- Cover
- Title page
- Preface
- Acknowledgments
- 1 An overview of data mining: The applications, the methodology, the algorithms, and the data
- Part I: The Methodology
- 2 Classification modeling methodology
- 2.1 An overview of the methodology for classification modeling
- 2.2 Business understanding and design of the process
- 2.3 Data understanding, preparation, and enrichment
- 2.4 Classification modeling
- 2.5 Model evaluation
- 2.6 Model deployment
- 2.7 Using classification models in direct marketing campaigns
- 2.8 Acquisition modeling
- 2.9 Cross-selling modeling
- 2.10 Offer optimization with next best product campaigns
- 2.11 Deep-selling modeling
- 2.12 Up-selling modeling
- 2.13 Voluntary churn modeling
- 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
- 3 Behavioral segmentation methodology
- 3.1 An introduction to customer segmentation
- 3.2 An overview of the behavioral segmentation methodology
- 3.3 Business understanding and design of the segmentation process
- 3.4 Data understanding, preparation, and enrichment
- 3.5 Identification of the segments with cluster modeling
- 3.6 Evaluation and profiling of the revealed segments
- 3.7 Deployment of the segmentation solution, design and delivery of differentiated strategies
- 3.8 Summary
- 2 Classification modeling methodology
- Part II: The Algorithms
- 4 Classification algorithms
- 4.1 Data mining algorithms for classification
- 4.2 An overview of Decision Trees
- 4.3 The main steps of Decision Tree algorithms
- 4.4 CART, C5.0/C4.5, and CHAID and their attribute selection measures
- 4.5 Bayesian networks
- 4.6 Naïve Bayesian networks
- 4.7 Bayesian belief networks
- 4.8 Support vector machines
- 4.9 Summary
- 5 Segmentation algorithms
- 4 Classification algorithms
- Part III: The Case Studies
- 6 A voluntary churn propensity model for credit card holders
- 6.1 The business objective
- 6.2 The mining approach
- 6.3 The data dictionary
- 6.4 The data preparation procedure
- 6.5 Derived fields: the final data dictionary
- 6.6 The modeling procedure
- 6.7 Understanding and evaluating the models
- 6.8 Model deployment: using churn propensities to target the retention campaign
- 6.9 The voluntary churn model revisited using RapidMiner
- 6.10 Developing the churn model with Data Mining for Excel
- 6.11 Summary
- 7 Value segmentation and cross-selling in retail
- 7.1 The business background and objective
- 7.2 An outline of the data preparation procedure
- 7.3 The data dictionary
- 7.4 The data preparation procedure
- 7.5 The data dictionary of the modeling file
- 7.6 Value segmentation
- 7.7 The recency, frequency, and monetary (RFM) analysis
- 7.8 The RFM cell segmentation procedure
- 7.9 Setting up a cross-selling model
- 7.10 The mining approach
- 7.11 The modeling procedure
- 7.12 Browsing the model results and assessing the predictive accuracy of the classifiers
- 7.13 Deploying the model and preparing the cross-selling campaign list
- 7.14 The retail case study using RapidMiner
- 7.15 Building the cross-selling model with Data Mining for Excel
- 7.16 Summary
- 8 Segmentation application in telecommunications
- 6 A voluntary churn propensity model for credit card holders
- Bibliography
- Index
- End User License Agreement
Acknowledgments
Special thanks to Ioanna Koutrouvis and Vassilis Panagos at PREDICTA (http://www.predicta.gr) for their support.
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