Maanda Rasuba

5/13/2018 Maanda Rasuba - slidepdf.com

http://slidepdf.com/reader/full/maanda-rasuba 1/173

STATISTICAL RELATIONSHIP OF CUSTOMER BEHAVIOURAL CHARACTERISTICS

IN PERSONAL BANKING

Maanda Rasuba

2009



STATISTICAL RELATIONSHIP OF CUSTOMER BEHAVIOURAL CHARACTERISTICS

IN PERSONAL BANKING

By

Maanda Rasuba

Student number: 202307832

Submitted in fulfilment of the requirements for the degree of Masters in the Faculty of

Science at the Nelson Mandela Metropolitan University

November 2009

Supervisor: Prof IN Litvine

Co-Supervisor: Prof M Struwig



i

ACKNOWLEDGEMENTS

I would like to thank the following people for their contributions with regard to this

dissertation:

My supervisor and co-supervisor, Professor IN Litvine and Professor M Struwig,

respectively for their support, advice and knowledge.

Mr S. Funani, Mr E. Werner and colleagues for their support and advice.

My family and friends for supporting and motivating me.

Above all, I thank God, who has given me the strength and courage to complete this

degree.



ii

EXECUTIVE SUMMARY

This study investigates the relationship of bank customers’ behavioural patterns based on

the customers past transactions, with respect to their profile characteristics. The main aim

of this study is to illustrate that different categories of customers (based on demographical

variables such as race, gender and age) have statistically significant differences in

behaviour, with respect to how they operate their accounts. A theoretical overview on the

literature of customer relationship management in the banking sector emphasises the

importance of understanding customers to ensure that a business is successful.

Four null-hypotheses where formulated based on a general research hypothesis. The

data base provided a major South African bank is used to achieve the objectives.

Extensive cleaning of the data set was necessary to ensure the validity of the results.

The data set had 7860 customer keys. The large data base used contributed to the

reliability of the results.

The following behavioural variables were used in the study namely, transaction data,

average debit and credit transaction amounts and average number of transactions per

month. The main results of study indicate that different customer categories have

statistically significant differences in behaviour, with respect to how customers operate

their accounts. This implies that it is important for the banking sector to consider

customer gender differences, age differences and race group differences in the

relationship strategies which they employ in their multicultural environment. Further

research in the area may be necessary before generalisation can be made on all banking

customers.

Keywords: Customers behaviour, Customer Relationship, Banking Sector in South Africa,

Race groups.



iii

TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS ..................................................................................................... i

EXECUTIVE SUMMMARY ................................................................................................... ii TABLE OF CONTENTS ...................................................................................................... iii LIST OF FIGURES ............................................................................................................. vii LIST OF TABLES ............................................................................................................... xi

CHAPTER 1: INTRODUCTION AND BACKGROUND TO THE STUDY ........................... 1 1.1 Introduction and research background ............................................................. 1 1.2 Problem statement, research questions and objectives and hypotheses ...... 3

1.2.1 Problem statement ..............................................................................................................................3

1.2.2 Research question ..............................................................................................................................4

1.2.3 Objectives ............................................................................................................................................ 4

1.2.4 Hypothesis ........................................................................................................................................... 5

1.3 Research design and methodology .................................................................... 6 1.3.1 Research methodology .......................................................................................................................6

1.3.2 Data collection .....................................................................................................................................7

1.3.3 Data analysis .......................................................................................................................................7

1.4 Summary ............................................................................................................... 8

CHAPTER 2: A THEORETICAL OVERVIEW OF CUSTOMER RELATIONSHIP

MANAGEMENT AND CUSTOMER BEHAVIOUR ............................................................... 9



iv

2.1 Introduction .......................................................................................................... 9 2.2 A definition of customer relationship management and customer churn ...... 9

2.3 Components of customer relationship management ...................................... 12 2.4 Customer relationship management in the banking industry ........................ 15 2.5 Customer relationship management models .................................................. 21 2.6 Components of churn ........................................................................................ 22

2.7 Customer churn in the banking industry ......................................................... 23

2.8 Summary ............................................................................................................. 29 Chapter 3: DATA CLEANING AND ANALYSIS ............................................................. 32 3.1 Introduction ....................................................................................................... 32 3.2 Data cleaning ...................................................................................................... 32 3.3 Data analysis ...................................................................................................... 34 3.4 Summary ............................................................................................................. 36 CHAPTER 4: RESULTS ................................................................................................... 37 4.1 Introduction ........................................................................................................ 37 4.2 Customer Data for all accounts key ................................................................. 38

4.2.1 Descriptive statistics for the average number of transactions and the amounts of transactions per

month. ............................................................................................................................................ 38

4.2.2 Inferential statistics for the average number of transactions and the amounts of transaction per

account key per month ..................................................................................................................... 50



v

4.3 Analysis for Race group 0 ................................................................................. 52 4.3.1 Descriptive statistics for the average number of transaction and transaction amounts for race

group 0. Table 4.5 illustrates the descriptive statistics for race group 0 customers. ....................... 52

4.3.2 Inferential statistics for the average number of transactions and the number of transactions per

account key per month for race group 0. ......................................................................................... 62

4.4 Analysis for Race group 1 ................................................................................. 68 4.4.1 Descriptive statistics for the average number of transaction and transaction amount for race group

1 ....................................................................................................................................................... 68

4.4.2 Inferential statistics for an average number of transactions and the amount of transactions per

account key per month for race group 1 .......................................................................................... 77

4.5 Analysis for Race group 2 ................................................................................. 81 4.5.1 Descriptive statistics for the average number of transactions and transaction amount for race

group 2. ............................................................................................................................................ 81

4.5.2 Inferential statistics for an average number of transactions and the amount of transactions per

account key per month for race group 2. ......................................................................................... 89

4.6 Analysis for Race group 3. ................................................................................ 93 4.6.1 Descriptive statistics for the average number of transaction and transaction amount for race group

3. ...................................................................................................................................................... 93

4.6.2 Inferential statistics for the average number of transactions and the amount of transactions per

account key per month for race group 3. ....................................................................................... 102

4.7 Summary ........................................................................................................... 108 CHAPTER 5: SUMMARY OF THE RESULTS, CONCLUSION, LIMITATION AND FUTURE

STUDY…………………………………………………………………………………………….110 5.1 Introduction ....................................................................................................... 110 5.2 Summary of the results .................................................................................... 110



vi

5.2.1 Summary of data set with the 7838 sample ................................................................................... 110

5.2.2 Summary of Race groups .............................................................................................................. 110

5.3 Conclusion ......................................................................................................... 126 5.4 Implications ...................................................................................................... 129

5.5 Limitations and future research ...................................................................... 130

References ................................................................................................................. 131 ANNEXURES ..........................................................................Error! Bookmark not defined.



vii

LIST OF FIGURES

Pa

Figure 4.1: Histogram plot for the average number of transactions per month per account

key ........................................................................................................................................ 3

Figure 4.2: Scatter plot for the average transactions debit amount per month per customer

key ........................................................................................................................................ 4

Figure 4.3: Scatter plot for the average credit transaction amount per month per customer

key ........................................................................................................................................ 4

Figure 4.4: Plot for the average transaction (debit-credit) amount per month per customer

key ........................................................................................................................................ 4

Figure 4.5: Average debit transactions amounts per account key vs. customer gender ....... 4

Figure 4.6 Average credit transactions amounts per account key vs. customer gender ....... 4

Figure 4.7: Distribution for Customer age groups ................................................................. 4

Figure 4.8: Average transaction credit and debit mean amounts per month vs. customer

age group. ............................................................................................................................. 4

Figure 4.9: Average number of transactions per month vs. debit transaction per month, by

sex ......................................................................................................................................... 4

Figure 4.10: Average number of transactions per month vs. credit transaction per month,

by sex .................................................................................................................................... 4

Figure 4.11: Relationship between the average number of transactions per month and

transaction amounts per month according to customer gender ............................................ 4

Figure 4.12: Histogram plot for the average number of transaction per month per account

key for race group 0 .............................................................................................................. 5



viii

Figure 4.13: Box plot for average debit transaction amount vs. customer gender ................. 5

Figure 4.14: Box plot for average credit transaction amounts vs. customer gender .............. 5

Figure 4.15: Box plot for average number of transaction per month vs. customer gender

group ..................................................................................................................................... 5

Figure 4.16: Customer age distribution for race group 0 ........................................................ 5

Figure 4.17: Average transaction debit and credit amounts per month vs. customer age .... 5

Figure 4.18.1: Scatter plot for average debit transaction amount per month vs. average

number of transaction per month ........................................................................................... 5

Figure 4.18.2: Scatter plot for average credit transaction amount per month vs. average

number of transaction per month ........................................................................................... 6

Figure 4.19: Relationship between the average number of transactions per month and

transactions amounts per month per account key according to customer gender ................ 6

Figure 4.20: Histogram plot for the average number of transactions per month for race

group 1 ................................................................................................................................. 7

Figure 4.21: Average transaction debit amount vs. customer gender race group 1 .............. 7

Figure 4.22: Average transaction credit amount per account key vs. customer gender race

group 1 ................................................................................................................................. 7

Figure 4.23: Average transaction debit and credit amount per month vs. customer age

race group 1 ......................................................................................................................... 7

Figure 4.24: Relationships between the average number of transactions per month and

transactions amounts per month per account key according to customer gender race

group 1 ................................................................................................................................. 7

Figure 4.25: Average number of transactions per month vs. account keys according to

customer gender ................................................................................................................... 7



ix

Figure 4.26 Histogram plot for the average number of transactions per month per account

key for race group 2 .............................................................................................................. 8

Figure 4.27: Average transaction debit amount per account key vs. customer gender race

group 2 ................................................................................................................................. 8

Figure 4.28: Average transaction credit amounts per account key vs. customer gender

race group 2 ......................................................................................................................... 8

Figure 4.29: Average transaction debit and credit amounts per month vs. account key per

customer age for race group 2 .............................................................................................. 8

Figure 4.30: Relationship between the average number of transactions per month and the

transactions amount per month per account key according to customer gender for race

group 2 ................................................................................................................................. 8

Figure 4.31: Average number of transactions per month vs. account key according to

customer gender race group ................................................................................................. 8

Figure 4.32 Histogram plot for the average number of transactions per month for race

group 3 ................................................................................................................................. 9

Figure 4.33: Average transaction debit amount vs. customer gender for race group

3…………………………………………………………………………………...... ........................ 9

Figure 4.34: Box plot for average transaction credit amount vs. customer gender for race

group3………………………………………………………………........................... ................... 9

Figure 4.35: Average number of transactions per month vs. account key according to

customer gender .................................................................................................................... 9

Figure 4.36: Customer age distribution for race group 3 ....................................................... 9

Figure 4.37: Average transaction debit and credit amount per month vs. customer age per

customer gender for the race group 3 ................................................................................... 9



x

Figure 4.38: Scatter plot for average debit transactions amount vs. average number of

transaction per month ............................................................................................................ 1

Figure 4.39: Scatter plot for average credit transactions amount vs. average number of

transaction per month ............................................................................................................ 1

Figure 4.40: Relationship between the average number of transactions per month and the

transactions amounts per month per account key according to the customer gender race

group 3. ................................................................................................................................. 1



xi

LIST OF TABLES

Page

Table 2.1: The Customer Relationship Model ................................................................... 2

Table 2.2: Summary of the various studies on churn behaviour ........................................... 2

Table 3.1: Criteria for Cohen’s and r2 interpretation ............................................................ 3

Table 4.1: Descriptive statistics for the average number of transactions and the average

transaction amount (Debit and Credit) per month for all accounts key ................................. 3

Table 4.2: Descriptive statistics of the transactions amounts and the number of

transactions per month according to customer gender ......................................................... 4

Table 4.3: t test statistics for gender differences, assuming unequal variances ................. 5

Table 4.4: Pair wise Correlations for transaction variables and account age......................... 5

Table 4.5 Descriptive statistics for average number of transactions and average

transaction amounts (Debit and Credit) per month for race group 0. ................................. 5

Table 4.6: Descriptive statistics for the transactions amount according to gender per

month for race group 0. ...................................................................................................... 5

Table 4.7: t-test statistics of male and female differences for race group 0. ........................ .6

Table 4.8: Correlation coefficient between customers’ behavioural patterns for race group

0 ............................................................................................................................................ 6

Table 4.9: Statistical report for R-Square statistics ............................................................... 6

Table 4.10: Statistical report for the analysis of variance (Models tests using F) ................. 6



xii

Table 4.11: Statistical report for regression significant estimates race group 0 .................... 6

Table 4.12: Descriptive statistics for the average number of transactions and the average

transaction amount (Debit and Credit) per month for race group 1 .......................................

Table 4.13 Descriptive statistics of the transaction amount according to customer sex per

month of race group 1 .......................................................................................................... 7

Table 4.14: t-test statistics of male and female differences for race group 1 ........................ 7

Table 4.15: Correlation between customers’ behavioural patterns for race group 1 ............. 7

Table 4.16: Statistical report for R-Square statistics ............................................................. 7

Table 4.17: Statistical report for analysis of variance (Models tests using F) ....................... 7

Table 4.18: Statistical report for regression significant estimates race group 1 .................... 8

Table 4.19: Descriptive statistics for average number of transaction and average

transaction amount (Debit and Credit) per month for race group 2 ....................................... 8

Table 4.20: Descriptive statistics of the transaction amount according to customer sex

per month of race group 2 .................................................................................................... 8

Table 4.21: t-test statistics of male and female differences for race group 2 ....................... 8

Table 4.22: The correlation between customers’ behavioural patterns for race group 2 ....... 9



Table 4.25: Statistical report for regression significant for estimates race group 2 ............... 9

Table 4.26 Descriptive statistics for the average number of transactions and the average

transaction amounts (Debit and Credit) per month for race group 3 ..................................... 9



xiii

Table 4.27 Descriptive statistics of the transaction amount according to customer sex per

month of race group 3 .......................................................................................................... 9

Table 4.28: t-test statistics of male and female differences for race group 3 ....................... 1

Table 4.29: Correlation between customers’ behavioural patterns for race group 3 ............. 1



Table 4.32: Statistical report for regression significant estimates for race group 3 ............... 1

Table 4.33: Summary of the results for the research hypothesis ........................................... 1

Table 5.1: Race group average transaction mean and average number of transaction

means ................................................................................................................................... 1

Table 5.2: Summary of t-test for the race groups and age groups behavioural patterns ..... .1

Table 5.3: Correlation coefficient of variables for all race groups (* significant correlation

between two variables) ......................................................................................................... .1

Table 5.4 Regression Summary of customers’ race groups ................................................. .1

Table 5.5 t-test statistics for the difference between customer genders behavioural

characteristics ....................................................................................................................... 1

Table 5.6: Practical significance for the statistical significant differences between males

and females .......................................................................................................................... .1



1

CHAPTER 1: INTRODUCTION AND BACKGROUND TO THE STUDY

1.1 Introduction and research background

The subject of customer loyalty, customer retention and customer churn is

receiving attention in many industries. The banking and most probably

telecommunications industry has been faced with the challenge of customer churn

as well as an introduction of appropriate retention models. There has been an

increase in competition and the imaging of new businesses, which offer similar

products which may traditionally be considered to be bank products; therefore

these have put the banking businesses under pressure. The main challenge is

how can a bank keep good relationships, retain its existing customers as well as

attract potential customers.

The banking business is characterized by many customers with different

characteristics and financial demands. This is indicates that the mass marketing

approach is not likely to succeed with the diversity of consumer business. The

banking business is likely to loose customers, and consequently profitability, if they

embarked on a mass marketing approach instead of a customer centric approach.

A customer centric approach may be advantageous in a developing country such

as South Africa because of its diverse population with different cultural

backgrounds. The business is faced with the challenge of maintaining and

satisfying these customers with different backgrounds, interests and approaches

towards their own personal finances.

The introduction of massive technology into the banking business, has helped the

business to keep track of the customer’s crucial information and behaviour

patterns. Businesses have massive amounts of data which may be useful to

support crucial business decisions (Wah, 2006). For example, analyzing customer

transactions data, may lead to an improvement in production and promotions to the

right segment of customers in terms of age, race group in a multicultural

environment, gender or income group.



2

Authors such as Storbacka (1994), Trubik and Smith (2000), Winer (2001),

Garland (2002), Van den Poel and Lariviere (2003), Mutanen (2006), and Mavri

and Loannou (2008), have paved a way in the area of customer relationship

management, customer churn and customer retention models. Retaining

customers becomes one of the most serious challenges facing customer service

providers (Au, Li & Ma, 2003). Colgate, Stewart and Kinsella (1996) and Storbacka

(1994) recognised that a reduction in defection may contribute to increases in

profits which are far more than the increases in market share. Colgate et al. (1996)

also found that the defection of university students which was reduced of from 15

percent to 17.8 percent was shown to increase profits by 105 percent.

With the aim of understanding the drive behind customer churn rather than the

percentage of churners, Trubik and Smith (2000) in their study, identify four

variables which help identify customers who are leaving a bank in the Australian

banking industry.

The four attributes are:

• Customers which had one product,

• The major channels were bank branches,

• Had no exemption fee,

• And were on their third month with the bank.

Mutanen (2006) also identify factors that contribute to customer churning

behaviour. The author describes customer age, account age, and income amount

as other factors which may help to identify customer churning behaviour.

The focus on customer churn has been placed on the influential behavioural

factors rather than the churning percentage of the customer. Businesses who

understand customers and their behavioural patterns stand a chance of sustaining

a good relationship with their customers. The main focus of this study is to identify

all variables which may be useful in the prediction of customer churn behaviour



3

and relationships between those variables. The literature on customer

relationship and customer churn behaviour will be presented in chapter two.

1.2 Problem statement, research questions and objectives and hypotheses

1.2.1 Problem statement

This study addresses the importance of understanding the customers’ relationship

management as well as the customers’ behavioural patterns in the retail banking

industry. Given all the important information as well as the more important areas

which are crucial to the growth of the business, the problem statement is as follows:

This study attempts to identify a statistical relationship between the customer

behavioural variables in personal banking, whilst using past transactions

information as well as the customers profile and biographical information, which

may be important to the study.

More formally, the main interest is to establish a statistical base relationship

amongst behavioural variables and customer profile variables. Furthermore, one

may identify similarities and differences between different categories of customers

with respect to how they operate their accounts.



4

1.2.2 Research questions

Through a number of intensive consultations with experts as well as reviews

of a number of previous studies, the following research questions emerged:

a) What is customer relationship management in the banking industry?

b) What are the behavioural relationships amongst different categories of bankcustomers?

c) Are there any differences in terms of behavioural patterns amongst different racegroups?

d) Are there any statistically significant differences between male and femalebehavioural patterns?

e) What is the difference among age groups behaviour with respect to how thesecustomers operate their accounts?

f) Are there any statistical relationships between or amongst the customer’sbehavioural patterns and customer profile characteristics?

g) If the answer was yes to the previous question then, how do these variables differas compared to other variables used in previous studies?

1.2.3 Objectives

The primary objective of this study is to investigate the relationship of customers’

behavioural patterns based on customers past transactions data, with respect to

their profile characteristic. The researcher should prove that different categories of

customers have statistically significant differences in behaviour, with respect to

how these customers operate their accounts. For example; to identify that male

and female customers have different transactions patterns. Furthermore, the

researcher may identify variables which may be useful to the customer churningbehavioural analysis.



5

1.2.4 Hypothesis

The following hypothesis has been formulated based on information provided by

the expert in this area as well as from reviews of previous studies.

The research hypothesis involves identifying the relationships of customers’

behavioural patterns with respect to how customers operate their accounts, based

on their transaction history and profile characteristics, such as customer age,

gender and race. The research hypotheses (H1) states that, different customer

categories have statistically significant differences in behaviour, with respect to the

manner in which customers operate their accounts. For example, male and female

customers have significant statistical differences in behaviour patterns.

The general null hypothesis (H0) can be formulated as follows,

Different customer categories have no statistically significant differences in

behaviour with respect to how customers operate their accounts. An example

would be where males and females have no significant statistical differences in

behaviour patterns. The following are example of null hypothesis that can be

formulated from the general hypothesis:

H 01: Males and females have no significant differences in behavioural patterns,

H 02 : No relationship exists between behavioural variables and customer

characteristics,

H 03 : Race groups have the same behavioural patterns,

H 04 : Different age groups have no significant differences in behavioural patterns.



6

1.3 Research design and methodology

The following section will address the following: the research methodology used,

the method which has been used to analyse the data in hand, as well as the data

collection procedure, which was used to correlate the data.

1.3.1 Research methodology

After the research area was identified, the author approached a major bank in

South Africa, for assistance with input and data set for the study. After some

discussion with the bank management, it agreed to provide a data set for the study.

The data provided consisted of the banks customers personal accountsinformation. The data at hand was collected for the time period of January 2003

until September 2008. The data provided consists of three sets of samples namely,

personal profile information (Biographic), accounts information and past

transactions information.

The final data used was a set which consisted of 7838 customer samples. The

behavioural variables calculated from the data include:

• The average number of transactions,

• The transaction debit and credit amounts per month and ,

• The account age from the customer profile information.

The sample data was first analysed to identify the behavioural patterns of all the

customers. The second part involved dividing the data into two sample sets,

namely, an active account and a non active account. Active customer accounts are

those which had not been closed within the period January 2003 until September

2008. Four race groups were considered for the analysis from the active data and

each was given a code as 0, 1, 2 and 3. The code 4 was not used as it only has



7

three customer accounts and no race groups were attached to the code 4. The

records which were identified by the ‘code 4’ were excluded from the study.

1.3.2 Data collection

The data used in this study was provided by a major retail bank in South Africa.

For the purpose of anonymity, this bank will be referred to as Bank R. The real

customer data of a selected group of customers was provided and used for the

analysis. The data was collected from January 2003 to September 2008. The data

collected, was provided in three different sets of files:

• Behavioural data, also known as transaction data, with 3880514 million

sample of transactions,

• Account information data with 7860 sample accounts data, and

• Customer profile information data with 13715 samples of customer data

This study focuses only on these types of customers and the final results will make

no generalisation about other bank customers.

