DM

This rule type is part of decision rule category in Pega. below are some of its Capabilities :1. Used to select the best "proposition" for each customer2. it comprises unit of reasoning by decision component3. each decision component adds distinct functionality to the holistic Next-Best-Action decision.

• Strategies define the decision that is delivered to an application.
• The decision is personalized and managed by the strategy to reflect the interest, risk, and eligibility of an individual customer in the context of the current business priorities and objectives.
• The result of a strategy is a page (clipboard or virtual list) that contains the results of the components that make up its output definition.

• referenced in interaction rule
• it is an instance of Rule-Decision-Strategy

1. Import2. Business rules3. Decision Analytics4. Enrichement5. Arbiteration6. Selection7. Aggregation

Used to bring data into the "Strategy"it includes: 1. Data import2. Interaction history3. proposition data

References business logic, such as Decision table, Decision tree, Map value, and Split

References logic that makes prediction about customer behavior. it includes:1. Adaptive model2. Predictive model3. Scorecard model

It sets "values" for properties that requires calculation. for example...margin on product. the calculation are defined using expression builder

This component FILTERS Out inappropriate proposition and PRIORITIZE those that remain by "margin" or "propensity"

Allows the strategy to switch between different business issues such as SALES,RETENTION, SERVICE etc...it contains "champion strategy", "exclusion" and "switch"

Used to calculate "aggregate" amount. example, the sum of all transaction in the past month

IMPROVING CUSTOMER EXPERIENCE WITH NBA

P.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 11.0px Helvetica}used for improving the results of any type of customer interaction-involving marketing, sales, service, retention, collections, risk, even data collection. Its goal is to determine the optimal action to take with a customer at a given moment under the circumstances. The optimal action is one that satisfies customer expectations while also meeting business objectives.

In real-time inbound environment, it starts when a customer takes action - calls into a call center, and each subsequent time the customer responds to an offer or takes another action.

Interaction history is what gives pega decision management its long term memory. customer response is captured for every next best action, even if the response is no-response

In this environment next best action is implemented via next best action advisor, which advises the call center agent on what to say to a customer or how to respond to a customer.

Its ultimate goal is to optimize customer value i.e. to increase the profitability of each customer relationship. to accomplish this objective, the next best action basis its decision on: customer need and business objective