Chapter 3.4 Marketing metrics and customer equity models · PDF file3.4 – 2 Chapter 3.4...

Chapter 3.4 : Marketing metrics and customer equity models

3.4 – 1Author/Consultant: Robert Shaw

Chapter 3.4

Marketing metrics and customer

equity models

This chapter includes

�� Understanding the context of marketing metrics

�� Monetary metrics 1 – the conceptual framework

�� Monetary metrics 2 – customer equity models

�� Merit measurement – non-financial metrics

About this chapter

In this chapter we will deal with the numbers that senior decision makers

need to manage the strategic issues concerning direct marketing. These

numbers need to be specially prepared to be suitable for their needs.

We will look at the context in which these numbers are created and used. There

are many sources of confusion and misunderstanding, and these need to be

considered and addressed prior to establishing a metrics framework.

Metrics divide between monetary measures and measures of marketing merit. We

look first at the monetary measures, and the conceptual framework behind them.

This will not be readily found in accounting literature, which tends to focus

almost exclusively on costs, and tends to neglect the revenue side of the business.

Next we look at the need for customer equity accounting – tracking customer

acquisition and retention. This is important to financial management, as a stock

of active customers is vital to the sustainability of the firm.

Finally, we turn to the non-financial factors that must be measured alongside the

financial ones. Here, the selection of measures is highly specific, and we look at

the wide range of factors that may be relevant to interpret the financial numbers.

3.4 – 2


Robert Shaw

Robert Shaw is an author,

businessman and

consultant, specialising in

profit-focused marketing.

He advises senior

executives and creates

insights, models and plans to demonstrate and

improve the profitability of marketing. He is

director of VBMF.COM (the Value Based Marketing

Forum) which researches best-practice in profit-

focused marketing and benchmarks organisations

against best practice. He is Professor of

Marketing Metrics at Cass Business School, City of

London, and his recent book Marketing Payback

was voted top marketing book of the year by the

Chartered Institute of Marketing and also by the

Marketing Society. He can be contacted at

[email protected]

T: +44 (0) 208 995 0008

M: +44 (0) 7940 526 833

Chapter 3.4

Marketing metrics and customer

equity models

Understanding the context of marketing metrics

Who needs marketing metrics?

In other chapters of this Guide, the following data sources are examined:

customer database records, market research data and financial data. So before

digging into the details of marketing metrics and customer equity, we

investigate who needs any more data, and what they will do with it.

Marketing metrics are the strategic numbers needed by senior decision makers in

organisations.

To understand the relationship between metrics and managers, it is useful to look

at the nature of senior managers’ work. Mintzberg’s landmark study (1980) of top

managers and several replicated studies suggest that managers perform 10 major

roles, which can be classified into three main categories: decisional, informational

and interpersonal:


3.4 – 3

Table 3.4.1 Mintzberg’s 10 management roles

Decisional

Entrepreneur Searches the organisation and its environment for

opportunities and improvement projects; supervises

implementation of some projects

Resource allocator Allocates organisational resources of all kinds – in effect the

making and approving of all significant organisational

decisions

Disturbance handler Triggers corrective action when the organisation faces

important unexpected disturbances

Negotiator Represents the organisation in important negotiations

Informational

Monitor Seeks and receives a wide variety of special information

Disseminator Transmits information to other managers. Some may be

factual; other information may require interpretation and

narrative

Spokesperson Serves as an expert on a particular aspect of the organisation

Interpersonal

Figurehead Performs a number of duties of a social or legal nature

Liaison Maintains a network of contacts inside and outside

Leader Motivation and activation of subordinates, staffing coaching

and training

The first three decisional roles in particular deserve careful attention.

Entrepreneurial decision makers are on the constant lookout for new ideas in

their monitor role, and when one appears, he or she initiates a development

project that they may supervise directly or delegate to a manager. An interesting

feature of these projects is that they do not involve a neat, tidy bundle of data, but

rather a messy cluster of facts, figures and associated decisions. These bundles

are often called strategies – the things that will drive up value. Entrepreneurs tend

to prolong these strategic projects so that they can fit them, bit by bit, into their

busy schedules.

If entrepreneurs are the main source of ideas and strategies, resource allocation is

the main process for making those ideas reality. Apart from allocating their own

time (perhaps the most important decision), managers must allocate money,

brainpower and muscle, and decide on the right level of resources.

Rightsizing is a crucial allocation concept. By the ‘right’ quantity we mean the

quantum of resources that will maximise (or optimise) the financial value

generated. Calculating the right quantity of resources is not something that

should be left to chance.

A key role of metrics is in helping managers evaluate how much is the right

level of resource allocation.

3.4 – 4


The third decisional role is also important. Whereas the entrepreneur is a

voluntary initiator of change, every manager must spend a good part of his or her

time responding to high pressure disturbances. Disturbances arise by and large

because poor subordinate managers allow situations to reach crisis proportions,

but also because good managers cannot possibly anticipate all the consequences

of the actions they take.

Another key role of metrics is in providing early warning indicators of

problems that lie ahead.

Uses and abuses of management information

Numbers are collected and studied by businessmen and women on a regular

basis. These numbers are often called ‘management information’ and their

collection and consumption has been going on for at least 100 years. The types of

numbers consumed have gradually expanded over time, especially since

inexpensive computers became widespread. More and more types of data have

been added to the menu. Although some of the uses of numbers are healthy, the

obsessive collection and consumption of numbers is not necessarily healthy, and

sometimes numbers are misused and abused.