1.3.3 Data analysis

It is indicated in section 1.3.2 that the data is provided in three different sets of

samples. To ensure that the data was ready for the analysis, the following steps

were taken:

• The transaction data was first sorted in ascending order according to account

key with the transaction date as well as the transaction amount per date.



8

• The sorted transaction data was linked to account information data using an

account key. Any account keys which did not match were discarded.

• The transaction and account customer data with only matched account keys

were then linked to the customer profile data using the customer key. Only data

that matched the customer key was considered for the next steps.

• The final data had 7860 customer keys. But due to a small number of missing

values in the transaction information of other accounts, as well as outliers, the

final data used was covered 7838 customers.

• From this data, the following were derived: the behavioural variables, the

average number of transactions, debit and credit transaction amounts and the

account age.

• Data was then divided into two sets of samples, namely, active and closed

accounts data. Only active data was then used for race group analyses.

The data analysis was performed using Excel, MATHEMATICA and JMP 8.0

program. The regression models and correlation models were used to investigate

the relationship between customer characteristics and behavioural variables.

1.4 Summary

This chapter outlines the research background, research problem, objectives of the

study and research methodology. The chapter illustrates the main goal of the study

and the procedure to be followed to answer research questions. To investigate the

research topic or questions, previous research studies as well as a literature review

is necessary to support the research study. In chapter two a theoretical overview ofcustomer relationship management and customer behaviour will be provided.



9

CHAPTER 2: A THEORETICAL OVERVIEW OF CUSTOMER RELATIONSHIP

MANAGEMENT AND CUSTOMER BEHAVIOUR

2.1 Introduction

The main objective of this study is to investigate the relationship between

customer profile characteristics and behavioural patterns. The main interest is

to identify those customer profile characteristics and behavioural variables,

which describe and define customer behavioural patterns.

Customer Relationship Management (CRM) remains a constant problem as

businesses continue to miss the mark when creating and cultivating long-term

customer relationships (Bailor, 2007). The financial and telecommunication

industries are two of the better known sectors which struggle with creating and

nurturing customer relationships. The low switching costs and ease of

movement between competitors in the financial services sector, as well as the

share amount of transactions and the volume with which the company deals

with customer’s, leave the companies open to a lot of dissatisfaction and churn

behaviour (Bailor, 2007). The impact of churn has changed the way financial

institutions are perceived by their customers. Most banks focus on customer

relationship management strategies and more frequently on churn reduction.

In this chapter, customer relationship management (CRM) and customer churn

will be defined first. Thereafter the components of CRM and churn will be

outlined.

2.2 A definition of customer relationship management and customer churn

Customer relationship management is the process of collecting and analysing

business information regarding customer interactions in order to enhance the

customer’s value to the business. By integrating various data, such as

operations or service logs, researchers can obtain a more complete view of

customer behaviour (Kamakura, Mela, Ansari, Bodapati, Fader, Iyenger, Naik,



10

Neslin, Sun, Verhoef, Wedel & Wilcox, 2005). Customer churn refers to the

tendency of a customer to cease services with a business. Current customers

are a businesses greatest potential source of sales and profits. For many

businesses, 80% of sales come from 20% of their existing clientele. Yet

businesses spend about five times more money on campaigns to attract new

customers than in developing and executing strategies to retain their current

customers (Furlong, 1993). Being able to identify “at risk customers” could

assist banks to avoid the expenses associated with losing their existing

customers and ensure that they are able to gather and establish new ones

(Trubik & Smith, 2000). These definitions emphasise the importance of

understanding the components of the customer relationship in customer churn

studies.

Some studies (Geppert, 2002), highlight some causes of churn behaviour.

These behaviours are, namely, price, service quality, fraud, lack of business

responsiveness, brand loyalty, privacy concern, and new technology or a

product introduced by a competitor. The business should maintain its brand

standard, evaluate its price and assure customer privacy regarding their

information.

The need to have a better understanding of customer behaviour as well as the

competition within the market has changed how marketers view the world. The

advances in information technology have forced marketers to focus on

managing customer relationships, focusing specifically on customers who may

deliver long term portfolio relationships within the business. The tracking of

these customer’s behaviours as well as the interaction between customers and

businesses is made possible by the existence of customer relationship

management strategies.

CRM is the process of collecting and analysing a firm’s information regarding

the customer’s value to the business (Kamakura et al ., 2005). CRM is the

outcome of the continuing evolution and integration of marketing ideas as well

as of data which has only recently been made available data, technologies, and



11

organizations (Boulding, Staelin, Ehret & Johnston, 2005). The emphasis is not

on how one sells the product, but rather on how one creates value for the

customer, and in the process, creates value for the business. Benaroch (2005)

defines Customer Relationship Management as a collective term for business

strategies, financial processes and software technologies relating to an

individualised relationship between an enterprise, customer prospects, and

business partners. These strategies are deployed with the goal of winning new

customers, extending existing customer relationships across the entire

customer lifecycle, as well as improving competitiveness and business success

by optimising the long term profitability of the individual customer relationship.

Bohling, Kumar and Ramani (2004) define CRM as the process of achieving

and maintaining an ongoing long term relationship with the customer, and

identifying the overall financial contribution of a customer to the business. In the

marketing literature, Hair, Lamb and McDaniel (2006) refer to CRM as a

company–wide business strategy designed to optimise profitability, revenue,

and customer satisfaction by focussing on a highly defined and precise

customer group.

These definitions reflect a dramatic shift in the way in which businesses should

view their customers, and understand the value of a strong and lastingrelationship with their customers. To initiate CRM, businesses should have a

relationship with their customers, understanding who the customers are, where

they are located and what type of product or service individual customers

prefer. This will help marketers to design a one-to-one marketing strategy

between an individual customer and the business, as customers differ in terms

of needs and personality. There is an increase in one-to-one customer

relationships where the “averaging” of customers is considered as an ineffective

marketing strategy since customers have different financial needs. There has

therefore been an increase in attention being focused on understanding each

customer and what an individual customer can deliver to the business in terms

of profits (Winer, 2001).



12

Churn is the tendency of customers to defect from or cease business with a

company (Kamakura et al., 2005). Furlong (1993) defines customer churn

(attrition) as the number of customers who leave during a year divided by the

number of new customers. For example, if a company loses half as many

customers as it gains each year, its churn is then 50 percent.

Burez and Van den Poel (2008) divide churn into two different categories,

namely, financial and commercial churn. Financial churn is defined as a

customer who stops paying because they can no longer afford the service.

Whereas commercial churn, are customers who made the choice not to renew

the subscription or service. Examples of this can be found in the Pay TV

channel, contract cell phone subscription, and some banking investments

where customers have to pay in a specific time frame on that investment or

service. The issue of CRM and customer retention as part of CRM, is important

in most businesses which depend on customers for their revenue.

2.3 Components of customer relationship management

With the CRM being criticised for not meeting its objectives, studies by Ryals

(2005); Srinivasan and Moorman (2005); Mithas, Krishnan and Formell (2005); and

Jayachandran, Subhash, Kaufman and Raman (2005) indicate that customer

relationship management is one of the most powerful tools in marketing and has

been successful in many industries. The customer relationship management

strategy has the following components (Winer, 2001).

These components are:

(1) A data base of customers,

(2) Analyses of the data base,

(3) Given the analysis, management will make decisions about which

customers to target,



13

(4) Tools for targeting the customers,

(5) How to build a relationship with the targeted customers,

(6) Privacy issues,

(7) Metrics for measuring the success of the CRM program.

The customer relationship is often described as a closed-loop system which

builds relationships with customers (Hair et al., 2006). The first step to a better

CRM, is to build a customer data base. The data base should contain important

information about the customers.

This includes information such as:

(a) Customer transaction: a complete purchase history with accompanying

details,

(b) Customer contacts,

(c) Descriptive information: this helps the company for segmentation

purposes,

(d) Response to marketing stimuli: this is an indicator of whether the

customer responded to any marketing initiatives, and

(e) The data should also be compiled over time (Winer, 2001).

Data analysis helps marketers to understand their past and present behaviourand forecast their future behaviour by using the appropriate statistical methods.

Given these analyses, management will know which customers to target and

how they can strengthen the relationship with these customers. The overall goal

of a relationship program is to deliver a higher level of customer satisfaction

than competing firms will deliver (Winer, 2001). A comprehensive set of



14

relationship programs includes customer service, loyalty programs,

customization, rewards programs and community building.

The concept of the customer relationship has two different categories of CRM.

These categories are analytical and behavioural (Kamakura et al ., 2005).

Analytical customer relationships involve using business’ data on a customer to

design appropriate models of choice over the breadth of the products and using

them prescriptively to increase the revenues from customers over the cycle of

their lifetime. A behavioural customer relationship uses experiments and

surveys to focus upon the psychological underpinnings of the service

interaction. An example of analytical customer relationship management can be

applied to the telephone, mobile, pay TV, insurance and banking businesses.

These businesses have large amounts of data from which the business can

retrieve information and behavioural patterns of existing customers.

The CRM can be organised through the customer life cycle, which is the

acquisition of new customers, development and retention strategies. The early

detection and prevention of customer attrition or churn can also enhance the life

time value of a customer base, if efforts are focused on the retention of valuable

customers (Hair et al ., 2006). The objective of any acquisition is to obtain more

profitable customers. Customer development pertains to the growth of revenue

from existing customers. Customer development can be achieved through

cross-selling. Banks for example, can achieve this by offering multiple services;

they can make it more difficult for customers to switch to a competing bank.

Customer retention as part of CRM has a significant impact on business

profitability. The reduction in defection (churn behaviour) of customers, can

contribute to an increase in profits far more than an increase in market share

(Colgate et al ., 1996).

Hair et al . (2006) describe the CRM cycle stages as follows:

• Identifying the customer relationship within the organisation,



15

• Understanding the interactions with current customers by collecting data

on customers, purchase history and other information,

• Capturing relevant customer data on interaction,

• Identifying profitable and unprofitable customers.

Building a “right” relationship with existing customers is always critical

(Reinartz, Krafft & Hoyer, 2004). A goal of CRM is to manage the various

stages of the relationship systematically and proactively. The continuous

balance of CRM activities at each stage, otherwise known as customer

acquisition, retention and relationship termination, should be guided by the

attempt to maximize the value of the group of concurrent customer relationships

and should thus be associated with overall performance of the company

(Reinartz et al ., 2004).

2.4 Customer relationship management in the banking industry

The original focus of CRM was to forge closer and deeper relationships with the

customer, being willing and able to change the behaviour towards an individualcustomer based on what the customer tells the service provider and anything

else which the company knows about the customer. The focus is on the fact

that existing customers are more profitable than new customers. It is less

expensive to sell products to existing customers than it is to attract new

customers. Thus the central objective of CRM is to maximize the life time value

of a customer to the organization.

The customer relationship has three types of approaches in the Financial

Services Industry (Geib, Reichold, Kolbe & Brenner, 2005):

• Customer satisfaction management,

• Customer contact management and,



16

• Customer profitability management.

These three approaches have been seen as the backbone to the success of

CRM in the banking, insurance as well as investment industries. Geib et al .

(2005) give an illustration of each approach.

CRM as customer satisfaction management aims at high customer satisfaction

by offering the customer a high quality service and proximity. Detailed

knowledge about the customer is not important; however it is also important

because customer satisfaction management does not distinguish between

individual customers. The HB bank in Norway, Finland and Denmark has

moved its positioning forward, in terms of customer satisfaction. In 1999, the

bank proved to be the best bank in Sweden in terms of service and satisfiedcustomers. The bank became number one in an official survey which

measures satisfied customers in various business sectors (Zineldin, 2005). This

is an indication that banks should consider customer satisfaction management

in their customer relationship approach.

CRM, as customer contact management, aims at reducing costs by improving

process efficiency and using media-based communication channels. Moreover,

customer contact management aims to provide customers with a consistent

interface across all communication channels. As customers have more choices

and targeted customers are more valuable to the business, customer service

must receive a high priority (Winer, 2001). It is important that the bank invests

money into strategies which provides information on product availability, and a

variety of other service-related topics.

CRM, as customer profitability management, tries to develop a long-lasting

profitability relationship with customers. This is possible by increasing customer

loyalty and exploiting the potential customer base. In customer profitability

management, the business should identify the nature of all profitable customers

and try to develop strategies to encourage unprofitable customers to become

profitable. This can be done by creating a long lasting relationship with



17

profitable customers. A bank may develop a new strategy to make an

unprofitable customer become more profitable.

“Going only after the customers it assumes are the most profitable, the bank

could miss opportunity. We found that some of our low-balance checking

account customers are very profitable because of the fees the bank obtains

from them” (Motley, 2005:43).

The financial industry has embarked on a CRM process and there has been an

increase in the use of customer relationship management, especially in the

European market. One example of the use of CRM is in the Swedish banking

industry (Zineldin, 2005). The purpose of the study was to theoretically and

empirically develop a better understanding of quality and CRM’s impact onbanking competitiveness. Zineldin (2005) found that the bank has to create a

customer relationship which delivers value beyond that provided by the core

product. This involves adding tangible and intangible elements to the core

products, therefore creating and enhancing the “product surroundings” (Zineldin,

2005). One condition necessary for the realisation of quality and the creation of

added value is quality measurement and control. This is an important function

to ensure the fulfilment of given customer requirements. The key ways to build

a strong competitive position are through CRM, product or service quality and

differentiation (Zineldin, 2005).

Even though they know that customers are different, many financial institutions

still treat them in the same way. There is a need to understand the value of

existing customers, potential long term value and the potential a customer may

bring to a financial institution (Peppard, 2000). Failure to identify each

customer’s needs and failure to understand that all customers cannot be

treated in the same way, leads to a rather costly investment.

Banks must move from traditional methods of choosing products for their

customers. Rather, customers should decide how they want to transact

business and their preferred channel. This can only be possible if the bank



18

introduces as many channels as is possible. CRM is also about analyzing

customer information for business decisions: the aim being to help an

organization understand customer needs; differentiate between customers via

market segmentation; predict the likelihood of customer churn; perform an

analysis of customer loyalty, customer profitability, channel effectiveness and

profitability as well as sale campaign performance (Peppard, 2000).

Customer relationship marketing has been successful in some financial or

banking industries. European banks like Merita in Finland, has 600’000 internet

banking customers, 500’000 of which actively use the service on a monthly

basis, representing 42 percent of the retail customer base in Finland. In the

bank, 1.5 million payments per month take place via the internet.

“The challenge for an organization is to move to a situation where the customer

starts buying from you rather than being sold to” (Peppard, 2000: 322).

The move of marketing into the one to one environment necessitates the need

for having access to large amounts of information about a customer. Without

this vast amount of data on the customer purchase trend, the one-to-one

strategy is not possible. Bank institutions understand the importance of the

profitability of their customers and they need to focus their resources on

acquiring information about these customers as it is crucial to the delivery of a

successful marketing strategy.

The data mining strategy is the key to a better understanding of a relationship

between the bank and a customer in the customer relationship context. Data

mining is defined as a sophisticated data search capability which uses

statistical algorithm to discover patterns and correlation (Rygielski, Yen &

Wang, 2002). This method finds and extracts knowledge buried in corporate

data warehouses. It discovers customer patterns and relationships hidden in

the data and is actually part of the process called “knowledge discovery” which

describes the steps which must be taken to ensure meaningful results. Data

mining does not identify the patterns. Data mining helps business analysts to



19

discover information about the business and also helps them to design

hypotheses and validate results.

The introduction of advanced algorithms, multiple processor computers, and

massive databases helps a company to engage in a prospective, proactive

information delivery. Information systems can store past data up to and

including the current level of business (Rygielski et al , 2002). Companies in the

financial and telecommunication industries establish new price structures and

services to encourage customers to make more deposits or take loans and

place more calls. This task requires an understanding of the past customer

service usage behaviour data in order to identify patterns for making these

strategic decisions. Data mining is particularly suited to this purpose. For a

financial industry, data mining is a tool which can help to address problems

related to how the industry may improve their services. They may ask questions

such as, “what is likely to happen to Bank A unit sales next month, and why?”

Then data mining should be defined according to the definition of the business

and its interest. For example if the bank wants to learn about a certain segment

group or account type, which has been performed on a savings account for

high-income customers aged between 20 and 40 years living in Sandton, South

Africa, the analysis should be restricted to those customers and theircharacteristics.

It is important that banks understand the relationship stages in their customers’

“life cycle” as it relates directly to customer profitability and customer revenue.

There are three ways in which to increase customer value. These methods are:

(i) Increase in the purchase of the product they have,

(ii) Sell them more products and to sell more higher-margin products; and

(iii) Try to keep customers for long time periods (relationship marketing and

retention its vital here).

The customer life cycle has four key stages:



20

(i) Prospects people who are not yet customers but are in the target market,

(ii) Responder-prospects who show an interest in a product or service,

(iii) Active customers who are currently using the product or service,

(iv) Former customers who have all had a negative relationship with the

service provider. This could be due to bad payment or shifting their service to

competitors.

In a situation where customers have left, the bank should look at how it can win

back those customers. Anderson (1996) suggests five steps which can be used

for successful happy returns of all customers who have strayed.

(i) The bank should develop a “most wanted” list of customers,

(ii) Find out why these customers left,

(iii) Ask for another chance of service offering,

(iv) Come up with a piece offering and,

(v) Practice routine maintenance.

The competition within the financial industry in South Africa has grown, with

other institutions offering some products which were traditionally for banks. With

the increase in the use of technology and the understanding of services within

such a multicultural environment, banks are faced with a big challenge of better

and attractive service delivery. One study conducted in South Africa, within the

Nelson Mandela Bay area, investigated the influence of customer relationship

management on the service quality of banks (Rootman, 2006). The study

reveals the significant positive relationship between both the knowledgeability

and attitudes of bank employees as well as bank CRM. Furthermore, bank

managers and employees should be aware of the fact that a bank’s interaction

with the clients influences the institution’s CRM and level of service quality .



21

Specifically, the knowledgeability of bank employees with regard to banking

products, services, policies and/or procedures as well as the attitude of bank

employees in each banking branch should be positively adapted, in order to

ensure high levels of CRM and service quality .

Banks should implement strategies, specifically on the understanding of

business attitudes of bank employees, in ways which would positively influence

their CRM and ultimately their service quality (Rootman, 2006). The current

study indicates the importance of implementing CRM and services quality within

the banking sector within South Africa as well as the importance which

employees play in the success of CRM.

2.5 Customer relationship management models

The customer relationship model as mentioned in previous paragraphs is an

important tool for a CRM strategy’s success. This model provides ideas as to

how the customer relationship should be implemented. Table 2.1 shows the

seven steps of the customer relationship model.

Table 2.1: The Customer Relationship Model

1. Create a data base

2. Analysis

3. Customer selection

4. Customer targeting relationship marketing

5. Building relationship with customer

6. Privacy issues

7. Metrics



22

Source: Winer, (2001)

From table 2.1, one may observe that businesses should collect important

information about their customers. An enterprise data warehouse is a critical

component of a successful CRM strategy (Rygielski et al ., 2002). The business

must perform an analysis on the information which they have, using statistical

tools and a data mining strategy. In this situation, marketing professionals need

to understand the customer data and business imperatives. From the analysis,

marketers should segment customers according to the relevant information

obtained from the data. The business will have to target sets of these

customers and build a relationship with them. In the process of building the

relationship with selected customers, the business should understand the

importance of the privacy of customer information. One way to achieve this goal

is to create anonymous architecture for handling customer information

(Rygielski et al ., 2002). The business should also evaluate the success of their

CRM strategy. In this situation, a business should be able to establish whether

or not the CRM strategy met their objectives.

2.6 Components of churn

Churn can be broken down into involuntary churn, where the business cuts the

service of a customer, often due to repeated non-payments, and voluntary

churn, where the customer chooses to disconnect the service. This is often due

to unsatisfactory service or a better service offer from a competitor.

Furthermore, churn can be separated into two categories, financial and

commercial churn (Burez & Van den Poel, 2008). The two types of churn areoften found in the contractual context, where a customer has a fixed term

contract or investment.



23

2.7 Customer churn in the banking industry

ABA reports that the average U.S. Bank has an attrition rate of 12-15 percent

and some suffer attrition rates as high as 20-25 percent (Lunt, 1993).

Relationship bank marketing is the starting point for growth and is based on the

fact that it is much easier and more profitable to keep existing customers whilst

cross-selling them into additional products and services, than it is to find and

sell to new customers.

In the retail banking environment, where more sophisticated consumers with

less banking loyalty is becoming the norm, customer service quality is an

essential competitive strategy (Mavri & Loannou, 2008). The quality of services

and products offered by the bank, in combination with the brand name has apositive effect in decreasing churn (Mavri & Loannou, 2008). Banks need to

develop a CRM strategy in which the intention of clustering clients across their

personal characteristics and exclusive attributes, will contribute to a decrease in

the rate of retention (Mavri & Loannou, 2008). Banks should also implement

strategies to specifically increase the knowledge of businesses and the

attitudes of bank employees, so that their attitudes can positively influence their

CRM and ultimately their service quality (Rootman, 2006). These strategies are

an important part of reducing customer churn behaviour and encouraging

loyalty to the bank. A loyal customer to a bank is referred to as a customer who

will stay with the same service provider, is likely to take out new products within

the bank and is likely to recommend the bank’s service. Thus, commercial

banks have embarked on different management strategies as a way of

promoting customer loyalty (Jamal & Naser, 2002). Bitner (1990) and Cronin

and Taylor (1992) emphasise the effects of time, money constraints, access to

information, lack of credible alternative, switching costs, convenience, price,

and availability as major attributes which may enhance customer satisfaction

and switching behaviour. Other issues of gaining customer loyalty in the

banking system include confidentiality in transactions, the banks

trustworthiness, the introduction of weekend banking, the extension of banking

hours and the provision of insurance (Ehigie, 2006).



