Marketing performance assessment: An exploratory investigation into current practice and the role of firm orientation

Full text

Journal of Marketing Management, 2004, 20, pages
ISSN0267-257X/2004/x-x/010001+01 £8.00/0 ©Westburn Publishers Ltd.
Tim Ambler1*,
Flora Kokkinaki^
and Stefano Puntoni*
Assessing Marketing Performance:
Reasons for Metrics Selection
London Business School*
University of Patras^
In recent years both practitioners and academics
have shown an increasing interest in the
assessment of marketing performance. This paper
explores the metrics that firms select and some
reasons for those choices. Our data are drawn
from two UK studies. The first reports
practitioner usage by the main metrics categories
(consumer behaviour and intermediate, trade
customer, competitor, accounting and
innovativeness). The second considers which
individual metrics are seen as the most important
and whether that differs by sector. The role of
brand equity in performance assessment and top
management orientations are ke
y
considerations.
We found consistency between orientation and
metrics. Within these categories we identified 19
metrics that could be regarded as primary and
could therefore serve as a short-list for initial
selection. However, the sector importantly
moderates that selection, not least because
competitive benchmarking requires similar metrics
to be available. Control, orientation and
institutional theories appeared to influence metrics
selection and the absence of agency theory is
p
robabl
y
due to the research method o
f
this
p
a
p
er.
We concluded with some propositions formally to
test the basis of metrics selection.
Keywords: marketing metrics; performance assessment; brand equity; UK
firms.
Introduction
Practitioners and academics have shown increasing interest in the assessment
of marketing performance (Clark 1999; Marketing Week 2001; Schultz 2000;
1 Corresponding author: Tim Ambler, London Business School, London NW1 4SA.
Email: tambler@london.edu, ph/fax: 020 7262 5050/7724 1145

2 Tim Ambler, Flora Kokkinaki and Stefano Puntoni
Shaw and Mazur 1997). The Marketing Science Institute has raised marketing
metrics to become its leading capital research project (MSI 2002).
The marketing performance literature has been criticized for its limited
diagnostic power (Day and Wensley 1988), its focus on the short term
(Dekimpe and Hanssens 1995, 1999), the excessive number of different
measures and the related difficulty of comparison (Ambler and Kokkinaki
1997; Clark 1999); the dependence of the perceived performance on the set of
indicators chosen (Murphy, Trailer and Hill 1996); and the lack of attention to
shareholder value creation (Doyle 2000). “Perhaps no other concept in
marketing’s short history has proven as stubbornly resistant to
conceptualization, definition, or application as that of marketing
performance” (Bonoma and Clark 1988, p.1).
This paper explores the usage of marketing metrics in the UK. Marketing
is broadly defined here as what the whole company does to achieve customer
preference and, thereby, its own goals (Webster 1992). Accordingly, every
business has some interest in assessing marketing in this sense. Although the
usage of marketing metrics has been increasingly reported (eg Shaw 1998,
Ambler 2000), this paper focuses on the categories of marketing metrics and
some reasons why metrics are chosen. The theoretic aspects of this research
area are not, as yet, developed and this paper represents a step in that
direction.
First we discuss four theoretical perspectives: control, agency, institutional
and orientation theories. While the literature is largely based on US
experience, some crossover has taken place (eg The Marketing Leadership
Council 2001) and transnational, or intra-national come to that, differences
seem unlikely to obscure the fundamental issues of metrics selection. One
relatively new factor has emerged in the last decade, namely brand equity
(Aaker 1991, 1996). The emergence of this intangible market asset from the
shadows has created the need to measure it and seems likely to be a factor in
metrics selection. After summarizing the theoretical background, we present
a framework for categorising metrics. This is used in two empirical studies
of metrics usage, the first for those categories and the second for individual
metrics.
After discussion, limitations and propositions for future research, we
draw some final conclusions.
Theoretical Perspectives
Control Theory
Monitoring performance provides one informational means to help
“planned marketing activities produce desired results”, as stated by Jaworski
(1988, p.24) in his definition of marketing control. Control theory assumes

