BIG DATA AND THE CHALLENGE OF MARKETING METRICS

rescueflipUrban and Civil

Nov 16, 2013 (3 years and 9 months ago)

76 views


BIG DATA AND THE CHALLENGE OF MARKETING METRICS

Markus Lamest
*

Scho
ol of Business, Trinity College

Dublin, Ireland

Tel.: +353 85 129 3984, Email:

mlamest@tcd.ie

Mairead Brady

School of Business, T
rinity College
Dublin, Ireland

Email:

mairead.brady@tcd.ie

*Corresponding author


Track: Marketing

WORKING PAPER


1

ABSTRACT

Recent

empirical studies
show
that successful companies are distinguished by their ability to
use

big data


for strategic decision
-
making at
senior
-
level
(Brown et al., 2011; LaValle et
al., 2011; Manyika et al., 2011; Shah et al. 2012)
.

What is missing is a study that thoroughly
explores and defines the phenomeno
n of metrics in the context of “big data”

and that
provides a holistic investigation of the interdependent role of marketing metrics and financial
metrics for
senior
-
level

management within the current information and technological
landscape.

A research agenda is suggested to study whether senior
-
level managers are guided
by a set of marketing metrics or whether the traditional financia
l metrics still domina
t
e

in
organisation
s
. In particular,

this agenda
explores
five
research
challenges
that deserve
the
attention of
current and future
marketing
r
esearch.

INTRODUCTION

Over the last decade, there has been an exponential increase in the amount and type of data
available to firms
(Leeflang, 2011)

often called “big data”

(Brown et al., 2011; LaValle et al.,
2011; Manyika et al., 2011; O’Leary, 2012)
. A recent survey

of 1,700 Chief Marketing
Off
icers (CMOs) worldwide reveals

that a majority of businesses across all industries are
facing an “explosion” of data
,

predominantly
customer data
(CMO
-
Council, 2011, p.3)
.

F
or many businesses, the ability to transform large amounts of data into information and
finally into usable knowledge has become th
e critical factor for differentiation and
competitive advantage
(see Lusch et al., 2007; Vargo, 2011)
. Brown et al.
(2011:2)

hold

that
“over time (…)

big data may well become a new type of corporate asset that will cut across
business units and function
s (…),

representing a key basis for competition
”.

In many industries, successful companies are separated from the less successful ones based
on their ability to cope with the vast amount of data
that has become available to them



2

(CMO
-
Council, 2011; LaValle et al., 2011)
.
For example, i
nterviews with 1700 CMOs
revealed
that
a

“data explosion” is perceived as a universal game
-
changer and that “CMOs
from outperforming organizations address these challenges differently from other CMOs”
(CMO
-
Council, 2011)
.

This paper regards metric
s as a
p
otential

lens through which to approach

and
engage
with

“big data”.
In other words,
do

m
etrics guide
senior managers

in
strategic decision
-
making?

R
ecent researc
h in the marketing domain has paid significant attention to
the role of metrics
in the context of performance measurement systems that contribute to strategy
implementation and enhanced learning

(Clark et al., 2006; Homburg et al., 2012; O’Sullivan
& Abela, 2007)
.
Research
ers have
also
suggested that marketing metrics can be tools that
drive engagement with the customer
(Rust, Moorman, & Bhalla, 2010)
,
decrease the misuse
of financial metrics that drive short
-
sighted management practices, or “myopic management”
(
Mizik, 2010
)

and support the shift from a product or finance centricity to a customer centric
logic
(
Shah et al., 2006
,
Wind, 2008
)
.