A simple but dangerously misleading explanation of the role of management

information is the widely used ’central nervous system’ metaphor. “Measurement

is the company’s nervous system” says Sir Peter Davis, in the forward to

Marketing and the Bottom Line by Tim Ambler (FT Prentice Hall, 2000). The

notion that organisations are controlled by an all-powerful brain that triggers

every movement and action of its many parts is deeply misleading. Yet this

misconception is commonplace and it has spawned information systems whose

sole purpose is to feed rivers of raw data to the Boardroom. In recent years there

has been a powerful backlash against this approach, epitomised by the ‘beyond

budgeting’ phenomenon whereby organisations are ripping out their traditional

information systems.

Figure 3.4.1 Patterns of information flow in ‘central nervous

system’

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A much better alternative to the big-brain image of organisations is the ant colony.

Here, individual workers collect information, for their own use and to pass to

others. Individual actions are not controlled centrally, and yet the colony serves a

collective purpose, despite the lack of a powerful command centre. Figure 3.4.2

shows the patterns of management information flow in a typical ‘ant heap’

organisation.

Figure 3.4.2 Patterns of information flows in an ‘ant heap’

organisation

At the top of the organisation is the Board. It has limited knowledge of what is

happening in the parts of the organisation underneath it. Without this knowledge

it cannot possibly control the many actions and decisions taking place every

moment 24/7. A modern view of the Board is that it guides and coaches its

subordinate parts, but it does not command them.

The one exception is control of discretionary spending – monetary expenditures

that can be delayed or cut at time of cash shortage. Boards can and do command

cuts to these costs in order to avert short-term cash crises. Marketing expenditure

is the most important such discretionary spend item, and Boards often do

command cuts in marketing spend at very short notice.

The role of the Board needs some explanation, given that its role as a command

centre is limited. Current thinking (Campbell, Mintzberg) is that the Board is

custodian of ‘value creating insights’ – deep penetrating ideas on what strategies

create value, and what destroy value. These strategies tend to focus on revenues

or costs, and it is the revenue strategies where marketing has an important

strategic role. Boards intervene with subordinates by coaching and guiding them,

rather than by command, and this distinction is an important one to understand.

Information flowing to the Board comes from transparency within the

organisation (i.e. the parts of the business feed critical information to the Board

summarising their activities) and it also comes from ‘strategic Key Performance

Indicators’. The latter summarise progress towards strategic goals, and these are

not necessarily revealed by the internal information alone.

At the lowest levels in the organisation, data is collected for self-control purposes,

by the supervisors and managers of operational activity (similarly to the ants in

our earlier analogy). Variance analysis is widely used as a control mechanism for

the myriad of detailed activities that occur at this level, often implemented by

computer systems. The parameters within which more junior managers act are

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often more tightly controlled than the senior managers, by middle managers who

set variance limits and prescribe behaviour of the junior team.

The middle managers therefore have a crucial role, which is not merely a matter

of following orders and responding automatically to variances. For them,

judgement is crucial, and this judgement is shaped by detective work – collecting

evidence, examining the facts, analysing them, interpreting them and drawing

conclusions. Middle managers need to accumulate a wide range of facts and

figures, not merely a narrow range of control data.

Why do we need yet more data and what will we do with it?

Metrics are needed to support senior and middle managers’ value creating insight

and foresight. There are three important types of insight and foresight, as

illustrated in figure 3.4.3:

Figure 3.4.3 Main types of insight and foresight

� Historical insight: a deep understanding of the driving forces that have

propelled the performance of the company over time to its current

performance levels

� Projective foresight: deep understanding about the path that performance

will follow if the company remains unchanged

� Visionary foresight: deep understanding of the changes that the company

must make to improve its performance

Data is used to support all three. Insights emerge from studying historical

information that illuminates and quantifies the driving forces behind past profit

patterns. Projective foresight is improved by creating quantitative models that

allow past patterns to be projected into the future.

Visionary foresight requires imagination to create ideas about changes that are

needed to improve future performance. Imagination is an important aspect of

vision, but even here numbers and models are important. Mental models are

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essential tools for the visionary, and vision is sharpened by constructing models

that apply the lessons of the past to quantify the impact of the visionary changes.

Metrics serve an important role in this context. They are preprocessed data,

suitable for senior and middle managers as a source of insights. Raw data is too

detailed and complex to yield significant insights or foresight, and it needs

working, cleaning, summarising and analysing to yield up its important payload of

insights.

Metrics provide decision makers with a helicopter view of the profit patterns and

drivers. The word metric has musical associations, implying a steady beat against

which the performance movements in the business are measured. Metrics are

extracted from raw data, sometimes automatically, but also manually, with checks

and tests to ensure that they are suitable for comparison.

Having collected the metrics, they need to be analysed to yield insights. As a first

step, you should plot a graph. Spreadsheets are the tool of choice for this, and

most novices can plot graphs. Even with limited data, there are some patterns

that you may notice:

� Trend which rises (or falls) steadily over the time period you are examining

� Seasonal pattern which repeats, more or less the same, every 12 months

� Short-term peaks that are the effects of events such as price promotions or

important sporting fixtures

These features are illustrated in figure 3.4.4:

Figure 3.4.4 Insights come from observing patterns in

marketing metrics

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Metrics are used as sources of value creating insights. Value, according to the Oxford

English Dictionary has two separate connotations, and both must be encompassed in the

metric system:

� Monetary worth

� Merit

The next two sections explore the monetary aspects of metrics, whereas the final

section looks at metrics and marketing merit.

