Doctor of philosophy



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Improvement of

Prediction of Customer Response

in Direct Marketing

THESIS
Submitted in partial fulfillment

of the requirements for the degree of



DOCTOR OF PHILOSOPHY

by

A. SUNDARAMURTHY

Under the Supervision of

Prof. Dr. M.J.XAVIER



BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCE

PILANI (RAJASTHAN) INDIA
2009

ACKNOWLEDGEMENT

I express my gratitude to Prof.L.K.Maheshwari, Vice Chancellor, BITS, Pilani for giving me this opportunity to pursue PhD and Prof.Ravi Prakash , Dean, Research & Consultancy Division, BITS, Pilani for his continuous encouragement and support in carrying out my PhD work smoothly. I thank Prof.S.P.Regalla, Assistant Dean and Dr.Hemant Jadav, Ms.Monica Sharma, Mr.Dinesh Kumar, Mr.Gunjan Soni, Mr.Sharad Srivastava and Mr.Amit Singh, nucleus members of Research &Consultancy Division, BITS, Pilani for their constant help and advice at all stages of my Ph/d work. I also thank the other office staff of Research & Consultancy Division, BITS, Pilani who rendered a lot of help in organizing various forms of paper work related to PhD progress.

I express my deep felt sense of gratitude and sincere thanks to my PhD supervisor Dr.M.J.Xavier, without whose constant guidance, help and tutelage this PhD work would not have been completed .He has been a constant source of inspiration and encouragement throughout my PhD work.

I thank Dr.Anil K Bhat and Prof.S.B.Mishra, my Doctoral advisory committee (DAC) members at BITS, Pilani for their constructive criticism and useful suggestions that helped me in immensely improving the contents and quality of presentation of my PhD thesis.

My heartfelt thanks for all the support and guidance to Dr.Anil K Bhatt, Group Leader of Management Department who has supported, inspired, encouraged and guided me during the various phases of my research work.

My sincere thanks to Dr.G.Balasubramaniam, Dean, IFMR who taught me data analysis, using artificial neural network and has been a constant source of encouragement.

I dedicate this thesis to my Beloved Mother who was a Noble Soul of Selflessness, who passed away recently.

A.Sundaramurthy

SYNOPSIS

Improving prediction of Customer Response in Direct Marketing

Objective

The objective of this Research is to improve the accuracy of prediction of customer response in direct marketing.



Scope

The Research focuses on methods used for prediction and their relative accuracy.



Milestones

1. Identification of methods of research

2. Data collection

3. Data Analysis and model development

4. Validation of model

Methodology

The sector chosen is financial services marketing and the model developed is based on qualitative and quantitative analysis of data from the data bases of customer. Data is used to score customers where improvement in prediction is shown and which is useful in direct marketing campaigns for cross selling and up selling.

The data is divided into three parts one for model building one for testing and the third for validation.

Different methods like Linear Regression, Logistic Regression, Discriminant analysis and neural network are used for model development and their prediction accuracy is compared.

First the transformation of business from the orbit of production centric to that of ever increasing consumer centric orbit is explained. Thus Customer insight as the heart of the business in the 21st century is brought out. The complexity of the consumer behavior , various attempts to model the consumer behavior by various experts in the field of marketing, economics and psychology and attempts to quantify the behavior in the science of psychology ,sociology and anthropology follow next.

The recent advances in studying human behviour in the fields of neuro psychology is explained.

Next the fundamentals of models and model building, different methods of model development, application of various mathematical methods, validation and application of models in various fields is brought out.

Response models and their crucial role in marketing in sustaining the competitive edge by improving the ROI of marketing is brought out. Specifically the effectiveness and efficiency of marketing campaigns, which are playing increasingly a dominant role in acquiring and retaining customers by direct marketing and maintaining customer relations to harvest their life time value is brought out.

Birth and evolution of financial services as a business and its birth and growth in India is discussed. How the rapid growth of the sector has given rise to the snowballing debt and non performing assets due to the bad repayment behavior of the borrowers and how this has led to the urgent and important need to assess the customer as good or bad and the development of the field of risk management is discussed.

Different methods of risk modeling and their merits and demerits are discussed.

Neural network has been used and compared with other methods for model development and neural network has been found to be most accurate. Hence NN its theory and application is discussed in detail. Once the classification of good and bad customers is done, the data of good customers are useful to profile customers based on various factors like their financial needs, repayment behavior, their risk ranking, their risk taking ability, their thirst for risk, their financial and buying behavior etc and other data to develop a response model.

The possible potential areas of future research are given briefly in the conclusion.

The improvement in prediction accuracy achieved in this research enables accurate scoring and profiling of customers in financial service
and its importance is globally important as has been proved by the global crisis triggered by Subprime lending.

LIST OF TABLES




Tables No.

Title

Page No.