The majority of existing

studies
, however,

test formal relationships between single marketing
metrics a
nd financial metrics
(Aksoy et al., 2008; Kumar & Shah, 2009; Luo

et al., 2010;
Morgan et al., 2009; Ngobo et al., 2011; O’Sullivan et al., 2009b; Rao & Bharadwaj, 2008)
.
Results are mixed
. For example, Kumar & Shah
(2009)

develop
ed

a framework that can
reliably predict the market capitalisation of a firm based on its customer equity

(CE)
.
They
found

that marketing strategies that are di
rected at increasing CE
(marketing metric)

lead to
increased

stock prices

(financial metric)
.
Kim & McAlister
(2011)

report a contingent
relationship between growth in advertising expenditure (marketing metric) and firm value
(financial metric):
W
hen a
certain advertising response threshold is reached, the relationship
turns from negative to positive.



3

However,

studies

of this kind

either fail to address the contemporary challenge arising as a
result of the altered data landscape or only address them to

a limited extend. Furthermore,

there is a lack of definitional rigour in existing studies. K
ey terms such as “marketing
metrics”, “customer metrics” or “marketing performance measures” are often not defined
(Gupta & Zeithaml, 2006; Homburg et al., 2012; Srinivasan et al., 2010)

and/or used
interchangeably.

The paper is structured as follows.

F
irst
, it

investigates the imbalance that traditionally
prevails

in practice

between
the domina
nt

financial metrics and
the lesser used
marketing
metrics
. It also explores

the data explosion

in the corporate world aligned to

advances in
technology

which support
s

th
e increased flood of data
.
Finally, the paper outlines
a research
agenda

that from a methodological standpoint differs from earlier studies in that it allows a
more explora
tive, in
-
depth investigation
of metrics

in the context of “big data”
.

THE IM
BALANCE
OF FINANCIAL METRICS AND MARKETING METRICS AT
SENIOR
-
LEVEL


Financial Metric’s Dominant Role for
Senior
-
level

Management

When it comes to
senior
-
level

decision
-
making, financial metrics traditionally play a
dominant role.
Studies

outside the marketing lite
rature have found
managers to be generally
biased

towards financial metrics
(Homburg et al., 2012)
. N
on
-
financial data often “lurk” in
departmental silos instead of guiding decision
-
making
(see Brown et al., 2011)
. In a study of
board
-
level managers, one third reported that
they spend less than 10% of their time
discussing marketing or customer
-
related issues
(McGovern et al., 2004,
see also Lusch &
Webster Jr, 2010)
.



4

If
marketing metrics are
used at all

at this level
,
senior managers frequently refer

to single
metrics such as net promoter score
(Keiningham et al
., 2007; Reichheld, 2003)
, customer
satisfaction or customer loyalty
(Arens & Rust, 2011; Bendle et al., 2010)
.

Over the past decades, the use
of
financial metrics has become

common practice as
they

are
established as

an
integral part of mandatory financial reports
(Beyer et al., 2010)
.
Further
contributing to the dominant role of financial
metrics is the fact that

most managers are, at
least to some degree, familiar with the underlying principles, so additional training is
unnecessary
(
Danielson & Scott, 2006)
. As Hanssens et al.
(2009, p.
117)

state:
“One of the
best things about balance sheets and financial statements is that
everybody understands what
most of the numbers mean

.

The reason is deeply rooted in the concept of shareholder theory,
according to which the primary goal of a company is the maximisation of both shareholder
wealth and “the valuation of each corporation by financial markets”
(McSweeney, 2009,
p.839)
.

Interestingly

the
recent financial crisis

has damaged the reputation of traditional financial
metrics.
While propon
ents of the
shareholder value (
SHV
)

approach stress that the model was
initially designed to emphasise long
-
term economic value
(
Danielson et al., 2008)
, both
proponents and opponents of SHV agree that, at least
during the boom
and pre
recession,
managers have frequently used a “perverted”
(Davidson, 2009, p.
24)

or “stylized”
(Danielson
et al., 2008, p.
63)

form of the SHV approach, which has led to an overemphasis of managers
on short
-
term goals at the expense of long
-
term value
(Natalie Mizik, 2010)
. The

severe
consequences of the recent recession have led to a heated debate on the suitability of
traditional financial metri
cs both among academics and practitioners
(
Ambler, 2010;
Danielson et a
l., 2008; Davidson, 2009; McDonald, 2010)
.
Senior

managers are believed to
“becoming wary of value
-
creating antics of financial managers who rely on techniques such
as leveraging and financial restructuring”
(van Doorn et al., 2010, p.
253)
.

