typically, the Payments argument should be a list of values,and not a single value. So that you can use a list of values to provide the Payments argument, use a dataimport component, and 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.

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• Prioritization components rank the components that connect to it based on the value of a strategyproperty, or a combination of strategy properties. These components can be used to determine theservice/product offer predicted to have the highest level of interest, or profit.

• Filter components apply a filter condition to the outputs of the source components.

Prioritization components express the arbitration through the Prioritization tab. The Priority field is usedto define the property providing prioritization criteria through an expression. Below this field, there are anumber of settings that allow you to control the criteria for selecting what the source components return.The Lowest First check box allows you to define how to sort the list of results from the source component.The Select options (Top 3, All, and Top) define how many results should be considered in the arbitration.The Top 3 option considers the first three results, All considers all results, and Top allows for a user-defined number of results by entering the corresponding number in the field next to the option.

Filter components express the arbitration through the Filter Condition tab. The Filter Condition field allowsyou to enter the expression used when filtering the results of the source components.

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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 68) 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 components randomly allocate customers between two or more alternativecomponents, thus allowing for testing the effectiveness of various alternatives. For example, you canspecify that 80% of customers are offered product X, and 20% are offered product Y.

• Switch components select between components on the basis of specified conditions. Thesecomponents are typically used to select different issues (such as interest and risk), or they selecta component based on customer characteristics, or the current situation. For example, a case canbe allocated to a sub-network dealing with recent customers with little history, or to a sub-networkdealing with long standing customers.

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 condition expressed in the If field is not met.

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%.

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Connecting ComponentsYou can connect components by selecting a component, and dragging the arrow to another component.Selecting one component is performed by mouse over on the center area of the component untilthe icon is displayed. A dotted red line is displayed in the process of dragging the arrow betweencomponents. As the arrow reaches another component, the component you are connecting is highlightedand, finally, the black arrow connecting the two components is displayed.

For components that select other components, the connections established this way determine theentries in the component's Source tab (the connected to component) or, for selection components, theSwitch 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,

Getting Started


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 72)• Expressions using financial functions (page 73)

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 63) that work on the strategy result data class, and not the AppliesTo class.

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.

Getting Started


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 73)• cumprinc (page 74)• db (page 74)• dbb (page 74)• fv (page 74)• ipmt (page 74)• nper (page 74)• pmt (page 74)• ppmt (page 74)• pv (page 75)• rate (page 75)• sln (page 75)• syd (page 75)• vdb (page 75)

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 75) applied to the loan.• Total number of periods: the total number of periods (page 74) for the loan.• Present value: the present value (page 75) 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.

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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 73).

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 74) 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 74). 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 75).• Number of periods: number of periods (page 74) for the payments.• Payments: the payment (page 74) (negative value) to be paid each period.• Present value: the present value (page 75) 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 75),period, number of periods (page 74), and present value (page 75) arguments, optionally using thefuture value (page 74) and type arguments.

Number of PeriodsCalculates the number of periods for an investment, optionally using the future value (page 74), 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 74). Typically, the payment containsprincipal and interest, and no other fees, or taxes.

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 74). This calculation can also be expressed by payment (page 74) minusinterest payment (page 74).

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Present ValueCalculates the net present value, optionally using the future value (page 74), 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 74), andcalculating the function at the beginning of the period. This function takes the number of periods (page74), payment (page 74), and present value (page 75) 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 74).

Sum-of-Years' DepreciationCalculates the sum-of-years' digits depreciation of an asset after a specified period taking the samesarguments as described in the depreciation function (page 74).

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 74). 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 specialized usedof strategies with propositions, this tab also allows you to add and remove properties at the class leveldetermined by the applicability of the strategy in the context of the proposition hierarchy.

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 available inthe 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.

Properties added through the Strategy Parameters tab have a specific configuration, which consists ofhaving the pyDecisioningItem custom field set to StrategyProperty.

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 if the data is displayed overmultiple pages allow you to navigate through the pages displayed for the selected component.

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OverviewThe Overview tab displays the segments (page 133) introduced in the strategy through segmentationcomponents.

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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.

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.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:

Getting Started


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

• 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.

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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 53), but they use different parts of the rule definition to operate.

• Run strategy (page 81)• Capture response (page 81)

Run StrategyThe Run Strategy shape uses the configuration in the interaction rule's run strategy tab (page 54) 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 (page55) 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 83)• Using strategy rules to guide processes (page 88)• Managing adaptive models (page 122)

Predictive Models and Scorecards in ProcessFlows• Using predictive models to guide processes (page 83)• Using scorecards to guide processes (page 85)

Related Topics

• Predictive Model (page 40)• Scorecard (page 46)

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 83), and then reference the predictive model rule inthe process flow (page 85).

