New Zealand Applied Business Journal
Volume 1, Number 1, 2002
CUSTOMER PROFITABILITY ANALYSIS:
FRUSTRATION LEADS TO EVOLUTION
Susan M. Jackman
School of Business, Christchurch College of Education
Christchurch, New Zealand
Yvonne Shanahan
Department of Accounting Finance and Information Systems
University of Canterbury
Christchurch, New Zealand
Abstract: This paper traces the evolution of a customer profitability analysis system
used by a major New Zealand retail bank. It is shown that the Bank currently uses a
first generation customer profitability analysis system, but dissatisfaction with the
system has developed. The current system’s limitations do not allow the Bank to
effectively use customer profitability information to support its marketing strategy.
As a result the Bank is in the process of designing and implementing a secondgeneration customer profitability analysis system. Issues concerning the design of
the new system are discussed. Although the firm is still in the process of
implementing the new system, a description of the decisions it is making during the
design phase provides useful insight for firms contemplating implementation. How
the new information will be used to support the firms marketing strategy is then
described. This amalgamation of marketing and management accounting has been
called for in the management accounting literature.
Key words: Customer profitability analysis, banking industry, case study research,
management accounting research
INTRODUCTION
In the early 1950s Drucker argued that the only reason for a company to be in business was
for the customer (Drucker 1954). Timewell (1994) continued this theme into the 1990s when
he regarded a customer focus as being “the critical success factor of the 1990s” (p. 29).
Several other authors note the importance of focussing on customers, building relationships
with them and keeping them (Christopher et al, 1991, 1991a, Johnson, 1992, Storbacka, 1993,
Peck, 1997, Intindola, 1991, Reichheld, 1994).
In order to react and respond to customer needs firms must have information on their
customers. Information on the profitability of customers (and products) is considered to be
one of the most important types of customer information. (Howell & Soucy, 1990, BellisJones, 1989)1. The early literature concerning customer profitability analysis focussed on
1
Other information should include: a measure of customer satisfaction, although satisfaction in itself does not
indicate a loyal or retained customer (Reichheld, 1994); depth of the customer relationship, that is number of
products or services a customer purchases (Heskett et al, 1994, Lian 1994); frequency of purchases (Reichheld,
1994), and segmentation and/or differentiation of the customer database (McKenzie, 1995).
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New Zealand Applied Business Journal
the use of first generation analysis (Foster and Gupta 1994). However, it was argued that this
form of analysis was too simplistic, and other authors suggested that firms move to second
generation analysis, which identified the lifetime value of customers (Storbacka, 1993, Foster
and Gupta, 1994, Foster et al 1996).
Research tracing the evolution of customer profitability analysis systems is scant. The
evidence is primarily anecdotal,2 however, Stuchfield and Weber (1992) and Storbacka
(1993) have published case study research describing customer profitability analysis in the
banking industry. In addition, Foster and Gupta (1994) argued that there should be a cross
over between marketing and management accounting, and studies of this nature are rare.
As a result this paper traces the evolution of a customer profitability analysis system used by
a major New Zealand retail bank from a first generation analysis (Bellis-Jones 1989) to its
proposed second generation analysis (Foster and Gupta 1994). It also seeks to show how the
Bank will use customer profitability analysis information to effectively implement its
marketing strategy. The Bank is one of the four major trading banks in New Zealand, with
assets in excess of $23 billion. It is owned by an off-shore parent company which operates
through more than 2,300 branches, in 17 countries, and employs more than 52,000 people.
Group assets exceed $197 billion and the parent bank has a long term rating of AA by the
rating agency, Standard & Poor’s. The Bank’s group is listed on the Stock Exchanges of
New Zealand, Australia, London, New York and Tokyo and as at 30 September 1996, had a
market capitalisation in excess of $21 billion.
A bank was chosen as the research site for two reasons. Firstly, this paper seeks to compare
and contrast the experience of a New Zealand banking institution with the existing literature.
Yin (1992) notes the importance of relating the research site to the literature: “each case
study and unit of analysis…should be similar to those previously studied by others” (p. 25).