Consumer choice model

76



ANN’s relative contribution factor

78



Out of sample forecast

80



C5 Algorithm training data

83



Comparative model performance

123



NN model -parameters for comparison

165



Logistic Regression- parameters for comparison

165



Markov Model -parameters for comparison

166



Multi Variate proportional hazard estimation -parameters for comparison

166



Logistic Binary regression model -parameters for comparison

167



Classification & Regression Tree-parameters for comparison

167



Evaluation of classification models

168



Summary of variables in creditworthiness models across the globe

186



Customer Profile-Age

205



Customer Profile-Educational qualification

207



Customer Profile-No. of dependents

209



Customer Profile-Income

211



Customer Profile-Other Income

213



Customer Profile-Experience

215



Customer Profile-Type of House

217



Customer Profile-Rent

219



Customer Profile-Down payment

221



Customer Profile-TV ownership

223



Customer Profile-Music system ownership

225



Customer Profile –Fridge

227



Customer Profile-Washing machine

229



Customer Profile-Two wheeler

231



Customer Profile-Four wheeler

233



Customer Profile-Amount Overdue

235



Customer Profile-no of Dues

237



Customer Profile-Classification

239



Age-Customer classification

241



Qualification-Customer classification

243



No. of dependents-Customer classification

245



Income-Customer classification

247



Other Income-Customer classification

249



Experience-Customer classification

251



Type of house-Customer classification

253



Rent-Customer classification

255



Down payment-Customer classification

257



TV ownership-Customer classification

260



Music system ownership-Customer

Classification



263



Fridge ownership-Customer classification

266-267



Washing machine-Customer classification

269



Two wheeler ownership-Customer classification

272



Four wheeler ownership-customer classification

275



Variables extracted in data analysis

296



Confusion matrix to measure model fit

325



Multiple Regression model Summary

328



Multiple Regression -ANOVA

329



Multiple Regression –Significant Variables

329



Multiple Regression-Coefficients

330



Logistic Regression- Significant Variables

330



Logistic Regression-Non significant Variables

331



Discriminant analysis-classification results

331



Factor Analysis-Rotated Component Matrix

332



Neural Network –performance Table

332



Classification Table for NN

334



Summary Of Models

334



Optimum MLP configuration

335

LIST OF FIGURES





Figure No.

Title

Page No.



Howard Model of Buyer behavior 1974 version

26



Howard Model 1977 version

27



Engel Black Well Kollat Model

31



Behavioral model –A model of decision making

56



Customer response model

82



Case based reasoning model 1

84



Case based reasoning model2

85



Sales Response Models

88



Rural Economy Statistics

139



Classification Tree

168



Process of credit appraisal and loan disbursal

190



An Integrated model for credit scoring

203



Customer Profile-Age

206



Customer Profile-Educational qualification

208



Customer Profile-No. of dependents

210



Customer Profile-Income

212



Customer Profile-Other Income

214



Customer Profile-Experience

216



Customer Profile-Type of House

218



Customer Profile-Rent

220



Customer Profile-Down payment

222



Customer Profile-TV ownership

224



Customer Profile-Music system ownership

226



Customer Profile –Fridge

228



Customer Profile-Washing machine

230



Customer Profile-Two wheeler

232



Customer Profile-Four wheeler

234



Customer Profile-Amount Overdue

236



Customer Profile-no of Dues

238



Customer Profile-Classification

240



Age-Customer classification

242



Qualification-Customer classification

244



No. of dependents-Customer classification

246



Income-Customer classification

248



Other Income-Customer classification

250



Experience-Customer classification

252



Type of house-Customer classification

254



Rent-Customer classification

256



Down payment-Customer classification

259



TV ownership-Customer classification

262



Music system ownership-Customer

Classification



265



Fridge ownership-Customer classification

268



Washing machine-Customer classification

271



Two wheeler ownership-Customer classification

274



Four wheeler ownership-customer classification

277



MLP NN architecture

288



Over learning in NN

309



Statistical Power

316



Probability of error Vs No of comparisons

321



Expected Error Vs Comparisons

321



Comparison of ROC curves

325



NN topology adopted

333



ROC of the NN Model

333

LIST OF ABBREVIATIONS

ANN - Artificial Neutral Network
AQRE - Agent Based Quantal Response Equilibrium
BF - Basis Function
BP - Back Propogation
CART - Classification and Regression Trees
CEM - Conditional Expectation Maximization
DM - Direct Marketing
FF - Feed Forward
FFT - Fast Fourier Transform
FMCG - Fast Moving Consumer Goods
GA - Genetic Algorithm
GDP - Gross Domestic Product
KNN - K Nearest Neighbour
MARS - Multiple Adaptive Regressing Splines
MCR - Magnetic Character Recognition
MFI - Micro Financial Institution
MFT - Mean Field Theory
MLP - Multi Layer Perceptron
NASA - National Aeronautics and space Administrators.
NFC - Need for Closure
NN - Neural Network
NPA - Non Performing Assets
OCH - Optimum Collective Harvest
OCR - Optical Character Recognition
OLS - Ordinary Least Squares
RBF - Radial Basis Function
ROC - Receiver Operating Curve
ROI - Return on Investment
SE - Sequential Equilibrium
SES - Socio Economic Status
SHG - Self Help Groups
T.E.D.I.C. - Technological, Economic, Demographic Institutional & Cultural Developments

ABSTRACT


Business is Consumer and all successful businesses have always understood their customers. This research is in consumer behaviour and the objective is to get an insight of customer. This work is basically to predict the customers who would be good customers among the hundreds of thousands of borrowers and then who could be targeted for cross selling and up selling to improve the response to campaigns. This work has compared various methods like regression, discriminant analysis, logistic regressions and neural network and compared their prediction accuracy and established that a neural network gives the maximum prediction accuracy. Input to neutral network is factor scores from factor analysis. Also both qualitative and quantitative analysis is done based on customer data. Another important feature of this study is that extensive discussion, interviews and focus groups study have been carried out with both good bad customers and an attempt has been made incorporate to the behavioural aspects of the customer in the model.

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