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Marketing Return on Investment: Seeking Clarity for Concept and Measurement



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Marketing Analytics
The Peer-Reviewed Journal
ISSN: 2054-7544
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Brand and Eminence, Deloitte
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Gordon Farquharson, Senior ROI Insight Manager,
Camelot UK Lotteries
Mousumi Ghosh, VP, Decision Tech, JPMorgan Chase
Susan Hammes, VP, Digital Brand & Social Media
Development, American Express
Dominique M. Hanssens, Bud Knapp Professor of
Marketing, UCLA Anderson School of Management
Christopher Hogan, Director of Marketing Analytics,
Christopher M. Johannessen, Vice President,
eCommerce Springfield Financial Services
Barry Keating, Professor, University of Notre Dame
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Richard Phillips, Head of Online Analytics, Virgin
Russell Pierce, Managing Director, Customer Data and
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Kevin Potcner, Principal, Expectation Labs, Inc.
Stewart Robbins, Analytics Practice Lead, EMEA,
Hewlett Packard
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© Henry Stewart Publications 2054-7544 (2015) Vol. 1, 3 267–282 Applied Marketing Analytics
Marketing return on investment:
Seeking clarity for concept and
Received (in revised form): 21st April, 2015
Paul W. Farris
is the Landmark Communications Professor of Business at the University of Virginia’s Darden
School of Business. Previously, he worked in marketing management for UNILEVER, Germany
and the LINTAS advertising agency. Professor Farris is a current or past board member for several
international companies and is a past academic trustee of the Marketing Science Institute. He is the
co-author of award-winning work in marketing metrics, advertising research, advertising budgeting
and retail power.
Darden Business School, University of Virginia, 100 Darden Blvd, Charlottesville, VA 22906, USA
Dominique M. Hanssens
is the Bud Knapp Distinguished Professor of Marketing at the UCLA Anderson School of
Management. From 2005 to 2007 he served as executive director of the Marketing Science
Institute. A Purdue University PhD graduate, Professor Hanssens’ research focuses on strategic
marketing problems, in particular marketing productivity. He is a Fellow of the INFORMS Society
for Marketing Science and a recipient of the Churchill and Mahajan awards from the American
Marketing Association. He is a founding partner of MarketShare, a global marketing analytics fi rm
with headquarters in Los Angeles.
UCLA Anderson School of Management, 110 Westwood Plaza, Suite B-417, Los Angeles, CA 90095-1481, USA
Tel: +1 310 825 4497; E-mail:
James D. Lenskold
is President of Lenskold Group, international speaker and author of the award-winning book
‘Marketing ROI, The Path to Campaign, Customer and Corporate Profi tability’. Jim’s fi rm specialises
in building marketing ROI capabilities for Fortune 1000 and emerging companies globally. Client
engagements include innovative measurement solutions, user-friendly ROI planning tools,
action-driven dashboards and ROI frameworks to drive more profi table marketing strategies and
Web:; E-mail:
David J. Reibstein
is the William S. Woodside Professor and Professor of Marketing at The Wharton School,
University of Pennsylvania. He served as executive director of the Marketing Science Institute
and as chairman of the American Marketing Association. He previously taught at Harvard,
Stanford, INSEAD and ISB (in India). David has authored seven books and dozens of articles
in major marketing journals. His most recent book is ‘Marketing Metrics: The Definitive Guide to
Measuring Marketing Performance’. He has consulted for companies ranging from Fortune 500
firms to start-ups, including Google, GE, British Airways and Royal Dutch Shell. David was
a co-founder of (Shopzilla) and on the founding board of And1, the basketball
apparel company.
3730 Walnut Street, 743 JM Huntsman Hall, The Wharton School, University of Pennsylvania, Philadelphia,
PA 19104, USA
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Farris, Hanssens, Lenskold, Reibstein
Applied Marketing AnalyticsVol. 1, 3 267–282 © Henry Stewart Publications 2054-7544 (2015)
Abstract As the need for accountable marketing spending continues to grow,
companies must develop sound metrics and measures of marketing’s contribution to
rm profi tability. The leading metric has been return on marketing investment (MROI),
following the widespread adoption of ROI metrics in other parts of the organisation.
