Customer Segmentation with Latent Class Analysis Approach

Tutorial on how to develop a customer segmentation solution with Latent Class Analysis approach in SAS
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  • Lectures 16
  • Contents Video: 1.5 hours
    Other: 0 mins
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
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About This Course

Published 7/2014 English

Course Description

In this class, I will show you how to develop a customer segmentation solution through hands-on training.

In this 2-hr training course, I will walk you through the Latent Class Analysis approach and show you how to develop a customer segmentation solution step-by-step. Together, we will develop the solution using SAS software. I will provide extensive demo on SAS coding, running codes, and explaining outputs for each step. All SAS codes and modeling data will be made available to you as well.

The goal for this training is for you to be able to develop a professional-level customer segmentation solution independently.

What are the requirements?

  • Microsoft Excel and PowerPoint or the similar softwares
  • SAS Software with Base/Stat/Graph Components

What am I going to get from this course?

  • Learn how to develop a customer segmentation solution from the beginning to the end
  • Learn how to interpret segmentation results
  • Learn how to write segmentation presentation

What is the target audience?

  • Working professionals who want to learn advanced analyics skills
  • Graduates or undergraduates who want to explore their career opportunities in advanced analytics

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Introduction
Objectives, outlines, and pre-requisites
Preview
05:26
What is customer segmentation, high-level approaches
Preview
05:04
Customer segmentation applications
Preview
05:20
Section 2: Project and Data
06:56

Web link to download the data for the project:

http://archive.ics.uci.edu/ml/machine-learning-databases/census1990-mld/

OR you can download the files from the following links:

https://s3.amazonaws.com/AnalyticsOnlineCourses/LCA/data/Summary+Tape+File+3+Technical+Documentation.htm

https://s3.amazonaws.com/AnalyticsOnlineCourses/LCA/data/USCensus1990.attributes.txt

https://s3.amazonaws.com/AnalyticsOnlineCourses/LCA/data/USCensus1990.data.txt.zip

https://s3.amazonaws.com/AnalyticsOnlineCourses/LCA/data/USCensus1990.mapping.sql.txt

https://s3.amazonaws.com/AnalyticsOnlineCourses/LCA/data/USCensus1990.readme.txt

https://s3.amazonaws.com/AnalyticsOnlineCourses/LCA/data/USCensus1990raw.attributes.txt

https://s3.amazonaws.com/AnalyticsOnlineCourses/LCA/data/data+dictionary.xlsx

Section 3: Methodology
03:54

Further Readings:

LCA mathematical model web site: http://methodology.psu.edu/ra/lca/example/math

LCA FAQ with some good references: http://www.john-uebersax.com/stat/faq.htm

Wiki with some references on LCA:

http://en.wikipedia.org/wiki/Latent_variable_model

http://en.wikipedia.org/wiki/Latent_class_analysis

Free LCA software download from The Methodology Center of Penn State University:

http://methodology.psu.edu/downloads/proclcalta

Section 4: Analysis
08:30

please download the sas code and save as .sas in order to run in sas software

05:47

please download the sas macro code and save as .sas in order to run in sas software

Step 1 - Prepare data - Data QC demo - Part 2
03:58
11:31

please download the sas code and save as .sas in order to run in sas software

09:31

please download the sas code and save as .sas in order to run in sas software

Step 2 - latent class analysis demo - Part 2
08:43
07:59

please download the sas codes and save as .sas in order to run in sas software

Step 3 - profiling analysis demo - Part 2
03:51
Step 3 - profiling analysis demo - Part 3
05:17
Section 5: Results Presentation
02:15

Results Summary

Section 6: Downloadable SAS Codes
SAS Codes
Article

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Instructor Biography

Predictive Analytics, Advanced Analytics Made Simple

15+ years of professional experience on predictive modeling in different industries. Specialized in logistic regression, linear regression, generalized regression, survival analysis, decision tree, discriminant analysis, cluster analysis, and other advanced analytic. In-depth knowledge and experiences in customer acquisition, retention, and credit risk management. Developed training classes and taught classes to graduate students and other working professionals.

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