24

The issue of the customer relationship and switching behaviour in the banking

industry is one of the problems which many banking businesses face on a daily

basis. The challenge is: ‘how does one keep the customer happy and ensure

that customers continue to do business with the bank?’ A study was conducted

on the effect of gender on customer loyalty, in Malaysian banks (Ndubisi, 2005).

The key finding of this study is that female customers are significantly more

loyal than males when the bank is very trustworthy. Bick, Brown and Abratt

(2004) on the other hand indicate that customers are not satisfied with the

services, products and levels of customer intimacy delivered to them by their

banks. Thus, they did not believe that they were getting the value they

expected. Therefore, it is essential for retail banks to achieve operational

excellence as a matter of urgency and to become more market or customer

focused and engage with the customers to seek their input. Some studies

estimate that between 65 and 85 percent of customers which defect said that

they were satisfied (Reicheld, 1993).

Trubik and Smith (2000: 27) conducting research at Australian banks, found

that if customers had one product at the bank, they were more likely to leave.

Of all customers who left, 80% had only one product. This is a challenge to the

bank, as they had to implement a strategy to encourage customers to takemore than one product with the bank. In terms of fee exemption of any kind, a

customer who has no fee exemptions is likely to leave. Of all customers who

left, only 7% of the customers who received the fee exemptions left the bank.

This is not expected as customers with fee exemption will not be influenced by

fees and charges of banks which will increase from time to time. The majority of

customers who make their transactions personally at the bank branch, are more

likely to leave. This is quite an interesting finding considering that most banks

have already introduced IT based transaction methods. More focus which has

been placed on the customers who prefer branch banking, should therefore

also be considered.

Danenberg and Sharp (1996), using data from a regional bank in South

Australia, reported an intended defection (churn) of 9.6 percent whilst using the



25

Juster scale, but after three months the actual defection (churn) was 2.6 per

cent. Stewart’s meta-analysis of defection (churn) rates from British and

European banks range from 4 to 18 percent. Trubik and Smith (2000) have

identified a strong, direct relationship between customer loyalty and customer

profitability in personal retail banking. A couple of the authors suggest that

retaining existing customers and trying to encourage them to become more

profitable customers, seems to be an appropriate customer service strategy

(Colgate et al .,1996). It was also suggested that banks should work on long

term customer management schemes where youthful customers who at an

earlier stage of their relationship with the bank are considered as unprofitable,

but become profitable customers as they move on through the family lifecycle.

Most banks have long seen the university student market as one of particular

interest. There are several reasons why this is so. The most significant reason

is that many university students after graduation will obtain employment in

relatively secure and high paying jobs (Colgate et al., 1996). A study of financial

service accounts held by university students was conducted in Ireland (Colgate

et al., 1996). The paper identifies the effects of churn rate on the profitability of

students’ accounts in the bank. It was found that reducing the defection (churn)

rate of the university student can increase profits. For example, if defection(churn) rate was reduced from 15.0 to 17.8 per cent, it can increase profit by

105 percent.

Garland (2002) found that in Australian banks, intention of defect (churn) was at

10 percent during the next 12 months, using Juster scale. Those who are from

rich families and are associated with the bank for less than eight years show

above average predisposition of 12 to 16 percent. The old age group of 65

years of age are below average with a 7 percent defect rate.

Van den Poel and Lariviere (2003) investigated customer attrition (Churn) in the

European market and focussed on reasons of failure which the business can

control. The authors used the data which included the past purchase behaviour

of customers, as well as the quantity of particular banking products and the



26

customers characteristics. The random sample data was 47,157 customers with

others experiencing a churn over a period of 77 years. The results indicate that

the longer the customer stays with the bank, the smaller the probability of

staying with the bank according to the survival distribution function. In general,

individuals experience higher attrition rates in the first few years of being a

customer. In this case, after seven years the chances of staying with the

service provider stabilises for a period of 15 years. After twenty years the

chances of staying with the bank decrease at a higher rate and continue to

decrease. In terms of demographic characteristics, men experience a shorter

duration of time and older people are less likely to end their relationship with the

financial services sector. Individuals experience high attrition tendencies in a

wealthier microenvironment.

Athanassopoulous (2000) examined the customer satisfaction cues in the retail

and banking services in Greece. The conceptual part of the study is customized

in the context of financial services as organized within the Greek national

framework. The frame of reference is the banking of the country as a whole, not

a single financial institution as is usual in many previous and present studies.

What this research brings about, is the external validation offered by the

statistical differences found in service satisfaction scores of different customer

segments. In the context of financial institutions, banking-specific, Laroche,

Rosenblatt and Manning (1986) reveal speed service, convenient location, staff

competence, and bank friendliness as important determinants of customer

satisfaction.

The assessment of customer switching behaviour forms a very important

question for banking institutions. The implications are significant for both market

leaders who seek to implement effective defensive policies, as well as for

smaller players who seek to expand their market share (Athanassopoulous,

2000). The sample used in this study shows a significant 13% switching of

business customers and 8% switching of individual customers who had



27

switched banks in the past 2 years. In these cases pricing was the main primary

factor for switching individual customers which shows the effect of growing

competition amongst the retail banking industry in Greece (Athanassopoulous,

2000).

Identifying the right customer and understanding what motivates them calls for

strong and well defined research (Wisskirchen, Vater, Wright, Backer & Detrick,

2006). Banks that do this begin by carefully identifying their existing customers,

to learn which customers are the most valuable to them, how their use of

products evolves over time, and what characteristics they possess. To support

these findings, a real life example is given of Grupo Banco Popular, Spain’s

largest banking group. They spotted an attractive segment in the nation’s fast-

growing population of affluent senior citizens. By examining consumption

patterns and banking needs, the bank discovered that senior customers valued

personal security and the convenience of personalized concierge-style

services.

In a survey of more than 26000 retail banking customers in Australia and New

Zealand, 80 percent of respondents said they would consider switching their

financial service provider (North, 2007). Poor service ranked as the number one

factor (31 percent) among financial services, three times greater than the next

biggest frustration, which is complexity and conditions. As a result of poor

service, 80 percent of survey respondents said they would consider switching

banks. Twenty-six percent said they would switch to find a better rate, 23 per

cent are looking for better service, 19 percent for a better product, and 17

percent for more loyalty rewards.

“With increased pressure on profitability, banks have been powering up sales

channels to grow their market share. However, as a result, the costs of

attracting new customers in Australia has jumped by more than two-thirds over

the past five years, now totalling between $100 and $800 per customer

acquisition. This compares with just $100 to cross-sell to an existing customer”

(Peppard, 2000: 322).



28

Table 2.2 outlines the summary of studies on customer churn (attrition or

defection).

Table 2.2: Summary of the various studies on churn behaviour

Source Main findings

1.Mavri and Loannou(2008)

The quality of the banking products andservices offered, in combination with thebank’s brand name, has significant positiveeffects on the decrease of churn.Furthermore, demographic characteristicshave little impact on switching behaviour.

2.Wisskirchen et al. (2006)

Ability to deliver products and services whichdelight the heart and win over the minds ofcustomers may be a formula for the successof banking business. Identification of the rightcustomer and understanding what influencestheir behaviour could be the wining strategy.

3. Mutanen (2006) Churning customers are those which have adeclining trend in their transaction numbers.Furthermore; the high customer age and asmaller customer bank age both have apositive impact on the churn probabilitybased on the coefficient values.

4.Van den Poel andLariviere (2003)

The findings indicate that demographicvariables, environmental changes andstimulating “interactive and continuous’relationships with customers, are of majorconcern when considering retention.Customer behaviour predictors only have alimited impact on attrition in terms of totalproducts owned as well as the inter-purchasetime.

5. Burez and Van denPoel (2008)

The study considers two types of churn,namely: commercial and financial. It wasfound that previous bad payment is far moreimportant in financial churn prediction ascompared to commercial churn. Financialchurn is easier to predict whilst commercialchurn is much easier to prevent. It is alsoimportant to know that different types of



29

churn exist, and even then it should beprevented with different actions.

6. Colgate et al.(1996)

Reducing the churn rate of university studentcard holder’s increases profits. If the bank

reduces defection (churn) from 17.8 percentto 15 percent, for example, it can increaseprofits by 105 percent.

7. Garland (2002) The percentage of customers who intend todefect, based on the Juster scale, from one’smain bank in the next 12 months, was foundto be 10 percent. Customers who have beenwith the bank for less than eight years andwere from rich family’s show an aboveaverage defection (churn) rate whilst old age

customers (65 and above) are belowaverage ( 7 percent)

8. Trubik and Smith(2000)

Four attributes to help to identify customerswho are leaving the bank were identified:customers having only one product with thebank, no fee exemptions, and a majorchannel with a bank branch and customerswho were on their third month of service withthe bank. Overall these attributes identify88.36 percent of customers who have left the

bank.

2.8 Summary

Literature on customer relationship management and customer churn has received

attention from many businesses and academics in recent years (Colgate et al.,

1996; Garland, 2002; Lejeune, 2001; Peppard, 2000; Mutanen, 2006; Rootman,

2006; Mavri & Loannou, 2008). The relationship marketing (RM) concept has

become part of the ‘plausible story’ of the customer relationship management. It

has been suggested that most businesses can leverage firms’ customer’s relations

to gain privileged information about customers needs and in turn provide more

satisfactory offerings than their competitors are able to (Hair et al ., 2006).



30

There is no doubt that the customer relationship marketing strategy is commonly

used in many businesses to promote, and advertise business to customers and to

keep track of a customer’s behaviour. Commercial banks have thus embarked on

different management strategies as a way of promoting a customers loyalty (Jamal

& Naser, 2002). The issue of churn in the banking industry is one of the problems

which many banking businesses face on a daily basis. The challenge is to

determine how to keep their customers happy and ensure that customers continue

to do business with the bank. The churn management strategy has been used as a

way of developing the banks customers’ loyalty whilst winning back customers at

the risk of ceasing the service with the service provider. Understanding customer

behavioural patterns over time is important to the relationship as well as with

regard to churn management strategies.

Recent studies also indicate that it is less expensive to cross sell products to

existing customers than to acquire new customers (Colgate et al ., 1996 and

Peppard, 2000). For banks to reach their maximum function potential, customer

defection (churn) rate should be considered and minimized. Some of the research

suggests that retaining existing customers and trying to encourage them into more

profitable customers seems to be an appropriate customer service strategy

(Colgate et al ., 1996).

This is only possible when businesses understand and know their customers. The

existence of technology within the banking business allows marketers and

management to identify how customers use the services. Statistical methods have

been used to identify hidden information on customer profiles and behavioural

patterns. Based on the previous literature, regression models have been in the fore

front of the analysis of customer churn behavioural patterns (Rygielski et al., 2002).

Understanding the interaction of customers with the service provider is crucial for

any business, which provides services to customers. The regression model is able

to investigate the relationship between customer behavioural patterns and to give

an overview as to how these factors affect their relationship with the business.



31

In chapter 3 the methodology which has been used will be discussed and

thereafter the results of the study will be explained in chapter 4.



32

Chapter 3: DATA CLEANING AND ANALYSIS

3.1 Introduction

Chapter 2 outlined the theoretical overview of customer relationship management

and the behavioural characteristics of customers with the banking business.

Research data can give misleading results if it is not properly dealt. It is imperative

to thoroughly check the data for all possible errors and to correct any errors found

before analysis may commence. This process is called data cleaning. The data

analysis process is the process of using the cleaned data and applying statistical

methodology to answer the research objectives. In this chapter, the detailed

research data cleaning and a data analysis of the study will be outlined.

3.2 Data cleaning

In this study a selected customer database provided by a retail bank was used for

the analysis. The data consists of customer accounts profile information as well as

their behavioural information transactions.

The data provided was collected from January 2003 to September 2008. The dataconsists of active and non active accounts within the motioned period. The data is

divided into three sets, namely, transaction data, accounts data and customer

profile data. The following procedure was followed to combine the data:

• The transaction data was first sorted in ascending order according to an

account key with transaction date and transaction amount per date.

• The sorted transaction data was linked to account information data using an

account key.

• Any account key which did not have a match was eliminated entirely from the

data set.



33

• The transaction and account customer data which matched using an account

key which was linked to customer profile data, using a customer key. Only

customers’ data set which has matched customer key was considered for the

steps hereafter.

• The data had 7860 customer keys. But due a small number of some missing

values in the transaction information of other accounts and outliers, the final

sample data was 7838.

• From this data set the following behavioural variables were calculated,

-average number of transactions per month,

-debit and credit transaction amounts per month,

-mean debit and credit amounts,

• Customer characteristics variables selected for the analysis

-Account age,

-Income estimate,

-Income amount,

-VIS_20_20_SEG (segmentation code),

-Customer number of children,

-MAX_SERV_FEE (maximum service fee),

-NO_BANK_SERVICE (No bank service),

-MRT_STAT_CODE (Marital status),

-CONSEN_INDICATOR (Consent indicator),

-CUSTOMER_SEX_CDE (Customer sex code),



34

-CUSTOMER_AGE (customer age),

The next section 3.3 illustrates the data analysis procedure for the total data

sample of 7838 and according to race groups.

3.3 Data analysis

The study involves analysing four sets of sample data according to race groups.

These four sets of sample data were derived from the sample data according to

race code indicators in the customers’ profile.

The study required the identification of relevant variables to be used in the analysis

from the data. The set of variables were derived from the customer database.

The variables include:

(1) Account transactions, which are considered as behavioural variables,

(2) Service indicators,

(3) Personal customer profile information.

The first attempt of analysis involved analysing whole sets of data and dividing the

data into two sets of sample data, namely, active and non active accounts. The

total of 7838 samples of data were analysed with the aim of understanding the

overall variation and relationship between active and non active accounts. The

second step is to divide the data into four sets of sample data from the active

accounts according to race group. Only active accounts in this study were used for

race group analysis.

Race groups are coded as 0, 1, 2, and 3 in the sample set of data. Race groups

have sample size of 1151, 66, 34, and 5056 respectively. For each of these

groups, graphical representations of the behavioural variables were presented. The

General least squares regression model as well as the Pair-wise multivariate



35

correlation is calculated to understand the relationship between the customer

behavioural characteristics and the customer profile characteristics.

The regression model assumptions were defined as:

1. The mean of ε is zero,

2. The variance of ε is 2σ ,

3. The probability of ε has a normal distribution,

4. The errors associated with any different observations are independent.

The descriptive statistics and inferential statistics result of the data are also

represented for each race group. The difference between gender behavioural

patterns is analysed using independent-measures t test statistics defined as:

pooled S

M M t

2

2121)()( µ µ −−−

= , where is the

pooled variance,1

M and2

M samples means for groups 1 and 2 respectively,2

2S

and2

1S are sample variances.

Since the researcher deals with a large sample size, it was necessary to employ

practical significance. Effect Size (ES) or practical significance is a statistic used to

determine the magnitude of a research result. Typically, it is used to determine the

magnitude of the difference in the mean scores of two groups on a measure. The

effect size does not actually specify the amount of points by which the groups

differ. Instead, the amount of difference is expressed in standard-deviation units.

The advantage of standard-deviation units is that effect sizes are calculated on

different measures within the same study or across studies have the same

meaning (Gall, Gall & Borg, 1999).

The practical significance of the statistically significant differences is measured

using Cohen’s d and 2r coefficient. The ‘Cohen’s d’ is

( ) ( )2

11

21

2

2

21

2

1

−+

−+−

=

nn

nsnss pooled



36

Defined as: pooled S

M M d sCohen 21

'−

= and df t

t r +

=2

22

where )(21 nndf += .

For ‘Cohen's d’, an effect size of 0.2 to 0.3 might be a "small" effect, around 0.5 a

"medium" effect and 0.8 to infinity, a "large" effect. But the “d” may be larger thanone. This study, will report the ‘Cohen’s d’ only for practical significant level. Table

3.1 illustrates the proposed general criteria for interpreting the effect size using

‘Cohen’s d’ and ‘r2’:

Table 3.1: Criteria for Cohen’s d and r2 interpretation: Gravetter and Wallnau,

(2005)

Small Moderate Large

Cohen’s d 0.2 < d < 0.5 0.5 < d < 0.8 d > 0.8

Correlation r 0.01 < r² < .09

0.09 < r² <0.25 r² > .25

3.4 Summary

The concept of data cleaning and data analysis is the most important process of

the research study. In this chapter the data cleaning and analysis process for the

research were outlined. The outliers and errors in the data were removed. The

method to be used in the study was outlined as well as the concept of practical

significant.

Chapter 4 outlines the presentations of the outcome of the statistical analysis of the

customer sample data. The first paragraph presents the outcome of analysis forthe whole sample of data, followed by the race groups.



37

CHAPTER 4: RESULTS

4.1 Introduction

Chapter 3 outlined the concept of data analysis as well as the data cleaning

process. In this chapter the results of the study is presented for analysis involve a

data set for closed and active accounts as well as active accounts for race groups.

These results will assist the researcher to answers the objectives of the study.

The primary objective of this study is to investigate the relationship of a customers’

behavioural patterns based on the customers past transactions, with respect to

their profile characteristics. The researcher should prove that different categories of

customers have statistically significances differences in behaviour with respect to

how these customers operate their accounts. An example would be where one

should identify that female and male customers have different transactions patterns

as well as to also identify variables which may be useful in the customer churning

behavioural analysis.

Businesses focus on knowing their customers as real people with real needs and

preferences, which leads to better customer satisfaction and therefore attraction.

Retaining loyal customers costs less than acquiring new ones and are a great

source of information to create new and innovative services (Colgate et al , 1996).

By knowing existing customers, a business can be assured of the fact that they will

always be kept ahead of their competition. The understanding of customers

behaviour is based on data transactions which enable the bank to understand the

customers without looking at their profiles and as well as link their behaviour to

their profile which may include age, gender, marital status, income amount,

number of children and other useful variables. For example, an unmarried person’s

behaviour might differ from a married person’s behaviour and therefore one may

deduce that different age groups have different behaviours. It is therefore important

to understand how customers behave with the aim of providing better service in the

future.



38

The result includes the overall analysis of closed and active data sets and that of

customer race groups. The results for each race group shall be presented

separately. The identification of relationships amongst dependent and independent

variables will be investigated amongst the customers’ race groups. Finally a

summary of results and conclusions will be drawn from these results and the

recommendations will be presented in chapter 5.

4.2 Customer Data for all accounts key.

4.2.1 Descriptive statistics for the average number of transactions and the amounts of

transactions per month.

Table 4.1 indicates the descriptive statistics of transaction amounts and the

average number of transactions for a sample of 7838.

Table 4.1: Descriptive statistics for the average number of transactions and the

average transaction amounts (Debit and Credit) per month for all account keys.

Statistics Debit Credit Number of transactions

Mean 1942 1988 5

Standard Error 74 76 0

Median 342 362 2

Standard Deviation 6585 6716 7

Sample Variance 43366579 45098727 44

Kurtosis 203 200 13

Skewness 11 11 3

Range 187274 183295 76



39

Minimum 0 0 0

Maximum 187274 183295 76

Sum 15220237 15578803 37388

N 7838 7838 7838

From table 4.1 it is evident that the sample data for debit transaction amounts,

credit transaction amounts and the average number of transactions is positively

skewed. The following figure illustrates the variation of the number of transaction

per month for customers. Figure 4.1 illustrates the histogram plot for a number of

transactions.

Figure 4.1: Histogram plot for the average number of transactions, per month per

account key.

0.10

0.20

0.30

0.40

0.50

0 10 20 30 40 50 60 70

Average number of transaction per month



40

Figure 4.1 indicates the average number of transactions which each customer

made per month over the past 69 months. It is clear from the graph that, the

majority of the customers made on average between 1 and 15 transactions per

month with few accounts having more than 15 transactions per month. Figure 4.2

and 4.3 indicates the variation of the transaction (credit and debit) amounts per

month.

Figure 4.2: Scatter plot for the average debit transaction amounts per month, per

customer key.

The debit transaction amount per account is indicated in figure 4.2. It follows from

this graph that majority of the accounts or customers have debit transactions of

less than R20000 per month. The average debit transaction amount per month for

Average Debit transaction vs. customer key

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

200000

0 1000 2000 3000 4000 5000 6000 7000 8000

Customer key



41

these customers is around R1’941.00. The next figure 4.3 indicates the credit

transaction amount per customer key.

Figure 4.3: Scatter plot for the average credit transaction amount, per month per

customer key.

In figure 4.3, the credit transaction amounts for the account keys are more or less

the same as the debit transaction amounts. Most accounts have credit transactions

which are less than R2000 per month, where all customers make around

R1987.000 per month on average. This is more than the debit transaction amount.

The next figure will indicate the variation in terms of debit and credit, where the

negative trend indicates that the customer key had more credit transactions per

month when compared to debit transaction amounts per month. Figure 4.4

illustrates the debit-credit transaction amounts per customer key.

Average Credit amount vs. customer key

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

200000

0 1000 2000 3000 4000 5000 6000 7000 8000 Customer key

C r e d i t t r a n s a c

t i o n a m o u n t



42

Figure 4.4: Plot for the average transaction (debit-credit) amount per month, per

customer key.

It follows from figure 4.4 that some of the customers have a higher volume of credit

transactions per month in comparison to debit transactions. The majority of

customers have debit-credit transaction amounts of plus or minus R10’000, with

less than four customers with a higher volume of credit transactions over the

R10’000 mark.

The three figures illustrate the patterns for all the customers, without considering a

customer’s gender, age, race etc. From this we may conclude that customers have

a higher credit transaction amount when compared to debit transaction amounts.