However, the ROI metric in marketing is typically interpreted and used in a variety
of ways, which causes ambiguity and suboptimal marketing decision making. This
paper seeks to remove the ambiguity around MROI to guide better measurements and
analytics aligned to fi nancial contribution. The authors fi rst provide a formal defi nition
of MROI and review variations in the use of MROI that are the root cause of ambiguity
in interpretation. The authors come to the conclusion that MROI estimates would be
more transparently described if those providing the estimates used the following form:
Our analysis measured a (total, incremental, or marginal) MROI of (scope of spending)
using (valuation method) over time period. The paper proceeds to describe fi ve case
studies that illustrate the various uses of MROI, covering different marketing initiatives
in different business sectors. The authors describe the important links between
marketing lift metrics (such as response elasticities) and MROI. The fi nal section of
the paper focuses on the connection between MROI and business objectives. While
management’s prerogative is to maximise short- and long-run profi ts, that is not
equivalent to maximising MROI. The authors demonstrate that MROI plays a different
role in the process of marketing budget setting (a marketing strategic task) versus
allocating a given budget across different marketing activities (a marketing operations
task). They highlight the role of setting MROI hurdle rates that recognise not only
marketing’s ability to drive revenue, but also the fi rm’s cost of capital. The authors
hope that their recommendations will help the marketing profession achieve a common
understanding of how to assess and use what they believe is its most important
summary productivity metric, MROI.
KEYWORDS: ROI, marketing impact, marketing metrics, marketing resource allocation,
marketing budgeting
An important responsibility of the marketing
function is to enable economic decisions
on budgeting and allocating the corporate
resources devoted to marketing efforts.
Marketing return on investment (MROI),
aka return on marketing investment (ROMI),
is the metric that is increasingly used to
evaluate marketing spending and to guide
strategic and tactical decisions. Practitioners
and academics agree that, if dollars are spent
or valuable assets committed to marketing
purposes, then the firm should strive to
monitor and improve returns to marketing
efforts in financial (dollar) denominated
metrics. MROI is arguably the most widely
employed measure of enterprise marketing
productivity (output/input), even if it is not
as universally embraced and implemented as
many would wish. As such, it is important to
ensure that definitional ambiguity does not
plague the already-difficult job of assessing
marketing’s contribution to the firm’s health
and profitability. The goal of this paper is to
improve conceptual and definitional clarity,
as well as to suggest specific terms to identify
the several variations of MROI that are
being used and reported by practitioners and
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Marketing return on investment
© Henry Stewart Publications 2054-7544 (2015) Vol. 1, 3 267–282 Applied Marketing Analytics
Although the history of MROI goes
back at least to 1971,
and was used
at AT&T in the late 1980s (Lenskold,
personal communication), the measure
has no clear genesis. Its adoption was
undoubtedly influenced by the widespread
use of ROI to measure firm and strategic
business unit (SBU) profitability in
the late 1970s; for example, the PIMS
project focused on ROI as the primary
performance metric.
For many years,
communicating marketing’s contributions
to the CFO and others in the finance
function has been important to marketers.
Part of this desire to demonstrate
marketing productivity is related to
budgeting, as finance often holds the
key to obtaining approval for marketing
spending, and hence the ambition of its
strategic objectives. So it is natural that
marketing would strive to speak the same
language. Finance also struggles with
finding the ‘right’ measure of profitability,
however. Consider these examples of
profitability metrics used by finance:
profit, economic profit (also known as
EVA — economic value added), EBITDA,
etc. Each has advocates and advantages
for particular applications, but the terms
are not interchangeable. We believe that
marketing should strive for the same kind
of precision in our common language and
that belief motivates this paper.
MROI can and is being used for a
number of different purposes: assessing
historical and projected marketing
productivity; reviewing and approving
marketing budgets; allocating limited
marketing funds among competing
products, markets, customers, marketing
mix elements and media, and evaluating
specific marketing campaigns for ‘go no-
go’ decisions.
