Excitement levels are high for the March 2020 release of SAS Customer Intelligence 360, which includes multiple years of research and development culminating in enhancements to the platform's underlying data model. The changes will introduce the unification of a comprehensive data model recording both: Customer behavior -- what users are
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Digital transformation. Yup, I said it. It's over-hyped. But as SAS Chief Operating Officer and Chief Technology Officer Oliver Schabenberger says, "It's also real and powerful. Our world is being liquefied from physical assets into virtual assets, and analog processes into digital processes - the world is turning into bits
In parts one and two of this blog series, we introduced hybrid marketing as a method that combines both direct and digital marketing capabilities while absorbing insights from machine learning. According to Daniel Newman (Futurum Research) and Wilson Raj (SAS) in the October 2019 research study Experience 2030: “Brands must
Your brand is customer journey obsessed, and every interaction with your company provides a potential opportunity to make an intelligent decision, deepen engagement and meet conversion goals. The hype of martech innovation in 2020 is continuing to elevate, and every technology vendor is claiming the following statement: "Bolster the customer
In part one of this blog series, we introduced hybrid marketing as a method that combines both direct and digital marketing capabilities while absorbing insights from machine learning. In part two, we will share perspectives on: How SAS Customer Intelligence 360 completes analytic's last mile. How campaign management processes can easily
The marketing industry has never had greater access to data than it does today. However, data alone does not drive your marketing organization. Decisions do. And with all the recent hype regarding the potential of AI, a successful cross-channel campaign is propelled by a personalized, data-driven approach injected with machine
In parts one and two of this blog series, we introduced the automation of AI (i.e., artificial intelligence) and natural language explanations applied to segmentation and marketing. Following this, we began marching down the path of practitioner-oriented examples, making the case for why we need it and where it applies.
In part one of this blog series, we introduced the automation of AI (i.e., artificial intelligence) as a multifaceted and evolving topic for marketing and segmentation. After a discussion on maximizing the potential of a brand's first-party data, a machine learning method incorporating natural language explanations was provided in the context
Marketers and brands have used segmentation as a technique to deliver customer personalization for communications, content, products, and services since the introduction of customer relationship management (i.e., CRM) and database marketing. Within the context of segmentation, there are a variety of applications, ranging from consumer demographics, psychographics, geography, digital behavioral
Competition in customer experience management has never been as challenging as it is now. Customers spend more money in aggregate, but less per brand. The average size of a single purchase has decreased, partly because competitive offers are just one click away. Predicting offer relevance to potential (and existing) customers plays a