book

Effective CRM using Predictive Analytics

Name: Effective CRM using Predictive Analytics
Author: Antonios Chorianopoulos
ISBN: 9781119011552

by Antonios Chorianopoulos

January 2016

Intermediate to advanced

392 pages

12h 6m

English

Wiley

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Cover
Title page
Preface
Acknowledgments
1 An overview of data mining: The applications, the methodology, the algorithms, and the data
1.1 The applications1.2 The methodology1.3 The algorithms1.4 The data1.5 Summary
Part I: The Methodology
2 Classification modeling methodology
2.1 An overview of the methodology for classification modeling2.2 Business understanding and design of the process2.3 Data understanding, preparation, and enrichment2.4 Classification modeling2.5 Model evaluation2.6 Model deployment2.7 Using classification models in direct marketing campaigns2.8 Acquisition modeling2.9 Cross-selling modeling2.10 Offer optimization with next best product campaigns2.11 Deep-selling modeling2.12 Up-selling modeling2.13 Voluntary churn modeling2.14 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 segmentation3.2 An overview of the behavioral segmentation methodology3.3 Business understanding and design of the segmentation process3.4 Data understanding, preparation, and enrichment3.5 Identification of the segments with cluster modeling3.6 Evaluation and profiling of the revealed segments3.7 Deployment of the segmentation solution, design and delivery of differentiated strategies3.8 Summary
Part II: The Algorithms
4 Classification algorithms
4.1 Data mining algorithms for classification4.2 An overview of Decision Trees4.3 The main steps of Decision Tree algorithms4.4 CART, C5.0/C4.5, and CHAID and their attribute selection measures4.5 Bayesian networks4.6 Naïve Bayesian networks4.7 Bayesian belief networks4.8 Support vector machines4.9 Summary

5 Segmentation algorithms
5.1 Segmenting customers with data mining algorithms5.2 Principal components analysis5.3 Clustering algorithms5.4 Summary
Part III: The Case Studies
6 A voluntary churn propensity model for credit card holders
6.1 The business objective6.2 The mining approach6.3 The data dictionary6.4 The data preparation procedure6.5 Derived fields: the final data dictionary6.6 The modeling procedure6.7 Understanding and evaluating the models6.8 Model deployment: using churn propensities to target the retention campaign6.9 The voluntary churn model revisited using RapidMiner6.10 Developing the churn model with Data Mining for Excel6.11 Summary
7 Value segmentation and cross-selling in retail
7.1 The business background and objective7.2 An outline of the data preparation procedure7.3 The data dictionary7.4 The data preparation procedure7.5 The data dictionary of the modeling file7.6 Value segmentation7.7 The recency, frequency, and monetary (RFM) analysis7.8 The RFM cell segmentation procedure7.9 Setting up a cross-selling model7.10 The mining approach7.11 The modeling procedure7.12 Browsing the model results and assessing the predictive accuracy of the classifiers7.13 Deploying the model and preparing the cross-selling campaign list7.14 The retail case study using RapidMiner7.15 Building the cross-selling model with Data Mining for Excel7.16 Summary
8 Segmentation application in telecommunications
8.1 Mobile telephony: the business background and objective8.2 The segmentation procedure8.3 The data preparation procedure8.4 The data dictionary and the segmentation fields8.5 The modeling procedure8.6 Segmentation using RapidMiner and K-means cluster8.7 Summary
Bibliography
Index
End User License Agreement

Overview

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.

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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