Marketers differ widely,
however, in their understanding, acceptance
and implementation of MROI. Better
understanding can help add precision
to the terms, increase acceptance for
appropriate applications, design necessary
analytic measurements, and speed
the implementation of sorely needed
metrics to assess and improve marketing
In a survey of 194 senior marketing
managers and executives,
77 per cent
reported that they believe that ROI is a
very useful measure and 67 per cent also
think that market share is very useful.
Less than half (49 per cent) reported
that ROMI was ‘useful in managing
and monitoring their business’. A major
reason why managers may not find
ROMI (aka MROI) as useful stems
from a lack of understanding of the
Another possible reason is that
respondents might have been confused
about if and how ROMI differs from
MROI or ROI. Furthermore, Rogers
and Sexton
report that there is a lack of
effort within companies to measure their
marketing ROI, in part because rewards
are not being tied to this measure. Yet
Ofer and Currim,
based on a survey of
439 managers in the USA, show that the
use of such performance metrics leads to
significantly better performance. Clearly,
there is a need for and a benefit to better
understanding measures that capture
marketing productivity.
In this paper the authors will:
(1) provide a formal definition of MROI;
(2) discuss the variations in specific MROI
calculations and confusion that may result
from differences in the domain of MROI
under consideration; (3) illustrate several
of those MROI variations with specific
management applications and suggest
specific names/labels for each major
variation; (4) analyse relationships of
these variations to other response metrics,
such as elasticities and linear response
coefficients of marketing mix models;
(5) review different perspectives on what
an appropriate objective function is for
marketing (since maximising MROI is
only sensible for fixed budgets); and
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Farris, Hanssens, Lenskold, Reibstein
Applied Marketing AnalyticsVol. 1, 3 267–282 © Henry Stewart Publications 2054-7544 (2015)
they generate revenue and profit returns over
multiple years, building cumulative impact
and creating assets with future value. More
transparency in reporting these outcome
types will help identify the situations
under which these comparisons and other
applications of MROI are more or less
appropriate. The next section addresses these
Although the maths is simple, the meaning and
significance of the MROI metric is anything
but straightforward. Below we will discuss
some important sources of variations that we
have identified in how MROI is estimated
and reported. Our discussion is intended
to support the marketing field’s efforts to
generate transparent and reliable metrics that
can be used to assess and report marketing
productivity, as well as to motivate an
objective-maximising allocation of resources
among competing marketing activities. As
such, sources of variations are important and
should be fully disclosed when marketers
report and apply MROI to decisions.
We have classified these three sources of
variations into three categories: (A) methods
of valuing marketing returns; (B) scope/
granularity of spending evaluated; and (C)
range of spending for which the MROI is
A. Methods for valuation of marketing returns
The most straightforward of marketing
returns used in calculating MROI is the profit
margin generated from incremental sales.
This is what we have termed a baseline-lift
valuation, based on the ability to establish a
reasonable measure of the lift over a baseline
level of existing sales, attributable to a specific
marketing initiative. A slight variation of this
is reporting incremental revenue as the return
in place of profit. When profit margins are
unknown or undisclosed, this calculation is
(6) conclude with some suggestions for
future work to help marketing achieve a
common understanding of how to assess
and use its most important summary
productivity metric, MROI.
MROI is the financial value attributable to
a specific set of marketing initiatives (net
of marketing spending), divided by the
marketing ‘invested’ or risked for that set of
initiatives. MROI (aka ROMI) is a relatively
new metric. It is not like the traditional
‘return-on-investment’ metrics because
marketing is a different kind of investment.
ROI metrics for firm or SBU performance
are almost always annual returns, but other
uses of ROI, such as the return on specific
financial investment, often leave unspecified
the time required to generate the return.
Marketing spending is typically expensed in
the current period and, usually, marketing
spending will be deemed as justified if the
MROI is positive and exceeds the firm’s
‘hurdle rate’.