Monetary metrics 1 – the conceptual framework

Monetary data is commonplace in most organisations. Yet familiarity breeds

contempt. Getting a deep understanding of the driving forces behind the financial

figures is certainly not self-evident, and most managers simply do not spend

enough time understanding where money comes from. Normally they know where

money went (i.e. the costs), but do they know where exactly it comes from, and

how to get more?

A framework is needed upon which to construct an understanding, or model, of

the sources and drivers of money. There are two key concepts that must be

understood to gain a deep understanding:

1. Deciding how much to spend to get more money

2. Choosing where are the best places to spend

Deciding the level of marketing spend

Spending money creates more money. This is the simplest, most basic way of

looking at marketing spend, an input-output model, as illustrated in figure 3.4.5:

Figure 3.4.5 A simple input-output model of marketing

payback

Marketing Profit

expenditure payback

The input-output model is so simple, it’s easy to overlook its importance. This

derives from the fact that input and output tend to be linked according to one of

the most important principles in marketing, and also in economics, and

underlying it is ‘the law of diminishing returns’,sometimes also referred to as

Pareto’s principle or the 80/20 rule. It is central to all aspects of marketing

decision making. Stated simply, when the expenditure on marketing is increased,

then the revenue response rises but its rate of increase diminishes. Graphically,

this is shown in figure 3.4.6:

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Figure 3.4.6 Law of diminishing returns – the marketing

saturation curve

Two parameters are all that’s needed to calibrate this curve. First is the initial

response rate, which is often available on the basis of past experience. Second,

the maximum revenue (or saturation level); total customer population can provide

useful clues to this.

The reason why this law is very important is because it implies that there is a

right level of marketing expenditure, above which and below which profits will be

lower. So it is crucial for decision makers to assemble good evidence about where

this right level is situated.

Another important corollary is that profit is the only appropriate objective.

Revenue will not do, nor will net contribution (revenue minus marketing spend).

Maximising any variable other than profit will result in a massive overspend with

profits below the maximum level, as shown in figure 3.4.7. For example,

maximising net contribution significantly diminishes profits.

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Figure 3.4.7 Maximising profits is the only appropriate

objective

Finally, the nature of the profit being maximised is important. Short-term profits

are less than long-term profits, and will lead to different maxima. Maximising

long-term profits will justify much higher expenditure levels than short-term, as

illustrated in figure 3.4.8:

Figure 3.4.8 Long-term and short-term maximisation

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Deciding where to target marketing spend

This is the second key decision. There are many dimensions that need to be

considered, especially in large organisations, as illustrated in figure 3.4.9:

Figure 3.4.9 Multiple dimensions of targeting in a large

organisation

In making a targeting decision, there are two types of situation:

1. Unconstrained marketing funds

2. Allocating a fixed marketing budget

Entrepreneurial businesses treat each funding opportunity on its own merits, and

will obtain funding for any campaign that creates profits. In these businesses, the

optimal funding required for a series of marketing strategies is found by

optimising the funding for each. Each stands or falls on its own merits, and the

funding process involves setting a hurdle rate for campaigns and finding funds for

all campaigns that generate surplus profits. This mode of operating requires that

all campaign managers have the time available to analyse their projects rigorously,

and the sophistication and objectivity to do a good job.

Few businesses operate in practice this way. It is far more common to set a fixed

marketing budget, and allocate this fixed amount between alternatives. Figure

3.4.10a shows this situation for three main alternative expenditure targets.

Option A generates fastest growth in profitability, as expenditure begins to grow.

Option B has a lower profit growth curve, and option C the flattest. For the

expenditure levels shown in the figure, only A and B are targets; Option C does

not generate sufficient incremental profit to be worth targeting.

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Figure 3.4.10a Targeting a fixed marketing budget at the

optimum opportunities

The optimal mix changes as gross expenditure changes, as shown in figure

3.4.10b. Option A is the only good investment when budgets are low. As gross

budget increases, Option B becomes attractive, as the incremental value of Option

A declines. Finally, Option C becomes a contender, and then all three options are

targets. Eventually all three hit their maximum returns. At that point, no further

expenditure can be justified, as going beyond that point will destroy value.

Figure 3.4.10b Changes in the optimum marketing mix as

expenditure changes

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Monetary metrics 2 – customer equity models

Monetary metrics in most direct marketing businesses have a further

complication that was indicated in figure 3.4.6, but not fully explored. This

section examines this complication. In acquiring new customers, the value of the

initial purchase is not the whole story. Repeat and add-on purchases contribute

value, far into the future. (While this section deals with this issue at the level of

corporate profitability, the next chapter (3.5) examines the same issue at the

individual level of customer lifetime value.)

Fundamentals of customer equity

According to customer equity theory, the simple input-output model gains one

extra dimension: the customer inventory, as shown in figure 3.4.11:

Figure 3.4.11 Input-output model plus customer inventory

From a pure financial perspective, this model contains an element that does not

appear in the company’s financial records; namely the customer inventory. This

customer inventory has potential future value. Customer equity’s main thesis is

uncomplicated. The customer is a financial asset that firms should measure,

manage and maximise just like any other asset.

Customer equity terminology

Customer accounting: maintaining detailed records and inventories of customers, in a

similar way to other balance sheet records

Customer equity optimisation: an approach to marketing payback optimisation that

makes an allowance for future sales from customers, in addition to the initial sales that

occur at the moment when the customer is first acquired

Customer equity management: a dynamic, integrative marketing system that uses

financial valuation techniques and data about customers to optimise the acquisition of,

retention of, and selling of additional products to a firm’s customers, and that maximises

the value to the company of the customer relationship throughout the life cycle

Customer accounting lies at the heart of this approach. While customers are not

formally accounted in company’s balance sheets (Accounting Standards does not

recognise them as a tangible asset), they are nonetheless an intangible asset. Good

practice organisations maintain detailed customer inventory records.