Assessing Marketing Performance 3
that management has a strategy and a known set of intermediary stages
(plans) with which actual performance can be compared. Metrics selection is
an essentially rational process by which “marketing managers can learn to
improve performance by altering the utility levels associated with marketing
control variables” (Fraser and Hite 1988, p.97).
Merchant (1998) defines control as being both reactive (like a cybernetic
feedback loop) and proactive in anticipating problems before they can
damage performance: “Controls, then, include all the devices managers use
to ensure that the behaviors and decisions of people in the organization are
consistent with the organization’s objectives and strategies.” (p.2) This
broadens the concept in an interesting fashion and implies that the costs of
control, including the behavioural effects, need to be balanced against the
benefits. At the same time, it does not materially change the theory on
control briefly summarised here.
Kotler (2003) lists four types of marketing controls (table 22.1, p.685):
annual-plan, profitability, efficiency and strategic. These distinguish
whether the company is selecting the right goals (strategic), whether they are
being achieved (effectiveness or annual-plan), where the company is making
or losing money (profitability) and the return on each marketing expenditure
(efficiency).
Thus control theory assumes that management establishes goals of
whatever type. Having done that the metrics necessary to compare goals
with performance are defined.
Agency Theory
A rational-actor perspective is also taken by agency theory (Jensen and
Meckling 1976). In this case, the focus is on the principal-agent contractual
relationship where the principal has delegated work to the agent. Agency
theory takes an economic perspective of how information will be transmitted
vertically within the organization: information that is positive for the agent
will be communicated to the principal to the extent that the gain obtained
from its disclosure does not exceed the costs of obtaining and disseminating
it.
This is related to control theory in that “agency theory looks at the relative
merits of behavior-based contracts (…) vis-à-vis outcome-based contracts (…)
as a means of efficiently ensuring the fidelity of the agents” (Nilakant and
Rao 1994, p.653). The greater the difficulty of effectively measuring
marketing performance, the greater should be the efficiency of behaviour-
based forms of control compared to outcome-based forms of control
(Eisenhardt 1985). This implies that when it is more difficult to evaluate
marketing results, more reliance is likely to be placed on marketing
expenditure controls. It also implies that specialist marketers are likely to

4 Tim Ambler, Flora Kokkinaki and Stefano Puntoni
propose metrics that will justify prospective expenditure (budgets) and past
activities.
Institutional Theory
Institutional theory (Meyer and Rowan 1977) suggests that organizational
action is mainly driven by both the cultural values and the history of the
specific company, as well as by those of its industry sector. Accordingly,
marketing information disclosure to top management can be predicted from
“perceptions of legitimate behavior derived from cultural values, industry
tradition, firm history, popular management folklore and the like”
(Eisenhardt 1988, p.492). The set of marketing metrics selected by a company
therefore is likely to reflect the intended subjective performance (the
indicators the Board is used to seeing) rather than measures independent
observers might consider appropriate. Since corporate cultures evolve with
time, we can expect metrics to similarly adapt as distinct from being created
from scratch, eg by a consultancy project.
Success measures can be classified broadly as either accounting or non-
accounting (Frazier and Howell 1982; Buckley et al. 1988). Early work on
firm-level measurement of marketing performance focused on accounting
measures: profit, sales and cash flow (Sevin 1965, Feder 1965, Day and Fahey
1988). Many authors however highlighted the problem with using only
accounting indicators in determining marketing performance (e.g., Bhargava,
Dubelaar and Ramaswani 1994; Chakravarthy 1986; Doyle 2000; Eccles 1991).
For example, Chakravarthy (1986) argues that: “accounting-measure-of-
performance record only the history of a firm. Monitoring a firm’s strategy
requires measures that can also capture its potential for performance in the
future” (p.444). The US Institute of Management Accountants reported the
growing use of non-financial measures (IMA 1993, 1995, 1996).
Clark (1999) showed how traditional accounting measures (profit, sales,
cash flow) expanded to include of non-accounting (market share, quality,
customer satisfaction, loyalty, brand equity) measures, as well as wider
considerations covering marketing audit, implementation and orientation.
Clark posited that the set of selected metrics evolved incrementally, as
suggested by institutional theory. In recent years the number and variety of
measures available to firms has risen significantly (Meyer 1998). A literature
search in five leading marketing journals yielded 19 different measures of
marketing “success”, the most frequent being sales, market share, profit
contribution and purchase intention (Ambler and Kokkinaki 1997).
Market Orientation
The literature on market orientation and corporate culture takes a similar
view in that the concept of marketing adopted within an organization

