Streamlined analytical model deployment
Provides a common method for accessing and managing information, selecting analytical models, and defining business rules that create context for production use.
Comprehensive real-time decisioning combined with industry-leading analytics.
Real-time analytics
Applies real-time analytics and business rules to historical data for immediate insights and recommended best actions during customer interactions and operational business processes.
Rapid decision process creation and management
Enables quick response to changing decisions and customer dynamics. Easy-to-use management console lets you build complex decision diagrams with reusable drag-and-drop nodes, and link processes with data sources and analytics.
Common decision authoring and deployment environment
Simplifies validation of the entire decision flow. Complete version control preserves documentation, testing traceability and continuity in an environment shared by IT and the business.
User-friendly interface
Enables intuitive decision process design with reusable out-of-the-box tasks, selection of data and models from existing repositories, and shared, flexible processing control logic.
Integrated with SAS machine learning capabilities
Integrates model development using automated registration. Speeds up common manual tasks, such as business rule definition and deployment. Facilitates centralized model governance and monitoring via a common model repository.
Improve interactions with customers, partners, suppliers and employees.
Make the right decisions – automatically and at scale.
Deliver relevant, interactive decisions based on automated, sophisticated analytics. IT and business users can use current and historical operational data – informed by analytical models and governed by business rules – to jointly engineer and deploy operational decisions that automatically define real-time best actions at scale across thousands of daily decisions. From direct, customer-facing decisions (e.g., offer targeting and credit decisioning) to complex, cross-functional decisions in areas like manufacturing, you can be confident that enterprise decisions are efficient, effective and timely.
Standardize analytical model deployment.
A common decision authoring and deployment environment dramatically reduces the time required for IT to validate and deploy analytical models – whether written in SAS, open source or custom code. From a single interface, you can natively integrate, manage and deploy SAS and Python analytical models, custom code and business rules, with identical logic for both batch and real-time web service execution. That means faster deployment and confidence in the integrity of your analytically driven operational decisions.
Make high-quality decisions in high-volume environments.
It doesn't matter how high your data and decision volumes go; you'll always be able to make the best operational and customer decisions exactly when you need to. No need to worry about sluggish, nonresponsive computing resources that will delay decisions when volumes are high. Built on a multitier architecture with server clustering capabilities, our solution delivers scalability and enterprise data throughput for timely, accurate decisions – even in high-volume, 24/7 businesses.
Intuitively design decisioning processes.
Not a technical guru? No need to be. An intuitive, user-friendly interface lets you easily construct and modify automated decisioning processes – and even incorporate SAS analytical models – without IT assistance. Instead of using cryptic programming and rules, you can design processes by dragging and dropping a set of reusable, out-of-the-box tasks. Shared, flexible processing control logic enables you to select data and models from existing repositories. And defining business rules in context ensures continuity and shared terminology across business functions.
This solution runs on SAS® Viya®, which has the breadth and depth to conquer any analytics challenge, from experimental to mission critical. SAS Viya extends the SAS Platform to enable everyone – data scientists, business analysts, developers and executives alike – to collaborate and realize innovative results faster.
Explore More on SAS® Intelligent Decisioning & Beyond
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Customer Story
Building customer satisfaction with better network monitoringSAS Analytics helps a major telecom organization analyze traffic, anticipate problems and improve customer service.
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Customer Story
Get the next best offer to a segment of oneSAS Customer Intelligence solutions help Telenor ensure more personalized sales and dialogue, with real-time offers that are relevant to the individual customer.
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White Paper
Operationalizing and Embedding Analytics for ActionThis TDWI Best Practices Report focuses on how organizations can and are operationalizing analytics to derive business value.
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White Paper
The Internet of Things: Marketing’s Opportunities and Challenges Learn how marketers can apply event-based marketing and real-time marketing capabilities to capitalize on IoT data in the moment by pinpointing significant customer events, which then trigger real-time customer decisions. The result? Best offers delivered in real time, given the consumer’s current context.
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Article
Three ways to improve real time marketing resultsReal-time marketing enables you to reach customers during purchase deliberations to provide the push a campaign needs be wildly successful.
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White Paper
Managing the Analytics Life Cycle for Decisions at ScaleLet the SAS Analytics Life Cycle guide you through the iterative process of going from raw data to predictive modeling to automated decisions, faster. This paper tells you how.
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White Paper
Real-Time Analytics: The Key to Unlocking Customer Insights & Driving the Customer ExperienceThis Harvard Business Review Analytic Services report looks at how businesses are using advanced customer data analytics, along with real-time analytics and real-time marketing, to enhance their customers’ experiences.
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Article
Effective real-time marketingEffective real-time marketing is about engaging with customers whenever and wherever they are in their buying journey. Organizations should also be focused on knowing what the customer wants (product, pricing, experience, etc.) based on real-time analytics and predictive modeling.
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White Paper
Six Keys to Credit Risk Modeling for the Digital AgeModernizing and automating the end-to-end process for origination and servicing – from data management to model development to credit decisions – can reduce credit losses and boost performance. This paper explores how infusing machine learning into this process supports more effective credit decisions for individuals, products or portfolios.
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Customer Story
Machine learning helps personalize the gift card experience across retail channelsEpipoli uses SAS artificial intelligence and customer intelligence solutions to craft relevant, real-time offers for customers.
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Article
IoT: The customer experience accelerator you can't afford to ignoreIoT represents a powerful source of data that, when combined with analytics, can yield insights on everything from behavior to emotions to health. And that's why it's key to improving customer experience.
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Customer Story
AI-driven personalization gives electronics retailer a boostConrad Electronic uses customer intelligence solutions from SAS to craft relevant offers in real time.
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SAS® Viya®Conquer your analytics challenges, from experimental to mission critical. SAS Viya extends the SAS Platform to enable everyone – data scientists, business analysts, developers and executives alike – to collaborate and realize innovative results faster.
