Branding Banks For Shareholder Value 2

Branding Banks – Discussion Draft Section 2.0

1

© Geoffrey Johns - 22 March 2010

Branding banks for

shareholder value

Section 2

Knowing Customers

Planned series of discussion drafts

Discussion Draft

Version

Release Date

Creating shareholder value - an outline 1.0 Mar-10

Knowing customers 2.0 Mar-10

How customer perceptions develop 3.0 Apr-10

Branding banks is vital

TBA

Branding banks is hard

TBA

Measuring customer perceptions

TBA

Gaps analysis

TBA

Bank structure and brand control

TBA

Six Sigma and brand control

TBA


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Section 2

Knowing customers

Introduction

This is the second in series of discussion drafts in which I attempt to trace the path from

customer perceptions to shareholder value. The first covers shareholder value as a goal

of the system and the analysis framework I use. This section deals with understanding

customer segments.

I have had to split this from the following section on the development of customer

perceptions because of file size. For a general introduction to this series of papers, see

Branding banks for shareholder value 1.0 also lodged on LinkedIn as a discussion draft.

In terms of the systems dynamics framework developed in the last section the exhibit

below shows where we are in the process.

Customer perceptions

Perception of price

Perception of specification fit to needs

Perceptions of service experience

Perceptions of brand

Bank efficiency

Bank financial structure

Mix of businesses

Corporate centre

performance

Business unit performance

Shareholder value


3


I prefer the term ‘perceptions’ to ‘satisfaction’. Important though satisfaction is I think

there is more we want to know about what is in on customers’ mind than just their level

of satisfaction. We shall discover later in this series that satisfaction alone is a poor

predictor of customer behaviour.

In terms of the Gaps model I described in the preceding section of these papers, we are

at the first gap between selecting the market a bank wishes to serve and the

understanding developed within the bank of customers’ beliefs, needs values and

behaviour. Later in this section, I want to discuss how customer perceptions are

developed. In the paragraphs which follow, however, I shall say something about

understanding who the customers are in the market. Also I shall describe some

customer segmentations that I have found most useful in understanding a bank’s

portfolio of customers..

Comprehension of customer beliefs, needs, values and behaviour

Design of product service offering range

Executing the solution

Selecting / specifying the market

Shareholder value

Communicating the product service offering

Creating the solution

Gap 1

Gap 2

Gap 3

Gap 6

Gap 7

Gap 4

Gap 5

Gap 8

There is great diversity among bank customers. This is something that tends to set

banking apart from some other brands. One way or another, banks have to service


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pretty much all the population, at least of developed countries. Whereas some brands

might attract a specific type of person with a profile that is partially defined by the brand

itself, banks cannot be selective in this wayi. They deal with everyone. Everyone has a

say in how the brand of the bank evolves.

In a section to be released later on, I shall explore in detail how brand transmissions are

received by different customer groups and how these groups interact. For now I shall

sketch an outline. The exhibit below (the focus of this paper in dark green) is a way of

picturing the markets served. Banks that I have worked for or with generally sell into all

of these markets. There is no one way accepted way of dividing them up and, in any

case, divisions are blurred. For example, in examining the roles of a group of private

bankers serving the affluent market and commercial bankers serving the Small &

Medium sized Enterprise (SME) markets, I found there was a large overlap between the

type of customers they dealt with and the sort of work they didii. Similarly there are

large overlaps between the SME and Corporate markets and between the Corporate

Market and Institutionaliii. Although a bank may deal with another Institution on a

relationship manager to relationship manager basis they also deal with them in

professional markets.

Retail Corporate InstitutionalProfessional

markets

Personal SME

AffluentMass

market

Bank


5


One reason I have included this exhibit is that among people who deal with banks, who

are not themselves bankers, there is often a misconception. There is a tendency for the

view from the high street to be of banks as serving only the mass market. In academic

studies I have read what they describe as market research into bank customers is really

only of the personal mass market. Banks vary a lot in their shape and balance.

However, large banks that I have been close to typically derive more than 30% of their

shareholder value from the Small and Medium sized Enterprise (SME) sector compared

to, say, 40% from the personal mass market.

A key point to understand is that bank brands are developed in each of these markets

and there is little opportunity to segregate sub-brands for different marketsiv. Moreover,

quite a lot of people either are in more than one of these market segments or interact

with people who are. A foreign currency trader, for example, is also a personal bank

customer. People are both producers and consumers and deal with their banks in both

capacities. As we shall see, multiple audiences are a key feature of the challenges facing

bank brands.

Product usage

The exhibit below shows the main classification of personal bank products that I have

been using for a number of years and have yet to improve on. An important distinction

is between customer who deal with the bank on a product by product basis and those

who believe they have a relationship with the bankv. Among those who deal with banks

product by product (and this is often only just one product – banks have difficulty selling

on average two products to a personal customer). A special case is customers who were

introduced to the bank by an intermediary. They often have stronger relationships with

the intermediary than their bank. Banks tend in this respect to become commoditised.