The majority of the customers have a debit and credit transaction between -R

10’000 and R10’000 with number of transactions ranging between and 1 and 15

per month. To get some insight as to how the behavioural patterns vary according

to age, gender or race group, the analysis is done based on these categories. The

following paragraphs will illustrate some behavioural patterns according to the

Average (Debit-Credit) vs. customer key

-50000

-40000

-30000

-20000

-10000

0

10000

0 1000 2000 3000 4000 5000 6000 7000 8000

Customer key

D e b i t - C r e d i t a m o u n t



43

customer gender and age. The race group code analysis will follow in the

sections below. Table 4.2 paragraph illustrates the customer gender descriptive

statistics.

Table 4.2: Descriptive statistics of the transaction amounts and the number of

transactions per month, according to customer gender.

Statistics Female Male

Credit transaction amount per month

Mean 1490 2405

Standard deviation 4647 8029

Standard error mean 78 123

Variance 21591612 64469595

Skewness 12 10

Debit transaction amount per month

Mean 1441 2362



Variance 20668780 62052904

Skewness 12 10

Average number of transactions per month



44

Mean 4 5


Standard err mean 0 0

Variance 35 51

Skewness 3 3

N 3578 4260

Table 4.2 illustrates the mean transaction amount per month for male and femalecustomers. It is evident that the mean for males in all three variables is greater

than for females. Male customers have the highest transaction amounts when

compared to females. Figures 4.5 and 4.6 illustrate the transactions differences

between male and female customers, using Box Plot.

Figure 4.5: Average debit transaction amounts per account key vs. customer

gender.



45

Figure 4.6 Average credit transaction amounts per account key vs. customer

gender.

It follows from figures 4.5 and 4.6 that female customers have less debit

transaction amounts and credit transaction amounts per month when compared to

male customers. The average transaction amounts for females and males is more

or less the same, where customers debit and credit transaction amounts mean is

approximately R1’500 for females and R2’500 for males. Male customers have

higher volumes of transactions when compared to female customers transactions

and can make up to R2’500 for both debit and credit amount per months. It is also

evident from Table 4.2 that the male and female customers differ in terms of the

volume of the transaction amount with the average transaction credit amount of

R2’405 and R1’490, the average debit amount of R2’362 and R1’441, and the

average number of transactions of 5 and 4 per month for male and female

customers, respectively. Figure 4.7 illustrates the customers’ age distributions in

the data set.



46

Figure 4.7: Distribution for customer age groups.

Figure 4.7 indicates the distribution of age groups in the data set, with the highestnumber of customers are aged between 20 and 40 years. In fact, an overwhelming

majority of customers are aged between 20 and 60 years (approximately 80% of

total percentage of customers).

Figure 4.8: Average transaction credit and debit mean amounts per month vs.

customer age group.

M e an ( A v ov er 6 9 m on t h s

( D e b i t ) )

M e an

A v ov er 6 9 m on t h s

C r e d i t



47

It is clear from figure 4.8 that transaction amounts of customers differ by age

groups and the volume of transaction tend to increase as the customers’ age

increases (But only up to about 65 years).

There exists a difference in the amount of transactions per age where young

customers between the ages of 0 and 24 years, have a small volume of

transactions when compared to other age groups. It is evident that the transaction

amount means increases with a normal curve as the age increases from the age of

19 to 38. From the retirement age of 60 to 64 years, the credit and debit

transaction slightly decrease with as the age increases up to 100 years of age. The

same pattern applies for both debit and credit amount. Figure 4.9, figure 4.10 and

figure 4.11 illustrate the relationship between customer transactions amounts and

the number of transactions.

Figure 4.9: Average number of transactions per month vs. debit transaction amount

per month, by sex.

Scatterplot of Av over 69 months(Debit) against average number of transaction per months;categorized by CUST_SEX_CDE

data for all customer key 7838.sta 147v*7838c

CUST_SEX_CDE: F Av over 69 months(Debit) = -510.511+462.7943*xCUST_SEX_CDE: M Av over 69 months(Debit) = -1003.255+642.9913*x

average number of transactions per month

A v o v e r 6 9 m o n t h s ( D e b i t )

CUST_SEX_CDE: F

0 10 20 30 40 50 60 70 800

20000

40000

60000

80000

1E5

1.2E5

1.4E5

1.6E5

1.8E5

2E5

CUST_SEX_CDE: M

0 10 20 30 40 50 60 70 80



48

Figure 4.10: Average number of transactions per month vs. credit transaction

amount per month, by sex.

Scatterplot of Av over 69 months(Credit) against average number of transaction per months; categorized byCUST_SEX_CDE


CUST_SEX_CDE: F Av over 69 months(Credit) = -454.4588+461.0301*xCUST_SEX_CDE: M Av over 69 months(Credit) = -988.8239+648.515*x

average number of transactions per month

A v o v e r 6 9 m o n t h s ( C r e d i t )

CUST_SEX_CDE: F

0 10 20 30 40 50 60 70 800

20000

40000

60000

80000

1E5

1.2E5

1.4E5

1.6E5

1.8E5

2E5

CUST_SEX_CDE: M

0 10 20 30 40 50 60 70 80

Figure 4.11: Relationships between the average number of transactions per month

and transaction amounts per month, according to customer gender.

Transaction amount vs. number of transaction per

customer gender

F M

Average number of transaction per months

0 10 20 30 40 50 60 70 D e b

i t a n d c r e d i t t r a n s a c t i o n a m o u n t

0

10000

20000

30000

40000

50000

60000

0 10 20 30 40 50 60 70

A v ov er 6 9 m on t h s ( D e b i t )

A v ov er 6 9 m on t h s ( C r e d i t )



49

It can be seen from figures 4.9 and 4.10 that the average transactions amount

per month increase as the number of transactions per month increases. The slope

of the fitted line for males is higher than that of females’ customers. From this, it is

evident that as the number of transactions increase, male customer transactions

amount rates are higher than the female customer’s amount rate. It is deduced that

female and male customers demonstrate similar patterns at a lesser number of

transactions per month and differ as transactions increase.

Figure 4.11 points out that as the number of transactions increase, the transaction

amount increases for males and females. Female customers’ transactions

decreases when the number of transactions are around 40, and then increases

again. Males’ transaction amounts decreases when the number of transactions

reach 40 and increases again when over 55 average number of transaction per

month. Male customers have a higher number of transactions per month and it

decreases as the number of transactions reach over 55 per month.

This set of graphs, (figure 4.9, figure 4.10 and figure 4.11) has demonstrated an

important aspect of the study. By looking at the transaction amounts over the

number of transactions, it is evident that females have less transaction amounts

per month when compared to males. Furthermore, both males and females reduce

the transaction amounts as the number of transactions reaches 39 per month on

average. Only male customers in this data set have more than 50 transactions per

month with less transaction amounts per month. The following paragraph

illustrates the statistical evidence, on the gender differences, in the behavioural

patterns of customers.



50

4.2.2 Inferential statistics for the average number of transactions and the amounts of

transactions per account key per month.

t-test statistics for a difference between male and female behavioural

patterns.

Table 4.3 illustrates the t-test statistics and practical significant p-values for the

difference between the customer gender average, the number of transactions and

the transaction amounts per month. It is evident that at a 5% significance level,

therefore reject the hypothesis that males and females behavioural patterns are the

same. Therefore, there is sufficiently significant evidence to conclude that there is a

difference, between the gender group behaviour at a 0.05 significance level. Using

a practically significant value, Cohen’s d, there is a moderate difference betweenfemale and male transaction behavioural patterns. The difference between the

numbers of transactions is large. This supports the observation illustrated from

figure 4.11 above, where males have higher transactions amounts as the number

of transactions increase.

Table 4.3: t-test statistics for gender differences, assuming unequal variances.

Variables Difference Std Err Dif Lower

CL

Upper

CL

p-Value Cohen’s d

Debit 920.66 148.98 628.62 1212.70 <.0001* 0.58

Credit 915.39 151.94 617.54 1213.24 <.0001* 0.58

Number of

transactions 1.02 0.15 0.72 1.31 <.0001* 1.36



51

Correlation between the behavioural variables

Table 4.4 illustrates the correlation coefficients for transaction variables, the

average number of transactions and account age.

Table 4. 4: Pair wise correlations for transaction variables and account age.

Variable By Variable Correlation Lower

95%

Upper

95%

Signif

Prob

Average transactions

amount(Debit).

Account age 0.2776 0.2570 0.2979 <.0001*


amount (Credit).

Account age 0.2730 0.2524 0.2934 <.0001*


amount (Credit).


amount (Debit)

0.9952 0.9950 0.9954 0.0000*

Average number of


Account age 0.4696 0.4521 0.4867 0.0000*

Average number of

transactions per month


amount (Debit)

0.5817 0.5668 0.5961 0.0000*

Average number of

transactions per month


amount (Credit)

0.5732 0.5581 0.5879 0.0000*

(All p values with * are significant)

It is evident from table 4.4 that there is a strong positive correlation between the

average debit transaction amounts and credit transaction amounts. This implies

that when the debit transaction amount increases, the credit transaction amount

increases. Average number of transactions and the transactions amount are

correlated to each other. Average number of transactions and account age, also

has a positive correlation value of 0.47. The transaction amounts are highly



52

correlated with a correlation value close to 1. Other correlation coefficients are

also indicated in the table 4.4. The regression analysis between the behavioural

variable and customer characteristics will be left out. The detailed regression

analysis will be conducted in the race group analysis. In the next section, the

relationship between the customer behavioural patterns will be analysed according

to race group.

4.3 Analysis for Race group 0

4.3.1 Descriptive statistics for the average number of transactions and the transaction

amounts for race group 0. Table 4.5 illustrates the descriptive statistics for race

group 0 customers.

Table 4.5 Descriptive statistics for the average number of transactions and the

average transaction amounts (Debit and Credit) per month, for race group 0.

Statistics Average debit Average credit Average number of transactio

Mean 7322 7545 10


Median 2192 2353 6


Sample Variance 222213465 231136220 119

Kurtosis 41 40 4

Skewness 5 5 2

Range 187274 183295 76

Minimum 0 0 0



53

Maximum 187274 183295 76

Sum 8427132 8684705 11584

Count 1151 1151 1151

It follows from the table 4.5 that the mean for debit transaction amounts is slightly

different from the credit transaction amounts with approximately 10 average

transactions per month. It is also evident that the distribution of the data is

positively skewed with 1151 observations. Other statistical measures are indicated

in the table. Figure 4.12 illustrates the variation of the average number of

transactions per month, for race group 0 customers.

Figure 4.12: Histogram plot for the average number of transaction per month per

account key, for race group 0.

It follows from figure 4.12 that the majority of race group 0 customers, made less

than the 15 average numbers of transactions per month on average with only less



54

than 10% of the customers having more than an average of thirty numbers of

transactions per month. It follows from table 4.5 that the debit and credit

transaction amounts per month of race group 0 clients, on average is very close to

R10’000.

The behavioural patterns according to age, gender and other characteristics will be

investigated. The differences between female and male customers according to

transaction amounts and the number of transactions per month are illustrated in

the next paragraph. Table 4.6 indicates the mean and other statistical values of

race group 0, for male and female customers.

Table 4.6: Descriptive statistics for the transactions amount according to gender,

per month for race group 0.


Credit(transaction amount)

Mean 4993 10138



Variance 96871065 354566424

Skewness 6 4

Debit( transaction amount)

Mean 4764 9920




55


Variance 92873044 340564283

Skewness 7 4

Average number of transaction

Mean 9 10



Variance 84 119

Skewness 2 2

N 580 571

It follows from table 4.6 that male and female customers have different average

transaction amounts per month and the average number of transactions where the

average mean is different. The variation of male customer’s transactions amounts

per month is higher than that of female customers. The debit average transactions

mean is less than the credit average transactions mean for both male and female

customers. This implies that race group 0 customers have a higher volume of

credit transactions, when compared to debit transaction amounts. Figure 4.13

illustrates the differences in the amount of transactions between male and female

customers using Box Plot.



56

Figure 4.13: Average debit transaction amounts vs. customer gender.

Figure 4.14: Average credit transaction amounts vs. customer gender.

It is also evident from Figures 4.13 and Figure 4.14 that female customers have on

average less volume of transactions when compared to male customers. Female

customers have an average transaction volume of around R 5’000 per month

whereas male clients transaction amounts are approximately R10’000, with few

accounts having up to R50’000 per month. Figure 4.15 indicates the box plot for



57

the average number of transaction differences between male and female

customers.

Figure 4.15: Average number of transactions per month vs. customer gender

group.

It follows from figure 4.15 that the number of transactions for female customers is

less than that for male customers. The box plots indicate that females have fewer

transactions when compared to males. The pattern from figure 4.15 for the

average number of transactions is more or less the same as the one in figure 4.13

and figure 4.14 for debit and credit transaction amounts. From this information,

descriptively it can be concluded that the transaction amounts and number of

transactions between males and females are different. Figure 4.16 illustrates the

customer age distribution. The highest number of customers in this group are

between the ages of 40 and 59 years, followed by age group between 20 and 39

years with only few customers over the age of 80. Figure 4.17 indicates the

transaction amounts over age.



58

Figure 4.16: customer age distribution for race group 0.

Figure 4.17: Average transaction debit and credit amounts per month vs. customer

age.

M e an ( A v ov er 6 9 m on t h s ( D e b i t ) )

M e an ( A v ov er 6 9 m on t h s ( C r e

d i t ) )



59

Figure 4.17 shows that the transaction amounts vary according to age groups.

Customers at a younger age, have a smaller amount of transactions per month

when compared to other age groups. Race group 0 transactions amount per month

increase as the age increases from 19 years, and slightly decrease at the age of

74, and then increases again. From this, it is applies that race group 0 customers

of different age groups, have different transaction amounts per month, with the

younger group having a low volume of transactions. Figure 4.18.1, 4.18.2 and 4.19

illustrate the relationship between average transaction amounts and the average

number of transactions per month.

Figure 4.18.1: Scatter plot for the average debit transaction amounts per month vs.

average number of transactions per month.

Scatterplot of Av over 69 months(Debit) against average number of transacton per months over 69 months;categorized by CUST_SEX_CDE

Race group 0 data.sta 206v*1151c

CUST_SEX_CDE: M Av over 69 months(Debit) = 119.2805+852.4479*xCUST_SEX_CDE: F Av over 69 months(Debit) = -225.7175+576.5591*x



CUST_SEX_CDE: M

0 10 20 30 40 50 60 70 800

20000

40000

60000

80000

1E5

1.2E5

1.4E5

1.6E5

1.8E5

2E5

CUST_SEX_CDE: F

0 10 20 30 40 50 60 70 80



60

Figure 4.18.2: Scatter plot for average credit transaction amounts per month vs.

average number of transactions per month.

Scatterplot of Av over 69 months(Credit) against average number of transacton per months over 69months; categorized by CUST_SEX_CDE


CUST_SEX_CDE: M Av over 69 months(Credit) = 285.1486+856.969*xCUST_SEX_CDE: F Av over 69 months(Credit) = 147.6994+559.9425*x


A v o v e r

6 9 m o n t h s ( C r e d i t )

CUST_SEX_CDE: M

0 10 20 30 40 50 60 70 800

20000

40000

60000

80000

1E5

1.2E5

1.4E5

1.6E5

1.8E5

2E5

CUST_SEX_CDE: F

0 10 20 30 40 50 60 70 80



61

Figure 4.19: Relationship between the average number of transactions per

month and transactions amounts per month, per account key according to

customer gender.

It is evident from Figure 4.18.1 and 4.18.2 that as the number of transactionsincrease, the transaction amounts for both males and females increases. Male

customers have a higher rate of increases in the average transaction amount per

month as the average number of transactions increase. Only male customers in

this group have more than 50 average transactions per month and have tendency

of making the highest volume of transactions amount per month on average.

Figure 4.19 illustrates the same pattern as figures 4.18.1 and 4.18.2 with a different

form of graph. It follows from figure 4.19 that for female customers, as the numberof transactions increase the volume amount of transactions increase between

R0.00 and R40’000 transaction amounts. For males, as the average number of

transactions increase from 0 to 45, the transactions amount increases from R0.00

to R50’000. The volume transaction decreases again as the number of

transactions increase over the 45 mark. It can be seen from these figures that male

Average transaction amount vs. average number

of transaction per customer gender.

F M

0 10 20 30 40 50 70 0

10000

20000

30000

40000

50000

60000

0 10 20 30 40 60

A v ov er 6 9 m on t h

( D e b i t )

A v ov er 6 9 m on t h

( C r e d i t )



62

customers dominate the female customers in terms of their behavioural

tendencies. The relationship between the average number of transactions and the

transaction amounts is also being investigated using the correlation coefficient.

4.3.2 Inferential statistics for the average number of transactions, and the number of

transactions per account key, per month for race group 0.

The relationship and difference in behavioural patterns between male and female

customers have been presented in the previous section. Graphically, it has been

found that the difference between males and females exists in terms of the

average number of transaction and average transactions debit and credit amounts

per month. Table 4.7 in this section will investigate the relationship between

behavioural variables, using the correlation differences on behavioural patterns of

male and female customers using t-test statistics.

t-test statistics for differences between male and female behavioural

patterns.

Table 4.7: t-test statistics of male and female differences, for race group 0.

t = 1.96202 Alpha = 0.05

Variables t-ratio Difference Std ErrDif

LowerCL

Upper CL p-Value Cohen’sd

Average

number oftransaction. 4.46 2.84 0.64 1.59 4.09 <.0001* 6.17

Transactionamount(credit). 5.80 5144.62 883.72 3410.73 6878.51 <.0001* 1.00

Transaction 5.93 5156.40 865.92 3457.43 6855.37 <.0001* 1.00



63


LowerCL

Upper CL p-Value Cohen’sd

amount(debit).

It follows from table 4.7 that it may be rejected that males and females have the

same behavioural patterns, where the p-values are less than 0.05 and therefore it

can be concluded that there exists behavioural differences between male and

female customers at a 5% significance level. Table 4.8 illustrates the correlation

amongst behavioural variables as well as between behavioural variables and the

customer account age. Cohen’s d shows a large difference between male and

female behavioural patterns, for average transaction amounts and the average

number of transactions.

Correlation between the behavioural variables.

Table 4.8: Correlation coefficient between customers’ behavioural patterns for race

group 0.

Variable By Variable Correlation Lower95%

Upper95%

SignifProb

Averagetransactionamount (Credit).

Average transactionamount (Debit).

0.9939 0.9932 0.9946 0.0000

Ave number of

transactions.

Average transaction

amount (Debit).

0.5627 0.5218 0.6009 <.0001

Ave number oftransactions.

Average transactionamount (Credit).

0.5494 0.5078 0.5885 <.0001

Account age Average transactionamount (Debit).

0.3394 0.2872 0.3895 <.0001



64


Upper95%

SignifProb

Account age Average transaction

amount (Credit).

0.3300 0.2776 0.3806 <.0001

Account age Ave number oftransactions .

0.4860 0.4406 0.5290 <.0001

(* significance p-value).

Table 4.8 indicates the correlation coefficient and the significant probability for

linear relationships between the two variables. There is a strong correlation

between the debit and credit transaction amounts for customers in race group 0.

The account age and the number of transactions have a correlation of 0.49.

Therefore, the increase (decrease) in the debit transaction amount will increase

(decrease) credit transaction amount. In the next paragraph, the relationships

between the behavioural patterns and customer characteristics are investigated

using the regression model. Table 4.9, 4.10, and 4.11 illustrates regression results

for race group 0.



65

Regression analysis for Race group 0.

Table 4.9: Statistical report for R-Square statistics.

Statistics Av no of Trans Av Credit Av Debit

R-Square 0.555756 0.995793 0.995701

R-Square Adj 0.532531 0.995573 0.995477

Root Mean SquareError

7.601162 1053.658 1044.247

Mean of Response 11.14805 8285.032 8037.908

Observation 947 947 947

Table 4.10: Statistical report for the analysis of variance (Models tests using F).

Source DF Sum of Squares Mean Square F Ra

Av. number of Trans

Model 47 64980.41 1382.56 23.929

Error 899 51942.12 57.78 Prob >

C. Total 946 116922.53 <.000

Average debit transaction

Model 47 2.2707e+11 4.8312e+9 4430.4

Error 899 980316962 1090452.7 Prob >

C. Total 946 2.2805e+11 0.0000

Average credit transaction

Model 47 2.3626e+11 5.0268e+9 4527.8

Error 899 998065660 1110195.4 Prob >

C. Total 946 2.3726e+11 0.0000



66

Table 4.11: Statistical report for regression significant estimates, race group 0.

