Transcript of "PEGA Decision strategy manager (DSM)"
Precursor to PEGA –
• Major Components of DSM
• Introduction to Predictive & Adaptive Analytics
• Differences between Predictive & Adaptive Analytics
• Decisioning Components
• Data Import
• Data Enrichment
• Decisioning Rules
• To be able to make the right decisions, become more effective, and deliver more relevant actions for the
customer, we need to be able to determine customer interest in a product and their likely behaviour.
• Decision Management capabilities help businesses optimize every customer relationship and interaction to
satisfy both customer needs and business objectives at the same time. With Decision Management, business
users can implement a management strategy that is personalized for each customer, and that guides every
customer interaction and decision.
• The Decision Strategy Manager (DSM) allows marketing managers to participate more directly in the evolution
of the applications by including facilities that support cross-sell, upsell, retention, and risk management.
• The Decision Strategy Manager (DSM) delivers:
• Proposition management
• Strategy development
• Driving process flows using interaction, scorecard, and predictive model rules
• Using third party models
• Multilevel decisioning
• Single and distributed batch execution of strategies
• Capturing interaction results using Interaction Services (IS), and associating interaction records with work
• Visualization, monitoring, and forecasting using Visual Business Director (VBD)
• Advanced adaptive analytics using Adaptive Decision Manager (ADM)
Decision Strategy Manager (DSM)
• The core decision management component, DSM allows business users to design customer interaction strategies
and propositions based on decisions and rules that reflect customer behaviour, preferences, legislation, corporate
policies and desired business outcomes.
• The decision model is highly visual, and consists of a rich set of decision components that allows business users to
create strategies directly on a canvas.
Adaptive Conversation Advisor (ACA)
• The user interface for agent-driven channels, leveraging the decision management capabilities to make Next-Best-
Action recommendations. Different conversation styles can be nominated such as assessment, negotiation of offers,
bundling, top-n offers, Q&A, etc.
• ACA assesses everything known and said by the customer in current and previous interactions and recommends the
Next-Best-Action to be taken.
Adaptive Decision Manager (ADM)
• Adaptive Decision Manger provides self-learning models that are able to learn from real-time customer behaviour
and adapt on-the-fly. ADM is especially helpful in situations where there is not enough data to create a robust
model, there are too many different products to create predictive models for, or where behaviour is very volatile.
• Once initiated, ADM will begin learning and indicate when it has gathered enough data to begin making accurate
predictions on its own. ADM also includes a monitoring capability to track the performance of the models.
Predictive Analytics Director (PAD)
• Is a predictive tool that offers a streamlined business-oriented process to quickly and safely develop accurate and
reliable models that predict customer expectations, propensities, and behaviours.
• PAD focuses on the complete process of data analysis, model development, model analysis, and deployment of
Visual Business Director (VBD)
• Enables planning, monitoring, simulation, and control of the customer experience across all customer segments and
product lines in all channels.
• It allows business to determine certain control parameters – effectively making tactical changes to the decision
model. VBD offers a highly visual 3D user interface.
MAJOR COMPONENTS OF DECISION MANAGEMENT
• Predictive analytics make predictions about future events by analysing current and historical data and applying it
to propensity models to make and display calculated predictions of the likelihood that a certain event will occur.
• The models that are created are based on a snapshot of data and are refreshed or rebuilt at regular intervals.
Predictive models could be used in a number of operational or customer service related contexts.
For example, a customer service organization that is using predictive models might be collecting purchase
behaviour data, competitive information, demographic data, and other data used to proactively provide
recommendations on what the customer might be willing to purchase based on their history.
• Adaptive analytics use an algorithm that learns about customer behaviour in real-time, instead of using a snapshot
based on a predictive model. After each response to a proposition the model adapts, resulting in increasingly
• Adaptive decisioning can calculate who is likely to accept/reject a proposition without little prior experience. It also
captures and analyses data to deliver predictions where customer behaviour data is volatile.
For instance, if a customer is offered a product and accepts it, the likelihood score of customers with a
similar profile will increase slightly.
• Predictive and adaptive models can be used independently or together in a champion-challenger approach. Pega
has capability (Predictive Analytics Director and Adaptive Decision Manager), but also provides import of
predictive models developed with 3rd party tools such as SAS or SPSS.
When to use Predictive Vs. Adaptive
Predictive Analytics requires historical data containing the
behaviour we want to predict
Adaptive Analytics can start from a blank sheet of paper
and learn from customer interactions.