Predictive Model Decision RuleSo that we can predict risk when customers apply for a loan, we create a predictive model rule (page 40)that uses information known about the customer to predict the probability of defaulting on payments.

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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.

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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 85), and thenreference the scorecard rule in the process flow (page 87).

Scorecard RuleSo that we can score each customer applying for a loan, we create a scorecard rule (page 47) 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


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.

Getting Started


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 88)2. Define proposition hierarchy, propositions, and proposition attributes (page 89)3. Define segmentation rules (page 94)4. Design strategy rules for each business issue, and define the top level NBA strategy rule (page

99)5. Define interaction rule (page 111)6. Define process, and user interface (page 113)

Class Structure & Data Models• Class structure (page 88)• Data model (page 88)

Class StructureThe Top 3 Offers use case assumes every rule is created and defined in the same class (DMSandbox),the organizational class is DMSandBoxCo, which is also the top level class for the proposition hierarchy.

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 IntegerAssets Text

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

• Critical Account• Past Arrears

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

Property Name Property Configuration

CustomerID TextBehavior TextResponse TextSegment TextSubSegment TextSelectedProposition Page, Data-pxStrategyResult page classOfferedProposition Page List, Data-pxStrategyResult page classSelect1 TrueFalse, pxCheckBoxSelect2 TrueFalse, pxCheckBox

Related Topics

• Decision Execution (page 9)• Defining Expressions (page 72)

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 14) 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 89)• Define hierarchy (page 90)• Define propositions (page 91)

Related Topics

• Decision Management Landing Pages (page 26)• Strategy Result (page 10)

Define Top Level ClassBy default, the top level class for the proposition hierarchy follows the <OrgClass>-<ApplicationName>-SR pattern. Changing the default assumed top level class is not necessary if this pattern suits yourapplication.

Follow the steps described below to change the top level class for the proposition hierarchy.

1. In the Pega menu, go to Issues tab (page 26) in the Strategies landing page.

2. Click the top level class link to go to your application's pxDecisioningClass field value rule.

Getting Started


3. Change the value in the Localized Label field.

4. After saving the rule, the new top level class is reflected in the Issue 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 10) supporting theproposition hierarchy.

1. In the Pega menu, go to Issues tab (page 26) 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 OK.

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 OK.

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

7. The complete hierarchy is displayed below.

8. Define propositions (page 91).

Define PropositionsPropositions and attributes are defined in the hierarchy using the Propositions tab (page 26) 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 does not need to take place directly in theprocess of defining the structure.

• Define attributes (page 91)• Define propositions (page 93)

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.

Getting Started


The following steps describe the process of adding the attributes required to define propositions. For theTop 3 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 26) in the Strategies landing page2. Select the applicable level for the proposition attribute. In the case of Cost, do not select issue, or

group.3. Click Manage Attributes.4. 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. Enter the name of the attribute in the Property Namefield, and select the Decimal data type in the Type field under Quick Create.

5. Click Quick Create.6. 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.7. Repeat the same steps in the Property: New rule form as described for the Cost attribute to add the

CreditLimit attribute.8. Depending on the selected scope, attributes are added as properties in the appropriate class.

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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 3 Offer use case.

1. In the Propositions tab of the Strategies landing page, click Manage Propositions.2. Manage propositions using the data table instances editor in PRPC. You can also click Edit in Excel

to manage the propositions from Excel.

3. 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.4. Define the following propositions:

• Loans group• CarLoan• HomeLoan

• Savings group• TermDeposit

Getting Started


• YoungSaverDeposit• Proactive group

• BronzeCard• SilverCard• GoldCard• PlatinumCard• NoCard

• Reactive group• FreeCarInsurance• FreeEvaluation

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

1. Select the issue and group, and click Manage Propositions.2. Using the button for each data instance representing the proposition, define the proposition

attribute values for every proposition.

3. Click Save, and then the Back button.4. In the previous steps, we defined the cost for the propositions in the Loans group, with 100 for

CarLoan, and 200 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• GoldCard: 50000• NoCard: 0• PlatinumCard: 75000• SilverCard: 100000

Segmentation RulesDefine the segmentation the strategy requires to rank, define the eligibility of propositions, and presentpersonalized data.