Estimate STD Error t-Ratio Prob>|t|

Average no of Trans

Account age 0.5056 0.047529 10.64 <.0001*

Mean (Debit) -0.0008 0.000116 -6.93 <.0001*

MAX_SERV_FEE 0.1741 0.037679 4.62 <.0001*

CONSENT_IND [N] -1.104089 0.275378 -4.01 <.0001*

VIS_20_20_SEG [U5] 4.36354 1.204333 3.62 0.0003*

CUST_AGE -0.110137 0.03129 -3.52 0.0005*

Av over 69 months (Debit) 0.0008235 0.000241 3.41 0.0007

VIS_20_20_SEG [U8] 6.2621002 1.935695 3.24 0.0013*

INCOME_AMOUNT -1.103e-6 3.799e-7 -2.90 0.0038*

VIS_20_20_SEG [U13] -3.632864 1.474573 -2.46 0.0139*

VIS_20_20_SEG [S12] 3.9795338 1.69268 2.35 0.0189*

NO_BANK_SERV 0.7661469 0.341996 2.24 0.0253*

VIS_20_20_SEG [S9] 3.0055617 1.349419 2.23 0.0262*

VIS_20_20_SEG [U12] 5.4666779 2.484041 2.20 0.0280*

VIS_20_20_SEG [S15] 2.7767134 1.322681 2.10 0.0361*

VIS_20_20_SEG [E2] -6.9772 3.34881 -2.08 0.0375*

Av over 69 months (Credit -0.00049 0.00024 -2.04 0.0416*

VIS_20_20_SEG [M7] -2.600131 1.274356 -2.04 0.0416*

VIS_20_20_SEG [LI] -1.888957 0.931764 -2.03 0.0429*

Debit Transaction

Ave transaction (Credit) 0.9860375 0.003326 296.43 0.0005*

Mean (Credit) -0.073319 0.001879 -39.03 <.0001*

INCOME_AMOUNT -0.000466 0.00005 -9.30 <.0001*

VIS_20_20_SEG [U12] -1542.637 338.2853 -4.56 <.0001*

Mean (Debit) 0.0705917 0.016139 4.37 <.0001*

Average number of

transactions 15.541773 4.552464 3.41 0.0007*

VIS_20_20_SEG [U5] -529.2485 165.7175 -3.19 0.0015*

VIS_20_20_SEG [U10] -1097.427 441.4824 -2.49 0.0131*



67

Credit Transaction

Av over 69 months (Debit) 1.0038898 0.003387 296.43 0.0000*

Mean (Credit) 0.0743935 0.001877 39.62 <.0001*

INCOME_AMOUNT 0.0005172 0.00005 10.34 <.0001*

VIS_20_20_SEG [U12] 1539.389 341.4204 4.51 <.0001*

Mean (Debit) -0.055758 0.016352 -3.41 0.0007*

VIS_20_20_SEG [U5] 508.58315 167.2991 3.04 0.0024*

VIS_20_20_SEG [U10] 1189.2702 445.2259 2.67 0.0077*

Average number of transactions -9.413445 4.612498 -2.040.0416

VIS_20_20_SEG [S13] -398.6346 196.5915 -2.03 0.0429*

It follows from table 4.9 that the R2 indicates a better fit for the debit and credit

transaction amount models with 55.6% for the number of transactions. Using the

independent account age, customer age, Debit mean, MAX_SERV_FEE,

NO_BANK_SERV, CONSENT_IND [N], INCOME_AMOUNT, VIS_20_20_SEG

[variables] and both transactions amount values explains 55.6 percent of the total

variation of the average number of transactions per race group 0 customers. It

follows that 99.6 percent of the sample variation of the average debit and credit

transaction amounts may be explained using the independent variables,INCOME_AMOUNT, VIS_20_20_SEG, Credit mean, Debit mean, average

transaction (debit), average number of transactions and the average transactions

(credit), average number of transaction, INCOME_AMOUNT, VIS_20_20_SEG,

debit mean, and credit mean, respectively.

To determine the adequacy of the models, the global F-test was conducted. It

follows from table 4.10 that at least one parameter is not equal to zero. Therefore

at the 0.05 significance level, it has been concluded that the models are statistically

significant. To determine how much each parameter contributes in the models, the

t-test statistical method is used and the parameters are indicated in table 4.11.

It follows from table 4.11 that independent variables such as the customer account

age, MAX_SERV_FEE, NO_BANK_SERV, debit transactions and other



68

VIS_20_20_SEG variables have a positive effect on the average number of

transactions for race group 0. Average credit transactions per month, CUST_AGE,

INCOME_AMOUNT, Mean (Debit), CONSENT_IND [N] and VIS_20_20_SEG

variables have a negative effect on the number of transactions per month. It

follows that the income amount, maximum service fee, consent indicator and other

variables play a role in the number of transactions that race group customers

make.

Average credit transaction amounts per month, average number of transactions

and the debit mean have, a positive influence on the average debit transaction

amounts per month. The VIS_20_20_SEG, INCOME_AMOUNT, and credit mean

variable have a negative influence on the debit transaction amounts per month.

Furthermore, the average debit transaction amount, credit mean, VIS_20_20_SEG

and customer income amount variables have a positive influence on the average

credit transaction amounts, whereas other variables have a negative effect on the

credit transaction amount. It is evident that the debit and credit transaction

amounts per month have an effect on each other. The income amount also

influences customer transaction amounts per month.

4.4 Analysis for Race group 1

4.4.1 Descriptive statistics for the average number of transactions and transaction

amounts for race group 1.



69

Table 4.12: Descriptive statistics for the average number of transactions and the

average transaction amounts (Debit and Credit) per month, for race group 1.

Statistics Debit Credit Number of transaction

Mean 4860 5097 8


Median 2220 2350 5

Mode 0 13 2


Sample Variance 56956434 57037791 78

Kurtosis 11 11 5

Skewness 3 3 2

Range 42664 42947 41

Minimum 0 1 0

Maximum 42664 42948 41

Sum 320762 336408 512

N 66 66 66

Table 4.12 indicates the descriptive statistics for race group 1 customers, where

the credit mean is different from the debit transaction mean. The distribution for this

group of customers is positively skewed. The median, mode and standard

deviation are indicated in this table. The next figures illustrate the variation in the

number of transactions per month per account key.



70

Figure 4.20 Histogram plot for the average number of transactions per month for

race group 1.

It follows from figure 4.20 that the race group 1 customer’s, have less than 30

transactions on average, with less than 2 customers in this set of data with the

number of transactions between above 30. It is also indicated in table 4.12 that the

transaction mean for both transaction amounts are around R5’000 per month. It is

important to understand how these customers behave based on customer gender,

age and other characteristics. In the next paragraph the differences in the number

of transactions, debit transaction amounts and credit transaction amounts per

month according to gender is illustrated.



71

Table 4.13 Descriptive statistics of the transaction amounts according to

customer sex per month of race group 1.


Credit transaction amount

mean 3939 6062

standard deviation 4515 9324

Standard error 824 1554

Median 2436 2297

Variance 20381011 86932728

Debit transaction amount

Mean 3773 5766



Median 1969 2282

Variance 20746324 86727990

Number of transaction

Mean 8 8


Standard error 2 1

Median 4 6

Variance 82 77

N 30 36



72

It follows from table 4.13 that the male and female customers in race group 1,

have different transactions amounts and have different numbers of transactions per

month. Male customers have a higher volume of transaction amounts, as well as

debit and credit amounts when compared to female customers. But the number of

male customers transactions per month, is less than that for female customers.

From this, it can be concluded that male clients of race group 1, have fewer

transactions per month with a higher volume of transactions whereas female

customers have a smaller volume of transactions per month with higher number of

transactions.

Figures 4.21 and 4.22 illustrate the difference between male and female

transaction amounts.

Figure 4.21: Average transaction debit amount vs. customer gender race group 1.



73

Figure 4.22: Average transaction credit amounts per account key vs. customer

gender race group 1.

It is evident from table 4.13 , figure 4.21 and figure 4.22, that male clients have a

higher volume of transactions amounts when compared to female clients, where

female clients make a transaction amount between R3’700 and R4’000 and males

between R5’700 and R6’200 per month. It is clear that the differences exist

between these two categories. The next figure 4.23 illustrates the variation of

transaction amounts per age.



74

Figure 4.23: Average transaction debit and credit amount per month vs.

customer age race group 1.

It is evident from figure 4.23 that the transaction amount varies according to the

age group. Between the ages of 30 and 40 years, female customers have fewer

transactions in comparison to male customers. Female customers have shown a

decrease in the transaction amounts between the ages of 40 and 60 years,

whereas male customers increase their transaction amounts. Female customers

increase their transactions amount over the age of 60 years, whereas male

customers’ transactions decrease. Figure 4.24 demonstrates the relationship

between the average transaction amounts and the number of transactions per

month.

Average transaction (debit and credit) vs. customer age per customers

F M

CUST_AGE

0 10 20 30 40 50 60 70 80 90 -2000

0

2000

4000

6000

8000

10000

0 10 20 30 40 50 60 70 80 90

A v ov er 6 9 m on t h ( D e b i t )

A v ov er 6 9 m on t h ( C r e d i t )



75

Figure 4.24: Relationships between the average number of transactions per

month and transaction amounts per month, per account key according to customer


From figure 4.24, the positive relationship exists between the female customers’

number of transactions and the transaction amounts between the ages of 0 and 30

and between the ages of 0 and 15, and over the ages of 30 for male customers in

this group. The next figure, illustrates the variation of the number of transactions

per month, per account key. It follows from figure 4.25, that female customers

between the ages of 20 and 30 have a higher variation, than when this is

compared to male customers. Female customers make between 2 and 20

transactions per month and male customers make between 5 and 15 transactions

per month.

Average number of transaction vs. Transaction amount

F M

Average number of transaction per months

0 10 20 30 40 0

5000

10000

15000

0 10 20 30 40

A v ov er 6 9 m on t h ( D

e b i t )

A v ov er 6 9 m on t h ( C

r e d i t )



76

Figure 4.25: Average number of transactions per month vs. account keys

according to customer gender.

Section 4.4.2, will also indicate the relationships between customer behavioural

patterns and the statistical test for the differences between customer gender

behavioural patterns. The regression is also presented to investigate the

relationship between behavioural characteristics and customer characteristic

variables.



77

4.4.2 Inferential statistics for an average number of transactions and the amount of

transactions per account key, per month for race group 1.

t-test statistics for differences between male and female behavioural

patterns.

Table 4.14: t-test statistics of male and female differences for race group 1.

t=1.99773 Alpha=0.05

Variables t ratio Difference Std Err Dif Lower CL Upper CL p-Value

Average number of

transaction.

-0.03 0.07 2.20 -4.33 4.47 0.9742

Debit transactionamount.

1.13 0.07 2.20 -4.33 4.47 0.2887

Credit transactionamount.

1.13 1993.74 1863.59 -1729.20 5716.67 0.2586

It follows from this table that we do not reject that there is no difference between

male and female customers’ behavioural patterns. The p-value is not significant at

a 0.05 significance level and the t-ratio is also not greater than the critical t-value.

Therefore we may conclude that there is insufficient evidence to say that both

males and females differ in their behavioural patterns. In the next table, we

investigate the relationship between the behavioural variables as well as between

customers account age and behavioural variables.




78

Table 4.15: Correlation between customers’ behavioural patterns for race group

1.


Upper95%

SignifProb

Average number oftrans.

Account age 0.4327 0.2129 0.6108 0.0003*


Account age 0.1550 -0.0904 0.3827 0.2140


Average number oftrans

0.3464 0.1139 0.5429 0.0044*

Average transaction

amount (Debit).

Account age 0.1893 -0.0553 0.4124 0.1280


average number oftrans

0.3687 0.1391 0.5607 0.0023*


Average transactionamount (Credit)

0.9898 0.9833 0.9937 <.0001*

Table 4.15 illustrates the correlation between the behavioural variables and the

account age with the behavioural variables. It follows from the table that credit and

debit have the highest correlation with a correlation value 0.9898. The increase or

decrease in debit will increase or decrease the credit amount. The account age

and number of transactions have a correlation of 0.43. The correlation between

the average number of transactions, debit and, credit amount are 0.3687 and

0.3464 respectively. The change in the number of transaction changes the

transaction amount. The next paragraph investigates the relationship between

behavioural variables and other customer characteristics using the regression

model.

Regression analysis for race group 1.



79



RSquare 0.774391 0.999475 0.999487

RSquare Adj 0.425722 0.998664 0.998695

Root Mean Square Error 6.928879 286.9449 284.0292

Mean of Response 8.68955 5656.392 5382.373

Observations 57 57 57

Table 4.17: Statistical report for analysis of variance (Models tests using F).

Source DF Sum of Squares Mean Square F Ratio

Av. number of Trans

Model 34 3625.3698 106.629 2.2210

Error 22 1056.2059 48.009 Prob > F

C. Total 56 4681.5757 0.0264*

Debit transactions amount

Model 34 3459839167 101759976 1261.395

Error 22 1774797.22 80672.601 Prob > F

C. Total 56 3461613964 <.0001*


Model 34 3448720050 101432943 1231.919

Error 22 1811421.87 82337.358 Prob > F

C. Total 56 3450531472 <.0001*



80

Table 4.18: Statistical report for regression significant estimates race group 1.

Estimate STD Error t-Ratio Prob>|t|

Average no of Trans

VIS_20_20_SEG [S13] 11.273719 4.332994 2.60 0.0163*

VIS_20_20_SEG [S8] 19.29228 7.54314 2.56 0.0180*

NO_BANK_SERV 6.1611235 2.43197 2.53 0.0189*

VIS_20_20_SEG [U11] -58.24685 25.4303 -2.29 0.0319*

Debit Transaction

Average transaction (Credit) 0.9794903 0.030436 32.18 <.0001*

Mean (Credit) -0.037928 0.007575 -5.01 <.0001*

MAX_SERV_FEE 71.291212 19.00546 3.75 0.0011*

INCOME_ESTIMATE 0.0048243 0.001397 3.45 0.0023*

Mean (Debit) 0.0407302 0.018583 2.19 0.0393*

Credit Transaction

Average transaction (Debit) 0.999703 0.031064 32.18 <.0001*

MAX_SERV_FEE -77.11591 18.27941 -4.22 0.0004*

Mean (Credit) 0.0332364 0.008666 3.84 0.0009*

Mean (Debit) -0.051299 0.017601 -2.91 0.0080*

INCOME_ESTIMATE -0.004337 0.001489 -2.91 0.0080*

VIS_20_20_SEG [S8] -743.8041 318.5127 -2.34 0.0291*

It follows from table 4.16 that the R2 indicates a better fit for the average number of

transactions and transactions amounts. Using the independent, NO_BANK_SERV,

and VIS_20_20_SEG variables explains 77.4 percent of the total variation of an

average number of transactions per month of race group 1 customers. It follows

that 99.95 percent of the sample variation of the average debit and credit



81

transaction amounts, may both be explained using the independent variables,

INCOME_ESTIMATE, MAX_SERV_FEE, average transactions amount, credit

mean, and debit mean.

To determine the adequacy of the models, the global F-test was conducted. It

follows from table 4.17 that at least one parameter is not equal to zero. Therefore

at the 0.05 significance level, it has been concluded that the models are statistically

significant. To determine how much each parameter contributes to the models, the

t-test statistical method is used and the significance parameters are indicated in

table 4.18.

It follows from table 4.18 that independent variables such as NO_BANK_SERV

and VIS_20_20_SEG [S8, S13], have a positive effect on the average number oftransactions for race group 1. The INCOME_ESTIMATE, MAX_SERV_FEE, mean

debit and credit variables have a positive effect on the debit transaction amounts

and a negative effect on credit transaction amounts. It follows that the transactions

amounts, which are debit and credit have an effect on each other. The

VIS_20_20_SEG [S8] variables have a negative impact on the transaction

amounts per month.

4.5 Analysis for Race group 2.

4.5.1 Descriptive statistics for the average number of transactions and the transaction

amounts for race group 2.

Table 4.19 illustrates the descriptive statistics for race group 2 customers.



82

Table 4.19: Descriptive statistics for an average number of transactions and

average transaction amounts (Debit and Credit) per month for race group 2.

Statistics Debit Credit Number transactions

Mean 1593 1618 4


Median 609 605 2


Sample Variance 10778168 11235855 41

Kurtosis 21 21 9

Skewness 4 4 3

Range 18208 18621 30

Minimum 0 0 0

Maximum 18209 18621 30

Sum 54173 55027 137

Count 34 34 34

It is evident from the table 4.19 that the debit transactions mean amount, is smaller

than the credit transaction mean amount. The data for all three variables ispositively skewed. In figure 4.26, the average numbers of transactions are

indicated, with the majority of the race group 2 customers having less than an

average number of 10 transactions per month. It follows that out of the 34

accounts key; only five of those have at least more than the average number of ten




83

Figure 4.26 Histogram plot for the average number of transactions per month per

account key for race group 2.

Figures 4.27 and table 4.20 illustrate the variation of the behavioural patterns of the

race group 2. In the following paragraphs, the behavioural patterns will be

investigated according to age and customer gender. Table 4.20 indicates the

descriptive statistics according to age.



84

Table 4.20: Descriptive statistics of the transaction amounts according to

customer sex, per month of race group 2.



Mean 5 3


Standard error 2 1

Median 1 2

Variance 65 19

Debit( transaction amount)

Mean 1626 1561



Median 168 725

Variance 18947921 3279764

Credit ( transaction amount)

Mean 1646 1591





85

Median 197 749

Variance 19812238 3360109

N 17 17

It follows from table 4.20 that the female customers have a higher average

transaction mean and average number of transactions, when compared to male

customers. The variation of transactions, when the amounts and the number of

transactions for female customers is higher for male customers. Figure 4.27

indicates the difference in the variation of transactions between customer gendersper account key.

Figure 4.27: Average transaction debit amount per account key vs. customer




86

Figure 4.28: Average transaction credit amounts per account key vs. customer


It follows from figure 4.27 and figure 4.28 that female customers have a high

volume of transaction amounts when compared to male customers. This is also

evident from table 4.20, where a female average debit and credit transaction mean,

is greater than the male transaction mean. Figure 4.29 illustrates the variation of

the average debit and credit transaction amounts over the customer age. It is

evident from figure 4.29 that female customers have a higher volume of

transactions when compared to male customers aged between of 35 and 50 years,

where female customers have the highest transaction amounts at the age 35. This

implies that race group 2 female customers between the ages of 35 and 50, have

the tendency of making a high volume of transactions per month in comparison to

male customers. But these customers seem to balance their debit and credit

transaction amounts from time to time since both their debit and credit transaction

amounts are almost the same.



87

Figure 4.29: Average transaction debit and credit amounts per month vs. account

key per customer age for race group 2.

Figure 4.30: Relationship between the average number of transactions per month

and the transactions amounts per month per account key, according to customer

gender for race group 2.

Average number of transaction per month vs. account key

F M

CUST_AGE

0 10 20 30 40 50 60 70 0

1000

2000

3000

4000

0 10 20 30 40 50 60 70

A v p er m on t h ( D e b i t )

A v p er m on t h ( C r e d i t )

Average transaction amount vs. number of transaction per month

F M

Average number of transaction per month

0 5 10 15 20 25 30

A v e r a g e

t r a n s a c t i o n a m o u n t p e r m o n t h

0

5000

10000

15000

20000

0 5 10 15 20 25 30

A v ov er 6 9 m on t h ( D e b i t )




88

Figure 4.30 illustrates the relationship between the average number of

transactions per month as well as credit and debit transaction amount. The

relationship between the transaction amount and the number of transactions is

different between male and female customers.

Figure 4.31 illustrates the variation in the number of transactions between female

and male customers per account. Female customers have a high variation of the

number of transactions when compared to male customers. Females make up to

11 transactions on average per month, whereas male transaction amounts vary

from 1 to 6 on average per month.

Figure 4.31: The average number of transactions per month vs. account key,

according to customer gender race group.



89

4.5.2 Inferential statistics for an average number of transactions and the amount of

transactions per account key per month for race group 2.

t-test statistics for a difference between male and female behavioural

patterns.


t = 2.03693 Alpha=0.05


Lower CL Upper CL p-Value

Number of transaction -0.70 1.56 2.22 -2.95 6.08 0.4860

Credit trans amount -0.05 54.96 1167.51 -2323.18 2433.10 0.9627

Debit trans amount -0.06 65.60 1143.46 -2263.56 2394.76 0.9546

The t-test statistics are conducted to determine the relationship between female

and male behavioural patterns. It follows from table 4.21 that the p-value is greater

than the 5% significance level. We can conclude that there is insufficient evidence

to wrap up the fact that male and female groups have different behavioural

characteristics. The next table illustrates the correlation between behavioural

variables.



90


Table 4.22: The correlation between customers’ behavioural patterns for race

group 2.

Variable by Variable Correlation Lower

95%

Upper

95%

Signif

Prob

Av(Debit) Account age 0.1990 -0.1492 0.5033 0.2592

Av (Credit) Account age 0.1908 -0.1576 0.4969 0.2798

Av (Credit) Av (Debit) 0.9995 0.9990 0.9998 <.0001*

Average number of

trans

Account age 0.3072 -0.0345 0.5846 0.0772

Average number of

trans

Av (Debit) 0.8430 0.7061 0.9191 <.0001*

Average number of

trans

Av (Credit) 0.8428 0.7058 0.9190 <.0001*

It follows from the correlation table 4.22 that there is a strong positive correlation

between the average number of transaction and transaction amounts, as well as

between debit and credit. There is a strong positive correlation between the debit

and credit transaction amounts per month in this group of customers. It is very

important that the retail bank is able to know that the group 2 customers’ number of

transactions, per month has relationship with the monthly transaction amount. The

regression analysis will determine the influences of other customer characteristics

on the number of transactions and transaction amounts per month as well as the

relationship between behavioural variables.



91

Regression analysis for race group 2.



RSquare 0.876 0.999 0.999

RSquare Adj 0.786 0.999 0.99


Mean of Response 4.127 1693.750 1666.586

Observations (or Sum Wgts) 32 32. 32.



Av. number of Trans

Model 13 1179.8268 90.7559 9.7789

Error 18 167.0541 9.2808 Prob > F

C. Total 31 1346.8809 <.0001*

Debit transaction amount

Model 13 352485484 27114268 26950.93

Error 18 18109 1006.0607 Prob > F

C. Total 31 352503593 <.0001*


Model 13 367394587 28261122 26642.87

Error 18 19093 1060.7388 Prob > F

C. Total 31 367413680 <.0001*



92

Table 4.25: Statistical report for regression significant for estimates race group 2.

Estimate STD Error t Ratio Prob>|t|

Average no of Trans

INCOME_ESTIMATE -3.818e-5 1.682e-5 -2.27 0.0358*

Debit Transaction amount

Average Trans(Credit)

0.9737234 0.004187 232.57 <.0001*

Mean (Debit) 1.3285338 0.095558 13.90 <.0001

Mean (Credit) -1.286208 0.092827 -13.86 <.0001*

Credit Transaction amount

Average trans (Debit) 1.026644 0.004414 232.5 <.0001*

Mean (Debit) -1.364244 0.098054 -13.91 <.0001*

Mean (Credit) 1.320865 0.095188 13.88 <.0001*

One may note in table 4.23 that 87.6 percent of the total variation in the average

number of transactions may be explained using INCOME_ESTIMATE variables.Using the independent credit transaction amount, mean debit and mean credit

variables for the debit transaction amounts and debit transaction amount, mean

debit and credit variables for credit transaction amount, explains the 99.9 percent

of the total variation of the average transaction amount per month for race group 2

customers.