Constructing a working model of customer equity requires some additional data

about acquisition response rates (the saturation curves that we examined in the

previous section), and loss rates. Customer inventory decays over time, due to

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customers becoming dormant, dying, or actually defecting to competitors. This is

shown in figure 3.4.12, and non-financial metrics are shown in italics in the

dotted boxes.

Figure 3.4.12 Schematic of customer equity model

This schematic model can be used as a basis for calculating profitability; for

example, using a spreadsheet. A sample calculation is shown in figure 3.4.13:

Figure 3.4.13 Integrated customer and financial model

Source: Shaw and Merrick 2005

The inputs to the model are the acquisition expenditure for each year and the

initial customer base. Our customer accounting assumptions are the base

acquisition cost, the maximum acquisition rate per year (so that we can allow for

an increasing marginal cost of customer acquisition), and the customer loss rate.

Our financial assumptions are the purchase rate per customer, the fixed costs of

the business and the variable costs per customer. With these assumptions, the

figure shows how some relatively simple arithmetic enables us to calculate how

the customer base, revenues, costs and profit change year by year.

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Putting the customer equity model to work

The ‘base case’ version of the model shown in figure 3.4.13 keeps acquisition

expenditure, acquisition costs and customer losses (and therefore the customer

retention rate) constant across the five-year period. As shown in the figure, under

these assumptions both the customer base and profits grow steadily over the five-

year period.

Many alternative acquisition expenditure patterns could be investigated using the

model structure outlined. In order to provide some interesting and thought-

provoking patterns that will help to understand the fundamental business

questions, we look at three contrasting scenarios.

Scenario A: Stability

The acquisition budget is adjusted each year in order to maintain a constant

size of customer base.

Scenario B: Growth

The acquisition budget is kept constant at £50 million per year (rather than

the £20 million shown in figure 3.4.13) with the objective of growing the

customer base.

Scenario C: Profit

The acquisition budget is set so as to maximise profits over the five-year

period, whatever the consequences for the customer base.

To make the models more interesting, and to yield some useful insights we now

abandon the assumptions in the base case, and consider a situation where:

� Customer losses increase steadily from 20 to 40 per cent over the five-year

period (and retention rates consequently decline from 80 to 60 per cent)

because of growing competition

� For similar reasons, base acquisition costs increase steadily from £80 to

£100 per customer over the period

The results for scenario A are shown in figure 3.4.14:

3.4 – 16


Figure 3.4.14 Results for the stability scenario


The cost of keeping the customer base constant under these assumptions is a

steadily increasing expenditure on customer acquisition, both to offset the

increasing losses and because of the higher cost of acquisition itself. Although

keeping the customer base constant preserves revenues, the increasing costs lead

to a steady decline in profits throughout the period.

The results for scenario B are given in figure 3.4.15:

Figure 3.4.15 Results for the growth scenario


The financial outcome of this is disastrous. The high acquisition spend in this

scenario leads to a loss in the first two years. However, the size of the customer

base nearly doubles by year four and the higher revenues lead to a return to

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profitability. By that time, however, the harsher competitive environment means

that profits are meagre. In the final year, the customer base declines slightly

because even the £50 million acquisition budget is not able to maintain this size

of customer base in the face of the higher losses and acquisition costs that apply

in year five.

The results of Scenario C are shown in figure 3.4.16:

Figure 3.4.16 Results for the profit scenario


With profit as the goal, the game plan is very different.

This is the classic ‘Grow then Milk’ strategy. In the first year there is a spurt of

acquisition, which gains market share. Thereafter, acquisition is steadily cut back,

due to deteriorating market attractiveness.

A healthy profit stream emerges in the second year. At the same time, having

gained market share early, when the market was attractive, finance pulls back

marketing expenditure.

However this situation is not sustainable in the long term. The initially high share

is allowed to decline. Share is conceded to competition, because the market

attractiveness is declining. Eventually, as size drops, diseconomies of scale will

emerge, and these will eat profits and the business will eventually need to be shut

down.

It is clear that none of the above scenarios is satisfactory. But is there a

combination that would offer a stable (and therefore long-term) strategy that

maximised profits? Clearly such a strategy will depend on the assumptions,

especially for customer losses (retention rate) and the acquisition cost. We can

use the model described above to calculate what levels of customer base are

supported by various levels of acquisition spend and how much profit is made in

each case.

To illustrate the point, we show in figure 3.4.17 below the results based on the

assumptions in year one of the previous examples:

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Figure 3.4.17 Customer base and acquisition spend


First look at the customer base. As we increase the annual acquisition spend, the

size of the customer base that can be supported increases. Note that these figures

refer to a steady state situation. In any one year, a real company would not

necessarily have reached a steady state and may be either growing or shrinking

even with a constant annual spend (as shown in scenario B above). Note also that,

although the steady state customer base increases with acquisition spend, the rate

of increase slows as the costs of acquiring additional customers become ever

more expensive.

Now let’s compare the profitability of the various levels of acquisition spend for a

customer base that has reached a steady state. The results are quite revealing –

there is a maximum profitability at an acquisition spend of about £55 million.

Spending less than this quickly reduces profitability as the customer base

becomes too small. Above this, the spend gradually becomes less and less

effective.

