Customer Segmentation Using SAS Enterprise Miner
No marketing or customer contact strategy can be effective without segmentation. While the concept of segmentation is deceptively simple, in practice it is extremely difficult to execute. Emphasizing practical skills as well as providing theoretical knowledge, this hands-on, comprehensive course covers segmentation analysis in the context of business data mining. Topics include the theory and concepts of segmentation, as well as the main analytic tools for segmentation: hierarchical clustering, k-means clustering, normal mixtures, RFM cell method, text-based clustering, time-series clustering, and SOM/Kohonen method. The course focuses more on practical business solutions rather than statistical rigor. Therefore, business analysts, managers, marketers, customer intelligence analyst, programmers, and others can benefit from this course.
Presented by Goutam Chakraborty
Goutam Chakraborty, Ph.D., is a professor of marketing and founder of the SAS and Oklahoma State University Data Mining Certificate Program.
Learn the following:
- Segmentation Basics
- Data Pre-processing Before Segmentation
- Art and Science of hierarchical cluster analysis
- Applications of Hierarchical Clustering
- k-means Clustering
- Self Organising Map (SOM) and Kohonen
- Decision Trees for Segmentation
- Wrap-ups and Take-aways
Who should attend
Anyone who wants to learn how to segment customers based on attitude, preference, or transaction data to develop effective targeted marketing communications and promotions for each segment; develop cross-sell and up-sell strategy based on customers' purchase patterns across product classes; track and develop models for predicting customer migration from bad to good segments; or develop, deploy, and monitor comprehensive customer segmentation systems in their enterprise.
Some prior exposure to SAS is useful, but not required. No experience with SAS Enterprise Miner, SAS Enterprise Guide, or JMP is required. This course addresses SAS Enterprise Miner software.
|Sydney, Canberra, Melbourne:||Wellington:|