Intermediaries can include mortgage brokers, financial advisers and mortgage brokers.

We shall see in a subsequent section that they are also part of the overall brand

audience.

Customer groups can be defined by product usage. Around 1980, when bank

information systems were generally undeveloped, I was intrigued to find how much could

be predicted about a customer’s intentions for the next product they would acquire

merely from knowing their age and the most recent product acquired. At about this

time, with no way accurately measuring the profitability of actual customers, I


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constructed some ‘customer archetypes’ based on assumed patterns of product usage.

At that time only one of the archetypes showed positive net present value at the time of

acquisition. These were people who at some time in their banking history had a

residential mortgage. Of a customer base of over two million there were less than three

hundred thousand of them on the bank’s books at that point in time. This picture has

now changed, of course. Mortgage margins have fallen and the cost of transactions is

better recovered. Nevertheless it was an eye opener for me on the issue of cross-

selling. For alfinanz banks cross-selling to and from traditional banking and wealth

management is a crucial issue today. Plotting the segments I shall describe below

against product usage patterns is almost always revealing.

Wealth

management

Transactions /

consumption

Owner occupied

residential

mortgages

Investment

property

mortgages

Superannuation

Mastertrusts

Direct

Investment in

shares and

securities

Term depositis Transactions

deposit accounts Credit cards Personal loans

Personal

banking and

finance

Consumer

finance

Business

relationships where

the customer is the

decision-maker

Cash

management Charge cards

General

Insurance

Auto

Home

Personal

Here are two fairly typical product usage clusters. It is useful to know how these

clusters match against the segmentation strategies discussed below.

Price driven cluster


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Wealth management

Transactions / consumption

Owner occupied

residential

mortgages

Investment

property

mortgages

Superannuation

Mastertrusts

Direct

Investment in

shares and

securities


deposit accountsCredit cards Personal loans

Personal banking and

finance

Consumer

finance

Business

relationships where

the customer is the

decision-maker

Cash


General Insurance

Auto

Home

Personal

Simple relationship cluster

Wealth management

Transactions / consumption

Owner occupied

residential

mortgages

Investment

property

mortgages

Superannuation

Mastertrusts

Direct

Investment in

shares and

securities


deposit accountsCredit cards Personal loans

Personal banking and

finance

Consumer

finance

Business

relationships where

the customer is the

decision-maker

Cash


General Insurance

Auto

Home

Personal

Actually the majority, by far, of bank relationships are quite simple. Wishful thinking can

sometimes lure us into making too much of what are in fact exceptions.


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Channel usage

I have done relatively little on channel usage, partially because of the difficulty in

integrating into the mainstream of my brand thinking without introducing unmanageable

complexity. However, for the sake of a nod in its direction I include one of the

frameworks I have developed as an appendix to this paper. I shall try and improve on

this when I have a chance to update it. For now though there are distinct segment

needs between those who prefer to deal with the bank online, by telephone and face to

face. I try to deal with this by segmenting on attitudes to finance below.

Segmentation

I argue that, ideally, each customer would be managed as a ‘segment of one’. In many

cases this is of course impractical because of the cost of face to face relationship

management time. Having said that, ICT enables much greater levels of differentiated

customer management than was hitherto possible. I am not saying that ‘segment of

one’ management must be achieved. I am saying that we should approximate it as well

as we can and recognise that the compromise is unsatisfactory. Nevertheless, there are

bankers who strongly disagree with me. I believe this to be the result of a psychological

difference in world view. It is the difference between a customer centric view and a

bank centric view. Is the customer base a resource to be exploited or is it an asset to

invest in growingvi?

My view is reinforced for me because of my perception of the importance of relationships

in achieving economies of scope. A recurrent theme of these papers will be that bank

products are commodities differentiated by service. The cost of maintaining a

relationship, even one based entirely on ICT, is determined by economies of scopevii,

supported to a lesser degree by economies of scale. All banks say they want more

cross-selling. They want this to achieve economies of scope.


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I believe that segmenting customers is poorly understood in banking. Too often a new

marketing team will go straight into a huddle to revamp the segmentation system (along

with all the disruption that that causes) when there are more pressing priorities.

Moreover they often unnecessarily Hardwire a segmentation. I want to distinguish

between Hardwired segmentation, Softwired and Unwired. By Hardwired I mean

segmentation that is built into the fabric of the bank. It is incorporated in the

organisation structure. For example when there is an Agribusiness Division. By

Softwired I mean a segmentation that is reflected primarily in ongoing measures but

which is also often intended to shape the way people within a bank think about the

market. By Unwired segmentation I mean segmentation that is flexible and entirely

contingent on the problem at hand. People unused to using modern information systems

are often averse to unwired segmentation. Often it’s a bit like holding up a series of X-

rays to a lamp and trying to discern the pattern of shadows. You have to hunt hard

among all among the shadows for the one shadow that matters.