However,


5

empirical evidence suggests that financial metrics still
dominate

managerial decision
-
making
across functions
(see Homburg et al., 2012)

and particularly at senior
-
level

(Arens & Rust,
2012; Bendle et al., 2010)
.


Marketing Metrics


Limited R
ole
for
Senior
-
level

Management

In
comparis
on to financial metrics,

marketing metrics
traditionally play a limited role for

senior
-
level

management.
Where they exist, evidence suggests that

there

is an over focus

on
one metric such as the

satisfaction metr
ic

which can be seen
as the “ubiquitous mantra for
corporate success”

(Bowden, 2009, p.63)
. This may
be a
result of the widely accepted
belief
that
high levels of satisfaction
lead

to
an
increase in

other key metrics such as
customer
loyalty, intention to purchase, word
-
of
-
mouth recommendation, profit, market share, and
return on investment
.


Another
shortfall
of many popular metrics such

as customer satisfaction or net promoter
score

is t
hat they
,

like most financial metrics, relate to past purchase experiences, i.e.

that
these metrics

a
re

backward
-
looking
. Petersen et al.
(
2009, p.
102
)

stress that “while these
metrics can show managers why the firm is at its current state, these metrics have been shown
to offer little to no predictive ability to future customer behaviour or firm performance”.

As
LaValle et al.
(
2011, p.
22
)

note, “knowing what happened and why it happened are no longer
adequate.
Organizations need to know what is happening now, what is likely to happen next
and what actions should be t
aken to get the optimal results

.

A practical and research challenge is to provide
the right set of metrics
(Homburg et al.,
2012; O’Sullivan & Abela, 2007)

to guide strategic decision
-
making, thereby shaking off


the image of being tacticians rather than strategi
sts capable of helping the CEO drive growth
and profitability


(Kumar & Shah, 2009:119)
.





6

The New Reality of Metr
ics in the Context of Big Data

Rust et al.
(2010, p.98)

noted that “d
espite large investments in acquiring customer data, most
firms underuti
lize what they know”.
Those

marketing metrics that have long been regarded as
“superior” and that have dominated managerial practice are no longer adequate to address th
e
current data landscape.

It is
also
accepted
,

within the marketing metrics literature
,

that there
are no “silver metrics”
(Ambler & Roberts, 2008 , p.733; see also
Keiningham et al. 2007)

that

enable firms
to engage with

real
-
time measurement of outcomes, trends, dynamics and
characteristics
(Hopkins & Brokaw, 2011; Verhoef et al., 2010)
.


Senior
-
level management is challenged by an increasing amount and variety of data, how to
allocate resources to them such as time, attention and process
ing

of

the data and the
(marketing and financial) metrics
(see Kim & McAlister, 2011; LaPointe, 2008; Raithel et al.,
2011)
. The challenge is to ensure that the skills needed are available both within mar
keting
(Fellenz & Brady, 2010)

and finance
(e.g.
Mizik & Jacobson, 2007)

to really understand and
exploit the d
ata and the resulting metrics.
While there is often a stated willingness to
measu
re, there appears to be a lack
of operationalisation by senior managers to allocate their
scarce resources in this domain. A study of 400 CFOs, CEOs and marketing employees
shows that 75% are highly interested in measurement and the same number are attempt
ing to
link marketing measurement to financial performance. However, only 25% of respondents
believe that their measurement efforts have had a positive impact on business results

(Carr &
Schreuer, 2010)
.