BookCo case study

BookCo is one of the world’s biggest book clubs. Its business model, that of

selling books by mail order to people who join its clubs, is one that has

been around for a long time, but recent changes in its markets and

channels have put it under severe financial pressures. This case study is

about how a formerly successful company has turned to customer equity

modelling techniques, and in particular optimisation of its marketing

spend.

Over the last five years, although the total book market has grown, sales

of books through direct channels, such as book clubs, has shrunk. The

reason is that major retailers, including supermarket chains, have taken a

greater share of the market, especially for popular, best-selling titles

which used to be the mainstay of book clubs. BookCo, however, remains

the biggest player in the direct sales channel.

BookCo’s portfolio ranges from general interest clubs such as BigBooks,

through to clubs targeted at special interests or particular age groups

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3.4 – 19

(such as trains, military history and cookery books). An important issue to

grasp about their business is that although the general clubs have more

members, and benefit from negotiating better terms with publishers

because of the greater volumes involved, for the most part these clubs are

not as profitable as the smaller specialist clubs, who, although they have

lower volumes, also tend to have higher unit prices and a greater

frequency of purchasing.

A further complication is that the general interest clubs have been proved

to be the recruiting ground for the special interest clubs, and as such, the

general catalogues often carry a selection of special interest titles

alongside their general titles. As BookCo is meticulous in tracking

customer acquisition costs for each club, they have realised that this

cross-selling phenomenon makes clear-cut invest/divest decisions

particularly difficult.

For all these reasons, BookCo management decided that radically

changing the current book club portfolio was not an option, and what they

needed was a more sophisticated approach to fine-tuning their customer

acquisition and customer maintenance expenditure.

BookCo appointed a new finance director. One of the FD’s first moves was

to invite us to construct a customer equity optimisation model of the

business. The solution took the form of an Excel model, which ran on data

from BookCo’s membership database. It handled data for 30 book clubs

(allowing a few extra for new clubs), 15 media channels and allowed for

450 different recruitment/spend decisions, reflecting the detail with which

BookCo managed this part of its operations.

This may appear complex, but what goes on underneath the model is

relatively straightforward, and can be applied more generally. It helped

that BookCo had good base data, however, and this may be a stumbling

block for other businesses. In particular, they had a clear idea of the

profitability profile of each member over their lifetime as a member, and

the recruitment costs by book club and media channel.

However, what they had not done up until this point was bring all the data

together in a form that allowed them to compare club profiles, nor had

they made any attempt to use this data to forecast future performance.

Our model allowed them to do that, with useful results.

An example of how the model was used is shown in figure 3.4.18:

3.4 – 20


Figure 3.4.18 The results of three iterations of projected profits


The initial optimisation resulted in some club closures and the reallocation of

acquisition spend away from some club and media channels towards others. This

resulted in a deterioration of profitability in the first year compared with the

initial budget, but with a much better result in three years’ time.

One of the key points about this project was that the model was owned and

operated by a team within BookCo’s management. The use of the model by this

team made it possible to have a rational discussion within the organisation about

the trade-offs between maintaining a large overall membership and cutting and

reallocating acquisition budgets. In the event, an alternative, less radical

optimisation emerged as a consequence of introducing some constraints into the

model. This preferred solution still achieved a substantial improvement in profits

in years two and three, but also improved on the initial budget in years one and

two as well.

The use of the optimisation model therefore gave BookCo guidance on:

� The best allocation of media spend

� Which clubs were candidates for closure

� What would need to happen to overheads as a result of the other changes

Merit measures: the non-financial marketing metrics

So far we have concentrated on monetary metrics. To finance people, the need to

go beyond monetary metrics is not totally self-evident. However, in the search for

value creating insights, we find that merit is something that must be measured

alongside money.

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3.4 – 21

Definitions (OED)

Value: material or monetary worth; the regard that something is held to deserve

Worth: an amount of a commodity equivalent to a specified sum of money; the value or

merit of someone or something

Merit: superior quality; excellence

Value in a business context is firstly about monetary worth. Shareholders and the

Boards that serve them certainly place monetary worth at the top of the agenda.

Ultimately, marketing communications are paid for out of the organisation’s

cashflow.

Merit as judged on aesthetic grounds, fashionability, likeability or other criteria, is

the second meaning of value in a business context. Merit is important as an

explanation or predictor of monetary worth. The best practice is to estimate the

monetary contribution of marketing first, to enquire about its creative merit

second and, last, to observe whether the two are related.

In going beyond the monetary metrics, three situations need to be assessed:

� Customer acquisition

� Customer retention

� Customer add-on sales

Customer acquisition metrics

Customer acquisition is a widely used term among companies that are active in

customer equity management. Sometimes, customer recruitment is used as an

alternative term. However, the definition of the terms needs to become rigorous

for them to be used in customer equity accounting.

The Customer Acquisition Phase of customer life cycle includes the first purchase as well

as all non-purchase encounters that precede and follow the purchase up until the time the

customer makes the first repeat purchase. The retention phase begins after the customer

makes the first repeat purchase.

A firm should continue to acquire customers until it can no longer cover the cost

of acquisition of the last incremental customer. Most firms apply this rationale to

capital investment decisions but not to customer expenditure decisions (which

are treated as current costs and not investments, despite their inescapable long

term consequences).

Two dysfunctional acquisition behaviours are commonly observed. The first firm

under-invests, stopping with prospects whose net present value (NPV) is far

greater than zero. The second firm sets a volume target for its customer base and

continues to acquire customers in order to hit its volume target, even when those

new recruits are terminally loss making.