Hardwired, Softwired and Unwired segmentation all have a place in bank marketing but

much trouble can be caused by poor design of the information flows. Where serious

problems occur it is often a structural issue within banks. Silos unable to exert control

over other silos that they need to work with may seek to control a shared paradigm.

Rigid segmentation frameworks can be an insidious part of this. These issues are

impediments to branding and I shall deal with them in detail in a forthcoming paper in

this series on bank structure and brand control.

There also must be balance between ‘segment of one’ thinking and the imposition of

segmenting dimensions. Managing segments of one with the management logic buried

in ICT systems can get out of control unless managers return from time to time to bigger

picture segmentation.

I want to turn now to the segmentation frameworks that I have found most useful in

most situations. There is no truly satisfying right or wrong choices just some that are

more or less useful for the problem at hand. But it is always good to guard against

solutions that work for now but later become themselves impediments to progress.

Foresight in design is a handy skill to have as I have often found to my dismay.

Managing the dichotomy between planned and well-framed explorations of the market

with the serendipity of discovered insights is an often unrecognised marketing

competence. Mental as well as ICT flexibility is vital.


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Three behavioural segmentation dimensions

for the personal banking market

There are three dimensions of segmentation that I have found consistently useful in the

finance sector. They are useful in that they provide fairly discrete grouping of needs and

choices which can be entered into product and process design. That is to say, in

operational terms, that the expressed benefits in a QFDviii exercise can be expressed with

greater clarity and precision.

Starting Building Consolidating Retiring

Affluent Comfortable Struggling

Leaders Controllers LaggardsFollowers

Life stage

Affluence

Attitude to finances

Taken as a group the three dimensions are hierarchical. Each adds greater granularity

to the one before. To my mind the natural sequence for most purposes is shown in the

exhibit above. However, that does not mean this natural sequence must be rigidly

adhered to. The cells in the framework shown in the exhibit below can be

juxtapositioned in many ways.


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Life

sta

ge

Attitu

de

to

fin

an

ces

Affluence

While going down to a high level of granularity is frequently desirable it can lead to

sample sizes that are too small to be representative. In market research we tend to

cross our fingers and mumble thirty-ish as being a reasonable sample size but really it is

only so in limited circumstancesix. Most often I am only satisfied with about a hundred

or so observations in a reasonably homogenous cell. Ideally then it is desirable to limit

the number of cells in the sampling plan. In addition to this it is frequently hard to get

sufficient differentiation between a large numbers of groups. Too many of the groups

seem a bit too similar and, because of this, difficult to operationally deploy in analysis.

I often find that gender and location, although it may be useful or necessary to report

results by these classifications, are not necessary to include as separate dimensions.

Differences of perception between the sexes or by location generally do not require

analysis by the main dimensions as well. That is to say differences in perception between

things like the genders and locations mostly can be adequately explored without a full

additional segmentation by life stage and affluence and attitude to finances.

Life stage segments

Segmenting by age, to categorise by life stage, is easiest in that it is a readily

identifiable characteristic and a key determinant of banking behaviour. I have found

most value comes from a four stage classification as shown below.


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Consolidating

45-59

Retiring

60+

Building

30-44

Starting

15 - 29

These stages are distinct in the needs of people. Their financial needs and the choices they

face. Also they tend to divide the market into groups of roughly equal importance by size

and value.

However many life stages groupings are used there is, among personal customers, often a

pivotal moment. I call this ‘taming the mortgage monster’.

18 -24 25 -39 40 -49 50 -59 60 +

Taming the

mortgage

monster


13


I don’t mean by this paying off the mortgage. The term just refers to that magical and

indefinable moment when you wake up one day and realise that the mortgage is no

longer the overwhelmingly dominant feature of your finances. For many it is the cusp

between borrowing and investing.

Adding the dimension of affluence

For personal markets there is a basic matrix where at least twelve cells should be

understood. This is made up of life-stage and affluence. Affluence is a significant

modifier of life stage needs and choices. There are some important decisions in setting

the metric. The first is income versus wealth. They are not necessarily connected. For

example Wealth can be inherited. For understanding the needs of the market I have not

found composite wealth / income measures to be useful generally. By this I mean, for

example income of more than £50,000 or disposable assets greater than £200,000. For

a specific customer management purpose it might make sense, for example in setting

conditions for a group of customers with special privileges such as with HSBC’s Premium

Customers. But to create a behavioural group that has useful predictive power for a

segment cell income usually suffices. Also it is easier to ascertain by through

interviews or application forms.

Another decision is household or personal income. Again it depends on the study but in

most cases the income of the main earner in a household works best for mex.

A final decision is whether to make whatever affluence categories we want to use

common across the life stages or vary depending on the life stage. That is to say the cut

limits will be higher in ‘building’ than ‘starting’, for example. Because the main intent

here is to distinguish the needs of and choices confronting sub-groups within the life

stage segments, my preference to have different cut lines for each segment.


