Branding Banks For Shareholder Value 2

Branding Banks – Discussion Draft Section 2.0

Branding banks for

shareholder value

Section 2

Knowing Customers
Planned series of discussion drafts

Discussion Release
Draft Date
Version

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

1
© Geoffrey Johns - 22 March 2010


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.

Perception of
Customer
price
perceptions

Perception of Bank Business unit
specification efficiency performance
fit to needs Shareholder
value
Perceptions Bank Corporate
of service financial centre
experience structure performance

Mix of
Perceptions
businesses
of brand

2


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..

Shareholder value

Gap 7

Executing the solution

Gap 6
Gap
5

Creating the solution

Communicating the product service offering Gap 3 Gap 8

Design of product service offering range
4
Gap

Gap 2

Comprehension of customer beliefs, needs,
values and behaviour

Gap 1

Selecting / specifying the market

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

3


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 did ii. 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.

Bank

Professional
Retail Corporate Institutional
markets

Personal SME

Mass
Affluent
market

4


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

5


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.

Personal Business
relationships where
banking and the customer is the
finance decision-maker

Wealth Transactions / General
management consumption Insurance

Owner occupied Transactions
residential Superannuation Term depositis Credit cards Personal loans Auto
deposit accounts
mortgages

Investment Cash Consumer
property Mastertrusts management Charge cards Home
finance
mortgages

Direct
Investment in
Personal
shares and
securities

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

6


Personal Business
relationships where


deposit accounts
mortgages

Investment Cash
property Mastertrusts Charge cards Consumer Home
management finance
mortgages

Direct
Investment in
Personal
shares and
securities

Simple relationship cluster
Personal Business
relationships where


deposit accounts
mortgages

Investment Cash
property Mastertrusts Charge cards Consumer Home
management finance
mortgages

Direct
Investment in
Personal
shares and
securities

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.

7


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 scope vii,
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.

8


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.

9


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
viii
operational terms, that the expressed benefits in a QFD exercise can be expressed with
greater clarity and precision.

Starting Building Consolidating Retiring Life stage

Affluent Comfortable Struggling Affluence

Attitude to
Leaders Controllers Followers Laggards
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.

10


Attitude to finances
Affluence

e
ag
st
fe
Li

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.

11


Retiring
60+

Consolidating
45-59

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’.

Taming the
mortgage
monster

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

12


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.

13


Age

Starting Building Consolidating Retiring
15 - 29 30-44 45-59 60+

Affluent
Main household Income

(> twice mean
income)

Comfortable
(mean income to
2X mean )

Struggling
(< mean
income)

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

14


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.

15


 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 below xiv. 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;

16


 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.

17


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

25.0%

19.7%
20.0%
Percentage

15.0% 13.4%
10.9% 11.6%
10.5% 9.8%
10.0% 8.1%
6.6%
5.7%
5.0% 3.7%

0.0%
Zero to $25k to $50k to $75k to $100k $150k $200k $500k $1m to $2m to
less less less less to less to less to less to less less less
than than than than than than than than than than
$25k $50k $75K $100k $150k $200k $500k $1m $2m $5m

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.

1,400,000
Number of busiinesses

1,200,000
1,000,000
800,000
600,000
400,000
200,000
0
Zero to less $200k to less $500k to less $1m to less $2m to less
than $200k than $500k than $1m than $2m than $5m
Turnover bands

18


40.0%

35.0% $2m to less than
$5m

30.0%
Share pf aggregate turnover

25.0%
$1m to less than
$2m
20.0%
$200k to less than
$500k
$500k to less than
15.0% $1m

10.0% Zero to less than
$200k

5.0%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0%
Share of business numbers

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.

19


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.

Building an
Running a business organisation
Sourcing an income
(buying and selling (something that
(a business instead
things, employing operates
of a job, freelancing)
people) 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

20


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.

21


Turnover
$ million

Lower
Micro Small Medium
corporate

>90%
Percentage of balances held

50% - 90%

< 50%

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.”

22


Share of value, business customer
quintiles
150%

100%
Share of EVA / NPV

50%

0%
1 2 3 4 5
-50%

-100%
Quintile

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

23


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.

Customers Non-customers

Committed Uncommitted Open Unavailable

Entrenched Average Weakly Strongly
Shallow Convertible Available Ambivalent
committed committed unavailable unavailable

Customers' perceptions should be shifted towards Non-customers' perceptions should be shifted towards
commitment 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 account xxi. 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

24


but none that do it customer by customer. This matters a lot because relative
commitment stays with the customer record however you segment the market xxii.

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 categories xxiii:
 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
25


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.

Commitment

Low High
Low

Watch Develop
Value

High

Recover Protect

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

Behavioural Outcomes
segmentation segmentation
Attitude to finances

Commitment

Low High
Low

Watch Develop
Customer – bank interaction
Value

Affluence
e
ag
st

High
fe

Recover Protect
Li

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

26


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.

Age

Starting Building Consolidating Retiring
15 - 29 30-44 45-59 60+

Affluent
Main household Income

40%
-
60% 60% - 80% 60% - 80% < 20%
(> twice mean
income)

Comfortable <
40% - 60%
40% -
20% 60% - 80% 60%
(mean income to
2X mean )

>
Struggling 8
0
60% -
80%
40% -
60%
40
%-
60
%
(< mean %

income)

Percent of customers commited

40% - 60% -
< 20% 20%- 60% 80% > 80%
40%

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

 Remain with the bank;
 Yield greater share of wallet;

27


 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.

Responsiveness
to marketing
communications
Improved More
retention share of
Wallet
Greater
acceptance of
pricing
Commitment
Lower
cost of
risk

Lower Favourable
relationship word of
management mouth
cost

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

28


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.

29


Appendix 1

Basic channel taxonomy – payments (work in
progress)

Domestic Major traveling Purchases Personal
Routine bills
Shopping expenditure abroad from overseas Transfers

Cash Cash over Cash over Cash over Cash face to
FX
counter counter counter face
Cheque face to
Cheque over Cheque over Travellers’ Cheque face
face POS cheque
counter counter cheques to face
Cheque by mail Mail order Cheque by Cheque by Cheque by Cheque by
shopping mail mail mail mail
Cr Card POS, Cr C over Cr C over
POP Credit card Cirrus
counter counter
Dr Card POS, Dr card over Dr card over
Debit card Maestro
POP counter counter
Cr Card, telephone
Card over Card over Card over Card over
- intermediary
phone phone phone phone
Cr Card,
Card over Card over Card over Card over
telephone- direct
phone phone phone phone
Dr Card, telephone Amex travel
Card at POS
card
Stored value cards

Payee initiated On-line On-line
ordering payment
On-line
On-line Cr On-line
transfer
On-line
On-line Dr transfer

30


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.

31


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

32


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

33


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.

34


xxiii Assignment to these categories is based on norms drawn from over7,000
Conversion Model™ studies that have been conducted in over 100 categories.

35

Branding Banks For Shareholder Value 2

Recomendados

Recomendados

Mais conteúdo relacionado

Mais procurados

Mais procurados (20)

Semelhante a Branding Banks For Shareholder Value 2

Semelhante a Branding Banks For Shareholder Value 2 (20)

Branding Banks For Shareholder Value 2