Secondly, the researchers had prior contact with the Bank based on previous research
undertaken and, once again Yin (1992) indicates that selection of a case study can be based
on prior contact.
The Bank currently uses a first generation customer profitability analysis system, which
identifies the costs and revenues associated with doing business with a customer (BellisJones, 1989), and has done so for the last seven years. Dissatisfaction with the system has
recently developed. Because of the system’s limitations, the Bank has been unable to
effectively use customer profitability analysis information to support its marketing strategy,
as the literature suggests. For example, income and other customer information have been
used as substitutes for profitability when marketing decisions, such as customer
segmentation, have been made.
Due to these “frustrations”, the Bank is in the process of designing and implementing a new
customer profitability system, which clearly exhibits the characteristics of a second
generation customer profitability analysis system (Foster & Gupta, 1994). This paper seeks
to describe this evolutionary process.
2
See references in bibliography.
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The outline of this paper is as follows. Information for this paper was collected using case
study techniques. How access was gained, the people interviewed and method limitations are
discussed. The next section describes customer profitability analysis. A description of the
Bank’s experience is given and compared and contrasted with the literature. This information
may be useful for other organisations contemplating the implementation of a customer
profitability analysis system. Finally a conclusion ends the paper.
RESEARCH METHOD
To collect the information for this case study, interviews were held with Bank employees
over a one day period at the Bank’s head office, with follow up telephone interviews used to
confirm data over a period of nine ensuing weeks.
An initial telephone approach was made to the Bank. A request for access to conduct a study
of the Bank’s customer profitability system received a positive response. A formal written
request followed, outlining the research topic and requesting the Bank’s assistance. Two
weeks following the submission of the request, a follow-up telephone call was made and
accessed granted.
Upon arrival at the site the majority of the interviewees had been arranged. As the Bank is
currently developing a new customer profitability analysis system to replace the existing
system, all people either using the existing system or involved in developing the new one
were interviewed. This resulted in a total of six interviews comprising a senior manager from
marketing, three large customer segment managers, namely middle business banking, rural
banking and home loans, the head of the new customer profitability analysis project and a
research analyst.
The questionnaire consisted of open-ended questions as advocated by Yin (1992) and all
interviews were conducted informally, so that advantage could be taken of new areas that
were uncovered as the interviews progressed. When this occurred, the interview became less
structured, in an attempt to gain a complete understanding of all aspects of the customer
profitability systems.
Prior to commencement of the interviews, all employees were provided with a brief
description of the study to ensure that the interviewees understood what information was
being sought. All interviews were tape recorded and transcribed, to enable complete
representation of the information collected.
At the completion of all interviews, the researchers requested permission to contact the
interviewees, following transcription of the tapes, to clarify any information that was unclear.
This was required in some cases, mainly to clarify bank terminology and policy.
This study was undertaken over a short duration and included six senior staff interviewees.
The restriction on the number of people interviewed was imposed by the Bank, due to the
availability of staff. The use of a short study, and the number of interviewees, may lead to
observer caused bias and other validity problems. To overcome these limitations, the
researchers used written documentation from the Bank where applicable and telephone
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New Zealand Applied Business Journal
follow-up as described above. Also, where possible, statements made about the systems by
one interviewee, were verified or discussed with other interviewees.
THE BANK’S EXPERIENCE AND COMPARISON WITH THE LITERATURE.
The Bank’s current system is a typical first generation customer profitability analysis system.
The model: “Only looks at total customer costs. It is nothing flash. It shows the volume of
all customer transactions and then the volume is multiplied by the fees the customer pays”.
The system does not currently break down costs into customer specific, general customer or
general corporate costs as Foster and Gupta, (1994) recommend. Customer specific costs are
costs that are directly traceable to a customer, for example, automatic teller machine
transaction costs which can be directly traced to a customer by analysing that customer’s
transactions. General costs arise from providing a service for customers. However, they are
difficult to trace to a specific customer, for example the costs of maintaining a branch
network. Corporate costs include areas such as the Bank’s treasury and marketing
departments etc. These costs, by their nature, are very difficult to allocate to customers.
