Everything happens somewhere, and much of our customer data includes location information. Websites include x, y coordinates in semi-structured click streams, and the mobile apps your prospects depend on frequently support device location to provide a personalized, targeted experience. As my SAS peer Robby Powell said: "Human brains are hardwired
Tag: customer analytics
One of the wonderful aspects about my client-facing role at SAS is the breadth of audiences that I get to work with. No matter where you fall on this list: Data engineer. Business or marketing analyst. Citizen data scientist. Data scientist. Statistician. Executive. One topic is certain: We all love
No matter what your brand's level of marketing maturity is, SAS can help you move from data to insight to action with rich functionality for adaptive planning, journey activation and an embedded real-time decision engine – all fueled by powerful analytics and artificial intelligence (AI) capabilities. Let's begin with a
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
Over the past couple of years, I've written about a variety of use cases and value props regarding SAS® Customer Intelligence 360. As powerful as words and images can be, let's transition to the ultimate show – the demo. In the forty minute video below, observe how SAS Customer Intelligence
A typical day brings countless business decisions that affect everything from profitability to customer experience. What is a reasonable price point? Which audience segments should I personalize offers for? When should I recommend specific content earlier in a customer journey? Daily decisions like these can alter the trajectory of a
In part one of this blog posting series, we introduced that the analytics lifecycle is much more than authoring models. As brands develop and invest into creating models to solve critical business problems, so does the requirement to manage these assets as valuable competitive differentiators. In part two of this
The universe of customer experiences, digital analytics, personalization and decisioning is massive. At times, it can seem as complicated and vast as the galaxy itself. With intricate subjects underneath this umbrella, you can lose direction, wander aimlessly, or feel a misleading sense of success or failure. When you lose vision,
In parts one and two of this blog posting series, we introduced machine learning models and the complexity that comes along with their extraordinary predictive abilities. Following this, we defined interpretability within machine learning, made the case for why we need it, and where it applies. In part three of
In part one of this blog posting series, we introduced machine learning models as a multifaceted and evolving topic. The complexity that gives extraordinary predictive abilities also makes these models challenging to understand. They generally don’t provide a clear explanation, and brands experimenting with machine learning are questioning whether they