Assessing Marketing Performance 5
influences the measurement system implemented for determining
performance (Moorman 1995; Jaworski 1988; Webster 1992). The extent to
which top management is interested in assessing marketing, or market
performance, depends on the extent to which they are market-oriented (Day
1994; Jaworski and Kohli 1993; Kohli and Jaworski 1990; Narver and Slater
1990) because market-driven firms need to gather and disseminate market
intelligence within the organization (Kohli and Jaworski 1990; Morgan,
Katsikeas and Appiah-Adu 1998; Slater and Narver 1995). As a consequence,
one of the main features of a market-oriented organizational culture is the
presence of organization-wide norms for market orientation (Homburg and
Pflesser 2000). These norms will shape in turn the dynamics of information
disclosure to the top management as well as the content of such information.
Institutional theory and the concept of market orientation are related
because, as argued by Dobni and Luffman (2000), “organizations with similar
market orientations have a tendency or aptitude to engage in similar
strategies when in the same industry, and the types of strategy chosen are
related to the operational behaviors manifesting a market orientation”
(p.909).
Brand Equity
Brand equity (Aaker 1991; 1996) is a widely used term for the intangible
marketing asset. Srivastava and Shocker (1991) define brand equity as “a set of
associations and behaviors on the part of a brand’s customers, channel
members and parent corporation that permits the brand to earn greater volume
or greater margins than it could without the brand name and that gives a
strong, sustainable and differential advantage” (p.5).
Brand equity may be measured financially (cf. Egan and Guilding 1994;
Simon and Sullivan 1993) and/or non-financially (cf., e.g., Agarwal and Rao
1996; Keller 1993, 1998). We treat financial measures synonymously with
accounting measures, i.e. they are expressed in currency or as ratios of
currency values. On the other hand, we distinguish between brand equity
(the intangible asset) and brand valuation (the financial worth of that asset).
As brands are autonomous units for marketing measurement purposes,
multi-brand companies, if they are assessing their brand equities at all,
would need to assess each brand separately.
For the purposes of this paper, we envisage the increasing recognition of
brand equity as creating the need for measures of those assets.
Framework for Categorising Metrics
Control theory looks to encouraging behaviour which leads to the
achievement of goals and these include, but are not limited to, the financial

6 Tim Ambler, Flora Kokkinaki and Stefano Puntoni
bottom line. The simplest framework would simply include a category for
the marketing actions and expenditures (inputs) and the profits and cash
flow (outputs). In practice, those links are not always clear and marketing
plans will have two stages in between: the “intermediate” measures and
consumer behaviours, such as purchases and loyalty. Intermediate measures
are of customer memories be they attitudes, intentions, awareness or other
cognitive or affective or experiential brand-linked characteristics. Thus
control theory works backwards: if the links to financial results are unclear,
consider the links with behaviours. If those are also unclear, consider the
links with intermediate measures and then those with behaviours and then
those with financial results.
Market-oriented companies will consider the links in the reverse order:
consumers first and then financial results but the categories are the same
except that competition will be more carefully considered. Simmonds
(1986b) pointed out that traditional financial accounting fails to give attention
to competitive factors and proposes that the competitors be tracked on
comparable measures such as sales and profits.
Figure 1 shows how these four categories of metrics link together. Brand
equity consists of the elements from inputs onwards which have not yet
materialized as sales revenue. Ambler (2000) describes brand equity as the
reservoir of cash flow that has been earned by good marketing but has yet to
materialise as sales or profits. Although brand equity, as defined above,
arguably lies in the heads of customers and other stakeholders, the difficulty
of measuring neural effects leads academics and practitioners to use proxies,
such as inputs and behaviour, to infer what lies between. Figure 1 shows
only one generic “brain” but in practice brand equity is measured for each
segments separately considered by management and typically distinguishes
immediate trade customers from end users.
Although competition, for conceptual purposes, is shown as an input,
competitive metrics arise in all categories and are usually expressed as
relative measures, eg market share, share of category requirements (loyalty).
Thus Figure 1 provides the framework for the categories of metrics
considered in the first study:
• Own inputs (marketing activities).
• Intermediate measures of memory (e.g. awareness and usage
satisfaction, and attitudes).
• Behaviours.
• Competitive measures.
• Financial outcomes.

Assessing Marketing Performance 7
Inputs Intermediate Behaviour
(eg awareness and commitment)
Competition Purchases
Loyalty
Word of mouth
Own
Financial
outcomes
Finally we need to consider performance. The paper assumes that
performance, in some sense, influences the selection of metrics (“what you
measure is what you get”). Swartz, Hardie, Grayson and Ambler (1996)
concluded that marketing activities broadly achieved planned performance
but the return on marketing investment could not be compared across
companies because their performance intentions differed. In the two studies
below, performance was first based on how they were seen by competitors
and self-rating of success relative others in their sector. The second study
also used self-ratings but across four variables: relative to plan, to previous
year’s sales, to competitors and overall. These variables emerged from the
empirical work.
Empirical Analysis of Current Practice
The exploration of how practitioners viewed metrics occupied two studies.
The first grouped metrics into categories and asked the basis for comparison,
eg plan (control theory) or competition (market orientation). Then we
explored how widely brand equity was used as a concept and how it was
measured. Finally we looked at whether orientation would be associated
with the frequency of use or importance of competitor or customer metrics.
The second study switched focus from the categories, inevitably a
somewhat arbitrary grouping, to the metrics themselves. We sought the
metrics perceived as most important and how their selection varied by
business sector.
Figure 1. Metrics Categories