• Decision table (page 95)• Scorecard (page 95)• Adaptive model (page 96)• Predictive model (page 98)

Related Topics

• Decision Management Rule Types (page 40)

Getting Started


• Strategies (page 99)

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 100) 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.

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 100) 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 he 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 40, existing repaid on time scores 20, and the default score formissing data or any other account status not defined in the previous conditions scores 30.

Getting Started


5. Switch to the Results tab, and define the cutoff values. The result is Accept if the scorecard outputsa value below or equal to 60, otherwise it is Reject. In the case of the scorecard we are creating, wecheck the option to show audit notes when executing the flow.

Related Topics

• Decision Management Rule Types (page 40)• Segmentation (page 63)

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 104) 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. Set Age to numeric predictor type, and CreditHistory to symbolic. You candefine as many values as necessary to use as positive/negative behavior. In this model, we defineone value for positive behavior (Positive-Accept), and one for negative 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 if the light weight analysisprocess (model refresh).

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.

Getting Started


Related Topics

• Decision Management Rule Types (page 40)• Decision Management Landing Pages (page 26)• Managing Adaptive Models (page 122)• Model Updates (page 16)• Model Learning (page 15)• Segmentation (page 63)

Predictive ModelThe predictive model rule described in this topic is designed to predict the risk of customer attrition.This rule is used in the NBA strategy (page 100) to configure the proposition selection by the switchcomponent based on the churn rate predicted for the customer in the interaction. The purpose of the rulewe are about to define is to provide the churn rate for the expression in the final decision component. Inthe expression, we define the threshold at which customer attrition becomes the most important issue todeal with in the interaction. For the model to become useful in the strategy, we need to map the predictivemodel's fields to properties, and define the results to drive the segmentation in the strategy.

In this topic, we assume a predictive model generated through PAD predicting the risk ofcustomer attrition to be available to you. Documentation on how to perform modeling withPAD is available in the online help or offline documentation provided with the PAD software.In PRPC, the purpose of the predictive model rule is to make the predictive model generatedthrough PAD available to PRPC applications.

1. Create a new predictive model rule named PredictChurn.2. In the Predictive Model tab, upload the OXL file.3. Go to the Input Mapping tab, and map the fields required by the predictive model (predictors) to

properties.

Getting Started


4. Go to the Results tab. The predictive model outputs 13 classes, but we only have two possibleresults. Click Edit to aggregate classes 1-6, and classes 7-10.

5. In the Result fields, enter Low for the first aggregated classes, and High for the second.

Related Topics

• Decision Management Rule Types (page 40)• Segmentation (page 63)

Strategies• Loans strategy (page 100)• Sales strategy (page 104)• Top level NBA strategy (page 108)

Related Topics

• Decision Management Rule Types (page 40)

Getting Started


• Strategies (page 16)

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.

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) to segment customers for credit risk. These aretwo alternative credit assessment patterns for we need to define the selection criteria.

a. Add a champion challenger component named Challenger.b. Set ScoreRisk to be selected in 60% 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 process describedfor the 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 AcceptsOnly.

a. Connect the two proposition components 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. Check the Public option to use the output of the AcceptsOnly component in the sales strategy

(page 104).

Related Topics

• Decision Table (page 95)

Getting Started


• Scorecard (page 95)• Propositions (page 89)

Sales StrategyThe objective of this strategy is to import the loans strategy, import the savings propositions, combinethem in a strategy set that defines in which channel and direction should the propositions be offered, usean adaptive model that uses adaptive learning to predict customer behavior, set the margin, and thenprioritize based on a calculation 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 Cost, 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 following properties: Margin, Propensity, and Performance.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 by using Import All in the

Proposition drop down.

b. In the Interaction History of the SavinsPropositions 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 this strategy set.b. In the Properties tab, add the pyChannel, pyDirection, and Margin properties. Provide the value

SMS for pyChannel, Inbound for pyDirection, and an expression to calculate the Margin propertybased on the Cost property (0.30*.Cost).

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

Getting Started


c. Go to the Properties tab, and map the adaptive model outputs to strategy properties.

6. So that our application does not offer a proposition that has been already presented to the customer,we add a filter component named FilterOfferedPropositions.

a. Connect the SalesModel component to this filter.b. In the Filter Condition tab of the FilterOfferedPropositions component, add the

@if(.Count==0,true,false) expression to check if the proposition has not been offered. If it has not,it returns true, otherwise false.

7. Finally, prioritize propositions. Add a prioritization component named TopSalesOffered.a. Connect the FilterOfferedPropositions filter 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 Propensity.

