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Effective CRM using Predictive Analytics

Book Description

A step-by-step guide to data mining applications in CRM.

Following a handbook approach, this book bridges the gap between analytics and their use in everyday marketing, providing guidance on solving real business problems using data mining techniques.

The book is organized into three parts. Part one provides a methodological roadmap, covering both the business and the technical aspects. The data mining process is presented in detail along with specific guidelines for the development of optimized acquisition, cross/ deep/ up selling and retention campaigns, as well as effective customer segmentation schemes.

In part two, some of the most useful data mining algorithms are explained in a simple and comprehensive way for business users with no technical expertise.

Part three is packed with real world case studies which employ the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner and Data Mining for Excel.  Case studies from industries including banking, retail and telecommunications are presented in detail so as to serve as templates for developing similar applications.

 

Key Features:

 

  • Includes numerous real-world case studies which are presented step by step, demystifying the usage of data mining models and clarifying all the methodological issues.

 

  • Topics are presented with the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner and Data Mining for Excel.

 

  • Accompanied by a website featuring material from each case study, including datasets and relevant code.

 

 

Combining data mining and business knowledge, this practical book provides all the necessary information for designing, setting up, executing and deploying data mining techniques in CRM. 

 

Effective CRM using Predictive Analytics will benefit data mining practitioners and consultants, data analysts, statisticians, and CRM officers.  The book will also be useful to academics and students interested in applied data mining.

 

Table of Contents

  1. Cover
  2. Title page
  3. Preface
  4. Acknowledgments
  5. 1 An overview of data mining: The applications, the methodology, the algorithms, and the data
    1. 1.1 The applications
    2. 1.2 The methodology
    3. 1.3 The algorithms
    4. 1.4 The data
    5. 1.5 Summary
  6. Part I: The Methodology
    1. 2 Classification modeling methodology
      1. 2.1 An overview of the methodology for classification modeling
      2. 2.2 Business understanding and design of the process
      3. 2.3 Data understanding, preparation, and enrichment
      4. 2.4 Classification modeling
      5. 2.5 Model evaluation
      6. 2.6 Model deployment
      7. 2.7 Using classification models in direct marketing campaigns
      8. 2.8 Acquisition modeling
      9. 2.9 Cross-selling modeling
      10. 2.10 Offer optimization with next best product campaigns
      11. 2.11 Deep-selling modeling
      12. 2.12 Up-selling modeling
      13. 2.13 Voluntary churn modeling
      14. 2.14 Summary of what we’ve learned so far: it’s not about the tool or the modeling algorithm. It’s about the methodology and the design of the process
    2. 3 Behavioral segmentation methodology
      1. 3.1 An introduction to customer segmentation
      2. 3.2 An overview of the behavioral segmentation methodology
      3. 3.3 Business understanding and design of the segmentation process
      4. 3.4 Data understanding, preparation, and enrichment
      5. 3.5 Identification of the segments with cluster modeling
      6. 3.6 Evaluation and profiling of the revealed segments
      7. 3.7 Deployment of the segmentation solution, design and delivery of differentiated strategies
      8. 3.8 Summary
  7. Part II: The Algorithms
    1. 4 Classification algorithms
      1. 4.1 Data mining algorithms for classification
      2. 4.2 An overview of Decision Trees
      3. 4.3 The main steps of Decision Tree algorithms
      4. 4.4 CART, C5.0/C4.5, and CHAID and their attribute selection measures
      5. 4.5 Bayesian networks
      6. 4.6 Naïve Bayesian networks
      7. 4.7 Bayesian belief networks
      8. 4.8 Support vector machines
      9. 4.9 Summary
    2. 5 Segmentation algorithms
      1. 5.1 Segmenting customers with data mining algorithms
      2. 5.2 Principal components analysis
      3. 5.3 Clustering algorithms
      4. 5.4 Summary
  8. Part III: The Case Studies
    1. 6 A voluntary churn propensity model for credit card holders
      1. 6.1 The business objective
      2. 6.2 The mining approach
      3. 6.3 The data dictionary
      4. 6.4 The data preparation procedure
      5. 6.5 Derived fields: the final data dictionary
      6. 6.6 The modeling procedure
      7. 6.7 Understanding and evaluating the models
      8. 6.8 Model deployment: using churn propensities to target the retention campaign
      9. 6.9 The voluntary churn model revisited using RapidMiner
      10. 6.10 Developing the churn model with Data Mining for Excel
      11. 6.11 Summary
    2. 7 Value segmentation and cross-selling in retail
      1. 7.1 The business background and objective
      2. 7.2 An outline of the data preparation procedure
      3. 7.3 The data dictionary
      4. 7.4 The data preparation procedure
      5. 7.5 The data dictionary of the modeling file
      6. 7.6 Value segmentation
      7. 7.7 The recency, frequency, and monetary (RFM) analysis
      8. 7.8 The RFM cell segmentation procedure
      9. 7.9 Setting up a cross-selling model
      10. 7.10 The mining approach
      11. 7.11 The modeling procedure
      12. 7.12 Browsing the model results and assessing the predictive accuracy of the classifiers
      13. 7.13 Deploying the model and preparing the cross-selling campaign list
      14. 7.14 The retail case study using RapidMiner
      15. 7.15 Building the cross-selling model with Data Mining for Excel
      16. 7.16 Summary
    3. 8 Segmentation application in telecommunications
      1. 8.1 Mobile telephony: the business background and objective
      2. 8.2 The segmentation procedure
      3. 8.3 The data preparation procedure
      4. 8.4 The data dictionary and the segmentation fields
      5. 8.5 The modeling procedure
      6. 8.6 Segmentation using RapidMiner and K-means cluster
      7. 8.7 Summary
  9. Bibliography
  10. Index
  11. End User License Agreement