Advances in the technology open new avenues to more sophisticate
d sets of metrics and the
ability to manipulate data. Particularly in the marketing domain “technology has driven new
approaches and techniques, such as developing customer insight, web analytics, dashboards,
search engine optimization, and social media ma
rketing”
(Harrigan & Hulbert, 2011, p.254)
.
Over 80% of 1700 CMOs surveyed indicate that they are planning to “deploy new


7

technologies to grapple with big data”
(
CMO
-
Council, 2011, p.26
)
. To the question “Where
are data
-
driven managers headed?” 3,000 executives, managers
and analysts answered that
the “ability to visualize data differently” is expected to be the most valuable technique in two
years
(
LaValle et al., 2011, p.27
)
.

Aligned to the
increase in data availability

there has been a huge increase in the number and
nature of metrics
(Srinivasan & Hanssens, 2009; Weir, 2008)

as well as the information they
impart
(Leeflang, 2011; Petersen et al., 2009)
. Figure 1
below
illustrates the development of
data over the past decades and shows a sample
of the range of metrics that has been dealt
with in the literature.

Figure 1: Sample of Data and Metrics Development


Source:
A
dapted from Leeflang
(2011)

TOWARDS A RESEARCH AGENDA

Existing empirical literature ha
s thoroughly investigated important aspects in the field of
metrics but with a dominance towards the use of financial data within strategic decision
-
making.
Empirical research in this area has struggled to both engage with

and study the
reality of the
fina
ncial dominant metrics

consolidated during the boom, the underuse of


8

marketing metrics and the explosion in
data and technological advancement.

In many ways,
the contemporary context “is challenging both researchers and practitioners, requiring fresh
and i
nnovative thinking as to how organizations need to be configured, measured and
managed”
(Bititci et al., 2011:12)
.
T
his paper suggest
s

a research agenda which

focuses on
methodological limitations of previous resea
rch in this area. There are five

dominant
research
issues arising from
current empirical studies

-

see appendix 1
and explored below.

1.
Developing a
contemporary definition of metrics:
Seminal artic
les
in the marketing domain
that deal with metrics
refer to a variety of related

terms such as “customer metrics” or
“marketing performance measure” without
further
defining the
se

term
s

in the current data
landscape
(Gupta & Zeithaml, 2006; Homburg et al., 2012)
.

While in most cases,
a definition
is not provided
(e.g. Gupta and Zeithaml, 2006)
, existing definitions vary
(e.g. Ambler, 2000;
Farris et al., 2009)
. Ambler
(2000:61)

for example defines a metric as “a performance
measure that top management should review”, that “ma
tters to the whole business” and that
“implies regularity”. Farris et al.
(2009:1)

regard a metric

as “a
measuring system that
quantifies a trend, dynamic or characteristic”
.

The development of a contempora
ry definition
of metrics in the

environment of “big data”

is needed to increase clarity and rigorousness for
further
research in this area.


2.
Inves
tigati
n
g

a range of metrics
:

Many of the existing articles investigate a predefined set
of metrics many of them financial and sometimes financial only
. When using marketing
metrics they often

focus on a single
marketing
metric, which in most cases
is

customer
satisfaction. However,
in order to increase top level’s engagement with marketing metrics,
academics
suggest that research should


develop a set of measures small enough to be
manageable but large enough to be compre
hensive”

(Clark, 19
99, p.
711; see also Clark et al.
2006)
.

The call is for a study which provides an in
-
depth investigation of the range of metrics
that
are
relevant to senior
-
level management today
.



9

3
.

A focus on
managerial
level
s
:

E
xisting empirical studies that investi
gate at managerial
level are
often based on a single informant approach
(Grafton et al., 2010; Harmancioglu et
al., 2010; Morgan et al., 2009
; O’Sullivan et al., 2009
a
)
, often capturing the view of the

CMO

only.
O’Sulli
van and Abela
(2007:90)

concede that,

“a
lthough the key informant approach is
common, the use of multiple informants from a single firm may allow

for a more rounded
perspective

.
A study is needed which
encompasses the
role of

metrics for

different
managerial levels, including
senior
-
level
.

What metrics do they use, should they use or could
they use?