Greater Control as we are directly involved in the
development process which gives us more influence over
Adaptive Analytics has greater automation capabilities
Predictive Modelling uses a range of powerful techniques
and as a user we can select the best model for our needs.
One straight forward modelling technique
Predictive Models should be used when predictability and
compliance is important. For example, risk related
behaviour such as credit risk, claims risk or fraud, etc.
Adaptive models learn constantly and therefore their
performance will change over time, which makes the
outcome less predictable than that of predictive models.
For example: Product Acceptance, Routing, Fulfilment.
Tens of Models. Don’t get a very quick response for some
behaviour types. It can take months or even years to
accumulate data. Predictive modelling should be used in
Possible to implement hundreds of adaptive models in a
solution because of the high degree of automation
Longer delay for outcome Shorter delay for Outcome
• The core function of
Decision Management is to
create and maintain
Decision Strategies. Business
users can define Decision
Strategies using a palette of
• The Decisioning
Components are the
building blocks of every
Decision Strategy and each
Decisioning component has
a business purpose.
• There are 16 Decisioning
Components in total, which
are grouped into the
following 6 categories
Segmentation Components: These are used to define sub-
groups of customers that need to be treated differently.
Typically the segmentation is based on the customer’s
likelihood of interest in a proposition and the relevancy of a
proposition to that customer.
Data Import Components: These allow us to bring data into
the context of our Decision Strategy, for instance information
regarding our proposition or Recommendations that come
from other Decision Strategies.
Data Enrichment Components: These can be used to add
additional data to our Decision Strategies, allowing
recommendations to be even more personalized and context
Arbitration Components: These enable us to arbitrate
between propositions by filtering out irrelevant propositions
on the basis of, for instance, eligibility rules, and then
prioritizing the remaining relevant propositions.
Selection Components: These allow us to choose between
different options. For instance to determine what business
issue is more important than other business issues and under
what circumstances. Or more randomly to test new
Aggregation Components: These give us the ability to calculate
aggregate values, for instance counting the numbers in a list, or
calculating an average for a range of values.
Decisioning Components –
SegmentationThe “Segmentation” Decisioning Components determine in which group, or segment, a customer falls. A different strategy, and therefore, a
different treatment, can be applied to each segment. There are 5 segmenting components; each employs its own approach to creating
segments. We can add one of each to the canvas by right clicking on the canvas and selecting the one we want from the Add tab.
• Decision Table: The Decision table is used to segment customers by properties.
For example, say we are creating rules for approval of a mortgage loan. Customers in a Decision Table would be evaluated based
on properties such as: Salary, Age, and the Amount of the Mortgage. If the customer is not accepted, the same properties are
used to determine if the customer will be referred to a specialist, or rejected.
• Decision Tree: Decision trees offer a bit more flexibility because different properties can be used for different evaluations.
For instance, if the customer’s mortgage application is not accepted based on the properties that were used in the decision
table, other criteria, such as arrears history, can be used to determine whether their application will be referred to a specialist.
• Predictive model: Used to segment customers based on Predicted Behaviour. These segments are also known as behavioural segments.
Being able to predict the customer’s likelihood of interest in a certain proposition helps determine if and how we want to recommend
that proposition. The predictive models used in this component are created in and imported from the Predictive Analytics Director tool.
• Scorecards: These are broadly used and most commonly known in credit scoring. The scorecard component allows segments to be
defined based on ranges of credit scores. Scorecards provided by third parties or created with Predictive Analytics tools can be
implemented in the Decision Strategy using this component.
• Adaptive model: This is similar to the Predictive model in that it allows segmentation based on predicted behaviour. The difference is
that adaptive models learn from data gathered during real-time interactions. And Adaptive models can be defined in the Decision
Decisioning Components – Data
Decision Strategies use data as input, and make decisions based on this data. The majority of the data items that are
used can be found in the Data Model. Data Import decisioning components provide access to additional data that
lives outside our Data Model that can be mapped to our strategy properties. There are three types of Data Import
• Type one is “Sub Strategy”: The sub strategy data import component is used when we want to leverage the
output from other Decision Strategies as input for the current Decision Strategy.
For instance, the Next Best Action Strategy can have references to multiple strategies, such as cross-sell,
retention, education, etc. The Next Best Action decision strategy uses the output from those strategies to
determine the Next Best Action.
• Type two is “Proposition”: A proposition is a potential action considered by the decision strategy. Which
proposition the decision strategy ultimately recommends is determined by a myriad inputs, like the margin
made by the organization, the cost to the organization, and the benefit to the customer.
• The “Proposition” component leverages the data as defined in the Issue-Group-Proposition hierarchy on our
Strategies Landing Page.