8 Tim Ambler, Flora Kokkinaki and Stefano Puntoni
Study 1
Method
Forty-four in-depth interviews were conducted with senior marketing and
finance managers from 24 UK firms in order not to restrict the perspective to
the marketers (Homburg et al. 1999). The issues addressed included: the type
of measures collected, the level of review of these measures (e.g. marketing
department, Board), the assessment of the marketing asset, planning and
benchmarking, practitioners’ satisfaction with their measurement processes
and their views on measurement aspects that call for improvement, and firm
orientation. Information on firm characteristics, such as size and sector, was
also obtained. Data were collected face-to-face and each interview was
recorded for subsequent analysis. After the first 10 interviews minor changes
were made to the interview guide for clarification and to reduce interview
length. Some respondents took the opportunity to brief us widely about
their companies.
The pilot broadly confirmed the validity of the five categories above
although respondents preferred to put all financial/accounting and
competitive measures into separate categories, ie not just competitive inputs
and financial outputs. In practice, since most of the inputs were financial
metrics, this removed the “own inputs” category. Non-retail respondents
also distinguished immediate (trade) customers from end-users or
consumers. They used a separate category to monitor innovation. Thus we
were left with six categories: consumer intermediate and behaviour, trade
customer, competitive, innovativeness and accounting (inputs and outputs).
These changes became obvious after the first 10 interviews and, after making
the changes, the categories were not challenged in the subsequent 34. The
categories may not seem strictly logical to an outsider but we were seeking to
understand how practitioners grouped their metrics.
The pilot stage was also used to refine the survey instrument that was sent
to 1014 marketing and 1180 finance senior executives in the UK, recruited
through two professional bodies (The Marketing Society and the Institute of
Chartered Accountants in England and Wales). A total of 531 questionnaires
were returned (367 from marketers and 164 from finance officers, response
rates 36 percent and 14 percent, respectively). Table 1 presents a description
of the sample. To encourage response, given the sensitive nature of this
information, the returns were anonymous so we were unable to check back
for missing values. The majority of the “other” category is probably
explained by large companies operating in more than one sector, eg high
street banks are both retail and B2B.

Assessing Marketing Performance 9
Table 1. Respondents by Business Size and Sector (Study 1)
(# employees) Retail
Consumer
goods
Consumer
services
B2B
goods
B2B
services Other Total
Small (<110) 8 7 14 6 44 32 111
Medium (<500) 8 13 6 7 21 12 67
Large (>500) 51 111 38 30 38 77 345
Missing values 8
Total 67 131 58 43 103 121 531
Respondents were asked to indicate the importance attached to the different
measurement categories by top management on a 7-point scale anchored by
very important / very unimportant. They were also asked to report how
regularly data are collected for each measure category and the benchmark
against which each measure category is compared (previous year,
marketing/business plan, total category data, specific competitors, other
units in the group).
Respondents were then asked whether they have a term for the main
intangible asset built by the firm’s marketing efforts and whether this asset is
formally and regularly tracked, through financial valuation or other
measures. Customer and competitor orientation were measured with eight 7-
point scales drawn from Narver and Slater (1990). Separate single indices of
customer and competitor orientation were computed as the mean of
responses to these items (Cronbach’s alpha .81 and .69, respectively).
Results
As shown by Table 2, accounting measures were reported as being seen
by top management as significantly more important than all other categories.
The t-tests comparing the importance of accounting measures with other
categories were all significant (p< 0.001). However, the differences between
customer and competitive measures were small.
Table 2. Importance of Measure Categories for Assessing Performance
(Overall)
Mean
t df
Sig.
t
Financial 6.51
Direct customer 5.53 -14.90 499 .000
Competitive 5.42 -16.78 523 .000
Consumer intermediate 5.42 -15.60 515 .000
Consumer behaviour 5.38 -15.60 522 .000
Innovativeness 5.04 20.13 524 .000
Note. t-tests refer to the comparisons between financial measures and each of the
other categories.

10 Tim Ambler, Flora Kokkinaki and Stefano Puntoni
Apart from a slightly greater concern with innovation by marketers, there
were no significant differences between the importance attributed to each
metric category by marketers and finance respondents (note that both
marketers and finance respondents were asked to report the importance
attached by top management). Moreover, there were no significant
differences in measurement category importance between different business
sectors.
Irrespective of the importance attached to different indicators, accounting
measures were more frequently collected than any other category (74.9
percent of the sample reported that accounting measures were collected at
least on a monthly basis). In 33.5 percent of the cases, consumer intermediate
measures were collected only rarely/ad hoc. Innovativeness, which some see
as the lifeblood of marketing (e.g. Simmonds 1986a), is least regularly
measured (55 percent of firms measure innovativeness rarely or never).
Larger firms, as might be expected, measure most categories more
frequently than smaller firms (p<0.01 for categories except innovativeness).
Similarly, business sector was found to have a significant effect on the
frequency of data collection, with the exception of innovativeness. Closer
inspection of mean frequency per business sector, however, did not reveal
any systematic differences across sectors. On average, irrespective of
measure category, consumer goods and retail firms tend to collect data more
frequently than other sectors (F (5, 512) = 11.81, p < .001).
Table 3. Frequency of Benchmarks Used (valid percent, where measure
category used)
Previous
year
Marketing/
Business
Plan
Total
cate
g
ory
data
Specific
competitor(s)
Other units
in the
Group
Accounting measures 80.4 85.1 17.5 23.0 22.0
Competitive 51.4 51.0 35.8 55.7 6.6
Consumer behaviour 47.1 42.0 27.1 31.6 6.4
Consumer
intermediate
36.7 30.3 22.0 27.7 5.1
Direct customer 40.3 37.7 17.3 22.8 7.3
Innovativeness 21.3
33.7 10.9 20.7 6.6
Metrics Comparisons
Plans provided the most frequent benchmarks of accounting and
innovativeness measures, where such measures are used (Table 3).
Competitive measures, however, were compared with market research rather
than forecast in plans. Consumer and direct customer measures were most