At the Bank, all current account transactions are entered into the spreadsheet and are
allocated the same cost. However, it is possible to transact many different ways using a
current account, for example, cheque writing, direct credits and debits, automatic teller
withdrawals, and electronic funds transfer at point of sale transactions. The costs for these
transactions vary greatly yet the current model fails to recognise these differences.
The current model for determining customer profitability at the Bank was described as being
“very limited”. This view reflects the frustration of first generation customer profitability
analysis. Recall Foster and Gupta (1994): “high quality information cannot be developed
from low quality data inputs” (p. 6). It was explained that: “the model as it stands now, dates
back a good six or seven years, and the information within it has not been updated for quite
sometime”. The outdated information means the Bank can no longer identify which of its
customers are profitable and which are unprofitable:
We had a pretty good idea which were our profitable customers in the past, but
our costing information certainly got out of date and we have now lost the ability
to identify the profitable customers. However, we are trying to regain that ability.
All interviewees believed that the ability to correctly determine customer profitability will be
regained by the Bank, with the introduction of the new customer profitability analysis system,
in approximately six months. The new customer profitability system is an example of a
second generation customer profitability analysis system (Foster & Gupta (1994).
The new model will have up to date costing information which will be separated into direct
and indirect costs, so that marginal costing can be calculated.
As suggested by Foster & Gupta (1994), splitting costs into direct and indirect is a feature of
second generation customer profitability analysis.
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Volume 1, Number 1, 2002
Another feature of second generation analysis is the lifetime projection of customer costs and
revenues. At the Bank, it was explained that the new system will “project customer costs and
revenues forward for the upcoming 12 month period, and roll the horizon over periodically”.
Therefore the Bank’s choice of a 12 month “rolling” time horizon is consistent with the
recommendation in the literature.
As stated earlier, Berry and Britney (1996) believe that the key step in calculating customer
profitability lies in understanding what drives profitability. At the Bank, interviewees
believed that profitability was driven by customer behaviour, i.e. the types of products and
services a customer uses and their relative costs:
It is very much a function of volume to value - the quantity of services and
products a customer uses against what value they (the customers) are to us at the
end of the day.
Others believed income was the key profitability driver: “income drives profitability, without
a doubt”. What is interesting about this viewpoint is that ‘income’ refers to the customer’s
gross income, not fee income as suggested by Berry & Britney (1996). Other staff mentioned
fee revenue: “fee revenue definitely” drives profitability; and margins: “margin management
is one of the key drivers of profitability”. Berry & Britney (1996) do not refer to margins as
being a profitability driver in the banking industry. However, others note that both margins
and fees are key profitability drivers for banks (Storbacka, 1993, Hartfeil, 1996).
Now the Bank has identified what drives profitability we describe their treatment of the
components of customer profitability, namely revenue and costs.
At the Bank, revenue was perceived to be “easily definable” for the purposes of the model,
however as is shown later the Bank is struggling with the issue of the use of averages for
margins and fees. Customer revenue comes from three sources: fees - the charge for using a
particular product or service, for example, cheque clearance fees, transaction volume fees,
cash handling fees etc; margins - the difference between what the Bank pays customers for
funds on deposit and what it charges customers for loans, and financial products commission collected from products such as life insurances.
Fee revenue is easily traced directly to the customer. This is because fees are charged
periodically to individual customers, however one problem facing the Bank is the treatment
of fee revenue when projecting lifetime revenue:
You get into issues of seasonality. Every single fee type that the Bank has, had to
be analysed to understand how they are charged. Some are one-off fees, whilst
others are charged monthly, quarterly, or yearly. The characteristics of each
individual fee type are being built into the new model.
Another source of revenue for the Bank is margins. Like fees, margins are also not difficult to
trace to customers:
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It is very simple. It is the difference between the cost of funds and the retail
purchase. If the cost of funds is 7% and the customer is being charges 9%, then
the margin is 2%. On a one million dollar loan, that is $20,000 of revenue.