• Type 3 is “Data Import” : This component is used to access data that is not directly available in the data model
from our Apply to Class. With Data Import we can map to other Pages in the system
For instance an invoice data coming from a billing system that is available on a Page elsewhere in the
Decisioning Components – Data
Components in the “Data Enrichment” category are used to add extra information to our decision
• Strategy Set: Strategy set components enrich data by adding information to the components they
are connected to. Using strategy set components, you can define personalized data to be delivered
when issuing a decision. Personalized data often depends on segmentation components , and
includes definitions used in the process of creating and controlling a personalized interaction, such
Instructions for the channel system or product/service propositions to be offered including
customized scripts, incentives, bonus, channel, revenue, and cost information.
Probabilities of subsequent behaviour, or other variable element.
• Data Join: Data join components import data from an embedded or named page using a key to
match data, and map its contents to properties from the imported data to strategy properties.
Decisioning Components – Arbitration
& SelectionArbitration Decisioning components filter propositions based on priority and relevance. There are two types of Arbitration
• Prioritization: We can rank propositions and then select the best or most relevant propositions for a customer or group of
customers. In reverse, we can rank a group of customers based on their predicted likelihood of interest in a set of
propositions and match them up that way.
• Filter component : We can filter out propositions that are not relevant for the situation or that we don’t want to offer,
such as credit cards, which we will only offer to people 18 years and older.
Selection group of Decisioning components. allow us to choose between options, such as one proposition or a group of
• Switch component : Used to select between options based on business rules.
A Next Best Action decision uses the Switch component when it selects between business issues such as Sales,
Retention, Risk Mitigation, etc.
• Champion Challenger: Component enables a random selection.
Champion Challenger is typically used to test different variations of propositions. Where the Champion is the
mainstream proposition and the Challengers are alternatives that we want to test to hopefully select a new Champion.
Decisioning Components –
Aggregation Decision Components give us the ability to make calculations from a list of values.
• Aggregation: We can use the aggregation component to set the value of a strategy property based on an
aggregation of values from a source component. We can for instance calculate the total number of
payments and the average value of those payments.
• Financial: The “Financial” component can perform one of the following functions:
• Net Present Value
• Internal Rate of Return
• Modified Internal Rate of Return
DSM : Rule Types
• Predictive Model
• Adaptive Model
• Decision Management introduces five new rule types that enable us to implement sophisticated Decision
Strategies in PegaRULES Process Commander (PRPC).
• Decision strategies are defined in the Strategy rule type. The strategy rule type allows us to arbitrate between
business issues and prioritize across a variety of propositions, recommendations and actions.
• The strategy rule type can leverage
predictions derived form three new rule
types – Predictive, Adaptive models
• The scorecard, predictive and adaptive
models can also be directly invoked
from the decision shape in a process
• To invoke a decision strategy from a
process flow we use the interaction
rule. The same interaction rule is also
used to capture customer responses,
which are used to monitor the
effectiveness of the decision strategies
and learn and adapt where needed.
• Scorecards are a well-known method to
predict or score the likelihood of a
certain behavior. They are widely used
to determine customer credit scores
• The first column shows the properties
that are included in the scorecard
• In the second and third column we can
see the “Condition” and the “Score”
• Final Score = Weight * Score
• The combination of all these scores
leads to one score for the whole
scorecard. This score is calculated by
the “Combiner Function” – SUM, MIN,
MAX and AVERAGE
• We specify the cutoff values on the
results tab, which is similar to what we
would do on a standard decision table
• Score ranges : The minimum and
maximum scores are calculated based
on the Combiner Function selected on
the Scorecard tab
• Result : Enter a name for a decision
• Cut Off value : Enter a numeric cut off
score value for the result based on the
minimum and maximum scores
displayed at the top of the tab
• Audit Notes : Check this box if you want
to capture scorecard details in the case
• Use a predictive model rule to
import a model produced by the
Predictive Analytics Director
(PAD)product and relate its outputs
to properties in your application.
• Predictive models can support your
strategies for customer retention,
risk management and maximization
of customer lifetime value..etc
• Predictive Models are developed
on the basis of historical customer
• Upload .OXL file i.e. exported from
PAD / any third party tool.
• On the “Input Mapping” tab we can
see the input fields, also known as
• The “Field Name” and “Field Type”
come from the predictive model
we uploaded and are therefore
defined by the data set that was
used to develop the model.
Pedictive Model • The “Results” tab shows that the
predictive model classifies a customer
into different behavioral classes.