More specifically, MROI is the dollar-
denominated estimate of the incremental
financial value to the entity generated by
identifiable marketing expenditures, less the
cost of those expenditures as a percentage of
the same expenditures:
Incremental financial value
generated by marketing
− Cost of marketing
Cost of marketing
Unlike other types of investments,
marketing funds are rarely tied up in
inventories, fixed assets or receivables, and
most marketing expenditures come from
what otherwise would be liquid funds.
Therefore, great care will need to be taken
to validate comparisons between the ROI
of marketing with other ROI estimates.
Some marketing actions are similar to other
investments, however, in that in many cases
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Marketing return on investment
© Henry Stewart Publications 2054-7544 (2015) Vol. 1, 3 267–282 Applied Marketing Analytics
a useful interim step to calculating MROI,
although it does not have the precision
needed to optimise spend levels. When
only incremental revenue is known, we
still consider it a baseline-lift valuation but
recommend reporting this as ‘revenue MROI’
to distinguish it from a net profit impact.
It may also be useful when comparing the
marketing productivity of two alternative
marketing initiatives for the same product or
The next two forms of valuation are
necessary to account for outcomes when
sales lift is unknown. The first is a funnel
conversion outcome, where the valuation
of marketing returns involves projecting
incremental sales by applying historical
or estimated funnel conversion rates. The
second is referred to as a comparable cost
valuation, which considers the financial
outcome of cost savings or opportunity cost
differences as the return from the marketing
We also need two forms of valuation
that capture the contribution beyond
immediate sales lift to reflect asset outcomes
that provide long-term financial benefit —
customer equity (CE) and marketing
assets. These capture less transparent
estimates of brand equity, cost of capital
or effects on market capitalisation (eg price–
earnings ratios) that are critical outcomes
from marketing but more challenging to
The same marketing efforts might be
valued in a number of different ways,
with each method potentially yielding a
different financial value based on the level of
measurement precision, and thus a different
MROI. We discuss these different valuation
methods and provide mini case study
scenarios demonstrating the application of
each in the next section.
Table 1 corresponds closely to the
chain of marketing productivity spelled
out by Rust et al.
They suggested that
marketing productivity could be measured
at the levels of marketing tactics, impact
on customers, the market, financial
performance and firm value. We use
a similar hierarchy for organising and
labelling MROI.
B. Scope/granularity of marketing spending
evaluated (full marketing mix versus individual
MROI measures can assess the financial impact
of a single marketing tactic or an integrated
combination of many tactics, including the
full marketing mix. The granular extreme
would be ROI measures for a specific
search advertisement, an e-mail campaign,
or the specific offer within a direct mail
campaign. The other extreme is obtaining
ROI measures for the full marketing
mix, or integrated marketing activities
such as the Intel Inside
campaign. This
multi-year effort would include costs for
market research, logo design and revisions,
cooperative advertising rebates and all
media. As the scope of the marketing efforts
Table 1: Five levels of marketing returns
Valuation methods Financial return assessed
Comparable costs Cost savings for achieving equivalently valuable contacts
Funnel conversions Future period incremental sales and profi ts based on estimated conversion rates
Current period
incremental sales and profi ts
Customer equity Changes in customer lifetime value
Marketing assets Changes in brand and fi rm valuations
Current period refers to ‘accounting period’, which may include short response lags. For example,
carryover effects of advertising on sales may be found one or two weeks after exposure. In most cases, such
delayed effects would still occur within a quarterly accounting period.
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Farris, Hanssens, Lenskold, Reibstein
Applied Marketing AnalyticsVol. 1, 3 267–282 © Henry Stewart Publications 2054-7544 (2015)
included in a particular MROI measure
increases, it becomes more important to
assess substitute, interaction and feedback
effects among elements of the marketing
mix. Evaluating a combination of mix
elements can lead to a valuation that is quite
different from the sum of separate return
C. Range: total, incremental or marginal returns
Holding constant the scope and
granularity of activities being evaluated,
there is an important distinction between
reporting total, incremental or marginal
MROI (see Figure 1). Total evaluates
return on all spending, incremental for a
specified additional spending ‘increment’,
and marginal is the estimated return on
the ‘last dollar’ of marketing spending.