4
.
A focus on internal data
:

A substantial part of th
e academic studies on
metrics is based on
data external to the firm, for example the publicly available
and widely used
American
Customer Sa
tisfaction Index (ACSI)
(
Ngobo et al., 2011
,
Luo et al., 2010
,
O’Co
nnell and
O’Sullivan, 2010
,
Jacobson and Mizik, 2009
,
Aksoy et al., 2008
)
. Businesses today, however,
are concerned with how to use the vast amount of data available internally

(
Rust et al., 2010
)

and across a wide range of
organisational

levels
(LaValle et al., 2011
)
.
Earlier

studies were

particularly

restricted by the fact that data on customers was regarded as being proprietary

(Srinivasan & Hanssens, 2009)

and in many cases
, “the availability of data defines the
problem that is being addressed rather than the
other way around” (Hanssens et al.,
2009:116)
.
However, the data landscape has changed enormously. We are moving towards “a
world of radical transparency, with data widely available”
(Brown et al., 2011:4)
. This
has
increased the

rele
vance of

qualitative

studies that investigate
how

these data are processed
and

the techniques needed to manage this

and focus
on data internal to the firm
.

5
.
A focus on technology:

The majority of studies in the area do not investigate the role of
technology for the processing of customer data a
nd metrics or are limited by the

conceptualization
of technology
(
e.g.
O’Sullivan & Abela, 2007)
.
For example, t
he

“handful
of academic papers” in this area do not address how the metrics visualised in dashboards are


10

selected or should be selected in order to guide
senior
-
level

management

(Pauwels et al.,
2009, p.2)
.

How
ever,
Rust et al.
(2010, p.96)

note that
“never before

have companies had
such powerful technologies for interacting directly with customers, collecting and mining
information about them and tailor
ing their offerings according”.
This research would include
the
role
of technology in supporting the visualizatio
n and communication of

metrics to
senior
-
level

management
.

Generally,
Homburg et al.
(2012:71)

suggest that a

more

detailed investigation of metrics
would require a “case
-
study setting to take manager
-

and firm
-
specific circumstances into
consideration”.
This study suggests a research design that investigates multiple case scenarios
of
whether

managers are guided by a

set of marketing metrics in comparison to financial
metrics
. To what
extent
is
a revised set, as proposed in the literature,
in evidence in
technology and dashboards and used to
guide strategic decision
-
making at
senior
-
level
. Such
research
would
allow fo
r a depth of information
,

including multiple metrics
,

across
multiple
functions

reflecting the current technological landscape and skill sets

rather than a positivist,
formal testing of relations
hips
.
A unique contribution of the study will be its use of
i
nternal
data in organisation both collected by the company and also provided
by
customer
through
interactive technologies
,

the internet and s
ocial networking.

Conclusion

In conclusion
,

the increase in the availability of “big data” for companies across in
dustries
has led to fundamental changes in the context

and use

of
both financial metrics and marketing
metrics
.

Managers are now exposed to a

range of metrics as a direct result of the increase in
information and data into organisations and the range of te
chnologies both for collection of
data at the customer interface and the ability of the technology to dice and slice with
sophisticated algorithm never
previously available or utilised

before.
This research will
reflect and engage with the current
challeng
ing

data and technology
landscape in order to


11

understand how
senior
-
level

managers

do and should
be
guide
d in making
strategic decisions

through use of
financial and
/or

marketing metrics.





12

Appendix 1:

Conceptual Model, Research Issues and Limitations of
Previous Studies




13

References

Aksoy, L., Cooil, B., Groening, C., Keiningham, T. L., & Yalcin, A. (2008). The long
-
term
stock market valuation of customer satisfaction.
Journal of Marketing
,
72
(4), 105
-
122.

Ambler, T. (2000). Marketing Metrics.
Business Strategy Review
,
11
(2), 59
-
66.

Ambler, T. (2010). Palaeomarketing: When the Masters Lock Horns.
Market Leader
,
1
(47),
12.