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Starting

15 - 29

Building

30-44

Consolidating

45-59

Retiring

60+

Affluent(> twice mean

income)

Comfortable(mean income to

2X mean )

Struggling(< mean

income)

Age

Ma

in h

ou

se

ho

ld In

co

me

I tend to use just three categories again to limit the number of cells and to achieve well

differentiated behavioural grouping. What I call ‘Struggling’ are people with fewer

options. It a recent study of the Australian superannuation market, for example, I

defined ‘struggling’ as people who are unlikely to ever contribute more than the legally

defined minimumxi. As I have defined it in the exhibit above, ‘struggling’ applies to more

than half the population because of income skews. However, apart from the lowest

income groups there is reasonable homogeneity because of the limit of choices and also

the group as a whole represents are lower contribution to bank value than the proportion

of their numbers. Naturally, in for example a specific study, say for a product designed

for lower income groups, you would change the measure accordingly. Ideally, though it

helps to keep new segmentation within the framework set as sub-groups rather than a

different overlay altogether.

Within affluent customers it is particularly difficult to identify the rich segment. Their

numbers are relatively small and they are hard to reach in normal surveys. Also their

needs and options are wider and less easy to generalise as a group. In any case, they

are mostly relationship managed and are true ‘segments of one’.

In distinguishing the ‘affluent’ from the’ comfortable’ the goal is to achieve two groups

that are distinct in their profiles and both large. Ideally, the cut off would be empirically


15


determined by analysis of a bank’s own customer base. However, an income of twice

the mean might not be far wrongxii.

Segmenting by attitude to finances

Within the life stage / affluence grid there are obviously psychological differences in how

people approach their finances that we should capture. I have seen approaches to this

based on, say, a person’s position on Maslow’s Hierarchy of Needs set against Jungian

archetypes as developed for the Myers Briggs personality inventory. This type of

approach perhaps enables the development of brand analysis methodologies that work

across all industriesxiii. However, in my opinion, based only on my own experience, this

type of approach is too general to yield actionable results for banks brands. As I shall

discuss at length in the fourth of these papers branding banks presents unique

challenges at the extreme end of the brand spectrum.

My preference therefore is to develop an attitudinal tool that is specific to banking. That

is to say one that asks specific questions that we need to know the answers to rather

than one designed to elicit deeply held beliefs and values and to then interpret these in a

banking context.

I have long sought a standard battery of questions that could be deployed over a

number of studies to build a picture of people’s attitude to their finances. I have used

the questions listed below in two analyses. They are not ideal but they perhaps point in

the right direction.

I consider myself to be a leader in the adoption of new ideas and technology

I am the sort of person who would take a measured risk if I could see clear

benefits

Tightly controlling my day to day finances is very important to me

In taking financial decisions I am open to the advice of trusted family and

friends.

I am cautious when it comes to adopting new ideas and technologies.

I am cautious/careful when it comes to making decisions about my finances.


16


I am confident that the security of my internet connection at home is very

good.

I am willing to try new things myself without receiving recommendations from

family/friends.

I need to know about the background of companies before I deal with them.

I prefer dealing with people rather than remote channels.

Respondents were asked respond to each on a scale of strongly agree to strongly

disagree. The responses yielded the four clusters shown in the exhibit belowxiv. The

differentiation was fairly good. This version was a little too biased towards willingness to

take up new technology. Nonetheless, preferences for distribution channels are

inevitably a feature of attitudes to finances these days. There is not much personal

banking that cannot be done online.

Gary Lembitxv, who worked with me on this, and I agree that some of the key things that

have to be included in measuring an individual’s attitude towards their finances are:

Risk adversity / propensity;

The extent to which they need to have tight, detailed day to day control over

their finances;


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Their openness to advice from others;

Their willingness to engage with innovation;

The extent to which they measure life success by their financial success.

Their willingness to do business other than face to face.

Their openness to having a relationship with the financial institutions that they

deal with.

I believe that life-stage tells much about the needs and choices that people face. This

knowledge is much amplified by affluence both in terms of the needs themselves and the

resources can bring to them.

Attitude to finances is not totally independent of life stage and affluence. These

attitudes will in many ways change with age and wealth in measurable ways. In other

respects though they seem to me to be independent. In my researches, for example, I

have found people with a high need to control their finances in both rich and poor people

albeit sometimes for different reasons. Irrespective of the underlying reason, however,

their banking behaviour, in this respect is very similar.

Segmenting SME markets

For SME markets the predominant descriptive attribute is size of the business. I have

found relationships between size of the business and the volume banking business in

loans and deposits. This is not surprising. Perhaps more surprising is that the

relationship is less strong than I thought it would be.