Table 4.24 shows a report for the global F-test results for all three models. It

follows from this table that the models are of statistical significance are at the 0.05

significance level. It is concluded that at least one estimate is not equal to zero

and that the models are statistically significant. The significance of each

independent variable is also presented in table 4.25.



93

The independent INCOME_ESTIMATE variable has a negative effect on the

average number of transactions per month. This implies that the

INCOME_ESTIMATE variable may give information on the pattern or variation of

the number of transactions for customer’s race group 2. The relationship between

the credit transaction amount and debit transaction amount is also evident from

table 4.25. It follows from table 4.25 shows that the debit transaction amount

depends on the credit transaction amount and vice-versa.

The next section 4.6, illustrates the results for race group 3 customers.

4.6 Analysis for Race group 3.

4.6.1 Descriptive statistics for the average number of transaction and transaction amount

for race group 3.

Table 4.26 Descriptive statistics for the average number of transactions and the

average transaction amounts (Debit and Credit) per month for race group 3.

Statistics Debit Credit number of transaction

Mean 1051 1069 4


Median 353 369 2


Sample Variance 4730063 4854209 26

Kurtosis 92 93 11

Skewness 7 7 3

Range 47185 47227 49

Minimum 0 0 0

Maximum 47185 47227 49

Sum 5314073 5407035 21099



94

N 5056 5056 5056

Table 4.26 shows that the data is positively skewed, where debit, credit and the

number of transactions have positive skewness. Race group 3 customers have a

higher transaction mean amount when compared to the debit transaction mean.

The monthly debit transaction has less variation as compared to the credit

transaction amount. Figure 4.31 indicates the variation of the average number of

transactions per month per customer account key.

Figure 4.32 Histogram plot for the average number of transactions per month forrace group 3.

It follows from figure 4.32 that the majority of race group 3 customers less than 20transactions per month on average with few customers having between 20 and 50

transactions per month. Table 4.26 gives the average number of transactions

which race group 3 customers made per month. It follows that all the customers

make about 4 transactions per month. The difference between male and female

customers behaviour, is illustrated by table 4.27 and also in figures 4.33-4.35.



95

It follows from table 4.27 that the male customer has a higher volume of the

transaction amount when compared to the female customers, where the male

average transaction mean amount is higher than the average female transaction

mean amount. This also applies to the average number of transactions per month.

Male customers have higher numbers of transactions when compared to female

customers. From this we can conclude that the average number of transactions per

month correspond with the average transaction volume amounts per month.

Table 4.27 Descriptive statistics of the transaction amount, according to customer

sex per month of race group 3.



Mean 4 5


Standard error 0 0

Median 2 3

Variance 21 31

Debit (transaction amount)

Mean 826 1248



Median 270 446

Variance 4135564 5168862

Credit (transaction amount)

Mean 843 1268



Median 280 457



96

Variance 4178856 5362354

N 2359 2697

Figure 4.33: Box plot for the average transaction debit amount vs. customer

gender, for race group 3.



97

Figure 4.34: Box plot for the average transaction credit amount vs. customer

gender for race group 3.

Figure 4.35: The average number of transactions per month vs. account key,

according to customer gender.



98

It follows from figures 4.33 and figure 4.34 that the transaction amount varies,

where female customers transaction amounts is between R400.00 and R1’400.00,

per month. Male customers have average transaction amounts between R1’000.00

and R1’600 per month. It also follows from figure 4.35 that male customers have

high number of transactions when compared to females. The difference is in

indicated in Table 3.37, where males have a high average transaction amounts

and an average number of transactions per month. Figure 4.36 indicates the age

distribution for race group 3, and figure 4.37 illustrates the mean transaction

amount over the age groups.

Figure 4.36: Customer age distribution for race group 3.

One may observe from figure 4.36 that customers race group 3 have the highest

number of customers between the age of 20 and 40 years, with lowest number of

customers over the age of 80.



99

Figure 4.37: Average transaction debit and credit amount per month vs. customer

age per customer gender, for the race group 3

Figure 4.37 illustrates the variation of transaction amounts according to age group.

Both male and female customers group 3, increase their transactions between the

ages of 20 and 42. The transaction amount decreases between the ages of 42

and 50, and again over the age of 60. The two groups of customers have the same

type of variation, but differ in terms of the transaction amount, whereas males have

a high volume of transaction amounts between the age of 40 and 60 years. The

relationship between the transaction amount and number of transactions is

presented in figures 4.38, 4.39 and 4.40.

Mean Av over 69 months Debit

Mean (Av over 69 months (Credit))



100

Figure 4.38: Scatter plot for the average debit transaction amount vs. average

number of transaction per month.

Scatterplot of Av over 69 months(Debit) against average number of transacton per months over 69 months;categorized by CUST_SEX_CDE

Spreadsheet2 200v*5056c


Average number of transactions per month over 69 month


CUST_SEX_CDE: F

0 10 20 30 40 50 600

10000

20000

30000

40000

50000

CUST_SEX_CDE: M

0 10 20 30 40 50 60

Figure 4.39: Scatter plot for the average credit transactions amounts vs. average

number of transactions per month.

Scatterplot of Av over 69 months(Credit) against average number of transacton per months over 69 months;categorized by CUST_SEX_CDE

Spreadsheet2 200v*5056c

CUST_SEX_CDE: F Av over 69 months(Credit) = -306.5661+319.1385*xCUST_SEX_CDE: M Av over 69 months(Credit) = -207.4826+315.6653*x

Average number of transactions per month over 69 month


CUST_SEX_CDE: F

0 10 20 30 40 50 600

10000

20000

30000

40000

50000

CUST_SEX_CDE: M

0 10 20 30 40 50 60



101

It follows from figures 4.38 and 4.39 that, when the number of transactions

increases, the transaction amounts increase. For this race group, females and

males have the same rate of transaction amounts as the number of transactions

increase. Male and females do not show any transactions of behavioural

differences as the number of transaction increase. Figure 4.40, illustrates the same

pattern using a different type of graph.

Figure 4.40: Relationship between the average number of transactions per month

and the transactions amounts per month, per account key, according to the

customer gender race group 3.

F M


0 10 20 30 40 50

A v e r a g e d e b i t a n d c r e d

i t t r a n s a c t i o n p e r m o n t h

0

10000

20000

30000

40000

0 10 20 30 40 50

A v ov er 6 9 m on t h ( D e b

i t )




102

It is observed from figure 4.40 that, as the average number of transactions

increases from 0 to 30, the transaction amount increase slowly from 0 to R10’000

for both male and females. Males and females have the same pattern as the

number of transactions increases to 30, with few customers having above 30

transactions per month. From this, it is clear that there is a relationship between

the average number of transactions and the transaction amount per month for both

female and male customers. In the next section the relationship between variables

and statistical differences in the behavioural variables, is indicated.

4.6.2 Inferential statistics for the average number of transactions and the amount

of transactions per account key per month for race group 3.

t-test statistics for difference between male and female behavioural patterns.


t = 1.96043 Alpha = 0.05

Variables t ratio Difference Std Err

Dif

Lower

CL

Upper

CL

p-Value Cohen’s

d

Average debittransaction

6.96 421.50 61.03 301.86 541.14 <.0001* 0.96

Average credittransaction

6.93 425.08 61.83 303.87 546.29 <.0001* 0.96

Average number oftransaction

7.54 1.07 0.14 0.79 1.35 <.0001* 1.75

It follows from table 4.28, that one rejects the null hypothesis that the mean for the

two groups do not differ. One then conclude that there is insufficient evidence to

say that the two groups of customers have the same pattern of behaviour. ‘Cohen’s

d’ shows that there is a large difference between male and female behavioural



103

patterns for the average transaction amount and the average number of

transactions. Table 4.29 indicates the correlation between the behavioural

variables.


Table 4.29: Correlation between customers’ behavioural patterns for race group 3.

Variable Variable Correlation Signif Prob

Av (Debit) Account age 0.3680 <.0001*

Av (Credit) Account age 0.3655 <.0001*

Av (Credit) Av (Debit) 0.9990 0.0000*

Average number of transaction. Account age 0.4485 <.0001*

Average number of transaction. Av (Debit) 0.7481 0.0000*

Average number of transaction. Av (Credit) 0.7406 0.0000*

From table 4.29, there is a strong positive correlation between the debit and credit

transaction amounts with the correlation value of 0.9990. There is also a positive

relationship between the average transactions and average number of

transactions. The correlation between debit and the number of transactions are

0.7481, and between credit and number of transactions is 0.7406. The account

age and the transaction amounts have a positive correlation, 0.3680 and 0.3655 forboth debit and credit amounts. Tables 4.30, 4.31 and 4.32 illustrate race group 3

regression results.



104

Regression results for Race group 3.



RSquare 0.733 0.999 0.999

RSquare Adj 0.722 0.999 0.999


Mean of Response 6.808 2180.494 2153.611

Observations (or Sum Wgts) 1106 1106 1106



Av. number of Trans

Model 46 33993.119 738.981 63.3429

Error 1059 12354.667 11.666 Prob > F

C. Total 1105 46347.785 <.0001*


Model 46 1.3637e+10 296449570 19990.92

Error 1059 15704133.9 14829.21 Prob > F

C. Total 1105 1.3652e+10 0.0000*

Credit transactions amount

Model 46 1.4e+10 304344644 19338.79

Error 1059 16666037.7 15737.524 Prob > F

C. Total 1105 1.4017e+10 0.0000*



105

Table 4.32: Statistical report for regression significant estimates for race group 3.

Estimate STD Error t Ratio Prob>|t|

Average no of Trans

MAX_SERV_FEE 0.1431224 0.012816 11.17 <.0001*Account age 0.2103125 0.021493 9.78 <.0001*

Average transactions (Debit) 0.0071263 0.000834 8.55 <.0001*

Average transaction (Credit) -0.005741 0.000818 -7.02 <.0001*

VIS_20_20_SEG [U13 -9.395604 1.761456 -5.33 <.0001*

CONSENT_IND [N] -0.587386 0.119606 -4.91 <.0001

CUST_AGE -0.051852 0.011791 -4.40 <.0001*

INCOME_ESTIMATE -1.333e-5 3.184e-6 -4.19 <.0001*

VIS_20_20_SEG [U3] -9.550512 2.508508 -3.81 0.0001*

VIS_20_20_SEG [U7] 8.9448276 2.78261 3.21 0.0013*

VIS_20_20_SEG [S1] 3.1035321 1.004759 3.09 0.0021*

VIS_20_20_SEG [U8] -9.683266 3.410545 -2.84 0.0046*

VIS_20_20_SEG [U11] 9.2734936 3.341484 2.78 0.0056*

VIS_20_20_SEG [E2] -2.639906 0.964008 -2.74 0.0063*

NO_BANK_SERV 1.23315 0.485903 2.54 0.0113*

INCOME_AMOUNT 8.4421e-8 4.049e-8 2.08 0.0373*


Average transaction (Credit) 0.9681029 0.002186 442.87 0.0000*Mean (Debit) 0.2599706 0.020748 12.53 <.0001*

Mean (Credit) -0.247928 0.020762 -11.94 <.0001*

Av number of transaction 9.0582929 1.059628 8.55 <.0001*

VIS_20_20_SEG [S5] 128.94782 21.89911 5.89 <.0001*

VIS_20_20_SEG [U3] -413.4538 89.14403 -4.64 <.0001*

VIS_20_20_SEG [U11] -509.0411 118.5375 -4.29 <.0001*

VIS_20_20_SEG [U8] -450.5087 121.2692 -3.71 0.0002*

VIS_20_20_SEG [U1] -252.5332 70.1487 -3.60 0.0003*

VIS_20_20_SEG [S1] 117.57616 35.80146 3.28 0.0011*

VIS_20_20_SEG [S4] 91.298584 28.08524 3.25 0.0012*

VIS_20_20_SEG [U7] 303.67947 99.25244 3.06 0.0023*

VIS_20_20_SEG [M8] 48.8622 17.08855 2.86 0.0043*

VIS_20_20_SEG [M9] 58.064248 20.77727 2.79 0.0053*

VIS_20_20_SEG [M6] 44.475437 20.33447 2.19 0.0289*



106

Credit transactions amount

Average transactions (Debit) 1.0274008 0.00232 442.87 0.0000*

Mean (Debit) -0.267974 0.021372 -12.54 <.0001*

Mean (Credit) 0.2579897 0.021359 12.08 <.0001*

Av number of transaction -7.744322 1.103258 -7.02 <.0001*

VIS_20_20_SEG [S5] -127.3831 22.58952 -5.64 <.0001*

VIS_20_20_SEG [U3] 482.18678 91.57052 5.27 <.0001*

VIS_20_20_SEG [U8] 519.45505 124.722 4.16 <.0001*

VIS_20_20_SEG [U1] 279.98431 72.19511 3.88 0.0001*

VIS_20_20_SEG [U11] 467.09208 122.3333 3.82 0.0001*

VIS_20_20_SEG [S4] -98.64817 28.91813 -3.41 0.0007*

VIS_20_20_SEG [S1] -124.0854 36.87231 -3.37 0.0008*

VIS_20_20_SEG [M8] -57.22761 17.58423 -3.25 0.0012*

VIS_20_20_SEG [M9] -67.68137 21.38201 -3.17 0.0016*VIS_20_20_SEG [U7] -299.9248 102.2835 -2.93 0.0034*

VIS_20_20_SEG [M6] -50.50528 20.9378 -2.41 0.0160*

VIS_20_20_SEG [E2] -74.28649 35.45811 -2.10 0.0364*

In table 4.30 R-Square statistics for race group 3 are indicated for three models.

That is an average number of transactions, the average debit and credit

transaction amount models. About 73.3 percent of the sample variation in the

average number of transactions can be explained using customer characteristics

variables, such as the account age, NO_BANK_SERV. CUST_AGE,

INCOME_ESTIMATE and CONSENT_IND [N]. For both debit and credit

transactions, about 99.9 percent of the sample variation of the data, can be

explained using the customer profile characteristic variables, such as average

number of transactions, mean debit, credit as well as VIS_20_20_SEG. The R2

indicates a better fit for the models. To determine the adequacy of the models, theglobal F-test was conducted. In table 4.30, all three models were statistically

significant at the 0.05 significance level.

Table 4.31 illustrates the significant relationship between customer characteristics

and behavioural variables. Account age, MAX_SERV_FEE,



107

VIS_20_20_SEG[U1,3,8,11], INCOME AMOUNT, transaction amount debit and,

NO_BANK_SERV variables which have a positive influence on the average

number of transaction made over the past six years by race group 3 customers.

The account age has also been indicated as a variable that has an impact on the

customers behavioural patterns. The results of the study by Van den Poel and

Lariviere, (2003) indicate that the longer the customer stays with the bank, the

smaller the probability that the customer will stay with the bank decrease,

according to the survival distribution function. After 20 years the chances to stay

with the bank decreases at higher rate and continue to decrease. In terms of

demographic characteristics, men experience a shorter duration time than woman,

and older people are less likely to end their relationship with the financial services.

In this study, the regression results illustrate that the average transaction (credit),

CONSENT_IND [N], VIS_20_20_SEG [E2, M2, S1, S4, S5, M6, M8, M9, U7, U13],

INCOME_ESTIMATE and customer age have a negative influence on the average

number of transactions per month. The relationships between the average credit

transaction amounts and the average number of transactions per month are also

illustrated by the correlation coefficient in table 4.29.

The average credit transaction amount, the mean debit, the average number of

transactions per month, INCOME_ESTIMATE and VIS_20_20_SEG [S1, S4, S5,U7, M6, M8, M9] have positive effects on the average debit transaction amounts

per month. It follows that the debit amount per transaction has a positive influence

on the total debit transaction amounts per month. This implies that the amount that

a customer makes per transaction have an influence on the monthly average debit

transaction amount. The relationship between the average credit transaction

amount and debit is also indicated by correlation coefficient table. Furthermore,

the average number of transactions per month, debit mean and VIS_20_20_SEG

[S1, S4, S5, U7, M6, M8, M9, E2] have a negative influence in the average credit

transaction amounts per month. The credit amount per transaction, average debit

transaction amount and VIS_20_20_SEG [U1, U3, U8, U11] has a positive

influence on the average credit transaction amounts per month. It is clear that



108

there is a relationship between the average credit transaction amount per month

and the average debit transaction amount per month.

The findings indicate that the variation of the transaction amount as well as the

debit and credit transaction amounts is an important tool which can help to

understand how customers of race group 3 behave from time to time. The account

age, INCOME_AMOUNT and customer age are also important variables which

may help one to determine or predict the number of transactions which an

individual customer makes over time. The INCOME_ESTIMATE or

INCOME_AMOUNT are one of the variables which can assist one to get an

understanding on how many transactions the customer with a certain income level

make and how much of the transaction amounts they make per month.

4.7 Summary

Table 4.33 illustrates the summary of the decisions on the hypothesis results. The

hypotheses were derived based on the general hypothesis (see paragraph 1.2.4).

It follows that different customer categories have a statistically significant difference

in behaviours with respect to how customers operate their accounts according to

race groups, gender and age. The results indicate that customers have different

behavioural patterns with respect to gender, race groups and other characteristics.

Table 4.33 summarises the hypothesis test results for each null hypothesis,

together with the statistical test used.



109

Table 4.33: Summary of the results for the research hypothesis

Hypothesis Fail to

reject

Reject Test Significant lev

or p-value

H01: Males and females

have no significant different

behavioural patterns

Race

group 0

& 3

yes t- test <0.05.

significance le

Race

group 1

& 2

Yes t-test >0.05.

significance le

H02: No relationship

between behaviouralvariables and customer

characteristics

yes Correlation <0.001.

significance le

yes regression <0001.

significance le

H03: Race groups have

same behavioural patterns

yes t-Test: Two-

Sample

Assuming

Unequal

Variances

<0.05.

significance le

H04: Different age group

have no significant different

behavioural patterns

Yes test: Two-

Sample

Assuming

Unequal

Variances

<0.05.

significance le

H0 : Different customers’

categories have no statistical

significant different

behaviour with respect to

how customers operate their

accounts

yes t-test,

Correlation

and

ANOVA,

regression

Significant at

<0.05



110

CHAPTER 5: SUMMARY OF THE RESULTS, CONCLUSION, LIMITATION AND

FUTURE STUDY

5.1 Introduction

Chapter 4 of the study outlined the results of the data analysis. Chapter 5 will

outline a summary of the results, conclusions and research limitations of the study.

This includes a summary of the entire data set as well as the race groups’ data set,

as well as descriptive and inferential statistics results.

5.2 Summary of the results

5.2.1 Summary of data set with the 7838 sample.

The results of the study have shown interesting patterns about the given set of

data. In general customers have shown variability in their behavioural patterns. To

illustrate this, the summary on the descriptive statistics is presented in table 4.1.

Table 4.1 indicates the transaction amount and the number of transactions for

closed and active customer accounts. The average transaction means are

different where customers have a high credit transaction amount compared with

the debit transaction amount. On average, customers have about R2000.00

transaction mean amounts per month and five numbers of transactions.

It is evident from figures 4.6 and 4.5 that the behavioural pattern is different for

both males and females. The t-test statistics in table 4.3 illustrate that gender

differences exist within this customers group. This implies that males and females

show behavioural differences in terms of financial demand and financial

management. From figure 4.8, it follows that there exists a difference in the

amount of transactions over age groups, where young customers between the

ages of 0 to 19, have a small volume of transactions, when compared to other age

groups. Between the ages of 20 and 40, there is a smooth increase in the

transaction amount and this could be the result of higher numbers of customers

within this age group.



111

Furthermore, customers demonstrate different patterns based on transaction

amounts. From figures 4.9, 4.10 and 4.11, one must notice as the average number

of transactions increase, the average transaction amount increases for both males

and females. The increase in average transaction amounts per month for males is

different from that of females, as males increase their transaction amounts faster

than the females.

In table 3.4 customers’ transaction amounts are highly correlated to each other

with a correlation coefficient of 0.999. The increase (decrease) in the average debit

transactions amounts of an individual will increase (decreases) the average credit

transactions amount. The account age and number of transactions, and accounts

age and transaction amounts have a positive correlation coefficient.

5.2.2 Summary of Race groups

Table 5.1 outlines the results of the transaction mean and the average number of

transactions per month for race groups.

Table 5.1: Race group average transaction mean and average number of

transaction mean.

Race group Av mean (debit) Av mean (credit) Av no of Trans (mean)

0 7321.574057 7545.356632 10.06416601

1 4860.028 5097.090231 7.759112868

2 1593.329241 1618.428193 4.043904518

3 1051.043 1069.429 4.17311043

From Table 5.1 one may note that, the race group 0 has different transactions

amounts per month, as there is a high volume of credit transaction amounts. Race

code 1 customers have a different transaction mean amount per month where the



112

debit transaction is higher than the debit transaction amount mean, with the

number of transactions being eight on average. Race group 2 transaction amounts

are slightly difference with average credit amounts being greater than the average

debit transaction amount per month. With regard to race group 3 customers, it

follows that the difference between debit and credit transaction amounts is very

small with four average numbers of transaction per month. All customer race

groups have high credit transaction amounts when compared to debit transaction

amount.

Race groups 2 and 3 have an average transaction mean of around R1’600.00 and

R1’100 respectively, and the average number of 4 transaction means per month.

Race group 0 has the highest transaction volume mean and on average number of

transactions. This implies that different race groups can have different behavioural

patterns regarding transaction amounts and the number of transactions. The

gender behavioural differences are also indicated in table 4.23.