Ambler, T., & Roberts, J. H. (2008). Assessing Marketing Performance: Don’t Settle for
a
Silver Metric.
Journal of Marketing Management
,
24
(7/8), 733
-
750.

Arens, Z., & Rust, R. (2012). The Duality of Decisions and the Case for Impulsiveness
Metrics.
Journal of the Academy of Marketing Science
,
40
(3), 468
-
479.

Bendle
, N., Farris, P., Pfeifer, P., & Reibstein, D. (2010). Metrics That Matter to Marketing
Managers.
Marketing Journal of Research and Management, January
, 5
-
10.

Beyer, A., Cohen, D. A., Lys, T. Z., & Walther, B. R. (2010). The Financial Reporting
Environment
: Review of the Recent Literature.
Journal of Accounting and Economics
,
50
(2
-
3), 296
-
343.

Bititci, U., Garengo, P., Dörfler, V., & Nudurupati, S. (2011). Performance Measurement:
Challenges for Tomorrow.
International Journal of Management Reviews
, 1
-
23.

B
owden, J. L.
-
H. (2009). The Process of Customer Engagement: A Conceptual Framework.
Journal of Marketing Theory & Practice
,
17
(1), 63
-
74.



14

Brown, B., Chui, M., & Manyika, J. (2011). Are You Ready For the Era of “Big Data” ?
McKinsey Quarterly
,
October
(4).

C
MO
-
Council. (2011). From Streched to Strengthened
-

Insights from the Global Chief
Marketing Officer Study.
Available online
, 1
-
72.

Carr, J. M., & Schreuer, R. (2010). Connecting the Dots.
Marketing Management
,
19
(2), 26
-
32.

Clark, B.H., Abela, A. V., & Am
bler, T. (2006a). Behind the Wheel.
Marketing
Management
,
15
(3), 18
-
23.

Clark, B.H., Abela, A. V., & Ambler, T. (2006b). An Information Processing Model of
Marketing Performance Measurement.
Journal of Marketing Theory & Practice
,
14
(3),
191
-
208.

Clark, Br
uce H. (1999). Marketing Performance Measures: History and Interrelationships.
Journal of Marketing Management
,
15
(8), 711
-
732.

Danielson, M. G., Heck, J. L., & Shaffer, D. R. (2008). Shareholder Theory
-

How
Opponents and Proponents Both Get It Wrong.
Jou
rnal of Applied Finance
,
18
(2), 62
-
66.

Danielson, M. G., & Scott, J. A. (2006). The Capital Budgeting Decisions of Small
Businesses.
Journal of Applied Finance
,
16
(2), 45
-
56.

Davidson, H. (2009). Shareholder Value: The Enemy of Good Marketing.
Market Leade
r
,
Quarter 4
(46), 24
-
29.



15

Farris, P. W., Bendle, N. T., Pfeifer, P. E., & Reibstein, D. J. (2009).
Key Marketing Metrics
.
Pearsons, London
.

Fellenz, M. R., & Brady, M. (2010). Managing customer
-
centric information: The challenges
of information and communic
ation technology (ICT) deployment in service
environments.
International Journal of Applied Logistics (IJAL)
,
1
(3), 88
-
105.

Grafton, J., Lillis, A. M., & Widener, S. K. (2010). The Role of Performance Measurement
and Evaluation in Building Organizational C
apabilities and Performance.
Accounting,
Organizations and Society
,
35
(7), 689
-
706.

Gupta, S., & Zeithaml, V. (2006). Customer Metrics and their Impact on Financial
Performance.
Marketing Science
,
25
(6), 718
-
739.

Hanssens, D. M., Rust, R. T., & Srivastava,

R. K. (2009). Marketing Strategy and Wall
Street: Nailing Down Marketing’s Impact.
Journal of Marketing
,
73
(6), 115
-
118.

Harmancioglu, N., Grinstein, A., & Goldman, A. (2010). Innovation and Performance
Outcomes of Market Information Collection Efforts: T
he Role of Top Management
Team Involvement.
International Journal of Research in Marketing
,
27
(1), 33
-
43.

