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 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, and SOM/Kohonen method. The course focuses more on practical business solutions rather than statistical rigor. Therefore, business analysts, managers, marketers, programmers, and others can benefit from this course.

Learn how to

• understand and apply both attitudinal and behavioral segmentation tools and techniques on customer 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
• create segments based on product affinity
• select variables for segmentation
• reduce dimensionality of data before segmentation
• analyze textual data (such as customer comments) for segmentation
• use segmentation results to build predictive models
• segment time-series 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

Formats available	Standard duration
Classroom:	3 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.

Introduction

• define customer segmentation
• business context of customer segmentation

Segmentation Basics

• segmentation bases
• segmentation descriptors
• segmentation methods

Hierarchical Clustering for Segmentation

• introduction to clustering analysis
• similarity, distance, and distance metrics
• types of clustering

Applications of Hierarchical Clustering

• finding segments in B2B customer survey data
• modifying segments in B2B customer survey data
• profiling segments with bases
• applying Ward's method to find segments in B2B customer survey data
• validating segments with descriptors and other managerially important variables

k-Means Clustering

• mechanics of k-means clustering
• applications of k-means clustering
• scoring new data
• evaluating stability of cluster solution over time
• customer migration across segments over time
• probabilistic clustering
• probability of cluster membership

A Priori Segmentation Using RFM Cells

• application of RFM cell-based segmentation

Product Affinity Clusters

• application of product affinity in segmentation

Segments That Use Customer Comments (Text) Data

• introduction to text mining
• applications of text mining in segmentation and predictive modeling

Variables Selection before Segmentation

• variables selection for segmentation

Self Organizing Maps (SOM)/ Kohonen for Segmentation

• introduction to SOM/Kohonen
• applications of SOM/Kohonen

Handling Missing Values, Handling Time Series, Wrap-up and Take-Aways

• missing data analysis
• clustering time series data in SAS Enterprise Miner
• wrap-up and take-aways