Table 5.2 illustrates the summary differences in race groups and age group

behavioural differences. Only age groups 20-39 and 40-60 were used to illustrate

the differences between age groups. Race groups 0 and 3 sample were used to

illustrate the differences in racial group patterns. Race group 1 and 2 were not

used as the data sample was too small.



113

Table 5.2: Summary of t-test for the race groups and age groups behavioural patterns.

Statistics R3(Debit) R0(Debit) R3(Credit) R0(Credit)

N of trans

R0 No of trans R3

Mean 1051.043 9074.271765 1069.429 9314.708 10.16261722 4.17311043

Variance 4730063 1021915582 4854209 1.05E+09 121.4106667 26.3566234

Observations 5056 1156 5056 1156 1156 505

t Stat -8.52886 -8.65324 18.03941736

P(T<=t) two-tail 4.57E-17 1.65E-17 5.92816E-65

t Critical two-tail 1.962016 1.962016 1.961830676

Practical significant Cohen’s d 0.576157902 0.584138392 0.90258544

t-Test: Two-Sample Assuming Unequal Variances ( 20-40 and 40-60) years Race group 0

Aver number of

transaction(20-39)

Aver number

of

transaction(40-

60)

Credit(20-39) Credit(40-60) Debit(20-39) Debit(40-60)

Mean 9.955019 12.90148953 4413.130509 11278.07207 4349.92 11062.93



114

Variance 95.44728 164.7338491 45271031.13 1172805561 44728854 1.11E+0

Observations 328 360 328 360 328 360

t Stat -3.40549 -3.725330904 -3.73843

P(T<=t) two-tail 0.0007 0.000223932 0.000213


0.256742857 0.272354747 0.27355406


Debit(20-39) Debit(40-60) Credit(20-39)

Credit(40-

60)

Aver number of

transaction(20-

39)

Aver number o

transaction(40-

60)

Mean 810.5545 1759.133 826.2993 1784.442 3.748218 5.87022786

Variance 3025968 8360765 3081987 8508386 23.05442 38.4716447

Observations 2778 1356 2778 1356 2778 135

t Stat -11.1366 -11.1502 -11.0813

P(T<=t) two-tail 6.3E-28 5.46E-28 8.46E-28



115


Practical significant Cohen’s d 0.434078658 0.434557214 0.40023558



116

It follows from table 5.2, that race groups and age groups are statistically

significant, with a moderate and large effect and are practically significant for

transaction amounts as well as the number of transactions, respectively. It can be

concluded that race groups demonstrate different behavioural patterns with regard

to how customers operate their accounts. Furthermore different age groups have

significantly different behavioural patterns. Table 5.3 shows a summary of the

correlation coefficients of transaction amounts, number of transactions and account

age of race group customers.

Table 5.3: A summary of the correlation coefficients for variables of all race groups.

Variable 1 Variable 2 Race group

0

correlation

Race group

1

correlation

Race group

2

correlation

Race group

3

correlation

Account

age

Aver no of

transaction

0.4860* 0.4327* 0.3072 0.4485*

Account

age

Credit 0.3300* 0.1550 0.1908 0.3655*

Account

age

Debit 0.3394* 0.1893 0.1990 0.3680*

Aver no of

transaction

Debit 0.5627* 0.3687* 0.8430* 0.7481*

Aver no of

transaction

Credit 0.5494* 0.3464* 0.8428* 0.7406*

Debit Credit 0.9939* 0.9898* 0.9995* 0.9990*

(* significant correlation between two variables).



117

Table 5.3 indicates the correlation coefficients amongst customer race groups’

behavioural variables. The average transaction amounts for all race groups are

highly correlated with a correlation coefficient of close to 1. The increase (decrease)

in the average debit transactions will increase (decrease) the average credit

transaction amount of all race groups. There is also a high correlation between

the average transaction amounts, debit and credit and average number of

transactions for race group 2 and 3. Race groups 0 and 1 have a week positive

correlation coefficient between the average number of transactions and transaction

amounts.

The account age is correlated with the average number of transactions, with a

positive correlation coefficient for all race groups. As the account age increases

(decrease), the average number of transactions increases (decreases). The table

below outlines a summary of regression output with, R-square and F-test statistics

for the models. The model assumption about the random error is defined as:

(1) For all data sets, random error has a normal probability distribution with a mean

of 0 and variance equal to 1.

(2) The random errors are independent (in a probabilistic sense).

Table 5.4 illustrates the regression summary of results for the average transaction

amounts and the average number of transactions for the various race groups.



118

Table 5.4 Regression summary of customers’ race groups.

Race group

0 Statistics

Av no of Trans Av Credit Av Debit

R-Square 0.528087 0.998504 0.998494

F Ratio

Prob > F

16.4783

<.0001*

9826.487

0.0000*

9760.877

0.0000*

N 803 803 803

Significant

variables

Account age

Mean (Debit)

MAX_SERV_FEE

PROP_OWNR_IND[O]

VIS_20_20_SEG[M9,U8

,U5,U12,LI,U10]

Av (Debit) amount

Mean (Credit)

INCOME_AMOUNT

Mean (Debit)

CUST_NO_CHILD

VIS_20_20_SEG[U

5,U10,U12]

Av (Credit) amount

Mean (Credit)

INCOME_AMOUN

T

Mean (Debit)

CUST_NO_CHILD

VIS_20_20_SEG[U

5,U10,U12]

Race group 1 Statistics

RSquare

0.806044 0.999502 0.999514

N 57 57 57

F Ratio

Prob > F

2.1341

0.0402

1029.608

<.0001*

1055.166

<.0001



119

Significant

variables

NO_BANK_SERV

VIS_20_20_SEG [S8]

Av over (Debit)

MAX_SERV_FEE

Mean (Credit)

Mean (Debit)

INCOME_ESTIMAT

EVIS_20_20_SEG[

S14,S8]

Average (Credit)

Mean (Credit)

INCOME_ESTIMATE

MAX_SERV_FEE


RSquare 0.789761 0.999182 0.99919

N 34 34 34

F Ratio

Prob > F

13.9527

<.0001*

4538.567

<.0001*

4581.224

<.0001*

Significant

Variables

Account age Debit Credit


RSquare 0.736035 0.998642 0.998697

N 2290 2290 2290

F Ratio

Prob > F

119.9542

0.0000*

31645.20

0.0000*

32964.44

0.0000*



120

Significant

variables

Account age

Av (Debit)

MAX_SERV_FEE

Av (Credit)

CONSENT_IND [N

NO_BANK_SERV

VIS_20_20_SEG[M8,M7

,U8,MC,S2,S1,U1,S3,U3

,E2,MB,S4,LI]

INCOME_ESTIMATE

MRTL_STAT_CDE[S]

Aver number of

transaction per

Mean Debit

Mean Credit

VIS_20_20_SEG[M

9,S5,U1,U3,U7,U8]

Average Debit

Mean Debit

Av Credit

Aver no oftransaction

Mean Credit

INCOME_ESTIMA

TE

VIS_20_20_SEG[

M9,S6,S2,U1,U7,U

3,U8,S5]

It follows from table 5.4 that the R2 for all race groups indicates a better fit for the

debit and credit transaction amounts and the average number of transaction

models. Independent variables indicated in table 5.4 explains a certain percentage

(refer to R-square of each variable for race groups in table 5.4) of the total variation

of the average transaction amounts and average number of transactions per month

for each race group.

The Global F-test statistics were conducted to determine the adequacy of the

model. It follows from table 5.4 that at least one parameter is not equal to zero for

all race group models. Therefore at the 0.05 significance level, it is concluded that

the models are statistically significant. The contribution of each parameter is

determined by the t-test statistical method and all significant variables are indicated

in table 5.4.



121

The relationship between the transaction amounts is evident from table 5.4,

where both credit and debit transaction amounts have an impact on each other for

all four customer race groups.

It follows that an account age, mean (debit), MAX_SERV_FEE,

PROP_OWNR_IND [O], and VIS_20_20_SEG [M9, U8, U5, U12, LI, U10]

variables have an influence on the average number of transactions of race group 0.

The number of transactions for race group 0 depends on the account age, the

income of the customer as well as the maximum service fee which the business

charges. INCOME_AMOUNT, Mean (debit), CUST_NO_CHILD and other

variables indicated in the tables, have an influence on the transaction amounts of

race group 0 customers. The transaction amounts depend on the customer

income, transaction amounts per transaction as well as the number of children.

For race group 1, NO_BANK_SERV and VIS_20_20_SEG [S8] has an influence

on the average number of transactions. INCOME_ESTIMATE, mean (credit), and

MAX_SERV_FEE have an influence on both debit and credit transaction amounts.

The transaction amount of race group 1 customers depends on the maximum

service fee, income and also credit transaction amounts per transactions.

VIS_20_20_SEG [S14, S8] variables also have an effect on the debit transaction

amounts.

In race group 2 the account age has a relationship the average number of

transactions per month. The debit and credit transaction amounts have an

influence on each other.

Furthermore, the average number of transactions per month for race group 3 have

a relationship with the account age, maximum service fee, marital status

(MRTL_STAT_CDE[S]),CONSENT_IND[N],NO_BANK_SERV,VIS_20_20_SEG[M

8,M7,U8,MC,S2,S1,U1,S3,U3,E2,MB,S4,LI], INCOME_ESTIMATE and transaction

amount variables. Average number of transactions, mean debit, mean credit,

VIS_20_20_SEG [M9, S5, U1, U3, U7, U8], and average debit have an influence



122

on the debit and credit transaction amount. The MAX_SERV_FEE also has a

relationship with the credit transaction amount.

In Table 5.4, the regression results as indicated it can prove that there a

relationship exists between customers’ characteristic variables and behavioural

patterns. However not all characteristics profile variables significantly influence the

behavioural patterns of customers; that is the transactions amount and number of

transactions.

It is also evident that customer race groups’ behavioural patterns are influenced by

profile characteristics which are similar and different. This means that some of the

profile characteristics variables which are significant in race group 0, may or may

not be significant to other race groups. This is evident from table 5.4; account agehas an influence on the average number of transactions of race groups 0, 2 and 3.

MAX_SERV_FEE has an influence on the average number of transactions of race

groups 0 and 3, and also on the transaction amount of race group 1.

This implies that there are specific variables for each customer race group which

have a significant relationship with the customer behavioural characteristics.

However, it is clear that there is a relationship between customer profile

characteristics and their behavioural patterns, specifically debit and credit

transaction amounts and the number of transactions. There is significant evidence

that there is a relationship between the behavioural patterns and the customer

profile characteristics, therefore the null hypothesis is rejected. It can thus be

concluded that the customer characteristics and their behavioural patterns have a

statistically significant relationship at the 5% significance level. Table 5.5 illustrates

the customer gender behavioural characteristics.



123

Table 5.5 t-test statistics for the difference between customer gender

behavioural characteristics.

Race group

Statistics

Mean F Mean

M

t-Ratio t

05.0=α

Reject

rule

P-Value

Race group 0

Average number

of transaction

9 10 4.46 1.96 reject <.0001*

Transaction

amount (credit)

4993 10138 5.80 1.96 reject <.0001*

Transaction

amount (debit)

4764 9920 5.93 1.96 reject <.0001*

Race group 1

Average number

of transaction

8 8 -0.03 1.99 Do not

reject

0.9742

Transactionamount (credit)

3939 6062 1.21 1.99 Do notreject

0.2586

Transaction

amount (debit)

3773 5766 1.13 1.99 Do not

reject

0.2887

Race group 2

Average number

of transaction

5 3 -0.70 2.04 Do not

reject

0.486

Transaction

amount (credit)

1646 1591 -0.05 2.04 Do not

reject

0.9627



124

Transaction

amount (debit)

1626 1561 -0.06 2.04 Do not

reject

0.9546

Race group 3

Average number

of transaction

4 5 7.54 1.96 Reject <.0001*

Transaction

amount (credit)

843 1268 6.93 1.96 Reject <.0001*

Transaction

amount (debit)

826 1248 6.96 1.96 Reject <.0001*

Table 5.5 summarises the statistical test of gender behavioural patterns. Using t-

test statistics, the statistical inference about the hypothesis that gender differs in

terms of behavioural patterns is illustrated in table 5.5.

Male and female customers in race group 0 and race group 3 demonstrate different

behavioural characteristics, therefore the hypothesis that males and females havethe same behavioural pattern is rejected. It can thus be concluded that there is

sufficient evidence that males and females have different behavioural

characteristics. This implies that male and female customers of these two race

groups differ significantly in their transaction amounts differ with regard to the

number of transactions.

According to t-test statistics, the results fail to reject that, males and females have

similar behaviours for race groups 1 and 2 where p-values are insignificant in all

variables. It can thus be concluded that there is insufficient evidence that male and

female customers have different behavioural patterns. These could be due to the

variation in the sample data of the two race groups where the sample size is

smaller when compared to race groups 0 and 3.



125

Table 5.6, illustrates the statistical practical significance when using ‘Cohen’s d’

for significant independent male and female behavioural differences. Cohen’s d

shows a large practically significant difference between males and females for both

average transaction amounts and the average number of transactions per month.

For race groups 1 and 2 practical significance is not calculated since the null

hypothesis is not rejected.

Table 5.6: Practical significance for the statistically significant differences between

males and females.

Race groupStatistics

Mean F Mean M Reject rule P-Value Cohen’s d

Race group 0


9 10 reject <.0001* 6.17

Transaction amount(credit)

4993 10138 reject <.0001* 1.01

Transaction amount(debit)

4764 9920 reject <.0001* 1.01

Race group 3


4 5 reject <.0001* 1.75

Transaction amount(credit)

843 1268 reject <.0001* 0.96

Transaction amount(debit)

826 1248 reject <.0001* 0.96



126

5.3 Conclusion

This study is a small step in examining the relationship between customers’

behavioural variables and customer profile characteristics, and by doing so; enable

the researcher to identify customer behavioural patterns based on customer

transaction history. Furthermore, one may identify variables that can be useful in

identifying and estimating customer behavioural patterns such as switching

behaviour in the banking business according to race groups. It is believed that this

exploratory study has brought forward significant evidence, by introducing the

relationship between customer behavioural variables and the customer profile

characteristics as well as their impact on customer behavioural patterns in a

multicultural environment.

The primary objective of this study was to investigate the relationship of customers’

behavioural patterns based on customers past transactions data, with respect to

their profile characteristics. The researcher should have proved that different

categories of customers have statistically significant different behaviour with

respect to how these customers operate their accounts.

The study has illustrated that the account age variable has an effect on theaverage number of transactions of race group 0, 2 and 3. The account age has

been considered to be one of the variables which indicate the behavioural

characteristics of bank customers. Van den Poel and Lariviere, (2003), and

Mutanen (2006) illustrate that customers experienced higher switching behavioural

rate within the first few years of being a customer or when they have a small age

account.

The average number of transactions, have had an influence on the transaction

amount of race group 3. The decrease in the number of transactions, indicates a

customers switching behaviour (Mutanen, 2006). The number of transactions may

play a significant role in determining the behavioural patterns of customers and

more specifically in race group 3.



127

The maximum service fee has an influence on the transaction amount, as well

as the debit and credit amounts for race group 1, and the number of transactions of

race group 3. Pricing is indicated as the main factor for bank customer switching

behaviour, which shows the effect amongst the growing retail banking industry

(Athanassopoulous, 2000). The impact of the maximum service fee should be

considered in the process of strengthening the relationship with customers and

identifying customers’ behavioural patterns.

Customer income has an impact on the transaction amount, debit and credit for

race groups 0, 1 and 3. Customer salary is an important variable for the prediction

of churn (Mutanen, 2006). Furthermore, there is high correlation between

transaction amounts.

The impact of other variables such as the number of children, VIS_20_20_SEG,

MRTL_STAT_CDE[S], NO_BANK_SERV, and other variables in table 5.4 with

regard to the behavioural patterns gives additional information as to how these

variables influence customer behaviour. Demographic variables have little impact

on switching behaviour (Mavri & Loannou, 2008). However, these variables still

play a big role on the analysis of customer behavioural patterns.

In this study, a general null hypotheses and four hypotheses were derived from the

general hypothesis (refer to section 1.2). The results of this study indicate that,

• Different customers’ categories have statistically significant differences in

behaviour, with respect to how customers operate their accounts.

It follows from the results for the derived hypotheses that:

• Customers’ personal characteristics have an influence on customer behavioural

patterns. However, race groups’ behavioural patterns are influenced by both

similar and, or different personal characteristics.

• It is also concluded that males and females have different behavioural patterns

when using closed and active data and, when using active data for race groups 0



128

and 3. The result indicates that there is a large practical significance in the

behavioural differences between males and females for race groups 0 and 3

when using Cohen’s d. This information supports the graphical presentation

which indicates that males have a higher rate of average transaction amounts

when compared to females, as the average number of transactions increase per

month.

• When using race groups 0 and 3, it follows that there exists a statistical difference

in customer age groups. Customers aged between 20 and 39 years behave

differently when compared to customers between the ages 40 and 60 years. The

results indicate a small practical significant on transactions amount and average

number of transactions between the two age groups.

• Race groups have statistical significance with respect to how customers operate

their accounts. Again using race groups 0 and 3, there is significant evident that

race groups 0 and 3 behave in different ways. The results indicate a moderate

practical significance on transaction amounts and the large practical significant

on the average number of transactions between the two race groups.

Therefore, it can thus be concluded that different customer categories have

statistically significant differences in behaviour with respect to how customers

operate their accounts. Male and female customers have different behavioural

patterns for race groups 1 and 3 and do not show any differences in race groups 0

and 2. When using the transaction data, it has been derived graphically that male

customers have higher rate of transaction amounts when compared to females.

Different age groups demonstrate different behavioural patterns with respect to

how customers operate their accounts. Customers of different race groups have

different behavioural patterns. Therefore, this implies that it is important to consider

customer gender differences, age differences and race groups’ differences in

relationship strategies and more importantly in a multicultural environment.



129

5.4 Implications

Theoretically, the outcome of the study provides empirical evidence of the

influence of customer personal characteristics on behavioural patterns. Customer

relationship management (CRM) is focused on the creation and maintenance of

long-term, mutually beneficial relationships with strategically important markets or

customers. It is important that financial business should understand behavioural

patterns of customers as well as the drive behind their behaviour according to race

group in a multicultural environment.

As for practical implications, the first conclusion is that the banking business which

wishes to strengthen its relationship with its existing customers, should consider

differentiating customers according to race group, and understanding personalcharacteristics of each race group which has an impact on the customer. The

business should know that the transaction data for customers can help identify

customer behavioural patterns without considering customer profiles. The

business should also consider customer characteristics, such as income, maximum

service fees, bank services, account age, number of children, CONSENT_IND [N]

and the number of transactions in the analysis of behavioural patterns or the

switching behaviour of the customer race group.

The second practical implication is that, customers’ age and, gender difference is

evident and should be considered for a race groups’ analysis. For a bank to get a

good overview of their customers’ behavioural patterns, businesses should not

assume that male and female customers of all age groups have the same

behavioural patterns. These two groups of customers, namely, males and females

should be dealt with differently, where necessary. There is evidence supporting

gender differences between men and women in the financial decision-making(Powell & Ansic, 1997). Female customers are significantly more loyal than male

customers when the bank is deemed very trustworthy in the Malaysian banking

sector (Ndubisi, 2006). These imply the importance of considering gender

differences in the customer behavioural analysis.



130

5.5 Limitations and future research

This study has demonstrated the relationships of customers’ behavioural patterns

with respect to how customers operate their accounts based on their transactions

history and profile characteristics, such as customer age, gender and race in the

banking business. While this research extends and contributes to past studies on

relationship management and on customer behavioural analysis, the potential

limitation of this study is the selection of sample data.

The researcher focuses on selected groups of customers in the branch of a major

South African bank. The study used both closed and active data for overall

analysis and, only active customer data was used for race groups’ analysis within

the period of January 2003 until September 2008. The analysis focused oncustomer race groups’ behavioural patterns. This implies that further research in

the area may be necessary before generalisation may be made on the entire bank

race group customers. Future research may include non-active data for analysis of

each race group to see if there are any differences in variation on the behaviour of

customers. More behavioural variables such as the time frequency of transactions

used, the transaction channels, and the type of transaction could be used in the

analysis. Furthermore, future research could try to expand the same research and

use identified behavioural and customer personal variables to predict the customer

switching behavioural rates of race groups in the banking business.



131

References

Anderson, R.E., (1996). Personal selling and sales management in the new millennium,

Journal of Personal Selling and Sales Management , 16(4), 17–32.

Athanassopoulous, A .D., (2000). Customer satisfaction cues to support market

segmentation and explain switching behaviour, Managerial Auditing , 15(5), 19-208.

Au, T., Li, S., and Ma, G., (2003). Applying and evaluating models to predict customer

attrition using data mining techniques, International Marketing Journal , 6(1).

Bailor, C., (2007). Exhausting customers in a fierce business environment erodes profits

through loyalty loss, CRM magazine , www.loyaltyfactor.com, [Accessed 02/08/2008].

Benaroch, M., (2005). Customer Relationship Management with SAP, Whitman School of

Management, CRM, myweb.whitman.syr.edu/mbenaroc/courses/syl-655.pdf, [Accessed:

02/07/2008].

Bitner, M.J., (1990). Evaluating service encounters: the effects of physical surroundings

and employee responses, Journal of Marketing , 5(22), 69-82.

Bick G., Brown, A.B., and Abratt, R., (2004). Customer perception of the value delivered

by retail banks in South Africa, International Journal of Bank Marketing , 20(7), 317-324.

Bohling, T., Kumar, V., and Ramani, G., (2004). Customer lifetime value approaches and

best practice applications, Journal of Interactive Marketing , 18(3), 60-72.

Boulding, W., Staelin, R., Ehret, M., and Johnston, W.J., (2005). A customer relationship

management roadmap: what is known, potential pitfalls, and where to go, Journal of

Marketing , 69 (4), 155-67.

