Decisioning engines

AI-powered automated decision-making for customer engagement

What is a decisioning engine?

Businesses use decisioning engines to deliver personalized, real-time experiences to customers across channels. By leveraging a decisioning engine, marketing and CX organizations can deliver the right message or action at the right time to each customer, making their experiences more relevant, seamless, and engaging. This leads to stronger loyalty, better engagement, and improved business outcomes.

Why are decisioning engines important?

Decisioning engines are critical to unifying and scaling customer interaction management. The future of modern customer journeys relies on delivering personalization at scale across all channels and devices, when customers are most receptive to a brand message. They centralize disparate data streams, and workflows to automate content activation faster and more accurately than human labor alone.

Benefits of decisioning engines

Personalization at scale
Decisioning engines analyze real-time data and customer interactions including customer context, past behaviors, current intent, and channel preferences to deliver personalized messages, offers, and experiences tailored to each individual.
Optimized resource allocation
Decisioning engines prioritize the most impactful actions by evaluating potential outcomes across channels and allocating resources to high-value opportunities while also reducing manual processes associated with marketing operations.
Enhanced agility and responsiveness
Decisioning engines enable marketers to respond instantly to changes in customer behavior, market trends, or competitive dynamics. By integrating data from multiple sources and leveraging AI-driven continuous learning, they provide a cohesive, evolving view of each customer.

How do decisioning engines work?

Decisioning engines analyze real-time data across channels, applying AI and machine learning to evaluate customer behaviors and preferences. They deliver personalized, context-aware decisions for marketing, sales, and service, continuously improving outcomes through adaptive learning and automation.

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Types of decisioning engines

Composable decision engine

Flexible, modular system
Designed to evaluate inputs and make complex decisions by combining multiple independent, interoperable components or services
Ability to adapt and integrate different functions, rules, data sources, and algorithms to meet specific needs dynamically, enabling businesses to respond rapidly to changing conditions or requirements
Requires significant effort to stitch together disparate tools and platforms and create technical debt and inefficiencies of fragmented systems

End-to-end decisioning engine

A comprehensive, unified system
Automates and manages the entire lifecycle of decision-making processes from data ingestion to action execution
Integrates all necessary components – data collection, analytics, decision logic, and activation – into a single platform
Enables real-time decisioning across all customer touchpoints, ensuring consistent experiences, faster time to value, and reduced complexity for marketing and CX teams
Outperforms composable systems by unifying data, analytics, rules, and execution in a single platform

Frequently asked questions about decisioning engines

A decision engine informs business decisions by analyzing real-time and historical data, applying AI and machine learning to predict outcomes, and recommending the best actions.

Re-decisioning is an adaptive AI capability that reads real-time data signals and adapts to that information to ensure that the customer interacting with your brand receives the most relevant and timely action – repeatedly – even seconds after new signals arrive. Without adaptive learning, that customer is unlikely to receive the correct, personalized interaction in their moment of need. This is the secret sauce of true real-time interaction management (RTIM).

RTIM is the process of delivering personalized, contextually relevant experiences to customers in the moment, across any channel, by leveraging data, analytics, and decisioning to optimize engagement in real time.

Some of the key capabilities for RTIM include:

Channel delivery & integrations
Change management
Decision management
Action & treatment processing
Customer analytics & AI
Data & event processing
Strategy optimization

Decisioning in real time with the Customer Decision Hub