Next best action

You build next-best-action responses by combining business rules for prioritizing decisions with machine learning algorithms that can predict customer behavior. By harnessing the power of decision management, you can determine the optimal action (the next best action), to take with a customer at a given moment.

Use the following decision management components to build your next-best-action logic and ensure that customer actions are appropriate and consistent at all times:

Proposition Management

Create a decision framework for your next best actions by identifying propositions. A proposition can be anything that you offer to your customers, for example, goods, services, advertising, and so on. In Pega Platform, propositions are organized into a hierarchy that consists of three levels: business issue, group, and proposition. The combination of these levels provides a unique identifier for each proposition. You can customize this business hierarchy to reflect your existing products and services.

Decision Strategies

Determine the best propositions to offer your customers by using decision strategies. Each strategy contains a sequence of components that represent a specific type of logic that contributes to the final next-best-action decision. You can then write the strategy results to a database or a clipboard page for further use in other strategies or business processes.

Simulations

Simulate and understand the impact of actual or proposed decision strategies across all channels and products. To ensure that your simulations are accurate enough to help you make important business decisions, you can deploy a sample of your production data to a dedicated simulation environment for testing. By running simulations on sample production data, you can predict the impact of changes on your decision logic, before applying the changes to your live production environment.

Artificial intelligence and machine learning

Increase the relevance of your next-best-action decisions by using artificial intelligence and machine learning to better understand what your customers want. Building adaptive, predictive, and text analytics models that can be applied to a wide range of business use cases will provide you with greater insight into customer behavior:

Adaptive Analytics

Automatically build and deploy adaptive models that learn and gather data in real time, to predict customer behavior without any historical information.

Predictive Analytics

Develop predictive models that are using historical data to predict future customer behavior.

Text Analytics

Analyze unstructured textual data to derive useful business information that is instrumental in retaining and growing your customer base.

Event Strategies

Detect meaningful events in real-time data streams and react to them in a timely manner by using event strategies. You can use event strategies to detect interactions such as Call Detail Records, prepaid balance recharges, or credit card transactions, to identify the most critical opportunities and risks in determining next best actions for your customers.

Data Flows

Make thousands of decisions at a time by using a Data Flow rule. Data flows are a flexible, scalable solution for managing multiple decisions simultaneously, that follow a simple input-process-output pattern. You establish data flows through a set of instructions in shapes of various types, on a canvas-based rule form, using a graphical interface.

Revision Management

Provide business users with the means to implement and test modifications to their applications outside of enterprise release cycles. You use revision management to quickly respond to the internal factors and changes in the external environment that influence business. For example, by updating the decision strategies and propositions that define your next-best-action decision framework, a company can respond more quickly to changes in customer behavior.

Interaction History

Capture every customer response to each of your next best actions in the Interaction History. You can then use the interaction history to train a predictive model to predict whether a customer is likely to accept a given proposition, based on all similar customer interactions that have been recorded over time.

Decision-making as part of a business process

You can invoke a decision at any step of a business process. For example, you can call an instance of a decision rule, a decision strategy, or a data flow, as part of a case type. You use the decision management functionality in a case type, with the Decision and Run Data Flow shapes:

Decision

Call one of the following decision rules: Predictive Model, Scorecard, Decision Table, or Decision Tree.

Run Data Flow

Call a decision strategy, an event strategy, a text analyzer, or any other DSM component through a Data Flow rule type. By using a data flow to call a decision strategy, you can separate the business process from the decision strategies, and eliminate the need to update either component when the other one changes.