Total and incremental MROI are typically
easier to estimate and often result from
A/B testing, or from models that use
linear response functions. Evaluating the
marginal returns to spending is more
challenging and, with the exception of
complex and expensive experiments,
will usually involve models that include
nonlinear response functions. Conceptually
and practically, these three types of
returns are different and they should not
be compared to each other. Although
diminishing returns will eventually be
encountered, there is no general rule
as to which of the three measures of
MROI will be higher or lower. Their
relative values will depend on the shape
of the response function and where on
that function the return is evaluated. In
other words, the critical difference among
the three is the comparison or reference
spending level. Because marketing impact
on revenue is nonlinear, it matters a great
deal which reference point is chosen.
In summary, we believe there are three
critical dimensions of MROI estimates:
valuation method, scope/granularity of
marketing mix elements assessed, and range
of spending evaluated. All three dimensions
need to be reported for full transparency and
consideration of what the concept MROI
represents in a particular application.
Figure 1: Total, incremental, and marginal MROI
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Marketing return on investment
© Henry Stewart Publications 2054-7544 (2015) Vol. 1, 3 267–282 Applied Marketing Analytics
All methodologies attempt to attribute
ROI from the additional financial value to
the firm created by marketing spending.
Their differences lie in how the valuation
is assessed and the scope and range of
marketing efforts evaluated. This scope
can range from a specific tactic to a single
campaign or even the full marketing mix.
As shown in Figure 1, given a scope of
marketing, the range of spending evaluated
can encompass the entire budget (total),
some portion of that budget that makes
sense to evaluate as an increment, or the
marginal returns of the last dollar of spend.
MROI estimates will be more transparently
described if those providing the estimates
would use the following form: Our analysis
measured a (total, incremental, or marginal)
MROI of (scope of spending) using (valuation
method) over time period. For example: ‘We
measured the total MROI of 2014 trade
promotions using baseline-lift to be 34 per
cent for the Q1, 2014 reporting period.
Baseline-lift is referring to the increase in sales
above what would have occurred had the
trade promotion not been run.
This section will illustrate with examples
each of the MROI return valuation types
discussed above and conclude these examples
with a statement that would report the
valuation method, scope and range of
the MROI reported. The examples will
start with baseline-lift MROI as the most
common and straightforward measure.
Baseline-lift MROI scenario
A technology company selling a software
package to small and medium-sized
businesses evaluates its targeted marketing
campaign and determines that the campaign
generated an incremental 190 units of
sales compared to a control group that did
not receive the marketing. The integrated
campaign consisted of direct mail, e-mail,
a landing page with a white paper and an
outbound sales contact. The total costs
were US$80,000. The company generates
a net profit per sale of US$522, for a total
of US$99,180 of incremental profit from
the 190 units of new sales in the year. The
integrated campaign generated a total ROI
of 24 per cent (calculated as (US$99,180 −
US$80,000)/US$80,000) using the baseline-
lift MROI valuation method.
This same method can be applied to a
broad range of marketing, from individual
tactics through to an annual multichannel
marketing spend when the incremental
sales and profits generated from the specific
marketing initiative can be determined. The
following example is roughly based on a
published case study and demonstrates how
this method is adapted for different forms of
A recent article in the business press
heralded the impact of the ‘Brand USA’
campaign, the country’s first coordinated
effort to promote the United States to
international travellers. The campaign spent
US$72m on various media ads in the 2013
fiscal year, targeting tourists from eight
different countries. According to a research
study, it resulted in an increase in visitors
from these countries of 1.1m (2.3 per cent)
over the expected visitor levels in 2013.
