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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