Getting Started


c. Check the Public option to use the output of the TopSalesOffered prioritization in the Next BestAction strategy (page 108).

Related Topics

• Propositions (page 89)• Loans Strategy (page 100)• Adaptive Model (page 96)

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 predicted by the predictive model rule.

Getting Started


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 prioritization component.

Select TopSalesOffered in the Component drop down.

d. Since the strategy we are importing is not in a different class, and should not run it on a differentpage, we do not define the Strategy Page, and Strategy Class fields.

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.

Getting Started


3. To drive the final selection, we need to add the segmentation component that provides the risk ofcustomer attrition, and map the applicable property.

a. Go to the Strategy Properties tab of the strategy rule, click to add a new property namedChurnRate, select the Decimal data type, and add it at the SR class of your application's top levelclass.

b. Go to the Strategy tab, and add a predictive model component named PredictChurn.c. In the Predictive Model tab of this component, select the PredictChurn rule.

d. In the Properties tab, add the churnrate property, and map it to the ChurnRate strategy property.

4. The final selection component is a switch component that selects the sales strategies if the probabilityof customer attrition is below a certain level (0.09). 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 PredictChurn.ChurnRate<0.9, and selectRetentionOffers in the Otherwise drop down.

Getting Started


Related Topics

• Sales Strategy (page 104)• Predictive Model (page 98)

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 for interaction history purposes.

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 to handle the capture of the data resulting from theinteraction.

Getting Started


4. Configure the fields in the Capture Response tab as described below.a. The Behavior and Response fields should be mapped to the properties providing behavior

dimension 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 3 Offers, we are defininga static exercise where the call reason is always a campaign, and the reason always a newaccount. Enter "Campaign" as category level, and "NewAccount" as reason level.

Getting Started


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 3 Offers is a single offer application, so do not check the option that enables workingwith proposition bundles. Provide the SelectedProposition property.

g. The Top 3 Offers use case is not designed to record measurement data. Leave the fields in theMeasurements section blank.

Related Topics

• Understanding Decision Management (page 7)• Strategies (page 99)• Process Flow (page 113)• Class Structure & Data Models (page 88)

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,along with the three outputs (behavior, response, and selected proposition). The capture response shapeis executed with the values passed through the three outputs is processed, the corresponding data recordis stored in the IS database, adaptive statistics are updated in ADM when the sales model is selected inthe strategy execution path, and (if VBD is enabled in the system) the response data is propagated to theVBD database.

• Design process flow (page 113)• Configure flow actions (page 120)

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 flow isdriven by running the strategy. Depending on the customer details, propositions are displayed, and thenthe interaction is handled to record the customer response to the product offers.

Getting Started


After creating a new flow process rule named Top3OffersFlow:

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 Top3Offers to reference the interaction rule (page 111).

Getting Started


3. Connect CollectUserInfo to Top3Offers, and define the CustomerInfo flow action (page 120) definedin the connector Flow Action field.

4. Add an assignment shape named PropositionsOffered to show the propositions output by thestrategy, and connect Top3Offers to PropositionsOffered.

Getting Started


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

120) in the connector's Flow Action field.

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

Getting Started


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.

Getting Started


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

9. End the flow.10. Configure flow actions (page 120).

Related Topics

• Class Structure & Data Models (page 88)• Interaction (page 111)• Flow Shapes (page 81)

Getting Started


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 120)• Display offered propositions (page 120)

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.• 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.• In the first row, define the label as Customer ID label, and map the field to the CustomerID

property.

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

1. Create a flow action named CustomerInfo flow action.2. Add a section to the flow action, and reference the CustomerInfo section rule.

Display OffersBefore proceeding with the configuration of the flow action, create an activity to dynamically set theSelectedProposition property with 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.

4. Configure the On Click behavior of the check boxes. Select1 sets the Set property defined in thePropositionSet activity to 1, and Select2 to 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 three top offers to thework user in the interaction.

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

Related Topics

• Class Structure & Data Models (page 88)• Process Flow (page 113)

Managing Adaptive ModelsThe Adaptive Models landing page (page 27) provides access to the adaptive models in the ADM systemthat have been created in the process of executing a strategy. Adaptive model components in a strategyreference an adaptive model rule, which is the rule configuring how the model is created, how it learns,and how it predicts behavior. The performance of models over time is reported in the adaptive modelslanding page.