Assessing Marketing Performance 11
typically compared with previous year results. Market share apart, it appears
that internal (plan) and external benchmarks were routinely used only by the
minority of respondent firms. The modal frequency for each row is
highlighted in the table.
Brand Equity
Moving now to the marketing asset, 62.2 percent of the respondents reported
the use of some term to describe the concept. The most common terms were
brand equity (32.5 percent of those who reported a term), reputation (19.6
percent), brand strength (8.8 percent), brand value (8.2 percent) and brand
health (6.9 percent). Twenty percent of those who used a term reported one
or more of 65 different terms, such as brand integrity, customer loyalty,
global image, quality, contact base and trademark value.
24.9 percent of the total sample regularly (yearly or more) valued the
marketing asset financially, and 41 percent regularly quantified it in other
ways, e.g. through customer/consumer based measures (see Table 4). Less
than 15 percent of the total sample did both.
Table 4. Regularity of Tracking the Marketing Asset (valid percent of the
total sample)
Never
Rarely/
Ad hoc
Re
g
ularly Yearly/
Quarterly
Monthly or
more
Financial valuation 51.4 23.6 16.9 8.0
Other measures 36.8 22.2 28.7 12.3
Our theoretical discussion suggested that the marketing performance
assessment system can be tested against three criteria: benchmarking against
internal expectations (plan) and external (competitor) performance, adjusted
by changes in brand equity. Of the 196 respondents who reported
quantifying their marketing assets (either financially and/or in other ways),
24 percent also quantify consumer, competitive or direct customer measures
in their business plan (internal) and use market or competitive benchmarks.
Thus, on this survey, less than one quarter of firms could meet all three
criteria. Of course, these data merely indicate that they have the measures
available for such comparisons, not that they necessarily make such
comparisons.
Market Orientation
In order to examine whether customer and competitor orientation have an
effect on performance assessment practices, regularity of tracking and

12 Tim Ambler, Flora Kokkinaki and Stefano Puntoni
importance attached to different measures were regressed simultaneously on
these constructs, after partialling out the effect of firm size and sector. As can
be seen in Table 5, customer orientation was strongly associated only with
the regularity of collection of consumer, direct customer and innovativeness
measures. As might be expected, more customer-oriented firms tended to
collect data on such measures more frequently than firms less so oriented.
Customer orientation does not seem to be related to the regularity of tracking
of accounting and competitive measures, whereas the regularity of collection
of competitor measures was found to be related to the level of competitor
orientation.
Table 5. Regression of Regularity of Tracking on Customer and
Competitor Orientation
Customer
Orientation
Competitor
Orientation
F R
R2 beta t beta t
Accounting 210.40*** .27 .07 .03 .83 .05 1.13
Competitive 39.17*** .48 .23 .03 .81 .22 5.31***
Consumer
behaviour
11.14*** .28 .08 .18 3.78*** .07 1.50
Consumer
intermediate
12.07*** .31 .09 .23 4.88*** .00 .16
Direct customer 8.53*** .26 .07 .16 3.29*** .00 .04
Innovativeness 9.01*** .26 .07 .24 5.05*** .03 .71
*** p < .001
Table 6. Regression of Measure Category Importance on Customer and
Competitor Orientation
Customer
Orientation
Competitor
Orientation
F R
R2 beta t beta t
Accounting 4.86*** .19 .00 .10 2.19*.03 .71
Competitive 27.88*** .42 .18 .05 1.30 .26 6.03***
Consumer
behaviour
19.05*** .36 .13 .25 5.64*** .16 3.57***
Consumer
intermediate
21.45*** .38 .14 .30 6.68*** .09 2.18*
Direct customer 9.40*** .26 .07 .22 4.67*** .07 1.62
Innovativeness 9.80*** .26 .07 .20 4.29*** .10 2.32*
*** p < .001, *p < .05

