Effective CRM Using Predictive Analytics

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1 Effective CRM Using Predictive Analytics

2 Effective CRM Using Predictive Analytics Antonios Chorianopoulos

3 This edition first published John Wiley & Sons, Ltd Registered Office John Wiley & Sons, Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, United Kingdom For details of our global editorial offices, for customer services and for information about how to apply for permission to reuse the copyright material in this book please see our website at The right of the author to be identified as the author of this work has been asserted in accordance with the Copyright, Designs and Patents Act All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher. Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic books. Designations used by companies to distinguish their products are often claimed as trademarks. All brand names and product names used in this book are trade names, service marks, trademarks or registered trademarks of their respective owners. The publisher is not associated with any product or vendor mentioned in this book. Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. It is sold on the understanding that the publisher is not engaged in rendering professional services and neither the publisher nor the author shall be liable for damages arising herefrom. If professional advice or other expert assistance is required, the services of a competent professional should be sought. Library of Congress Cataloging in Publication Data Chorianopoulos, Antonios. Effective CRM using predictive analytics / Antonios Chorianopoulos. pages cm Includes bibliographical references and index. ISBN (cloth) 1. Customer relations Management Data processing. 2. Data mining. I. Title. HF C dc A catalogue record for this book is available from the British Library. Cover image: Baitong333/iStockphoto Set in 10/12pt Times by SPi Global, Pondicherry, India

4 To my daughter Eugenia and my wife Virginia

5 Contents Preface Acknowledgments xiii xv 1 An overview of data mining: The applications, the methodology, the algorithms, and the data The applications The methodology The algorithms Supervised models Classification models Estimation (regression) models Feature selection (field screening) Unsupervised models Cluster models Association (affinity) and sequence models Dimensionality reduction models Record screening models The data The mining datamart The required data per industry The customer signature : from the mining datamart to the enriched, marketing reference table Summary 20 Part I The Methodology 21 2 Classification modeling methodology An overview of the methodology for classification modeling Business understanding and design of the process Definition of the business objective Definition of the mining approach and of the data model Design of the modeling process Defining the modeling population Determining the modeling (analysis) level Definition of the target event and population Deciding on time frames Data understanding, preparation, and enrichment Investigation of data sources Selecting the data sources to be used 34

6 viii contents Data integration and aggregation Data exploration, validation, and cleaning Data transformations and enrichment Applying a validation technique Split or Holdout validation Cross or n fold validation Bootstrap validation Dealing with imbalanced and rare outcomes Balancing Applying class weights Classification modeling Trying different models and parameter settings Combining models Bagging Boosting Random Forests Model evaluation Thorough evaluation of the model accuracy Accuracy measures and confusion matrices Gains, Response, and Lift charts ROC curve Profit/ROI charts Evaluating a deployed model with test control groups Model deployment Scoring customers to roll the marketing campaign Building propensity segments Designing a deployment procedure and disseminating the results Using classification models in direct marketing campaigns Acquisition modeling Pilot campaign Profiling of high value customers Cross selling modeling Pilot campaign Product uptake Profiling of owners offer optimization with next best product campaigns Deep selling modeling Pilot campaign Usage increase Profiling of customers with heavy product usage Up selling modeling Pilot campaign Product upgrade Profiling of premium product owners Voluntary churn modeling 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

7 contents ix 3 Behavioral segmentation methodology An introduction to customer segmentation An overview of the behavioral segmentation methodology Business understanding and design of the segmentation process Definition of the business objective Design of the modeling process Selecting the segmentation population Selection of the appropriate segmentation criteria Determining the segmentation level Selecting the observation window Data understanding, preparation, and enrichment Investigation of data sources Selecting the data to be used Data integration and aggregation Data exploration, validation, and cleaning Data transformations and enrichment Input set reduction Identification of the segments with cluster modeling Evaluation and profiling of the revealed segments Technical evaluation of the clustering solution Profiling of the revealed segments Using marketing research information to evaluate the clusters and enrich their profiles Selecting the optimal cluster solution and labeling the segments Deployment of the segmentation solution, design and delivery of differentiated strategies Building the customer scoring model for updating the segments Building a Decision Tree for scoring: fine tuning the segments Distribution of the segmentation information Design and delivery of differentiated strategies Summary 142 Part II The Algorithms Classification algorithms Data mining algorithms for classification An overview of Decision Trees The main steps of Decision Tree algorithms Handling of predictors by Decision Tree models Using terminating criteria to prevent trivial tree growing Tree pruning CART, C5.0/C4.5, and CHAID and their attribute selection measures The Gini index used by CART The Information Gain Ratio index used by C5.0/C The chi square test used by CHAID Bayesian networks naïve Bayesian networks 172