Related Topics

• Adaptive Decision Management (page 14)• Training Models (page 122)• Predictor Overview (page 123)• Behavior Reports (page 124)• Performance Overview (page 126)• Clearing & Deleting Models (page 127)• Model Parameters (page 128)

Training ModelsModels can be trained by uploading historical interaction results. The use of previous interaction resultsallows for the Adaptive Decision Manager to create models that are able to predict behavior. Only positiveand negative cases are considered by ADM. Positive and negative cases correspond to the behaviorthat will be taken into account by the settings defined in the adaptive model rule. Historical data needs tobe provided in a CSV file containing the input data for each case (page 129), and a set of interactionresults (page 130).

Follow the steps described below to upload historical data.

1. In the Pega menu, go to Decisioning | Adaptive Models.2. Click Upload Responses in the Actions menu for 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 129).7. Click Done to return to the Adaptive Models landing page.

Predictor OverviewThe predictor overview shows which predictors (page 132) 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. The Inactive Predictorstable shows the predictors that are not used for modeling. For each predictor, information is addedfor the number of positives and negatives used in each predictor, the range of values that have beenencountered for numeric predictors, or number of symbols that has been encountered for symbolicpredictors. Predictors can be inactive because their performance is below the threshold for theselected component. If the performance is above the threshold, the predictor is correlated with apredictor that is active and outperforms it.

Behavior ReportsBehavior reports contain the model's behavior analysis, and allow you to analyze the treatment ofpredictors (page 133) in a given model. The behavior analysis is centered around:

• Predictive performance (page 131) of the model, and its classification.• Predictor grouping (page 132)

• Using the Active Predictors Report, the grouping performed on active predictors, which is alsoreferred to as filtered behavior report.

• Using the All Predictors Report, the grouping performed on all predictors for which statistics arekept, which is also referred to as unfiltered behavior report.

Together with the classification output, these reports describe fully 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.

Getting Started


Analysis Description

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

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

Behavior The probability of being a positive.Z-ratio Z-ratio (page 134) 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 materialbut the difference may be due to chance, smaller red bars may also indicateinteresting differences.

Lift Lift (page 131) comparison.Interpretation Look for intervals and categories with larger red and orange dots, which is and

indication of intervals and categories that have distinctive behavior. Look forfields with few missing or residual values, which is an indication of fields thathave a more reliable relationship with the behavior to be predicted.

• Generating an unfiltered behavior report (page 125)• Generating a filtered behavior report (page 126)

Active Predictors ReportFollow 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 131) and predictor grouping (page132) based on predictors currently active in the model.

Getting Started


All Predictors ReportFollow the steps described below 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.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

125), but then based on all predictors for which statistics are kept (predictors currently used inpredictions, and predictors that are currently inactive).

Performance OverviewPerfomance overview charts allow you to monitor the predictive performance (page 131) evolution of aparticular adaptive model (page 129).

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 with the chart visualizing the performance in number of cases for

that 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


Clearing & Deleting ModelsTwo types of actions in the adaptive models landing page allow you to remove adaptive statistics.

• Clearing models (page 127)• Deleting models (page 127)

Clearing ModelsClearing models consists of removing all adaptive statistics (page 129) associated with an adaptivemodel. In this process, everything is cleared except for numeric predictors boundaries. Used inconjunction with historical data upload (page 122), it allows complete control over the contents of theadaptive statistics.

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.

Deleting ModelsThe delete action in the Adaptive Models landing page allows you to delete the current model. If used bya strategy, the model is recreated again when the strategy is executed. Deleting models implies the lossof adaptive statistics (page 129) 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.

Getting Started


Model ParametersModels are created based on the settings configured in the adaptive model rule (page 51) they areassociated with through the adaptive model component in the strategy rule.

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 settings in the adaptive model rule used when

creating the scoring model in ADM.

Getting Started


Glossary

Predictive Analytics DirectorPredictive Analytics Director (PAD) is a desktop application used to develop and create predictive models(page 131). 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 129), analyzing them, and producing individual scoring models (page 133)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 129). PRPC retrieves the scoring models from the database, and usesthem to calculate the prediction.

Adaptive Data StoreThe database scoring adaptive statistics (page 129) and adaptive models (page 129).

Adaptive ModelAdaptive models are ADM scoring models (page 133) that output predictions (page 131) calculatedand adapted in real time as responses are captured after executing a strategy (page 133). 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 132) 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 133) containing adaptive models(page 129).