Burez, J., and Van den Poel, D., (2008). Separating financial from commercial customer

churn: A model steps towards resolving the conflict between the sales and credit

department, Expert Systems with Applications: An International Journal, 35(2), 497-514.



132

Colgate, M., Stewart, K., and Kinsella, R., (1996). Customer defection: a study of the

student market in Ireland, International Journal of Bank Marketing, 24(7), 494-508.

Cronin, J. J., and Taylor, S. A., (1992). Measuring service quality: A re-examination and

extension, Journal of marketing , 56, 55 - 68.

Danenberg, N., and Sharp, B., (1996). Measuring loyalty in subscription markets using

probabilistic estimates of switching behaviour, Proceedings of ANZMEC Conference , 390-

401.

Ehigie B. O., (2006). Correlates of customer loyalty to their bank: case study in Nigeria,

European Journal of Operational Research , 15(7), 196-217.

Furlong, C., (1993). 12 rules for customer retention, Bank Marketing , January, 14-18.

Gall, J. P., Gall, M. D., & Borg, W. (1999). Applying educational research: A practical

guide (4th ed.). New York: Addison Wesley Longman.

Garland, R., (2002). Estimating defection in personal retailing banking, Financial

Publishing Service White Paper , 20 (7), 317 – 324.

Geib, M., Reichold, A., Kolbe, L.M., and Brenner, W., (2005). Architecture for customer

relationship management approaches in financial services", 38th Hawaii International

Conference on System Sciences 2005 (HICSS-38), Big Island, Hawaii, 3 January , IEEE

Computer Society, Washington, DC.

Geppert, C., (2002). Customer churn management: retaining high-margin customers with

customer relationship management techniques. KMPG.

Gravetter, F.G., and Wallnau, L.B., (2005). Essentials of Statistics for the Behavioural

Sciences. USA, Belmont, CA: Thomson Wadsworth.

Hair, F.H., Lamb, C.W., and McDaniel C., (2006). Introduction to Marketing . China:

Thomson Southern-Western.



133

Jamal, A., and Nasser, K., (2002). Customer satisfaction and retail banking: an

assessment of some of the key antecedents of customer satisfaction in retail banking,

International Journal of Bank Marketing , 20(4), 146-160.

Jayachandran, S., Subhash, S., Kaufman, P., and Raman, P., (2005). The role of rational

information process and technology use in customer relationship management, Journal of

Marketing , 6(October), 177-192.

Kamakura, W., Mela, C.F., Ansari, A., Bodapati, A., Fader, P., Iyenger, R., Naik, P.,

Neslin, S., Sun, B., Verhoef, C.P., Wedel, M., and Wilcox, R., (2005). Choice models and

customer relationship management, International Journal of Service Industry

Management , 10(3), 320-336.

Laroche, M., Rosenblatt, J., and Manning, T., (1986). Services used and factors

considered in selecting a bank, International Journal of Bank Marketing , 14 (1), 35-55.

Lejeune, M.A.P.M., (2001). Measuring the impact of data mining on churn management,

Internet Research , 11(5), 375 - 387.

Lunt, T.F., (1993). Detecting intruders in computer systems, Paper presented at the

Conference on Auditing and Computer Technology , 1993, available at:

www.sdl.sri.com/nides/index5.html , [Accessed: 22/08/2008].

Mavri, M., and Loannou, G., (2008). Customer switching behaviour in Greek banking

services using survival analysis, Journal for Consumer Marketing , 20(4), 294-316.

Mithas, S., Krishnan, M.S., and Formell, C., (2005). Why do customer relationship

management applications affect customer satisfaction?, Journal of Marketing , 69(4), 201.

Motley, L. B., (2005). The benefits of listening to customers. ABA Banking Marketing , 37,

43.

Mutanen, T., (2006). Customer churns analysis-a case study, Journal of Product and

Brand Management, 14(1), 4-13.



134

Ndubisi, N.O., (2005). Effect of gender on customer loyalty, Marketing

Intelligence and Planning, 25 (1), 98-106.

Ndubisi, N.O., (2006). Relationship marketing and customer loyalty, Marketing

Intelligence and Planning , 25(1), 98-106.

North, M., (2007). Poor service drives banking customers away, Fujitsu Australia and New

Zealand, www.Fujitsu.com, Archives , [Accessed on 22/08/2008].

Peppard, J., (2000). Customer Relationship Management (CRM) in financial Services.

European Journal of Management , 18(3), 312-327.

Powell, M., and Ansic, D., (1997). Gender differences in risk behaviour in financial

decision making: an experimental analysis, Journal of Economic Psychology , 18 (6), 605-27.

Reicheld, F.F., (1993). Loyalty based management, Harvard Business Review , 71(4), 64-

73.

Reinartz, W., Krafft, M., and Hoyer, W.D., (2004). The customer relationship management

process: its measurement and impact on performance, Journal of Marketing Research , 6,

293-305.

Rootman C., (2006). The influence of customer relationship management on the service

quality of banks. Port Elizabeth. NMMU, (Thesis- MCom ).188

Ryals, L., (2005). Making customer relationship work: the measurement and profitability

management of customer relationship, Journal of Marketing , 6(9), 252-261.

Rygielski, C., Yen, D. C., and Wang, J., (2002). Customer relationship management in the

network economy, International Journal of Services Technology and Management, 3(3),

297-310.

Srinivasan, R., and Moorman, C., (2005). Strategic firm commitments and rewards for

customer relationship management in online retailing, Journal of Marketing , 6(9), 193-

200.



135

Storbacka, K., (1994). The Nature of customer relationship profitability, International

Journal of Service Industry Management, 5(5), 21-23.

Trubik, E., and Smith, M., (2000). Developing a model of customer defection in the

Australian banking industry, Journal of Business Research , 47 (47), 191-207.

Van den Poel, D., and Lariviere, B., (2003). Customer attrition analysis for financial

services using proportional hazard models, European Journal of Operational Research ,

157 (1), 196–217.

Wah, B.Y., (2006). Some application of data mining, conference paper.

www.emaraldinsight.com, [Accessed on 23/08/2008].

Winer, R., (2001). Customer Relationship Management: A Framework, Research

Directions, and the Future, 332 – 340.

Wisskirchen, C., Vater, D., Wright, T., De Backer, P., and Detrick, C., (2006). The

customer-led bank: converting customers from defectors into fans, Strategy & Leadership,

ISSN 1087 8572 , 34(2), 10-20.

Zineldin, M., (2005). Quality and customer relationship management (CRM) as

competitive strategy in the Swedish banking industry, International Journal of Bank Marketing , 14 (3), 23-29.



136

ANNEXURES

RACE GROUP 0

Figure A.1 Scatter plot for the average transaction debit amount per month per accountkey for race group 0.

Debit transaction amount er month vs. acc ke

0

100000

200000

300000

400000

500000

600000

700000

0 200 400 600 800 1000 1200 1400

Acc key

A v e r a

e d e b i t t r a n s a c t i o n a m

o u n t



137

Figures A.2 Histogram for transactionamount over age for male and females

Figures A.3 Histogram for averagenumber transaction vs. age for male andfemales

Y % of Total average transaction Debit amount)

% of Total average Credit transaction amount)



138

Average Debit Least Squares Predictionequation

Average Credit transaction Least SquaresPrediction equation



139

Average number of transaction LeastSquares Prediction equation

Figure A.4: Average number oftransaction per month vs. account age

Figure A.5: Average transaction amountper month vs. account age

Average Debit & credit transitions amount vs. account age

Account age 0 5 10 15 20

i t

T r an s a c t i on am o un t s

0

5000

10000

15000

20000

25000

30000

Average number of transaction vs. account age

Account age 0 5 10 15 20

N um b er of t r an s a c t i on

0

5

10

15

20



140

RACE GROUP 1


Average Credit transaction Least SquaresPrediction equation



141




142

Figure A.6 Average number oftransaction vs. account age

Figure A.7 Average transaction amount(debit and credit) vs. account

t- Test for different between two group (Males and females) behavioural

Analysis of average number of transactions

Assuming unequal variances

Difference -0.0714 t Ratio -0.03234Std Err Dif 2.2086 DF 61.11742Upper CL Dif 4.3447 Prob > |t| 0.9743Lower CL Dif -4.4875 Prob > t 0.5128Confidence 0.95 Prob < t 0.4872

Means ComparisonsComparisons for each pair using Student's t

t Alpha1.99773 0.05

Abs(Dif)-LSD F MF -4.59472 -4.3277M -4.3277 -4.19439

Avera e number of transaction vs. account

Account age

0 5 10 15 20

A v e r a g e n u m b e r o f

0

5

10

15

A v n o of T r an s p er m on t h s

Avera e transaction amount vs. account

Account age

0 5 10 15 20

T r a n s a c t i o n

0

2000

4000

6000

8000

10000

12000

A v ov er 6 9 m on t h s

D e b i t )




143

Positive values show pairs of means that are significantly different.

Level - Level Difference Std Err Dif Lower CL Upper CL p-Value DifferenceF M 0.0714171 2.202056 -4.32770 4.470529 0.9742

Debit transaction


Difference 1993.7 t Ratio 1.132246Std Err Dif 1760.9 DF 52.73351Upper CL Dif 5526.0 Prob > |t| 0.2627Lower CL Dif -1538.5 Prob > t 0.1313Confidence 0.95 Prob < t 0.8687


t Alpha1.99773 0.05

Abs(Dif)-LSD M FM -3549.68 -1729.2F -1729.2 -3888.48


Level - Level Difference Std Err Dif Lower CL Upper CL p-Value DifferenceM F 1993.736 1863.585 -1729.20 5716.674 0.2887

Credit transactions


Difference 2123.1 t Ratio 1.20699

Std Err Dif 1759.0 DF 52.45309Upper CL Dif 5652.1 Prob > |t| 0.2329Lower CL Dif -1405.9 Prob > t 0.1164Confidence 0.95 Prob < t 0.8836



144


t Alpha1.99773 0.05

Abs(Dif)-LSD M FM -3548.01 -1598.06F -1598.06 -3886.65


Level -Level Difference Std Err Dif Lower CL Upper CL p-Value Difference

M F 2123.124 1862.706 -1598.06 5844.307 0.2586

Race group 2

Figure A.8: Scatter plot for the average(debit) transaction amount per month peraccount key for race group 2.

Figure A.9: Scatter plot for the averagecredit transaction amount per month peraccount key for race group 2.

Average Credit amount vs. account key

0

2000

000

6000

8000

10000

12000

14000

16000

18000

20000

0 5 10 15 20 25 30 35 0 Account key

C r e d i t a m o u n t

Average Debit transaction vs. acc key

0

2000

000

6000

8000

10000

12000

14000

16000

18000

20000

0 5 10 15 20 25 30 35 0

Account key

D e b i t a m o u n



145

Figure A.10: Average number oftransaction vs. account age

Figure A.11: Average transaction amount(debit and credit) vs. account age

Average Debit Least SquaresPrediction equation

Average Credit Least SquaresPrediction equation

Account age

0 2.5 5 7.5 10 12.5

T r a n s a c t i o n a m o u n t

0

1000

2000

3000

4000

5000

6000

7000

A v ov er 6 9 m on t h s ( D e b i t )


Average number of transactions per months vs. accountage

Account age

0 2.5 5 7.5 10 12.5

A v e r a g e n u m b e r o f t r a n s a c t i o n p e r

0

2

4

6

8

10

12

A v er a g en um b er of t r an s a c t i on s



146


t- Test for difference between two group (Males and females) behavioural patterns

Debit transactions




t Alpha2.03693 0.05

Abs(Dif)-LSD F MF -2329.16 -2263.56M -2263.56 -2329.16



147


Level - Level Difference Std Err Dif Lower CL Upper CL p-Value

F M 65.60309 1143.464 -2263.56 2394.764 0.9546

Credit transactions




t Alpha2.03693 0.05

Abs(Dif)-LSD F MF -2378.14 -2323.18M -2323.18 -2378.14


Level - Level Difference Std Err Dif Lower CL Upper CL p-ValueF M 54.95952 1167.510 -2323.18 2433.099 0.9627

Average number of transactions


Difference -1.5627 t Ratio -0.70491Std Err Dif 2.2168 DF 24.61778Upper CL Dif 3.0066 Prob > |t| 0.4875Lower CL Dif -6.1319 Prob > t 0.7563

Confidence 0.95 Prob < t 0.2437



148


t Alpha2.03693 0.05

Abs(Dif)-LSD F MF -4.51552 -2.95286M -2.95286 -4.51552


Level - Level Difference Std Err Dif Lower CL Upper CL p-ValueF M 1.562660 2.216825 -2.95286 6.078184 0.4860

Race Group 3

Figure A.12: Scatter plot for the average(debit) transaction amount per month vs.account key

Figure A.13: Scatter plot for the averagecredit transaction amount per month vs.acc key

Figure A.14: Average number oftransaction vs. account age

Figure A.15: Average transaction amount(debit and credit) vs. account age

Average credit amount vs. acc key

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

0 1000 2000 3000 4000 5000

Acc key

D e b i t

Average debit per month vs. account key

0

10000

20000

30000

0000

50000

60000

0 1000 2000 3000 4000 5000

Acc key

D e b i t t r a n s a c t i o n a m o u n t

Average transaction amount vs. account age

Account age

0 5 10 15 20

A v e r a g e t r a n s a c t i o n

0

500

1000

1500

2000

2500

3000

3500

A v ov

er 6 9 m on t h s

( D e b i t )

A v ov

er 6 9 m on t h s

Avera e number of transaction er month vs.

Account age

0 5 10 15 20

A v e r a g e n u m b e r o f t r a n s a c t i o n s

0

2

4

6

8

10

12

A v er a g en um b er of t r an s a c t i on s p er

m on t h s



149


Average Credit Least Squares Predictionequation



150




151

Figures A.16 Histogram for transactionamount over age for male and females

Figures A.17 Histogram for averagenumber transaction vs. age for male andfemales

t- Test for difference between two group (Males and females) behavioural patterns

Debit transaction


Difference 421.499 t Ratio 6.958027Std Err Dif 60.577 DF 5051.467

Upper CL Dif 540.257 Prob > |t| <.0001*Lower CL Dif 302.741 Prob > t <.0001*Confidence 0.95 Prob < t 1.0000

Y

% of Total average transaction Debit amount)

% of Total average Credit transaction amount)



152


t Alpha1.96043 0.05

Abs(Dif)-LSD M FM -115.575 301.8561F 301.8561 -123.578


Level - Level Difference Std Err Dif Lower CL Upper CL p-Value

M F 421.4993 61.02892 301.8561 541.1424 <.0001*

Credit transaction


Difference 425.081 t Ratio 6.93256Std Err Dif 61.317 DF 5053.569Upper CL Dif 545.288 Prob > |t| <.0001*Lower CL Dif 304.874 Prob > t <.0001*Confidence 0.95 Prob < t 1.0000


t Alpha1.96043 0.05

Abs(Dif)-LSD M F

M -117.087 303.8725F 303.8725 -125.194


Level - Level Difference Std Err Dif Lower CL Upper CL p-ValueM F 425.0806 61.82722 303.8725 546.2888 <.0001*



153

Average number of transactions


Difference 1.07235 t Ratio 7.541991Std Err Dif 0.14218 DF 5040.073

Upper CL Dif 1.35110 Prob > |t| <.0001*Lower CL Dif 0.79361 Prob > t <.0001*Confidence 0.95 Prob < t 1.0000

Means Comparisons

Comparisons for each pair using Student's tt Alpha

1.96043 0.05

Abs(Dif)-LSD M FM -0.27261 0.790146F 0.790146 -0.29149


Level - Level Difference Std Err Dif Lower CL Upper CL p-ValueM F 1.072353 0.1439513 0.7901460 1.354560 <.0001*

Figure A.18: Average transaction debit and credit amount per month vs. customer age per customer

gender for the race group 3.

Average Debit & Credit transaction amount vs. account age

F M

CUST_AGE

0 20 40 60 80 100

T r a n s a c t i o n a m o u n t

0

500

1000

1500

2000

2500

0 20 40 60 80 100

A v o v e r 6 9 m on t h s ( D e b i t )

A v o v e r 6 9 m on t h s ( C r e d i t )



154

Table 6.1: Race group differences t-Test: Two-Sample Assuming Unequal Variances

Statistics R3(Debit) R0(Debit) R3(Credit) R0(Credit) N of trans R0 No of

Mean 1051.043 9074.271765 1069.429 9314.708 10.16261722 4.173

Variance 4730063 1021915582 4854209 1.05E+09 121.4106667 26.356

Observations 5056 1156 5056 1156 1156 5056Hypothesized Mean Difference 0 0 0

df 1157 1157 1272

t Stat -8.52886 -8.65324 18.03941736

P(T<=t) one-tail 2.28E-17 8.25E-18 2.96408E-65

t Critical one-tail 1.646172 1.646172 1.646052437

P(T<=t) two-tail 4.57E-17 1.65E-17 5.92816E-65


Table 6.2: t-Test: Two-Sample Assuming Unequal Variances for Race group 0, 20-40 and

40-60 age behavioural differences


aver number of transaction(20 -39)

aver number of transaction( 40-60)

Credit(20-39) Credit(40- 60) Debit(20-

39) Debit(40 -60)

Mean 9.955019 12.90148953 4413.130509 11278.07207 4349.92 11062.93

Variance 95.44728 164.7338491 45271031.13 1172805561 44728854 1.11E+09

Observations 328 360 328 360 328 360

Hypothesized

MeanDifference

0 0 0

df 665 389 391

t Stat -3.40549 -3.72533090 -3.73843

P(T<=t) one-tail 0.00035 0.000111966 0.000106

t Critical one-tail

1.647148 1.648780174 1.64876

P(T<=t) two-tail 0.0007 0.000223932 0.000213

t Critical two-tail

1.963538 1.966080989 1.96605



155

Table 6.3: t-Test: Two-Sample Assuming Unequal Variances for Race group 3, 20-40 and40-60 age behavioural differences

t-Test: Two-Sample Assuming Unequal Variances

Debit(20- 39)

Debit(40- 60)

Credit(20- 39)

Credit(4 0-60)

aver number

of transaction(20 -39)

aver number

of transaction(40

-60)

Mean 810.5545 1759.133 826.2993 1784.442 3.748218 5.870227865

Variance 3025968 8360765 3081987 8508386 23.05442 38.47164472

Observations 2778 1356 2778 1356 2778 1356

HypothesizedMeanDifference 0 0 0

df 1848 1848 2173

t Stat -11.1366 -11.1502 -11.0813

P(T<=t) one-tail 3.15E-28 2.73E-28 4.23E-28

t Critical one-tail 1.645679 1.645679 1.645555

P(T<=t) two-tail 6.3E-28 5.46E-28 8.46E-28


Figure A.19: Scatter plot for average debit transaction amount per month vs. averagenumber of transaction per month for all acounts

Scatterplot of Av over 69 months(Debit) against average number of transaction

per months; categorized by CUST_SEX_CDE


average number of transaction per months

A v

o v e r 6 9 m o n t h s ( D e b i t )

CUST_SEX_CDE: F

CUST_SEX_CDE: M0 10 20 30 40 50 60 70 80

0

20000

40000

60000

80000

1E5

1.2E5

1.4E5

1.6E5

1.8E5

2E5



156

Figure A.20: Scatter plot for average credit transaction amount per month vs. averagenumber of transaction per month for all accounts

Scatterplot of Av over 69 months(Credit) against average number of

transaction per months; categorized by CUST_SEX_CDE


average number of transaction per months


CUST_SEX_CDE: F

CUST_SEX_CDE: M0 10 20 30 40 50 60 70 80

0

20000

40000

60000

80000

1E5

1.2E5

1.4E5

1.6E5

1.8E5

2E5

Figure A.21: Scatter plot for average debit transaction amount per month vs. averagenumber of transaction per month for race group 0

Scatterplot of Av over 69 months(Debit) against average number of transacton permonths over 69 months; categorized by CUST_SEX_CDE


CUST_SEX_CDE: M Av over 69 months(Debit) = 119.2805+852.4479*xCUST_SEX_CDE: F Av over 69 months(Debit) = -225.7175+576.5591*x

average number of transacton per months over 69 months


CUST_SEX_CDE: MCUST_SEX_CDE: F

0 10 20 30 40 50 60 70 800

20000

40000

60000

80000

1E5

1.2E5

1.4E5

1.6E5

1.8E5

2E5



157

Figure A.22: Scatter plot for average credit transaction amount per month vs. averagenumber of transaction per month for race group 0

Scatterplot of Av over 69 months(Credit) against average number of transactonper months over 69 months; categorized by CUST_SEX_CDE


CUST_SEX_CDE: M Av over 69 months(Credit) = 285.1486+856.969*xCUST_SEX_CDE: F Av over 69 months(Credit) = 147.6994+559.9425*x



CUST_SEX_CDE: M

CUST_SEX_CDE: F0 10 20 30 40 50 60 70 800

20000

40000

60000

80000

1E5

1.2E5

1.4E5

1.6E5

1.8E5

2E5

Figure A.23: Scatter plot for average debit transaction amount per month vs. averagenumber of transaction per month for race group 3

Scatterplot of Av over 69 months(Debit) against average number of transacton permonths over 69 months; categorized by CUST_SEX_CDE




CUST_SEX_CDE: FCUST_SEX_CDE: M

0 10 20 30 40 50 600

10000

20000

30000

40000

50000



158

Figure A.24: Scatter plot for average credit transaction amount per month vs. averagenumber of transaction per month for race group 3

Scatterplot of Av over 69 months(Credit) against average number of transacton permonths over 69 months; categorized by CUST_SEX_CDE

CUST_SEX_CDE: F Av over 69 months(Credit) = -306.5661+319.1385*x

CUST_SEX_CDE: M Av over 69 months(Credit) = -207.4826+315.6653*x



CUST_SEX_CDE: FCUST_SEX_CDE: M

0 10 20 30 40 50 600

10000

20000

30000

40000

50000

Documents

Maanda Rasuba