18


The exhibit above is for the Australian business market. It is sourced from the

Australian Bureau of Statistics (ABS). Dollar amounts are AUD. These figures do not

take into account the smallest of Australian business as, at the lower end of the size

band they are based on Goods and Services Tax (GST) returns. Even, however on these

figures a substantial number of businesses are so small that their value to the bank is

likely to be less than a residential housing mortgage. I assume Australia is not all that

different from other developed economies. My caution is that segmentation by size

bands, though a natural starting point, must recognise that there are a very large

number of micro businesses of limited value.

10.9% 10.5%11.6%

9.8%

13.4%

6.6%

19.7%

8.1%

5.7%

3.7%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

Zero to less than $25k

$25k to less than $50k

$50k to less than $75K

$75k to less than

$100k

$100k to less than

$150k

$150k to less than

$200k

$200k to less than

$500k

$500k to less than $1m

$1m to less than $2m

$2m to less than $5m

Perc

enta

ge

Distibution of businesses with turnover < $5m in ABS 8165.0

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

Zero to less

than $200k

$200k to less

than $500k

$500k to less

than $1m

$1m to less

than $2m

$2m to less

than $5m

Turnover bands

Num

ber

of

busiin

esses


19


Because of this there is a need to segregate a category of micro businesses. In general

the smaller the businesses in a group the less variety there is in the banking behaviour

of the group. Among the practical implications of this is that smaller sample sizes can be

used.

It was also a surprise that I found no relationship between industry sectors and banking

business. Breaking down government statistics to four digit ANZIC level and breaking

that further into four size categories by number of employees, I found no relationship at

all with borrowings. This was true even of such factors as sector capital intensity to

borrowings. It seems that firm by firm propensity to gear greatly outweighs industry

sector considerations. For this reason, in setting another dimension to accompany size

of turnover, I think loans to total assets or to turnover is the most useful. It tends to

define independent behavioural characteristics. Size of the business is the main

determinant of how a firm deals with its bank. It is possible that propensity to borrow is

the next most important.

A third metric that has some use is proportion of turnover generated by exports. I see

this as a proxy for global exposure and outlook. However, I have not had the

opportunity to explore this in any detail.

Zero to less than

$200k

$200k to less than

$500k$500k to less than

$1m

$1m to less than

$2m

$2m to less than

$5m

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0%

Share of business numbers

Share

pf

aggre

gate

turn

over


20


Thinking of attitudinal dimensions that might apply, I have found that there are three

mindsets to consider in the SME market. These are shown below. Generally, these

relate closely to size and are better used as descriptions of behaviour within a segment

than as definers of segments themselves. However, they do tend to lead me to think in

terms of the following cut-off delineations in turnover bands.

Micro < AUD $1 million

Small AUD1 million – AUD $5 million

Medium AUD 5 million – AUD 50 million.

Sourcing an income (a business instead

of a job, freelancing)

Running a business (buying and selling things, employing

people)

Building an organisation

(something that operates

independently of me)

But perhaps a more important distinction for bank segmentation is the differences

between businesses when the main decision make is the proprietor and those where

there is a specific finance person, for example a financial controller or CFO. The

existence of a real CFO (who might not have a high level title but who is more than an

accountant in that they have decision making authority) is a strong indicator that a firm

is moving to the ‘building an organisation’ stage. While I never had the opportunity to

analyse this in my work with the TNS Business Finance Monitor I certainly recommend

doing so.

Propensity to borrow segmentation

There are several reason why propensity to borrow is an important metric in segmenting

the business banking market. First a borrowing business is likely to be significantly more

valuable to a bank (providing the cost of risk is recovered. Secondly, the relationship

between the customer and the bank is likely to be stronger. Thirdly, the interaction and

intensity of management by the bank is likely to be greater. Fourth, the borrowing


21


policy of the customer becomes an issue. However, it is necessary to decide the extent

to which it is:

Bank borrowing because investment is unavailable (mainly small businesses);

High risk propensity (mainly middle); and

Adopting sector normal gearing (mainly corporate).

Primary, secondary and tertiary

relationships segmentation

I define a primary relationship as bank with which the customer has the majority of its

balances. In the Australian market I have found there to be a marked tendency for

businesses to have a large proportion of their primary bank. Moreover, it seems to be

hard for banks to make much headway with their secondary relationships in terms of

achieving commitment. I shall deal with this in more depth in a subsequent section. For

my present purpose, outlining various segmentation strategies, I shall remark only that

b banks that can raise commitment among secondary relationships stand a good chance

of market share growth in balances. Balances are likely to seep to them from

customers’ primary banks.

The exhibit below is one of the most powerful in the SME sector. The cells should be

explored both or commitment and for value.


22


Micro Small MediumLower

corporate

>90%

50% - 90%

< 50%

Turnover $ million

Pe

rce

nta

ge

of b

ala

nce

s h

eld

Segmenting by value

Segmenting by value and commitment applies equally to personal and business markets.

