book

Customer Segmentation and Clustering Using SAS Enterprise Miner, Second Edition, 2nd Edition

by Randy Collica

November 2011

Beginner to intermediate

378 pages

10h 48m

English

SAS Institute

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1.1 - What is Segmentation in the Context of CRM?1.2 - Types of Segmentation and Methods1.2.1 - Customer Profiling1.2.2 - Customer Likeness Clustering1.2.3 - RFM Cell Classification Grouping1.2.4 - Purchase Affinity Clustering1.3 - Typical Uses of Segmentation in Industry1.4 - Segmentation as a CRM Tool1.5 - References
2.1 - Mass Customization Instead of Mass Marketing2.2 - Specialized Promotions or Communications by Segment Groups2.3 - Profiling of Customers and Prospects2.3.1 - Example 2.1: The Data Assay Project2.3.2 - Example 2.2: Customer Profiling of the Buytest Data Set2.3.3 - Additional Exercise2.4 - References
3.1 - What is Similar and What is Not3.2 - Distance Metrics as a Measure of Similarity and Association3.3 - What is Clustering? The k-Means Algorithm and Variations.3.3.1 - Variations of the k-Means Algorithm3.3.2 - The Agglomerative Algorithm3.4 - References
4.1 - Introduction to Cell-Based Segmentation4.2 - Segmentation Using Cell Groups—RFM4.2.1 - Other Cell Types for Segmentation4.3 - Example Development of RFM Cells4.4 - Tree-Based Segmentation Using RFM4.5 - Using RFM and CRM—Customer Distinction4.6 - Additional Exercise4.7 - References4.8 - Additional Reading
5.1 - Motivation for Clustering of Customer Attributes: Beginning CRM5.2 - How Can i Better Understand My Customer Base of Over 100,000?5.3 - Using a Decision Tree to Create Cluster Segments5.4 - References5.5 - Additional Reading
6.1 - Closer to Reality of Customer Segmentation6.2 - Representing Many Attributes in Multi-dimensions6.3 - How Can i Better Understand My Customers of Many Attributes?6.4 - Data Assay and Profiling6.5 - Understanding What the Cluster Segmentation Found6.6 - Planning for Customer Attentiveness with Each Segment6.7 - Creating Cluster Segments on Very Large Data Sets6.8 - Additional Exercise6.9 - References
7.1 - What is the Shelf Life of a Model, and How Can it Affect Your Results?7.2 - How to Detect When your Clustering Model Should be Updated7.3 - Testing New Observations and Score Results7.4 - Other Practical Considerations7.5 - Additional Reading
8.1 - The Basis of Breaking up the Data Space8.2 - Predicting a Segment Level8.3 - Using the Segment Level Predictions for Customer Scoring8.4 - Creating Customer Value Segments8.5 - References8.6 - Additional Exercises
9.1 - Missing Data and How it Can Affect Clustering9.2 - Analysis of Missing Data Patterns9.3 - Effects of Missing Data on Clustering.9.4 - Methods of Missing Data Imputation9.5 - Obtaining Confidence Interval Estimates on Imputed Values9.6 - Using the SAS Enterprise Miner Imputation Node9.7 - References
10.1 - Motivation of Estimating Product Affinity by Segment10.2 - Estimating Product Affinity Using Purchase Quantities10.3 - Combining Product Affinities by Cluster Segments10.4 - Pros and Cons of Segment Affinity Scores.10.5 - issues with Clustering Non-normal Quantities10.6 - Approximating a Graph-Theoretic Approach Using a Decision Tree10.7 - Using the Product Affinities for Cross-Sell Programs10.8 - Additional Exercises10.9 - References
11.1 - When Ordinary Clustering Does not Produce Desired Results11.2 - What is a Self-Organizing Map?11.3 - Computing and Applying SOM Network Cluster Segments11.4 - Comparing Clustering with SOM Segmentation11.5 - Customer Distinction Analysis Example11.6 - Additional Exercises11.7 - References
12.1 - Background of Textual Data in the Context of CRM12.2 - Notes on Text Mining versus Natural Language Processing12.3 - Simple Text Mining Example12.4 - Text Document Clustering12.5 - Using Text Mining in CRM Applications12.6 - References
13.1 - What is Association Analysis and its Uses in Business?13.2 - Market Basket Association Analysis13.3 - Revisiting Product Affinity Using Clustered Associations13.4 - The Business and Technical Side of Clustering Associations.13.5 - References
14.1 - Typical Market Research Surveys.14.2 - Match-back of Survey Responses14.3 - Analysis of Survey Responses: An Overview14.4 - Developing a Predictive Segmentation Model from a Survey Analysis14.5 - Issues with Scoring a Predictive Segmentation on Customer or Prospect Data14.6 - Assessing the Confidence of Predicted Segments14.7 - Business Implications for Using Attitudinal Segmentation.14.8 - References
15.1 - Survey of Methods of Ensemble Segmentations15.2 - Two Methods for Combining Attitudinal and Behavioral Segments15.3 - Presenting the Business Case Simply from a Complex Analysis15.4 - References15.5 - Additional Exercise
16.1 - Measuring Transactions as a Time Series.16.2 - References16.3 - Additional Reading16.4 - Additional Exercise

Content preview from Customer Segmentation and Clustering Using SAS Enterprise Miner, Second Edition, 2nd Edition

Segmentation of Customer Transactions

16.1 Measuring Transactions as a Time Series.

16.2 References

16.3 Additional Reading

16.4 Additional Exercise

16.1 Measuring Transactions as a Time Series

“If time be of all things the most precious, wasting time must be the greatest prodigality.” Benjamin Franklin coined that phrase. So, how can we best define a time series? Essentially, a time series is a collection of observations ordered sequentially in time. Figure 16.1 shows a representation of a time series.

Figure 16.1 A Typical Time Series

There are many examples of time series data in a variety of industries. People measure things that ...

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Publisher Resources

ISBN: 9781607648109