3.4 – 22


Breaking out of this value-destroying pattern involves accounting for customer

acquisition and evaluating their retention value and add-on value. Yet many

organisations are making good progress on these calculations and turning the

insights into shareholder value.

Customer acquisition accounting consists of the following steps:

� Quantifying the number of prospects contacted over a fixed time period

from a given campaign and media source

� Calculating the associated prospecting costs

� Profiling the acquired customers to assess their potential long-term value

(i.e. categorising them into value segments)

� Quantifying the number of customers and first-purchase revenues by source

(campaign and media) and by value segment

� Calculating the associated customer conversion costs and ongoing servicing

costs by source and value segment

� Calculating the net present value of the entire pool of customers for the first

purchase revenue minus the stream of costs (past, present and future)

Calculating these figures sets the stage for developing the full customer equity

model. An understanding of the non-financial factors that drive acquisition is

important, and for this reason, non-financial metrics are needed in addition to

financial metrics.

Figure 3.4.19 Drivers and results of effective acquisition

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There are three main drivers of customer acquisition:

� Direct response acquisition activities (e.g. direct mail, press, email and

sales calls)

� Advertising ‘halo’ effects

� Word of mouth

Direct response activities are widely used to drive customer acquisition.

They are not the exclusive drivers of acquisition, however. Advertising ‘halo’

effects are important in two ways: increasing direct response rates; and

generating unsolicited enquiries and sales.

‘Word of mouth’ comments about a brand or product have similar effects to

advertising.

Targeting is one of the main determinants of response rates. One of the main

rules that applies to targeting is:

When you broaden the acquisition targeting, be prepared for lower

response rates.

It is often useful to differentiate between target audience groups (or segments).

Acquisition accounting should maintain separate records for each target audience.

Targeting can also be done on the basis of scoring, where the target is selected on

the basis of a propensity score (see below).

Introductory offers are also important drivers of response rates. Different target

audiences may have different offer responsiveness. Where addressable media such

as direct mail are used, the offer can be varied according to target audience, and

an optimum offer calculated on the basis of customer equity modelling. Records

must be kept of these introductory offers, in order to analyse the insights from

past activity.

The message in the direct communication, and the creative execution, both also

affect the response rates. This chapter is not the place for expanding on direct

response creativity, and the interested reader should refer to chapter 10.3, or to

(Bird, Drayton) or (Stone 1996). Records of the creative execution are important

for future analysis purposes.

Awareness is a useful measure for diagnosing acquisition effectiveness. Prospects

need to know about a product and what it costs, before they will engage in trial

purchase. Many different measures of awareness are potentially available, and the

reader is referred to (Shaw 1998) for a more detailed account.

Attitudes to the product and its price also provide useful measures for diagnosing

acquisition effectiveness. Prospects need to have positive attitudes if they are

likely to buy. Measuring attitudes to competing products is also important for

diagnosing acquisition effectiveness in competitive markets (see Shaw, 1998).

Advertising and word of mouth both have an influence on awareness and

attitudes. For this reason, an allowance must be made for the cost of advertising

halo, and potentially the cost of word of mouth, if the acquisition cost is to be

3.4 – 24


accurately calculated. In our experience, most companies significantly

underestimate the acquisition costs because of this phenomenon.

Trial purchase is when the customer tries the product for the first time. Many

firms identify trial as a key strategic objective. From the company’s perspective, it

provides an opportunity to demonstrate to customers that their products and

services offer good value.

Product experience and retention pricing play a key role in determining whether

and when a customer will continue buying the product. Experience is affected

both by the evaluation of product features and benefits against expectations, and

also by service experiences. The cost of service during the acquisition phase

should be included in acquisition accounting. The price of the product (other than

the initial offer) will also have a significant influence on the repeat purchase.

Customer retention metrics

Customer retention is a widely used term among companies that are active in

customer equity management. Sometimes, customer defection or customer loss

are used as antonyms for retention. However the definition of the terms needs to

become rigorous for them to be used in customer equity accounting.

The length of the purchase cycle is highly relevant, and two alternative definitions

are proposed, depending on the cycle length.

A) Short Purchase Cycle (less than one year)

Customer Retention Phase of the customer life cycle includes the first repeat

purchase as well as all subsequent repeat purchases until the customer defects or

the customer’s purchasing becomes dormant.

B) Long Purchase Cycle (more than one year)

Customer Retention Phase of the customer life cycle includes the first repeat

purchase and continues for as long as the customer indicates their intention to

purchase the product or service at the next purchase occasion.

Attrition (defection) and silent attrition (dormancy) are also important terms.

Attrition occurs when the customer has decided not to use the service any further

and has communicated the fact to the supplier. However, most customers do not

communicate. Silent attrition occurs when the customer has decided not to use

the service any further and has not communicated the fact to the supplier. The

concept of dormancy is important. Often, during the retention phase, purchase

frequency declines and eventually stops. Attrition can usefully be divided into two

types:

Natural attrition occurs when customers no longer use the product or service.

Competitive attrition or switching occurs when the customer continues to use

the product or service but switches its supplies to a competitor.

Share of wallet is another concept that is important in the context of competitive

attrition. Many customers do not switch totally, but place their business with

multiple suppliers. The share of their spending with one company is an important

factor in diagnosing customer equity opportunities.


3.4 – 25

Customer retention accounting consists of the following steps:

� Identifying customers who are on their second purchase or more

� Removing customers who have defected or are dormant

� Calculating how many have been customers (since their second purchase)

for one month, two months etc.