I have never known a useful high level banking that did not have customer value in

some form as one of its main axes. Customers vary significantly in their value to the

bank. I conducted two detail studies into business customer values in one bank in 1991

and in another 1994-6. In the former I measured economic valuexvi and in the second

both economic value and net present value. Both cases the profile by quintile of value

looked very much like the illustrative exhibit below. In the 1991 analysis the negative

value of the fifth quintile was caused predominantly by underpriced risk and partially by

unrecovered transaction costs. In the 1994 – 96 study the negative value was almost

exclusively caused by underpriced risk. In the words of Neville Cox, General Manager

during the second study:

“Banking is the only industry where you don’t know if a customer is going to be

profitable at the time you acquire them.”


23


It may be that this skew in value has been eliminated in the last decade or so. Certainly

I put in place campaigns to mitigate value destruction in the bottom quintile. But

somehow I doubt it. Banks are exposed to the tyranny of published numbers such as

market share. There is always a temptation to buy market share by under-pricing. It is

always hard to re-price customers once a bad deal has been done.

Given this disparity of value it makes a lot of sense to want to treat customers in

different value groups differently. A key issue then is should the calculation be made on

present value to the bank or present value to the banking industry. Otherwise could it

be made on potential life-time value to the bank or to the industry? Ideally, of course

you should do both. I have found valuing business customers to be demanding but

possible. I expect that bank information systems have improved since I last did it so it

should be easier now. Calculating value to the industry is a little harder but achievable

in cases where banks have central access to customer credit assessments.

Segmenting by commitment

I want to turn now to the Conversion Model™. When I first discovered this it seemed to

me the Holy Grail of bank marketing and my opinion has not changed. This model has

great importance for the rest of this series of papers. I shall describe its main features

-100%

-50%

0%

50%

100%

150%

1 2 3 4 5

Shar

e o

f EV

A /

NP

V

Quintile

Share of value, business customer quintiles


24


here but defer my discussion of why it seems to me better than its key competitors in

measuring satisfaction until the section Measuring Customer Perceptions.

Entrenched

committed

Average

committedShallow Convertible Available Ambivalent

Weakly

unavailable

Strongly

unavailable

Committed Uncommitted Open Unavailable

Customers Non-customers

Customers' perceptions should be shifted towards

commitment

Non-customers' perceptions should be shifted towards

openness

The Conversion Model was developed by Jan Hofmeyr and documented in ‘Commitment-

Led Marketing written by Hofmeyr with Butch Ricexvii. It has been applied in at least 200

industries worldwidexviii. My comments in this paper, however, relate only to financial

services. The principal study on which I have been closely involved in its application is

the TNS Australia Business Finance Monitor (BFM)xix. This is, to the best of my belief,

one of the largest detailed studies of banking (specifically business banking) in

existence. I shall draw on my experience working with it extensively during the course of

these papers.

Commitment is a measure of satisfaction but it is also an important tool for

segmentationxx. It is applied to both existing customers and non-customers. It

measures the commitment of customers to the banks that they use and their openness

to the banks they do not use.

It does not measure commitment instead of satisfaction. Rather, the Conversion Model™

builds on satisfaction ratings to become a better predictor of customer behaviour and

hence value. While customer satisfaction (fit to the customer’s needs) is the bedrock of

commitment, three other factors are taken into accountxxi. The first is the customer’s

perception of alternatives. A customer may be very satisfied with a product or service

and yet believe that another offering may be just as good or better. Note here that this

is done for each respondent individually. When making comparisons between banks,

there are other satisfaction measures that compare satisfaction by groups of customers


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but none that do it customer by customer. This matters a lot because relative

commitment stays with the customer record however you segment the marketxxii.

The second is how important the decision is. If the decision about, say, the supplier of

pot plants to a customer’s offices is not important to them, they will be unlikely to be

committed to the provider.

The third is how ambivalent the customer is about making the choice between providers.

Essentially, this is a measure of how high the need to make a decision has risen on the

customer’s agenda. It helps to identify the point at which the natural inertia of staying

with an existing supplier will be overcome.

Based on these questions, the Conversion Model™ assigns customers of banks to one of

the following four categories for each bank.

Entrenched committed (to the bank under review).

Average committed.

Shallow.

Convertible (at risk of loss for the bank under review).

Non-customers for each bank are grouped into the following four categoriesxxiii:

Available (to the bank under review).

Ambivalent.

Weakly unavailable.

Strongly Unavailable.

The Conversion Model™ is as much a segmentation tool as it is a satisfaction measure.

The two most important measures to monitor are the:

percentage of committed customers to all customers; and

percentage of available non-customers to all existing customers.

Naturally, a large number of other measures can be derived from the data.

Managing the customer portfolio by value

and Commitment


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The exhibit below shows a matrix of value against commitment. This is akin to other

management tools for making comparisons among a portfolio. It is a ‘do I want it? / can

I get it? Matrix similar to, say the Boston Consulting Group matrix for General Electric’s

portfolio of businesses. This measures growth of industry (do I want it?) against market

share (can I get it?) The variant below simply uses value and commitment.

