Book Image

IBM SPSS Modeler Cookbook

Book Image

IBM SPSS Modeler Cookbook

Overview of this book

IBM SPSS Modeler is a data mining workbench that enables you to explore data, identify important relationships that you can leverage, and build predictive models quickly allowing your organization to base its decisions on hard data not hunches or guesswork. IBM SPSS Modeler Cookbook takes you beyond the basics and shares the tips, the timesavers, and the workarounds that experts use to increase productivity and extract maximum value from data. The authors of this book are among the very best of these exponents, gurus who, in their brilliant and imaginative use of the tool, have pushed back the boundaries of applied analytics. By reading this book, you are learning from practitioners who have helped define the state of the art. Follow the industry standard data mining process, gaining new skills at each stage, from loading data to integrating results into everyday business practices. Get a handle on the most efficient ways of extracting data from your own sources, preparing it for exploration and modeling. Master the best methods for building models that will perform well in the workplace. Go beyond the basics and get the full power of your data mining workbench with this practical guide.
Table of Contents (17 chapters)
IBM SPSS Modeler Cookbook
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Creating time-aligned cohorts


In this recipe we will create a table that combines customer information, monthly statements, and churner identifiers conditioned by cohort information.

Why we would do this is best explained by means of an example. Suppose we wish to identify the best predictors of whether a customer is going to churn. To do this we might be tempted to throw everyone into a pot of data and see what algorithm best predicts who are churners and who are not churners. There are two immediate problems with this: one, the results would be skewed where we would have many more non-churners than churners going into the analysis, and two, the process used would be insensitive to everything going on within similar customer traits. After all, while John churned in January 2012, Sally (who came from the same region) has not churned. Wouldn't it make more sense to fine-tune the analysis so that we are comparing customers with similar experiences but different outcomes? That way we get the...