Effective CRM using Predictive Analytics

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.

Autorentext
Antonios Chorianopoulos, Alpha Bank Greece.

Zusammenfassung

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.

Inhalt

Preface xiii

Acknowledgments xv

1 An overview of data mining: The applications, the methodology, the algorithms, and the data 1

1.1 The applications 1

1.2 The methodology 4

1.3 The algorithms 6

1.3.1 Supervised models 6

1.3.1.1 Classification models 7

1.3.1.2 Estimation (regression) models 9

1.3.1.3 Feature selection (field screening) 10

1.3.2 Unsupervised models 10

1.3.2.1 Cluster models 11

1.3.2.2 Association (affinity) and sequence models 12

1.3.2.3 Dimensionality reduction models 14

1.3.2.4 Record screening models 14

1.4 The data 15

1.4.1 The mining datamart 16

1.4.2 The required data per industry 16

1.4.3 The customer signature: from the mining datamart to the enriched, marketing reference table 16

1.5 Summary 20

Part I The Methodology 21

2 Classification modeling methodology 23

2.1 An overview of the methodology for classification modeling 23

2.2 Business understanding and design of the process 24

2.2.1 Definition of the business objective 24

2.2.2 Definition of the mining approach and of the data model 26

2.2.3 Design of the modeling process 27

2.2.3.1 Defining the modeling population 27

2.2.3.2 Determining the modeling (analysis) level 28

2.2.3.3 Definition of the target event and population 28

2.2.3.4 Deciding on time frames 29

2.3 Data understanding, preparation, and enrichment 33

2.3.1 Investigation of data sources 34

2.3.2 Selecting the data sources to be used 34

2.3.3 Data integration and aggregation 35

2.3.4 Data exploration, validation, and cleaning 35

2.3.5 Data transformations and enrichment 38

2.3.6 Applying a validation technique 40

2.3.6.1 Split or Holdout validation 40

2.3.6.2 Cross or nfold validation 45

2.3.6.3 Bootstrap validation 47

2.3.7 Dealing with imbalanced and rare outcomes 48

2.3.7.1 Balancing 48

2.3.7.2 Applying class weights 53

2.4 Classification modeling 57

2.4.1 Trying different models and parameter settings 57

2.4.2 Combining models 60

2.4.2.1 Bagging 61

2.4.2.2 Boosting 62

2.4.2.3 Random Forests 63

2.5 Model evaluation 64

2.5.1 Thorough evaluation of the model accuracy 65

2.5.1.1 Accuracy measures and confusion matrices 66

2.5.1.2 Gains, Response, and Lift charts 70

2.5.1.3 ROC curve 78

2.5.1.4 Profit/ROI charts 81

2.5.2 Evaluating a deployed model with testcontrol groups 85

2.6 Model deployment 88

2.6.1 Scoring customers to roll the marketing campaign 88

2.6.1.1 Building propensity segments 93

2.6.2 Designing a deployment procedure and disseminating the results 94

2.7 Using classification models in direct marketing campaigns 94

2.8 Acquisition modeling 95

2.8.1.1 Pilot campaign 95

2.8.1.2 Profiling of highvalue customers 96

2.9 Crossselling modeling 97

2.9.1.1 Pilot campaign 98

2.9.1.2 Product uptake 98

2.9.1.3 Profiling of owners 99

2.10 Offer optimization with next best product campaigns 100

2.11 Deepselling modeling 102

2.11.1.1 Pilot campaign 102

2.11.1.2 Usage increase 103

2.11.1.3 Profiling of customers with heavy product usage 104

2.12 Upselling modeling 105

2.12.1.1 Pilot campaign 105

2.12.1.2 Product upgrade 107

2.12.1.3 Profiling of premium product owners 107

2.13 Voluntary churn modeling 108

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 111

3 Behavioral segmentation methodology 112

3.1 An introduction to customer segmentation 112 3...