Watch Develop

Recover

Low HighL

ow

Va

lue

Protect

Hig

h

Commitment

This matrix essentially evaluates the outcomes of how behavioural segments respond to

investment,

Life s

tage

Att

itu

de

to

fin

an

ce

s

Affluence

Watch Develop

Recover

Low High

Lo

w

Va

lue

Protect

Hig

h

Commitment

Behavioural segmentation

Outcomes segmentation

Customer – bank interaction

Grouping customers by common needs and expectations.

Grouping customers by common value to the bank. Commitment adds futurity to value.


27


For each of the cells established in behavioural segmentation we would, ideally, be able

to see the pattern formed on the outcomes matrix. The illustrative exhibit below uses

colour for commitment and size of the circle for value. There are many potential

variants on this way of showing it, of course.

Starting

15 - 29

Building

30-44

Consolidating

45-59

Retiring

60+

Affluent(> twice mean

income)

Comfortable(mean income to

2X mean )

Struggling(< mean

income)

Age

Ma

in h

ou

se

ho

ld In

co

me

< 20%

40% -

60%

> 80%

60% - 80%

60% - 80%

40% - 60%

40% - 60%

40% - 60%

< 20%

40% - 60%60% - 80%

60% - 80%

40% - 60%< 20% 20%-

40%

60% - 80% > 80%

Percent of customers commited

Proponents of the Conversion Model™ argue that a committed customer is more likely

to:

Remain with the bank;

Yield greater share of wallet;


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Be more susceptible to the cross-selling of products;

Be more open to the bank’s innovations;

Be less price sensitive;

Be more receptive to marketing communications;

Promote the bank through word of mouth;

Have lower credit risk (because they are more likely to discuss business problems

early); and

Have lower administrative costs because both the mistakes of the bank and those

of the customer can be more easily dealt with.

To the extent all these things are true they represent a good indicator of potential as

opposed to existing value. I shall cover each of these elements in future papers.

Improved retention

Commitment

More share of Wallet

Responsiveness to marketing

communications

Lower cost of

risk

Greater acceptance of

pricing

Lower relationship

management cost

Favourable word of mouth

Summary and conclusions

While ‘segment of one’ marketing should always be considered ideal, it is necessary to

flexibly and creatively segment the customer base. Ideally, this should not confuse


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segregation to understand customer needs and behaviour with segmentation that

measures the outcomes. One measures the other.

We should distinguish between segmentation dimensions that are intended to group like-

behaviour from dimensions that relate to outcomes of customer management such as

commitment and value. In banking customer value is always a key dimension because

there is such high variety in it among customers even when they share other

characteristics..

I have discussed there are some segmentation dimensions that stand out in their

usefulness and which can be deployed with each other in a number of different ways.

My judgement criteria are always:

their usefulness for the problem at hand; coupled with

the extent to which they (ideally) fit an overall framework which explains how

investment in changing customer perceptions and / or extracting value from them

leads to shareholder value.

Commitment is my preferred metric for satisfaction. A satisfied customer may be

equally satisfied with a rival offering: a committed customer is not. I shall compare

ways of measuring satisfaction in a future paper in this series.

Because of file size this paper was separated from the next, which is about how

customer perfections develop.


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Appendix 1

Basic channel taxonomy – payments (work in

progress)

Cheque face to

face

Cr Card POS,

POP

Cr Card, telephone

- intermediary

Payee initiated

On-line Cr

Cash

Routine billsDomestic

Shopping

Personal

Transfers

traveling

abroad

Purchases

from overseas

Cheque by mail

Dr Card POS,

POP

Dr Card, telephone

On-line Dr

Major

expenditure

FX

Stored value cards

Travellers’

cheques

Cirrus

Maestro

Amex travel

card

Cash over

counter

POS cheque

Mail order

shopping

Credit card

Debit card

Card over

phone

Card at POS

On-line

ordering

Cash over

counter

Cheque over

counter

Cheque by

mail

Cr C over

counter

Dr card over

counter

Card over

phone

On-line

payment

Cash over

counter

Cheque over

counter

Cheque by

mail

Cr C over

counter

Dr card over

counter

Cr Card,

telephone- direct

Cheque by

mail

Card over

phone

Card over

phone

Card over

phone

Card over

phone

Cash face to

face

Cheque face

to face

Cheque by

mail

On-line

transfer

On-line

Card over

phone

Card over

phone

On-line

transfer


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Notes to Section 2.0

i As a side issue, banks grow partially though merger and acquisition over a long period.

For example National Westminster bank – part of Royal Bank of Scotland, is the result of

a combination of Westminster Bank, National Bank and District Bank. When this

happens there is little chance of a well-defined and distinct customer base.

ii Later in this series I shall argue that distinction between business banking and

personal banking is less important than that between decision makers that should have

face to face management and those that should not.

iii The definition of these markets will vary from bank to bank and the cut-off between

these groups and their sub-groups may be defined in different ways. Some banks for

example limit the term Institutional to financial institutions. Other may include

governments and large corporate. I tend to use the word to mean any entity where

financial competence is equal to that of the bank.

iv Except in the case of the brand of an overseas subsidiary – with some exceptions bank

brands do not travel well.

v Through these papers i shall use the term relationship in a way that I do intend to

imply a personal relationship either face to face or over the telephone. I consider all

customers to have a relationship with their bank(s) although some may be very weak.