Assessing Marketing Performance 13
The relation between orientation and measure importance is less clear (Table
6). Customer orientation was positively related with the importance attached
to most measures except competitive measures, although the correlation with
accounting measures was relatively weak. Competitor orientation strongly
correlated with the importance of competitor and consumer behaviour
measures and less robustly with intermediate and innovative measures.
Both customer and competitor orientation were significantly associated
with assessment practice. No moderating effects by sector were observed, but
firm size appeared to moderate the importance attached to measures.
The results indicate that customer orientation is a stronger predictor of the
importance of competitive measures for large firms, compared to small firms
(beta of the interaction term = .50, t = 2.60, p < .01). However, competitor
orientation was found to be a stronger predictor of importance for small
firms, compared to large firms (beta of the interaction term = -.46, t = -2.57, p
< .01). We have no simple explanation for these results. It is possible that
larger UK firms need to be more customer oriented in order to be effective,
perhaps because they already are competitor oriented. In contrast, in the case
of smaller UK firms, competitor orientation seems to be more important,
perhaps because they already are customer oriented.
Study 2
Up to this point, we have explored practitioner usage based on the metric
categories first identified in the framework and revised in the pilot stage of
Study 1. The next step was therefore to perform a study specifically aimed at
designing a list of most frequently used metrics. Providing useful indications
to managers and stimulating further academic research, such endeavour was
thought to be important for a complete understanding of current practice in
the area of marketing performance assessment. In particular, while we
expected to find wide variation by sector and firm, we explored the extent to
which certain metrics stood out as more valuable, or at least more widely
used, than others.
Method
An initial survey instrument with 54 metrics was developed, using the
relevant literature, and piloted to establish any additional measures and to
eliminate those measures that were not used or were redundant. No
additions were made but 16 were eliminated. The resulting 38 measures were
classified into the six categories described above.
A telephone survey was then conducted with 200 UK marketing or
finance senior executives drawn from the lists supplied by The Marketing
Society and The Institute of Chartered Accountants in England and Wales. In

14 Tim Ambler, Flora Kokkinaki and Stefano Puntoni
both cases, only senior practitioners were selected. The acceptance level for
the telephone interviews, i.e. response level, was 50.1 percent. This excludes
wrong numbers and other technical blockages. Since the survey instrument
was not materially altered by the pilot stage (the eliminated metrics had been
left blank) we added the responses of the 31 executives who participated in
the Study 2 pilot.
Respondents were asked to indicate the importance of each measure for
assessing the overall marketing performance of the business on a 5-point
scale. They were also asked to indicate the highest level of routine review of
this metric within the firm, on a scale ranging from the [group’s] top board
level (5) through junior marketing (1) to not used at all (0). Respondents were
also asked to add any relevant measures not listed.
Contextual data were also collected in order to determine the impact of
environmental factors such as firm size, business sector, organization
structure, and age of business. Table 7 shows a broad spread across firm size
and sector.
Table 7. Respondents by Structure and Sector (Study 2)
# Employees Retail
Consumer
goods
Consumer
Services
B2B
goods
B2B
Services Other Total
One unit
without
marketing dept
4
4
27
4
23
9
46
One unit with
marketing dept
5 7 6 11 15 6
50
Subsidiaries
with one board
7 8 5 11 13 5
49
More complex 6 13 10 15 15 25 84
Missing values 2 2
Total 22 32 23 41 66 47 231
The great majority of the firms had been in business for more than five years
and therefore have reporting systems that have evolved beyond the start-up
phase and to that extent become established. As was the case in Study 1, the
principle reason for the companies not attributed to any sector is due to large
firms that trade in more than one sector.

Assessing Marketing Performance 15
Results
Table 8 ranks the top 15 (> 62 percent usage) metrics by frequency of use
compared with the frequency that it was rated as “very important” and the
frequency that it reached the top level of management.
Table 8. Ranking of Marketing Metrics
Metric
%
claiming
to use
measure
% firms
rating as
very
important
%
claimed
to reach
top level
Pearson
Correlation
between
Level and
Importance
1. Profit/Profitability 92 80 71 .719**
2. Sales, Value and/or
Volume
91 71 65 .758**
3. Gross Margin 81 66 58 .827**
4. Awareness 78 28 29 .732**
5. Market Share (Volume or
Value)
78 37 34 .727**
6. Number of New Products 73 18 19 .859**
7. Relative Price
(SOMValue/Volume)
70 36 33 .735**
8. Number of Consumer
Complaints (Level of
dissatisfaction)
69 45 31 .802**
9. Consumer Satisfaction 68 48 37 .815**
10.Distribution/Availability 66 18 11 .900**
11. Total Number of
Customers
66 24 23 .812**
12. Marketing Spend 65 39 46 .849**
13. Perceived
Quality/esteem
64 37 32 .783**
14. Loyalty/Retention 64 47 34 .830**
15. Relative perceived
quality
63. 39 30 .814**
n = 231, ** p < .01
The last column of Table 8 highlights the expected correlation between the
measure being seen as very important and its review by the top management
level. Business sector was found to have a significant effect on the usage of
specific items, particularly consumer intermediate, competitive and
accounting measures. Table 9 shows the 15 most significant differences of 38.