The problem associated with projecting margin revenue for second generation analysis, is the
difficulty in estimating what volume of margins are going to be generated by customers and
the margin itself:
The margin side has two components, the margin percentage and the margin
volume. We have to try and estimate what is going to happen to interest rates in
the up-coming 12 months to determine our margin percentage. We also need to
estimate what volume of each product a customer is going to consume.
Relationship managers will be called on to help with this process. The information gathered
at the CARE3 interview, along with information gathered from every interaction with a
customer, will be used to project a customer’s future consumption:
Their (the customer’s) facilities may have changed recently. Or, in our
discussions, we determine that they are actually going to borrow money, or they
have some extra money coming in from overseas. Those factors are going to
affect their account balances, and hence our revenue projections.
Gathering information from customers regarding their future intentions to purchase or cancel
margin based products does not obviate the problem. As customers deposit funds into their
accounts or repay loans, their balances fluctuate, affecting the margin projections. To deal
with the problem of margin volume, the Bank currently uses monthly averages:
The margin balances we currently show are the average monthly margin balances
and from this we calculate an average monthly margin transfer rate for each
customer to reflect fluctuations in the margin balances. The margins will be based
on the actual rate that is applicable to the customer multiplied by the average
transfer rate. (However) We can change the margin depending on the customer.
If they are beating us around the head on price for one particular product, we may
let them have it to keep the relationship group4 with us.
Howell & Soucy (1990) warn that averages should only be used as a short-term solution. The
Bank is planning to follow this recommendation and use actual margins in the new second
generation system. This will highlight customers who are unprofitable due to low margins,
allowing management to take appropriate action. Furthermore, customers who are highly
profitable due to excessive margins can be identified and their margins reviewed to alleviate
any subsidisation of customers that are not profitable. Thus the new system will be used for
price negotiation (Storbacka, 1993, Booth, 1994, Hudson, 1994).
3
Interview held with customers to determine customer needs
4
A relationship group includes all business, and personal accounts of all company principals and their families.
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The third source of revenue for the Bank is from financial products. As with revenue from
fees and margins, the Bank can readily allocate revenue from financial products to customers:
We are in a partnership with one of the big players in the insurance industry. We
simply collect commission on all premiums that are paid. We know exactly how
much we are going to get and which customers it relates to.
Having discussed the Bank’s treatment of revenue issues, costs are now considered.
At the Bank it was acknowledged that there were different costs associated with different
customer activities (Hartfeil, 1996):
Yes, obviously branch transactions, are the most expensive type, followed by
cheques, and down on through the electronic transactions, which tend to be less
expensive than the branch transactions.
Transaction intensity (Berry and Britney, 1996) was recognised at the Bank as an important
aspect of customer costs:
Some customers have very heavy transaction requirements. Customers who pay
large payrolls, or receive a large number of automatic payments, need to be
monitored closely so that we can provide them with the best service and at the
same time minimise their costs as well as our own.
Activity costs and transaction intensity will be included in the model. The Bank intends to
concentrate on analysing transaction intensity and type by looking at customer behaviour.
Many authors recommend this technique (Howell and Soucy, 1990, Storbacka, 1993, Booth,
1994). Customer behaviour will be modified to minimise activity costs for both the customer
and the Bank.
At the Bank, standard costs are used for pricing both products and services:
Most of our fees are standardised across the board; our cheque clearance fee is
standard, regardless of who the customer is or how much that product or service
actually costs us to sell to that customer, our monthly activity fee on cheque
accounts is based purely on number of transactions, not transaction types, loan
set-up fees are a percentage of the amount borrowed regardless of who the loan is
for.
However, the Bank sometimes alters these charges for different customers:
Some customers are given fee free banking. This is usually based on the value of
the customer relationship to us.