Customers, of course, may not consider there to be any relationship at all.

vi Despite having been treated to some forthright opinions on this subject, I have never

been given reasons for holding them – otherwise I should have included them in this

paper. If anyone is able to enlighten me, I should be most interested.

vii By ‘economies of scope I meaning selling more products / services ro the same

customer or selling more through fixed cost distribution channels.


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viii Quality Functional Deployment – a quality management / Six Sigma tool for relating

organisationally conceived product / service features to customer expressed benefits that

I described in Branding banks for shareholder value 1.0.

ix Such as when the variety within the sample is small.

x Government statistics tend to focus more on household income. Where this is

important and individual income has been sampled,, usually a modifying algorithm can

be created that gives sufficient accuracy.

xi At present this is 9%. Technically it’s an employer contribution but practically it is

taken from the employment cost package.

xii Calculating standard deviations from the mean helps sometimes.

xiii For example, I think the TNS Needscope™ proprietary methodology is intended to do

this although I cannot pretend to be fully conversant with it.

xiv I am not happy with the choice of names for the groups that we used here. They are

too close to descriptions used in product innovation cycles. But I couldn’t come up with

anything better at the time and better names might come from additional exercises.

xv Gary Lembit was for most of the decade to 2009 Director of Finance and Business

services for TNS Australia where we worked closely. Before that I was his client. He is

the best market researcher in the banking and finance sector I have known.

xvi By economic value I mean operating profit before tax less the cost of risk measured

by the assessed contribution to the general provision for an asset in this risk class plus

the cost of capital required to provide support for unexpected losses.

xvii ‘Commitment-Led marketing – The key to brand profits is in the customer’s mind’.

John Wiley & Sons 2000, Jan Hofmeyr and Butch Rice ISBN 0=471-49574-3. This, for


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me, made a lot of things possible in market research that I had never known about

before reading it.

xviii http://www.tnsglobal.com/business-information/commitment-research/

xix The TNS Business Finance Monitor (BFM) has been running since June 2001. Twelve

thousand interviews are conducted each year with businesses with up to $300 million

turnover. Using Computer Assisted Telephone Interviews TNS collects data about

businesses’:

firmographics;

banking behaviour, including deposit and loan balances; and

perceptions of banks.

Interviews are with the firm’s financial decision-maker. In the case of a smaller business

this may be the owner. In the case of a larger one, it could be the financial controller.

Nearly all Australian banks have subscribed at one time or another. The principal

contact within TNS is Jenny Powell in The Sydney office.

xx The version of the Conversion Model™ that I describe here is a somewhat older one

than that used by TNS at present. However, it is the one in which I have extensive

experience. I expect there are rivals to the Conversion Model which replicate it’s key

features but the only one that I have knowledge of is the TNS proprietary research tool.

I am aware that Jan Hofmeyr, who developed the Conversion Model has introduced a

similar methodology in Synovate, another market research company. In a subsequent

section in this series of papaers i shall compare the Conversion Model with the Net


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Promoter Score and with Customer value Analysis. Both od these are widely used by

banks I have dealt with but are, in my opinion inferior to the Conversion Model.

xxi The following questions are asked in the BFM to measure commitment and openness.

I'd now like to ask you how you feel about different institutions with regard to

business banking. Please indicate your feelings about each institution by giving a

score between 1 and 7. If you have a very negative attitude towards a particular

institution then you should give it a score of 1. On the other hand, if you have an

exceptionally positive attitude towards it, you should give it a score of 7.

Overall, on a scale from 1 to 10, where 10 means 'perfect in every way' and 1

means 'completely unsatisfactory', how would you rate your experience with

[NAME OF PROVIDER] for business banking?

Thinking about the selection of a bank or other financial institution for your

business, on a scale of 1 to 5 where 5 is not important at all and 1 is extremely

important, how important to you is the decision about which provider to go to?

I’m now going to read three statements about business banking institutions.

Thinking about your business banking with [EACH BANK recorded], please tell me

which statement best describes how you feel about this institution?

There are many good reasons to continue dealing with [BANK] and no good reasons to

change.

There are many good reasons to continue dealing with [BANK] but there are also many

good reasons to change.

There are few good reasons to continue dealing with [BANK] and many good reasons to

change.

xxii I expect that there are derivatives of the Conversion model, however, of which I

have no knowledge.


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xxiii Assignment to these categories is based on norms drawn from over7,000

Conversion Model™ studies that have been conducted in over 100 categories.

Branding Banks For Shareholder Value 2

Documents

Transcript of Branding Banks For Shareholder Value 2