Automated Explanation and Supervised Segmentation with SAS Customer Intelligence 360
One of the wonderful aspects about my client-facing role at SAS is the breadth of audiences that I get an opportunity 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 infamous “ah-ha” moment of analysis that makes us leap out of our seats!
However, I never hear clients express how much free time they have. The feedback is typically centered on not having enough resources or talent to meet objectives.
Figure 1: Accelerate to “ah-ha” moments
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 proceeding paper from SAS Global Forum, there is a confluence of trends that is driving the demand, feasibility, and availability of automated analysis.
- Growing sizes of data (sensors, websites, apps, social, external, etc.).
- The awareness of the value that predictive analytics and machine learning enables 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 that combines automated machine learning and natural language explanations of segmentation results. The objective will be to derive actionable audiences who have higher propensities to meet our brand’s conversion goal (i.e. revenue-driving event). The benefit of this effort will provide guidance on who qualifies for marketing tactics like retargeting, 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 here.