The Bank does this by charging the customer the appropriate fees, but then credits the fees
back to the customer. The Bank aims to conduct multi-scenario analysis on customers, by
calculating customer profitability in the new system, with and without fees, making the
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effects of providing free fees on a customer’s profitability evident. This represents another
example of the use of customer information for price negotiation (Storbacka, 1993).
In regard to origination costs, all such costs will be included and allocated in the new model:
We have three main costs for a product: an opening cost (of an account), the cost
of maintaining (the product and/or account), and the cost of closure. We pick up
how many accounts for a particular product have been opened in the period and
allocate the origination costs accordingly.
Unfortunately it was not possible to ascertain how origination costs were amortised over the
life of the product and or the life of the relationship. This information was seen as
commercially sensitive. Hartfeil (1996) does recommend allocation, however he does not
describe how this should be done.
Costs of closure refer to the actual costs of closing an account should a customer leave the
Bank. This includes, for example, any costs that are associated with early closure of a loan.
Hartfeil (1996) believes attrition5 can be identified by customer profiling and segmentation
(discussed later). Customer profiling takes place at the Bank’s marketing department:
“marketing has done some customer profiling to identify which customers are at risk of
taking off.”
It is envisaged that information from the new customer profitability analysis system will be
combined with customer profiling information to be used for projection:
The other side is the projection side. It is a ‘what if’ capability. It means taking a
customer’s performance and trying to predict what the impact will be if they
leave. We will know what the impact of price negotiations on fees and/or
margins will be on profitability so we can negotiate to keep the customer or let
them go.
Whilst it was recognised that delinquency costs are a cost that can be directly traced to a
customer as suggested by Hartfeil (1996), the Bank does not propose to track delinquency
costs in the new system.
Foster & Gupta (1994) advocate the use of cost hierarchies. The Bank does identify cost
hierarchies:
We currently have average general costs for activities that directly relate to
servicing the customer. With the new model, we will go to the person directly in
contact with the customer, and they will be able to input the exact amount of time
spent on that customer. From this we can then strip out all the directly traceable
costs such as business manager costs, analyst’s salary costs and non-salary costs
and put them back in the model based on actual not average costs.
5
Attrition is the loss of customer accounts.
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While the “vast majority” of indirect costs will be included in the new model, general
corporate costs “aren’t taken down to a customer level”. These costs are taken “straight from
our general ledger down to a product level”. The remaining indirect costs and “extraordinary
costs and abnormal items will not be allocated to customers”. This view is inconsistent with
the recommendations in the literature to track indirect costs to customers to gain a more
accurate picture of customer profitability (Foster et al, 1996, Storbacka, 1993).
Stuchfield and Weber (1992) note that customer profitability analysis does not include the
effect of exit barriers. Exit barriers are evident at the Bank, for example moving mortgages is
difficult and “with fixed rate products, there are often penalties to leave.” The outdated data
in the current customer profitability system means that reliable customer profitability figures
are not available. Therefore, it was not possible to determine whether customers that remain
with the Bank, despite being unsatisfied, are profitable or un-profitable. The effect of exit
barriers on customer profitability is an area that is still under investigation.
The final issue the Bank faces is how risk will be handled:
The risk is quite an interesting point actually. We are still trying to get to grips
with exactly how risky a customer is going to be so we can present it through the
model. The way it is looking at the moment, is that if you are a high risk
customer you are going to appear more profitable.
This occurs because higher risk customers are charged a higher margin on products and
services and therefore their revenue is higher than a lower risk customer. As Rose (1991)
notes, only a handful of banks have customer profitability models capable of assessing risk,
and this bank may prove no exception. The Bank has not yet decided what approach it is
going to take when it includes risk into the new customer profitability analysis model: “we
have our resident expert working on that at the moment”. It was recognised that: “it is an
area of concern.” The current system does not allow for an adjustment of customer risk, and
because riskier customers are charged a price premium on many products to compensate for
their inherent risk, the current model shows riskier customers as being more profitable. This
has led to an apprehension by the Bank to distribute the information from the old and new
systems, to relationship managers, until this issue is resolved.
