SAS Customer Intelligence 360: Automated explanation and supervised segmentation


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 data. It’s beautiful, surprising, inspiring, emotive, compelling and persuasive. Data is power. But we only feel these emotions when we arrive at the “ah-ha” moment of analysis that makes us leap out of our seats!

Unfortunately, my clients don't have a lot of free time to explore data, and often tell me that they don't have enough resources or talent to meet objectives.

Figure 1: Accelerate to “ah-ha” moments.

But what if we could accelerate to “ah-ha” moments without sacrificing quality? SAS has been investing in research and development efforts around analytical automation designed to support the needs of a business analyst and citizen data scientist, as well as a statistician or data scientist. The question I’d like to address is:

What makes analytical automation useful in assisting people who are making or influencing changes to improve performance outcomes? 

At the end of the day, anyone working with data has the potential to persuade decision makers. According to Rick Styll’s proceedings paper from SAS Global Forum, there's a confluence of trends driving the demand, feasibility and availability of automated analysis, including:

  • The growing volume of data (from sensors, websites, apps, social, external, etc.).
  • Awareness of the value that predictive analytics and machine learning provides has soared.
  • Data scientists and available time are in limited supply.
  • High-performance computing now enables interactive modeling on very large volumes of data.
  • Cloud infrastructure and technology has reduced costs and deployment times dramatically.
  • Natural language processing is making conversational analytics a reality.

Let’s walk through an analysis together using SAS 360 Discover to combine automated machine learning and natural language explanations of segmentation results. The objective will be to derive actionable target audiences who have higher propensities to meet our brand’s conversion goal (i.e. revenue-driving event). The analysis will provide guidance on who qualifies for marketing tactics like re-targeting, personalization and testing. In other words: Which segments are worth targeting with our limited resources, and which aren’t.

The term "marketing data management" describes how SAS Customer Intelligence 360 can enhance and extend customer data activation, while allowing users to move beyond a traditional customer data platform with our hybrid architecture. Learn more about how the SAS Platform can be applied for marketing data management and customer use cases.


About Author

Suneel Grover

Advisory Solutions Architect

Suneel Grover is an Advisory Solutions Architect supporting digital intelligence, marketing analytics and multi-channel marketing at SAS. By providing client-facing services for SAS in the areas of predictive analytics, digital analytics, visualization and data-driven integrated marketing, Grover provides technical consulting support in industry verticals such as media, entertainment, hospitality, communications, financial services and sports. In addition to his role at SAS, Grover is an professorial lecturer at The George Washington University (GWU) in Washington DC, teaching in the Masters of Science in Business Analytics graduate program within the School of Business and Decision Science. Grover has a MBA in Marketing Research & Decision Science from The George Washington University (GWU), and a MS in Integrated Marketing Analytics from New York University (NYU).

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