Harrigan, P., & Hulbert, B. (2011). How Can Marketing Academics Serve Marketing
Practice? The New Marketing DNA as a Model for Marketing Education.
Jo
urnal of
Marketing Education
,
33
(3), 253
-
272.

Homburg, C., Artz, M., & Wieseke, J. (2012). Marketing Performance Measurement
Systems: Does Comprehensiveness Really Improve Performance?
Journal of Marketing
,
76, May
, 56
-
77.



16

Hopkins, M. S., & Brokaw, L. (201
1). Matchmaking With Math: How Analytics Beats
Intuition to Win Customers.
MIT Sloan Management Review
,
52
(2), 35
-
41.

Keiningham, T. L., Cooil, B., Andreassen, T. W., & Aksoy, L. (2007). A Longitudinal
Examination of Net Promoter and Firm Revenue Growth.
Journal of Marketing
,
71
(3),
39
-
51.

Kim, M., & McAlister, L. M. (2011). Stock Market Reaction to Unexpected Growth in
Marketing Expenditure: Negative for Sales Force, Contingent on Spending Level for
Advertising.
Journal of Marketing
,
75
(4), 68
-
85.

Kumar,
V., & Shah, D. (2009). Expanding the Role of Marketing: From Customer Equity to
Market Capitalization.
Journal of Marketing
,
73
(6), 119
-
136.

LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big Data,
Analytics and the Path Fr
om Insights to Value.
MIT Sloan Management Review
,
52
(2),
21
-
32.

Leeflang, P. (2011). Paving the Way for “Distinguished Marketing.”
International Journal of
Research in Marketing
,
28
(2), 76
-
88.

Luo, X., Homburg, C., & Wieseke, J. (2010). Customer Satisfac
tion, Analyst Stock
Recommendations, and Firm Value.
Journal of Marketing Research (JMR)
,
47
(6), 1041
-
1058.

Lusch, R. F., & Webster Jr, F. E. (2010). Marketing’s Responsibility for the Value of the
Enterprise.
Marketing Science Institute Working Paper Seri
es
,
10/111
, 1
-
48.



17

Lusch, R., Vargo, S., & Obrien, M. (2007). Competing through service: Insights from
service
-
dominant logic.
Journal of Retailing
,
83
(1), 5
-
18.

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H.
(2011).
Big data: The next frontier for innovation, competition and productivity.
McKinsey Global Institute, May
.

McDonald, M. (2010a). A Brief Review of Marketing Accuntability and a Research Agenda.
Journal of Business & Industrial Marketing
,
25
(5), 383
-
394.

McD
onald, M. (2010b). Shareholder Value is a Friend of Good Marketing.
Market Leader
,
1
(47), 10
-
11.

McGovern, G. J., Court, D., Quelch, J. A., & Crawford, B. (2004). Bringing Customers into
the Boardroom.
Harvard Business Review
,
82
(11), 70
-
80.

McSweeney, B.

(2009). The Roles of Financial Asset Market Failure Denial and the
Economic Crisis: Reflections on Accounting and Financial Theories and Practices.
Accounting, organizations and society
,
34
(6
-
7), 835
-
848.

Mizik, N, & Jacobson, R. (2007). Myopic Marketing
Management: Evidence of the
Phenomenon and its long
-
term performance consequences in the SEO context.
Marketing Science
,
26
(3), 361
-
379.

Mizik, Natalie. (2010). The Theory and Practice of Myopic Management.
Journal of
Marketing Research
,
47
(4), 594
-
611.

Mo
rgan, N. A., Slotegraaf, R. J., & Vorhies, D. W. (2009). Linking Marketing Capabilities
with Profit Growth.
International Journal of Research in Marketing
,
26
(4), 284
-
293.



18

Ngobo, P.
-
V., Casta, J.
-
F., & Ramond, O. (2012). Is Customer Satisfaction a Relevant

Metric
for Financial Analysts?
Journal of the Academy of Marketing Science
,
40
(3), 480
-
508.