The Bank will calculate customer profitability using the simple formula: “customer
profitability will simply be the difference between revenues and costs”. However, as was
discussed in the previous section, the Bank is undecided how it will approach risk. This will
obviously impact on the risk adjusted revenue and cost figures incorporated in the new
system.
HOW THE NEW SYSTEM WILL IMPACT ON THE BANK’S CUSTOMER DECISION
MAKING
The current system does not allow the Bank to use customer profitability information to
effectively implement its marketing strategy. Yet interviewees believe the new system can be
used for analysing customer behaviour and price negotiations (as discussed previously). The
new system will also be used for segmentation and cross selling.
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In regard to segmentation decisions, the Bank currently assumes a correlation between
income and profitability because, “income is at the core of a customer’s ability to purchase
products and services”. However, it is now envisaged that information from the new
customer profitability system be used extensively for customer segmentation in all core
business.
When the new customer profitability analysis system is introduced, the segmentation model
will be modified, and the income segments will be further segmented into levels of service
requirements. The new segmentation model will enable the Bank to tailor the appropriate
level of relationship manager involvement that will best suit the customer. Furthermore it is
predicted that it will enhance customer profitability by reducing unnecessary relationship
manager involvement with particular customers.
In regards to cross-selling the Bank has no evidence of its effect on customer profitability, yet
it was “strongly believed” to be the case:
I think that the profit from a customer who has a range of products, is better than
a customer who is operating a single transaction account unprofitably. We are
better to try and cross-sell and deepen that relationship by finding out what they
are using, what they need and whether they can utilise other services that we
offer.
It is hoped that this conjecture can be substantiated with information from the new system.
Information from the new system will initially go to business managers, who will use it: “to
get a picture of those customers who are making money for the Bank”. It is hoped that:
The business manager will be able to determine the profitability drivers of customers. For
example, if it is determined that we have a number of products that will meet a customer’s
needs, then an understanding of the profitability of the customer, as well as the product, will
enable the least expensive product to be chosen.
CONCLUSION
This paper has described the evolution of a customer profitability analysis system at a major
New Zealand retail bank. The Bank currently uses a first generation customer profitability
analysis system, however, it has proven to be ineffective for the Bank’s purposes due to
outdated and unsophisticated cost information. As a result the system can not be used by the
Bank to effectively implement its marketing strategy.
To overcome the current system’s limitations, the Bank is in the process of designing and
implementing a new customer profitability analysis system. The new system will exhibit
many of the characteristics of a second generation system, most noticeably the recognition of
the ‘lifetime’ value of customers using a rolling time horizon of 12 months as recommended
by Storbacka (1993).
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The Bank’s view of what drives customer profitability is consistent with the literature except
that interviewees believed that gross income was also a driver. This new driver needs to be
added to the literature.
The Banks proposed treatment of revenue is consistent with the literature. The aim is to
move from the use of average margins (first generation analysis) to actual margins in the new
model, as recommended by Howell and Soucy (1990).
Activity costs and transaction intensity will be included in the new model. Standard costs
will be used for pricing products and services. Origination costs will be allocated over the
life of the relationship but how the allocation will take place was not disclosed due to its
commercial sensitivity. These treatments of cost are consistent with the literature.
While it was recognised that delinquency costs should be directly traced to customers, the
Bank proposes not to do so. The reasons for non-tracing require further investigation. The
treatment of exit barriers also remains a significant issue.
A major problem for the system designers is how risk will be incorporated in the model. The
‘resident expert’ is still working on it.
Given these unresolved issues, it remains to be seen whether the Bank will be able to use the
new system effectively to support its marketing strategy. At the time of interviewing there
was confidence that the information could be used for all sources proposed in the literature,
namely analysing customer behaviour, segmentation, cross- selling and price negotiation.
This amalgamation of marketing and management accounting has been called for in the
management accounting literature (Johnson, 1992, Foster & Gupta, 1996). Further, this
paper provides useful insight into the decision making processes an organisation faces in the
transition from first generation to second generation customer profitability analysis.
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