� Calculating how long ago their last purchase occurred (i.e. recency)

� For each group, calculating the number of purchases they have made

(frequency) and their monetary value and gross margin

� Estimating the cost of serving these customers, and the costs of any

retention programmes

� Calculating the net present value of the entire pool of retained customers for

the repeat purchase revenue/gross margin minus the stream of costs


model. A firm may not want to retain all its customers, but the retention of

desirable customers is an important goal.

Many managers believe that product experience, and associated product

satisfaction, are the main drivers of retention. In this section we examine the

drivers more broadly. Figure 3.4.20 shows the main factors that are relevant.

Reese (1996) in Happiness isn’t Everything reported that the 20 companies that

scored well in Baldridge awards increased their satisfaction ratings – however,

customer retention levels declined or at best remained constant.

Reichheld (1996) in Learning from Customer Defections in a study of the auto

industry reported that although 90 per cent of the industry’s customers reported

satisfaction with their purchases, repurchase rates were only around 30 to 40 per

cent.

Lowenstein (1996) in Keep Them Coming Back polled over 200 large American

corporations and discovered that more than 90 per cent of them have ongoing

processes for measuring and improving customer satisfaction. However, only two

per cent of them could show increases in sales or profits resulting from their

increases in customer satisfaction.

An alternative approach to customer retention suggests that the process actually

begins during acquisition, which differentially targets a particular type of

customer and sets customer expectations about product value and uniqueness.

This model is shown in figure 3.4.20.

3.4 – 26


Figure 3.4.20 Drivers and results of effective retention

Customer attitudes towards the product and price are very important in

retention. These include attitudes towards price and product benefits. Value (as

perceived by the customer) is often used as a proxy for price and product

benefits. Expectations are especially important attitudes with regard to

customers’ assessment of their experiences.

Product experience and the associated service experience can be important.

They are generally evaluated against expectations. Raising expectations generates

trial, but overly high expectations depress retention.

Ease of purchase is an important driver. Some products are very inconvenient or

even difficult to buy, which hurts retention. More generally, good distribution and

display all help secure repurchase.

Repurchase reminders are also important in many sectors. Catalogues,

brochures, emails and letters all contribute to the steady flow of repurchases.

Consideration of privacy and permission marketing are important and can make

the difference between success and failure in this regard. Frequency of contact

and message are both important.

Repurchase incentives are sometimes useful triggers, especially in areas such as

office supplies where there is low involvement. The repurchase price can also be

a factor, especially when the introductory price is significantly less.

Competitive attrition is significantly driven by actions outside the firm’s control.

Competitive intensity is key – when competitors heat up activity, by spending

more, retention is likely to suffer. Competitive message and introductory offer are

both factors too.

Ease of exit is also important in determining how many customers will switch.

This is a combination of several factors: perceived product uniqueness (often

brand is a factor here), product suitability, and perceived switching costs all

contribute to the likelihood of retention.

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3.4 – 27

Natural attrition is another factor largely outside the supplier’s control. It

depends mostly on changing customer needs and availability of funds. Separating

the natural from the competitive attrition is essential to diagnosing retention

issues accurately.

Before it implements retention programmes, a company needs to determine which

customers warrant retention. The three primary tools for this are Pareto analysis,

RFM analysis and econometric modelling.

Pareto analysis involves dividing customers into ‘deciles’ – 10 per cent groupings

of customers, calculated by ranking each customer according to their purchases,

or according to other variables of interest, such as profitability. There is nothing

magic about 10 per cent groupings; merely they are easy to construct and

implement. Typically the top two or three deciles represent the bulk of revenues.

This is the 80/20 rule, also called the Pareto principle.

RFM analysis stands for Recency, Frequency and Monetary value. Recent

purchasers are more likely to repurchase. Frequent purchasers are generally

more attractive than infrequent ones. High monetary value purchasers are also

attractive. The main problem with this as a technique is that it is difficult to

combine with insights from demographics and other CRM database insights.

Econometric modelling provides a solution to this issue. It allows the factors,

including RFM, into a single formula predicting each customer’s propensity to

purchase and its value. This application of econometrics is also called propensity

modelling.

Add-on selling metrics

Add-On Selling is often the main profit driver for companies engaged in customer

equity management.

Add-on selling phase of the customer life cycle includes the first additional product sale

(that is not a repeat purchase of the first product) plus all subsequent additional product

sales, until the customer becomes dormant.

Cross-selling is part of the add-on selling process. It involves interactions or

relationships between products. For example, selling printers with personal

computers is an example of cross-selling.

The most obvious role of add-on selling is its ability to directly increase customer

equity through higher product holdings and profits per customer.

Successful add-on selling can allow a company to increase investment in

acquisition, by targeting customers with lower acquisition equity value, but whose

propensity to add-on purchase makes them profitable in the long run.

Cyclical offers are usually made, say quarterly or even monthly. More

sophisticated companies may vary contact frequency, depending on the customer

profile.

3.4 – 28


Add-on sales accounting methods depend on the type of product or service. For

transaction products, such as books bought from a book club, the following steps

are appropriate:

� Calculating the total number of customers on file

� Calculating how long ago they were acquired (i.e. length of relationship) and

when last purchase occurred (i.e. recency)

� For each group, calculating the frequency of add-on sales offers during a

given period, and the marketing cost of each offer

� For each direct add-on sales communication, calculating the response rate

and the sales value per add-on offer

� Calculating the dependency between response rate/sales value and length of

relationship/recency

� Calculating the net present value of the entire pool of customers, based on

forecasts of future add-on sales

For usage products, a different accounting method is needed:

� First three steps same as for transaction products

� Calculating the product holding statistics before and after the offer.

