Preface

This book is in a way the “sequel” of the first book that I wrote together with Konstantinos Tsiptsis. It follows the same principles, aiming to be an applied guide rather than a generic reference book on predictive analytics and data mining. There are many excellent, well-written books that succeed in presenting the theoretical background of the data mining algorithms. But the scope of this book is to enlighten the usage of these algorithms in marketing applications and to transfer domain expertise and knowledge. That’s why it is packed with real-world case studies which are presented with the use of three powerful and popular software tools: IBM SPSS Modeler, RapidMiner, and Data Mining for Excel.

Here are a few words on the book’s structure and some tips on “how to read the book.” The book is organized in three main parts:

  • Part I, the Methodology. Chapters 2 and 3: I strongly believe that these sections are among the strong points of the book. Part I provides a methodological roadmap, covering both the technical and the business aspects for designing and carrying out optimized marketing actions using predictive analytics. The data mining process is presented in detail along with specific guidelines for the development of targeted acquisition, cross-/deep-/up-selling and retention campaigns, as well as effective customer segmentation schemes.
  • Part II, the Algorithms. Chapters 4 and 5: This part is dedicated in introducing the main concepts of some of the most ...

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