O’Leary, S. (2012). All Fired Up Massachusetts: The State’s New Wave of Big Data
Companies.
MIT Sloan Management Review
,
February
, 1
-
5.

O’Sullivan, D., & Abela, A.

V. (2007). Marketing Performance Measurement Ability and
Firm Performance.
Journal of Marketing
,
71
(2), 79
-
93.

O’Sullivan, D., Abela, A. V., & Hutchinson, M. (2009). Marketing Performance
Measurement and Firm Performance: Evidence from the European High
-
t
echnology
Sector.
European Journal of Marketing
,
43
(5/6), 843
-
862.

O’Sullivan, D., Hutchinson, M. C., & O’Connell, V. (2009). Empirical Evidence of the Stock
Market’s (Mis)pricing of Customer Satisfaction.
International Journal of Research in
Marketing
,
26
(2), 154
-
161.

Pauwels, K., Ambler, T., Clark, B. H., LaPointe, P., Reibstein, D., Skiera, B., Wierenga, B.,
et al. (2009). Dashboards as a Service: Why, What, How, and What Research Is
Needed?
Journal of Service Research
,
12
(2), 175
-
189.

Petersen, J. A., M
cAlister, L., Reibstein, D. J., Winer, R. S., Kumar, V., & Atkinson, G.
(2009). Choosing the Right Metrics to Maximize Profitability and Shareholder Value.
Journal of Retailing
,
85
(1), 95
-
111.

Rao, R. K. S., & Bharadwaj, N. (2008). Marketing Initiatives, E
xpected Cash Flows, and
Shareholders’ Wealth.
Journal of Marketing
,
72
(1), 16
-
26.



19

Reichheld, F. F. (2003). The One Number You Need to Grow.
Harvard Business Review
,
81
(12), 46
-
54.

Rust, R. T., Moorman, C., & Bhalla, G. (2010). Rethinking Marketing.
Harvard

Business
Review
,
January
-
Fe
, 94
-
101.

Shah, S., Horne, A., & Jaime, C. (2012). Good Data Won’t Guarantee Good Decisions.
Harvard Business Review
, (April), 23
-
26.

Sidhu, B., & Roberts, J. (2008). The Marketing Accounting Interface
-

Lessons and
Limitations.

Journal of Marketing Management
,
24
(7), 669
-
686.

Srinivasan, S., & Hanssens, D. M. (2009). Marketing and Firm Value: Metrics, Methods,
Findings, and Future Directions.
Journal of Marketing Research
,
46
(3), 293
-
312.

Srinivasan, S., Vanhuele, M., & Pauwels,

K. (2010). Mind
-
Set Metrics in Market Response
Models: An Integrative Approach.
Journal of Marketing Research
,
47
(4), 672
-
684.

Vargo, S. L. (2011). On marketing theory and service
-
dominant logic: Connecting some dots.
Marketing Theory
,
11
(1), 3
-
8.

Vargo,

S. L., & Lusch, R. F. (2007). Service
-
dominant logic: continuing the evolution.
Journal of the Academy of Marketing Science
,
36
(1), 1
-
10.

Verhoef, P. C., Reinartz, W. J., & Krafft, M. (2010). Customer Engagement as a New
Perspective in Customer Managemen
t.
Journal of Service Research
,
13
(3), 247
-
252.

Weir, K. (2008). Examining the Theoretical Influences of Customer Valuation Metrics.
Journal of Marketing Management
,
24
(7/8), 797
-
824.



20

Wind, Y. (2008). A Plan to Invent the Marketing we Need Today.
MIT Sloan

Management
Review
,
49
(4), 21
-
28.

van Doorn, J., Lemon, K. N., Mittal, V., Nass, S., Pick, D., Pirner, P., & Verhoef, P. C.
(2010). Customer Engagement Behavior: Theoretical Foundations and Research
Directions.
Journal of Service Research
,
13
(3), 253
-
266.