16 Tim Ambler, Flora Kokkinaki and Stefano Puntoni
Table 9. ANOVA for Significant Metric Variations by Business Sector
Level of Importance Level of Review
df F df F
Other attitudes e.g. liking 228 8.88*** 229 4.66***
Image/personality/identity 226 7.91*** 229 4.36**
Penetration 225 7.67*** 229 3.07**
Salience 225 3.81 229 3.15**
Commitment/purchase intent 228 6.88*** 229 3.45**
Distribution/availability 218 6.83*** 228 6.14***
Awareness 227 5.84*** 229 3.05**
Relevance to consumer 226 5.34*** 228 2.61**
Marketing spend 228 5.12*** 229 2.55**
Market share 226 5.02*** 229 2.63**
Share of voice 225 4.93*** 229 4.82**
Brand/product knowledge 228 4.54** 229 2.62**
Conversions 225 2.55** 228 2.96**
Margin of new products 225 3.95** 228 2.60**
Purchasing on Promotion 225 11.87*** 229 5.88***
n = 231, ** p < .05, *** p < .001
Distinctions between sectors were greater for level of importance measures
than level of review. As would be expected, consumer oriented items were
found to be more important for consumer sectors. Perhaps the most
surprising result is the variation in importance ascribed to market share
across business sectors. As might be expected, consumer goods firms
consistently rated metrics as more important and reviewed at more senior
levels than business-to-business services.
Developing Primary Metrics
Finally we analysed which metrics are candidates for being seen as the
most valuable or primary, irrespective of sector or size. Content validity
(Churchill 1979) using a 50 percent cut-off (Cronbach and Meehl 1955) left 30
metrics that were then subjected to scale purification procedures. Construct
validity was assessed with the guidelines outlined by Churchill (1979) and
Gerbing and Anderson (1987). We examined item-to-total correlations and
the factor structure (through principal components) for each scale. The
decision criterion for item deletion was an improvement in corresponding
alpha values to the point at which all items retained had corrected item-total
correlations greater than 0.5. Eight items were eliminated, varying slightly as
to whether the level of review or level of importance was considered.
Both level of review and importance are shown for comparison in Table

Assessing Marketing Performance 17
10. 19 items match and could be considered as the primary general metrics:
Awareness, Perceived quality, Consumer satisfaction, Relevance to
consumer, Perceived differentiation, Brand/product knowledge, Number of
new customers, Loyalty/retention, Conversions, [Trade] Customer
satisfaction, Number of complaints, Relative consumer satisfaction,
Perceived quality, Number of new products, Revenue of new products,
Margin of new products, Sales, Gross margins, Profitability.
Table 10. Primary Metrics
If level of review is measured If level of importance is measured
Construct Alpha Items Alpha Items
Consumer
Attitudes
.85 Awareness
Perceived quality
Consumer satisfaction
Relevance to consumer
Image/personality
Perceived differentiation
Brand/product
knowledge
.84 Awareness
Perceived quality
Consumer satisfaction
Relevance to consumer
Perceived differentiation
Brand/product
knowledge
Consumer
Behaviour
.78 Total number of
consumers
Number of new
consumers
Loyalty/retention
Conversions
Number of consumer
complaints
.83 Number of new
consumers Loyalty
Leads generated
Conversions
Trade
Customer
.80 Customer satisfaction
Number of complaints
.79 Distribution/availabilit
y
Customer satisfaction
Number of customer
complaints
Relative to
Competitor
.79 Relative consumer
satisfaction
Perceived quality
.80 Relative consumer
satisfaction
Perceived quality
Share of voice
Innovation .84 Number of new products
Revenue of new products
Margin of new products
.81 Number of new
products
Revenue of new
products
Margin of new products
Accounting .81 Sales
Gross margins
Profitability
.77 Sales
Gross margins
Profitability
TOTAL : 22 items TOTAL : 22 items
n = 231