• Each class has been detected by
Predictive Analytics Director as having
statistically different when it comes to,
in this case, their probability of Churn
• Churn Rate: (Values between 0 and 1)
Probability of the customer being loyal
to the product he / she are purchasing.
FORMULA: churned count /
(churned count + loyal count)
• Lift: Lift is a measure of the
effectiveness of a predictive model
calculated as the ratio between the
results obtained with and without the
FORMULA: (churned behavior of
classification * 100) / total churned
The “Edit” button gives us the ability to
• Additional meta data about the
predictive model and the
development process is collected
by Predictive Analytics Director.
This information is shown on the
• The “Attributes” section contains
information about the
configuration of the predictive
model, including information such
as who created the model, when
the model was created, What kind
of behavior it predicts, etc.
• Adaptive Decisioning is an algorithm
that learns about customer behavior in
real-time from interactions with
customers. For instance, whether they
accept an offer presented to them by
an agent in the call center.
• The adaptive model will learn from
each response that is identified as
positive or negative. When a customer
responds positively to a proposition,
the model changes so that customers
with a similar profile will get a higher
likelihood of interest.
• In this example of the “Sales Offers
Model” adaptive model we see on the
“Configuration” tab which of the
properties can be used as predictors
• The “Settings” tab is used to
configure the Adaptive Decision
• The “memory” property specifies
what number of responses from
recent interactions will be used to
calculate the model. All evidence
from interactions will be used if the
value is left as Zero, otherwise the
specified number of responses will
be used to create the memory. A
memory of 1000 responses is a
very short memory, meaning the
model will adapt quickly when
behavior changes. A memory of
100,000 responses will result in a
less volatile model and will
therefore learn more slowly from
changes in behavior.
• This rule allows a strategy designer
to model sophisticated business
• The “Strategy” tab is a canvas that
allows us to graphically compose
the decision strategy
• Strategy rules are used in
interaction rules, and in other
strategy rules through the Strategy
• The Decision Strategy rule has a
very powerful capability which
enables us to arbitrate between
different directions and prioritize
between many options
• Right click on Work area and add
components to your strategy
• A strategy is defined by the
relationships of the components
that are used in the interaction that
delivers the decision
Strategy - Data Import
• Proposition components import
propositions defined in the
• Sub strategy components reference
other strategy rules. They define
the way two strategies are related
to each other, access the public
components in the strategy they
refer to, and define how to run the
strategy if the strategy is in another
• Embedded page components
import data in an embedded page
• Named page components import
data in a named page
Strategy – Segmentation
• The output of a scorecard rule is a
• The output of a predictive model
rule is statistics generated by the
PAD model that provides the
• The output of an adaptive model
rule is a partial list of adaptive
statistics (evidence, propensity, and
Strategy - Data Enrichment
• Strategy set components enrich data to
define personalized data to be
delivered when issuing a decision.
Personalized data often depends on
segmentation components, and
includes definitions used in the process
of creating and controlling a
personalized interaction, such as:
Instructions for the channel system or
product/service propositions be offered
including customized scripts, incentives,
bonus, channel, revenue, and cost
• Data join components import data from
an embedded or named page using a
key to match data, and map its contents
to properties from the imported data to
Strategy - Arbitration
• Filter components apply a filter
condition to the outputs of the
• Prioritization components rank the
components that connect to it
based on the value of a strategy
property, or a combination of
strategy properties. These
components can be used to
determine the service/product
offer predicted to have the highest
level of interest, or profit
Strategy - Selection
• Switch components express component
selection through the Switch tab. Add
as many rows as alternative paths for
the decision as necessary, use the
Select drop down to select the
component, and enter the selection
criteria as an expression in the If field.
The component selected through the
Otherwise drop 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 cases for each decision
path. All alternative decision paths
need to add up to 100%
• Interaction rules define parameters for
running a strategy, how to prepare the
interaction history and how to save the
• Interaction rules are used in flows
through the Run Strategy and Capture
• On the “Interaction History” tab we
define how the customer is identified.
This information is important when
determining Next Best Actions,
including applying contact rules such as
not offering the same proposition twice
within a certain period of time or when
the customer has already accepted or
declined the proposition
• On the “Run Strategy” tab we
reference the decision strategy, in
this example the “NextBestOffer”
• There must be at least one strategy
but there can also be multiple
public components. Public
components are defined in the
• On the “Capture Response” tab we
configure the information we want
to capture from each interaction.
• First of all we define the behavior
• We define the segment and
potential sub segments the
customer can be assigned to.
• In the “Customer Response”
section we define the proposition
the response refers to. In this
example it is the