Examples of product holdings for a retail bank include: current account

only, credit card only, current account and credit card etc. The change in

product holding is the basis for the response rate calculation

� Calculating the product usage volumes and values for the add-on products

� Calculating the dependency between response rate/sales value and length of

relationship/recency

� Calculating the net present value of the entire pool of customers, based on

forecasts of future add-on sales.


model. Add-on selling is vital to the health of almost all customer equity

businesses.

To successfully add-on sell, a company needs to decide:

� Frequency with which to make product offers

� Number of customers to be targeted each offer cycle

� Product targets for individual customers on the database

� Offers and messages that are most likely to elicit purchases


3.4 – 29

Figure 3.4.21 Drivers and results of effective add-on selling

Frequency of offers is perhaps the most critical element of add-on selling, and

also the most badly managed. As (Blattberg, Getz, et al. 2001 #8500) note, the

frequency of offers is not simply a matter for optimisation modelling. It also

depends on the capacity of the organisation to sustain a given cycle of offers. As

frequency increases, a law of diminishing returns will set in, and usually there is

an optimum frequency.

Response rate and sales value per offer are the two most important outcomes to

model, in order to evaluate the optimum frequency. The higher the response rate,

the lower the add-on cost per customer, and consequently the more frequent the

affordable offers become – until the law of diminishing returns sets in.

Marketing cost per offer (also known as direct selling cost) is another key factor.

CRM technology has the potential for significantly reducing this cost, by

improving targeting accuracy and consequently decreasing marketing waste. The

offer cost can also make a significant difference in some situations, and

companies such as Viking Direct focus significant time and effort sourcing

attractive gifts at a minimum cost.

Brand halo effects can have an important influence on response rates. Experience

with previous purchases of the brand will have an effect on response rates.

Perceptions of product value and features are also likely to have an effect.

Retention can also increase as customers purchase a wider range of products

from a given supplier. This ‘dependency effect’ may occur due to higher perceived

switching costs as the product holding broadens.

The CRM database is the main tool for targeting add-on sales offers. Several

approaches can be used to gain insights into potential add-on sales prospects.

Identifying which products to offer to what customers is crucial. This is referred

to as product affinity. A common approach used in modelling product affinity is

to develop customer profiles of ‘typical’ purchasers of each product, and then

search the database for similar customers who have not yet made the purchase.

Amazon, the internet bookseller uses this technique extensively in its add-on

selling. Their “we have recommendations for you” represents one of the most

sophisticated applications of this method to the new economy.

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An alternative approach is to gather data directly about customer needs and

wants, by asking the customers themselves. Some of the commercially available

‘lifestyle databases’ contain many millions of records of this type.

Cross-purchase models determine which products are purchased together and

hence provide evidence of which products to add-on sell.

Collaborative filtering is another technique. If two customers buy product A and

then one of them also purchases product B, the other customer is more likely to

buy B. Collaborative filtering is based on the assumption that similar purchasing

implies similar tastes and interest.

Propensity, response and suppression modelling are the final techniques. They

identify current customers most likely to respond to an offer. These methods

model offer-response rates, using explanatory variables such as frequency, recency

and monetary value. Using the outputs of these models, companies can suppress

contacts with customers who are unlikely to respond to the offer.

Summary

This chapter has examined marketing metrics and customer equity. We have

started with an overview of the senior management context, and stressed the

importance for senior decision makers to become more deeply attuned to these

types of management information. We have then examined the basic financial

framework, and the need for ‘rightsizing’ as a principle – choosing the right

number of customers and the right level of marketing spend. The impact of long-

term value from customers and customer equity was then examined. Finally, we

looked at the non-financial metrics that need to be measured to optimise the

payback from customer acquisition, retention and add-on sales.

References

1. Bird, Drayton – Commonsense Direct Marketing, Kogan Page; 1996.

2. Blattberg, Robert C; Getz, Gary, and Thomas, Jacquelyn S. Customer Equity – Building and

Managing Relationships as Valuable Assets. Harvard USA: HBS Press; 2001.

3. Hope, Jeremy; Fraser, Robin, and Horngren, Charles T. Beyond Budgeting: How Managers

Can Break Free from the Annual Performance Trap. Harvard USA: HBS Press; 2003.

4. Lowenstein, Michael. Keep Them Coming Back. American Demographics. 1996 May; 54-57.

5. McDonald, Malcolm. Marketing Plans. Fourth ed. Butterworth Heinemann; 1999.

6. McKinsey & Co . The New Era of Customer Loyalty Management [Web Page]. 2003. Available

at: www.mckinsey.com/practices/marketing/ourknowledge/customerloyalty.asp.

7. Reese, Shelley. Happiness isn’t Everything. American Demographics. 1996:52-58.

8. Reichheld. The Loyalty Effect. HBS Press; 1996.

9. Reichheld F.R. and Sasser. Zero Defections: Quality Comes to Services. Harvard Business

Review/ Sept./Oct./ 301-7. 1990;

10.Reichheld, Frederick. Learning from Customer Defections. Harvard Business Review. 1996;

No 2 (March-April):56-69.

11.Shaw, Robert . Improving Marketing Effectiveness. London: Economist Books; 1998; ISBN: 1

86197 054 4.

12.Stone, Bob. Successful Direct Marketing Methods. Chicago: NTC; 1996.

13.Shaw, R and Merrick, D: Marketing Payback, FT Prentice Hall, 2005

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