Business Knowledge Series course
Presented by Goutam Chakraborty, Ph.D., professor of marketing and founder of the SAS and Oklahoma State University Data Mining Certificate Program
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.
Learn how to
- manage segmentation project cycle
- understand and apply both attitudinal and behavioral segmentation tools and techniques on customer or prospect data
- use descriptive as well as predictive segmentation
- profile and validate segments
- evaluate stability of segments over time
- assign probability of segment membership to observations
- explore customer migration from bad to good segments over time
- create segments based on product affinity
- analyze textual data (such as customer comments) for segmentation
- find segments using time-series data
- use segmentation results to build predictive models
- do data preprocessing tasks such as selecting a smaller number of variables from a large pool of input variables, reducing dimensionality of data for building better models, identifying outliers in data via density and distance methods, applying scale and shape transformation for better models, and handling missing values in your data.
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
| Classroom:|| 3.0 days |
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.