Customer Segmentation using SAS Enterprise Miner Training Course and Workshop in Bangalore, Mysore, Chennai, Hyderabad, Pune, Mumbai, Delhi, Noida, Gurgaon, Kolkata

This Customer Segmentation using SAS Enterprise Miner training 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, -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.

By attending Customer Segmentation using SAS Enterprise Miner workshop, delegates will learn 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.

Some prior exposure to SAS is useful, but not required.

The Customer Segmentation using SAS Enterprise Miner class is ideal for:

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

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

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Customer Segmentation Using SAS Enterprise Miner

COURSE AGENDA