Teradata Customer Intelligence Framework FAQ

The Customer Intelligence Framework is Teradata’s strategic architecture that transforms raw data into real-time, actionable intelligence. It enables autonomous decision-making by detecting, interpreting, and activating customer signals across the enterprise using AI-ready data products, scalable models, and application-layer integration.

• Fragmented data across channels and life stages.
• Friction in customer journeys leading to higher costs.
• Inability to act in real time, limiting responsiveness and personalization.

• Data Products: Reusable assets that organize customer data.
• Signals: Actionable patterns reflecting behavior, intent, or opportunity.
• Agents: Intelligent entities that execute and adapt decisions autonomously.

Data: To harmonize structured, semi-structured, and unstructured data into reusable, AI-ready data products.

• Data Products: Multi-dimensional, reusable assets that organize customer data (e.g., transactions, behaviors) for analytics and AI.
• Industry Data Models (iDM): Standardized frameworks tailored to industries (e.g., banking, healthcare) to accelerate Customer 360 views.
• Industry Analytic Schemas (iAS): Define how data is analyzed using KPIs, dimensions, and fact tables to support scalable AI/ML.

Analytics: To extract predictive and prescriptive insights from data using AI/ML.

• Feature Engineering: Transforms raw data into model-ready features.
• Enterprise Vector Store: Manages billions of vector embeddings for semantic search and GenAI use cases.
• ClearScape Analytics: In-database AI/ML engine for scalable model training and real-time scoring.
• AI Workbench: Unified workspace for model development, lifecycle management, and integration with tools like LangChain and RAG.

Signals & Activation: To detect, score, and activate customer signals in real time, embedding them into workflows for adaptive decision-making.

• Signals: Patterns in data that indicate behavior, context, or intent (e.g., churn risk, fraud likelihood).
• Activation: Embedding signals into business workflows to trigger actions (e.g., next-best offers, fraud alerts).
• Vantage Customer Experience (VCX): Real-time decisioning engine that streams intelligence into applications.

Agents: To enable autonomous, intelligent decision-making across the data and application layers.

• AgentBuilder: Low/no-code tool to build, deploy, and govern multi-agent systems.
• Expert Agents: Pre-built agents with deep domain knowledge (e.g., CLTV analysis, complaint analytics).
• AI/GenAI Applications: Tools like CIM and VCX that support agentic workflows and real-time orchestration.
• Natural Language Interfaces: Allow users to interact with agents using conversational commands via Model Context Protocol (MCP).

Agent Architecture:

• Data Layer Agents: Automate data preparation and governance.
• Application Layer Agents: Execute contextual actions in real time.
• Communication Layer: Natural language interface for business-user interaction.

• Customer Lifetime Value Expert Agent: Uses LLMs and structured data to analyze and improve CLTV.
• AI for CX: Hyper-personalization, churn prediction, recommendation engines.
• Fraud Prevention: Detects anomalies and triggers proactive interventions.
• Complaint Analytics: Uses agentic classification to prioritize and resolve issues.
• Intelligent Document Processing: Uses vector search and RAG to interpret documents in real time.
• Natural Language Understanding Interfaces: Enable business users to trigger workflows using plain language.

In the Customer Intelligence Framework, data products are structured, reusable assets that transform raw Customer 360 data into intelligence-ready formats optimized for AI and analytics. They enable scalable signal detection by representing customers across behavioral, transactional, and contextual dimensions, supporting both strategic insights and personalized actions. These multi-dimensional assets feed models that isolate signals—patterns of behavior, intent, or risk—which are then activated across business functions like marketing, risk, and service. This modularity allows organizations to reuse the same data product across multiple use cases, accelerating deployment, reducing cost, and driving enterprise-wide alignment around customer-centric intelligence.

Customers can use Teradata Services with the Customer Intelligence Framework to accelerate deployment, simplify integration, and ensure governance across all layers. Services help ingest and organize data using industry models, operationalize AI with tools like ClearScape Analytics and AI Workbench, activate real-time decisions via VCX, and deploy pre-packaged use cases like churn analytics and hyper-personalization for faster value.

Available Services include:

• Data services: Integration, governance, and architecture deployment.
• Analytics services: Support for AI tools like ClearScape and Vector Stores.
• Signal activation: VCX environment support for real-time decisioning.
• Use case deployment: Fast rollout of AI for CX solutions.

It unifies data into reusable products, detects real-time signals, and activates intelligence through autonomous agents. Pre-built use cases like churn prediction and personalization accelerate outcomes.

• Real-time, individual-level intelligence.
• Personalization at scale using in-database execution.
• Agentic execution of industry IP.
• Support for multi-structured, multi-dimensional data.
• Scalable modeling across thousands of users and applications.

Agents bring adaptability and automation, orchestrating decisions and activating intelligence in real time. Tools like AgentBuilder and Expert Agents simplify deployment and scale across business functions.

Customer Intelligence Framework supports multi-structured data, scalable modeling, and real-time activation across millions of customers and billions of queries—powered by Teradata Vantage and ClearScape Analytics.

As customer journeys become increasingly digital and dynamic, companies must move beyond dashboards and reports that simply describe the past. Modern enterprises require customer intelligence that predicts, personalizes, and drives timely action. Customer 360 initiatives have laid the groundwork by unifying data across systems, but without signal-driven activation, these initiatives fall short of delivering business impact. The Customer Intelligence Framework outlines a modern architecture for converting raw customer data into real-time, actionable intelligence.