18 Tim Ambler, Flora Kokkinaki and Stefano Puntoni
Discussion
The primacy of accounting metrics, both in terms of importance attributed by
the respondents and of regularity of assessment, is consistent with the
literature (e.g. Clark 1999). We were surprised by the relatively low levels
reported for basics such as sales and profitability. Every board must see these
figures as part of their financial accounts but the respondents here were
reporting on what they perceive to be marketing. The role of “marketing”
varies widely across UK companies (Ambler 2000).
In our analysis, brand equity can provide the bridge between short- and
the long-term effects as regarded important by Dekimpe and Hanssens
(1995). Although a substantial proportion of our respondents were
measuring brand equity financially or non-financially, it seems likely that a
formal (control theory) process rarely meets all three marketing performance
assessment criteria above, i.e. internal and external benchmarking adjusted
by any change in brand equity. Less than 25 percent of our respondents had
the data to do so.
Orientation was consistent with metrics usage as shown for regularity and
importance (Tables 5 and 6 respectively).
Thus we found some support for control, institutional (sector differences)
and orientation theories in the selection of metrics but little support for
agency theory. This last would require analysis of the interactions between
the Board and junior levels of the firm, notably in respect of budget
approvals. From the literature and the two studies, we can advance some
propositions about how metrics are adopted with some implications for the
way they should be.
P1: Strongly control oriented top management will review those metrics
projected in the marketing plan. Apart from accounting measures, we found
that between a third and one half of measures were compared to plan but we
expect that to be moderated by control orientation.
P2: Following institutional theory, metrics will evolve a-rationally in
conformity with sector norms. We do not expect that firms can provide
rational explanations for the metrics they adopt.
P3: Following orientation theory, metrics selection will reflect the primary
interests of top management, e.g. customer, competitor or internal
accounting measures.
P4: Agency theory will provide explanation of metrics selection only in
the context of budget negotiation and subsequent inter-level evaluation of
performance.

Assessing Marketing Performance 19
Limitations and Future Research
Our findings are limited in several respects. First the research, as with other
survey-based methods, does not capture causality nor the dynamics of the
development of measurement, orientation and performance. In future
research linking metrics selection with performance, it may be important to
distinguish the types of performance sought by management rather than
build a signal performance construct, ie do companies, in fact, get what they
measure?
We examined the potential effects of size and sector, but we did not
consider external environmental effects. Market turbulence, for example,
may moderate metrics selection and their value (Greenley 1995, Harris 2001).
Although we used the literature from elsewhere, notably the US, the
empirical work was conducted in the UK. It is likely that there are variations
internationally in metrics selection, not least because the metrics available
from suppliers will differ in other countries. It would be more interesting to
explore what, if any, theoretical differences exist. Finally, the whole area of
linking non-financial market measures to financial outcomes, such as
shareholder value, has barely been explored.
Conclusions
This paper has contributed two exploratory UK studies to the increasing
interest in the assessment of marketing performance. Accounting remains
the dominant metrics category relative to consumer behaviour and
intermediate, trade customer, competitor, and innovativeness. Brand equity
is widely measured but rarely integrated into a formal assessment system.
We found consistency between orientation and metrics. Within these
categories we identified 19 metrics that could be regarded as primary and
could therefore serve as a short-list for initial selection. However, the sector
importantly moderates that selection, not least because competitive
benchmarking requires similar metrics to be available. Control, orientation
and institutional theories appeared to influence metrics selection and the
absence of agency theory is probably due to the research method of this
paper. We concluded with some propositions formally to test the basis of
metrics selection.
Acknowledgements
We are grateful to Debra Riley who conducted much of the earlier research
and to The Marketing Society, The Marketing Council, Institute for
Practitioners in Advertising, Sales Promotions Consultants Association,
London Business School and Marketing Science Institute for sponsoring this
research.

20 Tim Ambler, Flora Kokkinaki and Stefano Puntoni
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About the Authors
Tim Ambler is Senior Fellow at London Business School. His primary
research interests concern international marketing and the measurement of
marketing and advertising performance. More recently he has broadened
these to include the quantification of government waste (bureaucracy and
unnecessary regulation and red tape). His books include Marketing and the
Bottom Line: The New Metrics of Corporate Wealth (2000), Doing Business in

24 Tim Ambler, Flora Kokkinaki and Stefano Puntoni
China (2000), The SILK Road to International Marketing (2000) and Marketing
from Advertising to Zen (1996). He has published in the Journal of Marketing,
Journal of Marketing Research, International Journal of Research in Marketing,
Journal of Advertising Research and International Journal of Advertising. He was
previously Joint Managing Director of International Distillers and Vintners
which is now part of Diageo plc.
Flora Kokkinaki is a lecturer in Social Psychology at the University of Patras.
After the completion of her PhD in Psychology at University College
London, she joined London Business School as Research Fellow working on
the assessment of marketing effectiveness. After appointments as lecturer at
University College London and the London School of Economics and
Political Science, she joined the University of Patras. Her research revolves
around the areas of Social Cognition, Consumer Behaviour and Economic
Psychology. Her publications include papers in the British Journal of Social
Psychology, the Journal of Economic Psychology and the Journal of Marketing
Management.
Stefano Puntoni is a doctoral student in marketing at London Business
School. He holds a Master in Statistics from the University of Padua (Italy).
Stefano has published in the areas of marketing metrics, consumer
behaviour, marketing communications and pricing and participated to
various marketing conferences and symposia. He is currently working on a
research project devoted to a systematic analysis of the antecedents of
advertising polysemy.