This course focuses on best practices when doing cluster analysis, particularly when applied to CRM. However, the techniques presented are easily applicable to other analytic fields. Students with different backgrounds and skill levels will benefit from the course, which explores theory, practice, and application.
Use clustering and segmentation.
Determine the number of clusters present in data.
Find the most effective clusters.
Apply various approaches to clustering, including K-Means and Kohonen/SOM networks.
Assess the quality of clusters.
Use clustering in CRM, risk, fraud, and predictive models.
Determine which variables should be used in clustering.
Incorporate categorical and binary variables in clustering.
Approximate clusters with decision trees.
Incorporate soft “fuzzy” clustering.
Who Can Benefit
Analysts from different backgrounds and with varying skill levels who work with large data sets
Before attending this course, you should have some experience with the SAS programming language and SAS Enterprise Miner, as well as some knowledge about how to develop predictive models. Some experience with clustering is helpful but not required. Managers should have a basic understanding of analytics and how it is applied within their organization.
Rules of clustering: Lesson 1 summary.
Rules of clustering: Lesson 2 summary.
Distance Metrics – Normalize/Standardize Data
Distance metric variables and scale.
Transform to similar scales.
Transform variable example.
Rules of clustering: Lesson 3 summary.
Distance Metrics – Data Cleansing
Rules of clustering: Lesson 4 summary.
Clustering Algorithms: Results
Clustering results are inconsistent.
Rules of clustering: Lesson 5 summary.
Different Clustering Algorithm Results
SAS code used in examples.
Random seeds example.
Cluster seed selection methods.
SAS Enterprise Miner seed selection methods.
SAS Enterprise Miner – how many clusters?
Rules of clustering: Lesson 6 summary.
Monte Carlo Cluster Selection
Cluster node data collection example.
Rules of clustering: Lesson 7 summary.
Rules of clustering: Lesson 8 summary.
Continuous Categorical Variables
Identifying a specific class.
Rules of clustering: Lesson 9 summary.
Kohonen/SOM Monte Carlo clustering.
Rules of clustering: Lesson 10 summary.
Boolean and fuzzy logic.
Fuzzy cluster macro.
Example of fuzzy cluster approximation.
Application of fuzzy clusters.
Rules of clustering: Lesson 11 summary.
Hierarchical clusters overview.
Hierarchical cluster Example 1.
How many clusters.
SAS code for hierarchical clustering.
Hierarchical cluster Example 2: Lesson 7 data set.
Incorporating hierarchical clusters into SAS Enterprise Miner.
Using hierarchical clusters in Monte Carlo clustering.
https://www.exitcertified.com/it-training/sas/advanced-analytics/stat-analysis/-57355-detail.htmlBPCLUSBest Practices in Cluster Analysis for Customer Relationship Management (CRM)https://assets.exitcertified.com/assets/CourseImages/c54858acce/AdobeStock_124444025__FitMaxWzEwMDAsMTAwMF0.jpg1650.00USDInStock/Training/SAS/Advanced Analytics/Statistical AnalysisThis course focuses on best practices when doing cluster analysis, particularly when applied to CRM. However, the techniques...1650.00SASClassroom2019-07-22T17:37:18+00:00USD