8 x contents 4.7 Bayesian belief networks Support vector machines Linearly separable data Linearly inseparable data Summary Segmentation algorithms Segmenting customers with data mining algorithms Principal components analysis How many components to extract? The eigenvalue (or latent root) criterion The percentage of variance criterion The scree test criterion The interpretability and business meaning of the components What is the meaning of each component? Moving along with the component scores Clustering algorithms Clustering with K means Clustering with TwoStep Summary 213 Part III The Case Studies A voluntary churn propensity model for credit card holders The business objective The mining approach Designing the churn propensity model process Selecting the data sources and the predictors Modeling population and level of data Target population and churn definition Time periods and historical information required The data dictionary The data preparation procedure From cards to customers: aggregating card level data Enriching customer data Defining the modeling population and the target field Derived fields: the final data dictionary The modeling procedure Applying a Split (Holdout) validation: splitting the modeling dataset for evaluation purposes Balancing the distribution of the target field Setting the role of the fields in the model Training the churn model Understanding and evaluating the models Model deployment: using churn propensities to target the retention campaign 248

9 contents xi 6.9 The voluntary churn model revisited using RapidMiner Loading the data and setting the roles of the attributes Applying a Split (Holdout) validation and adjusting the imbalance of the target field s distribution Developing a Naïve Bayes model for identifying potential churners Evaluating the performance of the model and deploying it to calculate churn propensities Developing the churn model with Data Mining for Excel Building the model using the Classify Wizard Selecting the classification algorithm and its parameters Applying a Split (Holdout) validation Browsing the Decision Tree model Validation of the model performance Model deployment Summary Value segmentation and cross selling in retail The business background and objective An outline of the data preparation procedure The data dictionary The data preparation procedure Pivoting and aggregating transactional data at a customer level Enriching customer data and building the customer signature The data dictionary of the modeling file Value segmentation Grouping customers according to their value Value segments: exploration and marketing usage The recency, frequency, and monetary (RFM) analysis RFM basics The RFM cell segmentation procedure Setting up a cross selling model The mining approach Designing the cross selling model process The data and the predictors Modeling population and level of data Target population and definition of target attribute Time periods and historical information required The modeling procedure Preparing the test campaign and loading the campaign responses for modeling Applying a Split (Holdout) validation: splitting the modeling dataset for evaluation purposes Setting the roles of the attributes Training the cross sell model Browsing the model results and assessing the predictive accuracy of the classifiers 301

10 xii contents 7.13 Deploying the model and preparing the cross selling campaign list The retail case study using RapidMiner Value segmentation and RFM cells analysis Developing the cross selling model Applying a Split (Holdout) validation Developing a Decision Tree model with Bagging Evaluating the performance of the model Deploying the model and scoring customers Building the cross selling model with Data Mining for Excel Using the Classify Wizard to develop the model Selecting a classification algorithm and setting the parameters Applying a Split (Holdout) validation Browsing the Decision Tree model Validation of the model performance Model deployment Summary Segmentation application in telecommunications Mobile telephony: the business background and objective The segmentation procedure Selecting the segmentation population: the mobile telephony core segments Deciding the segmentation level Selecting the segmentation dimensions Time frames and historical information analyzed The data preparation procedure The data dictionary and the segmentation fields The modeling procedure Preparing data for clustering: combining fields into data components Identifying the segments with a cluster model Profiling and understanding the clusters Segmentation deployment Segmentation using RapidMiner and K means cluster Clustering with the K means algorithm Summary 359 Bibliography 360 Index 362

11 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 popular and powerful data mining algorithms for classification and clustering. The data mining algorithms are explained in a simple and comprehensive language for business users with no technical expertise. The intention is to demystify the main concepts of the algorithms rather than diving deep in mathematical explanations and formulas so that data mining and marketing practitioners can confidently deploy them in their everyday business problems. Part III, the Case Studies. Chapters 6, 7, and 8: And then it s action time! The third part of the book is the hands on part. Three case studies from banking, retail, and telephony are presented in detail following the specific methodological steps explained in the previous chapters. The concept is to apply the methodological blueprints of Chapters 2 and 3 in real world applications and to bridge the gap between analytics and their use in CRM. Given the level of detail and the accompanying material, the case studies can be considered as application templates for developing similar applications. The software tools are presented in that context. In the book s companion website, you can access the material from each case study, including the datasets and the relevant code. This material is an inseparable part of the book, and I d strongly suggest exploring and experimenting with it to gain full advantage of the book. Those interested in segmentation and its marketing usage are strongly encouraged to look for the previous title: Konstantinos Tsiptsis and Antonios Chorianopoulos. Data Mining Techniques in CRM: Inside Customer Segmentation. Wiley, New York, 2009.

12 xiv Preface Finally, I would really like to thank all the readers of the first book for their warm acceptance, all those who read or reviewed the book, and all those who contacted us to share kind and encouraging words about how much they liked it. They truly inspired the creation of this new book. I really hope that this title meets their expectations.

13 Acknowledgments Special thanks to Ioanna Koutrouvis and Vassilis Panagos at PREDICTA ( for their support.

Effective CRM Using. Predictive Analytics. Antonios Chorianopoulos

Effective CRM Using. Predictive Analytics. Antonios Chorianopoulos Effective CRM Using Predictive Analytics Antonios Chorianopoulos WlLEY Contents Preface Acknowledgments xiii xv 1 An overview of data mining: The applications, the methodology, the algorithms, and the