Behavioral ProfileA behavioral profile represents a univariate model. The probabilities of a positive outcome for eachinterval/category are score bands (page 133) and can be used to predict in the same way as those ofany 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 132) 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 129). 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 130) sensitive to thecomplete range of score bands (page 133) 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.

Continuous BehaviorContinuous behavior is a typically ordered range of values (for example, purchased mount, length of arelationship).

Data SourceData about customers, and their previous behavior. This data can be used for modeling and strategydesign. A source should contain one record per customer with the same structure for each record. Ideally,data should be present for all fields, and customers, but some missing data can be tolerated.

DecisionThe result of running a strategy in the interaction context. Several decisions can be involved in a singleinteraction (page 130).

DimensionsDimensions provide a hierarchical context for the facts and responses associated with an interaction(page 130). Dimension levels are stored as delimited strings called FQN (page 130). 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 132). Interaction results are recorded in the ISdatabase tables, and propagated to ADM and VBD.

IntervalTypically, the values of numeric predictors (page 132) are grouped in intervals. Each interval provides auseful building block for understanding behavior.

Getting Started


LiftA measure (multiplied by 100) of the improvement in behavior exhibited by cases (page 129) in oneinterval (page 130) or segment (page 133) over the average of all cases.

MeasurementsMeasurements are the primary storage of numeric information associated with interactions (page 130),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 130) 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 133) allows applications to take the best decision (page130) in a multidimensional context (retention, recruitment, risk, recommendation, etc.).

Next Best OfferNext Best Offer decisions (page 130) deliver the facilities to take the best proposition (page 132)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 131) as published usingPredictive Analytics Director.

PopulationThe group of cases (page 129) 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 132) areextracted for modeling and validation.

PredictionThe outcome (page 131) 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 segments (page133) given the input of the required data about a case (page 129). Predictive models are developed inPredictive Analytics Director.

Predictive PerformanceSome measure of the scores (page 132) or segments (page 133) generated by models. Performancecan be measured in terms of predictive power (page 132), value, or rate achieved under selectedconditions.

Getting Started


Predictive PowerFor scoring models (page 133), the predictive power is the measure of the ability of a model to separatecases (page 129) with a positive outcome (page 131) 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 132) 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 131).Predictors contain information available about the cases (page 129) 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.

RobustnessA measure of the consistency of a model over many data samples (page 132). It is assumed that themore consistent, the more reliable the model. Robustness is the standard deviation of the distributionof predictive power (page 132) of a model when applied to a large number of standard sub-samplesof the development sample. The lower the standard deviation, the more robust. The values have noabsolute meaning as they depend on the number and size of the sub-samples, and the measure in whichpredictive power is expressed. Model comparison is possible given the use of the same sub-samples.

SampleA sub-set of historical data extracted by applying a selection and/or sampling method on the data source(page 130). 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 131).

ScoreThe value calculated by a model. Score intervals (page 130) are aggregated under a score band (page133).

Getting Started


Score BandA score band is a set of score intervals (page 130).

ScorecardThe scorecard rule calculates segments by combining a number of properties. The resultingsegmentation is translated in a score (page 132).

Scoring ModelThe value calculated by the model, known as the score (page 132), places a case (page 129) ona numerical scale. High scores are associated with good performance and low scores are associatedwith bad performance. Typically, the range of scores is broken in intervals (page 130) of increasinglikelihood of one of the two types of behavior (positive, or negative), based on the behavior of the cases inthe development sample (page 132) 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 133), and they drive the decisionflow by placing a customer in a given segment for which actions/results are defined.

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 130) support to manage the interaction (page 130) 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 132) 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 132) are configured with different performancewindow sizes to determine the time frame in number of cases (page 129) over which the performanceis calculated. Implementing trend detection requires a combination of strategy design patterns, and usingcompatible adaptive model rules with different memory settings.

Value FieldTypically, a value field is data that is known after the time of prediction (page 131) (for example, thecost of recruitment, purchased value, subsequent probability of up sell or cross sell). Frequently, valuefields are frequently associated with one of the outcome (page 131) values: people who respond arelikely to have a cost of sale and a purchased amount, people who do not respond are not.

Getting Started


Wide of SchemeWhen other samples (page 132) are analyzed, values may be detected that were not present in thedevelopment sample. From these values, Wide of Scheme cases (page 129) are formed, and thecorresponding values reported separately.

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 133). The Z-ratio is positive when expected behavior is above the average behavior, and negative when below.

Version 6.2 Getting Started - Pega Community€¦ · Finalizing Rules 78 Testing Rules 78 Flow...

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