Those visitors spent about US$3.4bn in
the same fiscal year. While there are many
benefits that can result from this campaign,
from a purely financial standpoint, many tax-
funded tourism organisations will run the
analysis based on the tax returns generated,
with the hope of recovering or exceeding
the expenditure made. If we assume an
average corporate tax rate of 12 per cent,
the US$3.4bn in incremental revenue would
generate US$408m in taxes. The total ROI
of the fiscal year 2013 Brand USA campaign
is estimated to be 466 per cent (calculated
as (US$408m US$72m)/US$72m) based
on the baseline-lift MROI valuation method
for fiscal year 2013. It should be noted that
often MROI can be a rather large number,
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Farris, Hanssens, Lenskold, Reibstein
Applied Marketing AnalyticsVol. 1, 3 267–282 © Henry Stewart Publications 2054-7544 (2015)
given that these estimates tend only to look
at the impact of the marketing spend and do
not reflect the allocation of the fixed costs
of infrastructure that often make the delivery
feasible. If more plant or staff were necessary
to be added to deliver on the increase in
sales, then that expenditure would have to be
taken into consideration. Otherwise, it really
is the impact of the marketing spending
to be able to fully utilise the existing
underutilised capacity of the firm.
Comparable cost MROI scenario
Most of an internet retailer’s traffic is
currently generated through paid search
advertising. The cost per click through
(CTR) for a group of search terms is
averaging US$1.50 and the firm is spending
US$6,000 per year on search advertising.
Assume the CMO decides to invest
US$1,000 on improving the site’s organic
search ranking, resulting in an increase in
organic clicks migrating away from paid
search, thereby reducing paid costs to
US$4,000 per year. Total traffic (organic
and paid search) remains the same. The
reduction in search advertising spending
for the year is US$2,000 while the overall
traffic has remained the same. The MROI
is the cost savings in paid search minus the
cost of improvement to generate the search
traffic divided by these costs or (US$2,000
US$1,000)/US$1,000 = 100 per cent. We
estimate the total annual MROI of the site
improvements on a comparable cost basis to
be 100 per cent for the year. Obviously, there
could be additional benefits in years to come
and through subsequent purchases from the
acquired customers.
Funnel MROI scenario
A company launches a content-based
marketing campaign at a cost of US$30,000
that generates 6,000 views of its educational
video. Based on historical funnel tracking
of similar campaigns, they project 12 per
cent of viewers will become qualified leads
within six weeks and 10 per cent of those
leads will convert to a sale in nine months,
resulting in 72 sales. At a profit of US$500
per sale, the campaign is projected to
generate US$36,000 in incremental profit for
an estimated short-term ROI of (US$36,000
US$30,000)/US$30,000 = 20 per cent.
The analysis identified a total MROI for the
educational videos of 20 per cent using the
funnel conversion method for the nine-
month period.
Customer equity MROI scenario
A small financial institution catering to the
high wealth segment has 10,000 customers
with average annual profits of US$2,000
per customer. A senior bank executive
was concerned about the attrition rate
among its customers, which stood at
20 per cent annually, somewhat higher than
the competitive benchmark of 15 per cent.
She authorised a US$4m investment in
customer service enhancement, including
upgrades to the bank’s digital technology and
higher customer support staffing. One year
after implementation, the bank’s customer
attrition rate had dropped to 17 per cent,
while the sector benchmark stayed the same.
There are different ways to calculate CE and
this bank’s approach was to look at the future
profit stream of its customer base, ignoring
any changes in customer acquisition levels
or the time value of money. Before the
service improvement, CE stood at
(10,000 × US$2,000)/0.20 = $100m.
After the improvement, the CE rose to
(10,000 × US$2,000)/0.17 = US$117.6m. The
total MROI of the retention initiative using
the CE MROI valuation method is 340 per
cent (calculated as (17.6m 4m)/4m) over
the life of the acquired customers. The return
is considered ‘equity’ and not ‘incremental
profit’ as measured with the baseline-lift
method because the future profits require
additional marketing investments and can
easily change over time based on other
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Marketing asset MROI scenario
Two railroads merge, creating a new firm.
The new firm is not well known and the
stock price falls below what top management
believes it should be. A US$100m advertising
campaign in the financial press is launched
and, relative to the industry, the new firm’s
market cap grows by US$115m after one
year, attributed to the advertising that they do
not believe would have occurred otherwise.
Based on their historical P/E ratio and that of
the industry, the CFO decides the campaign
is a success by comparing the increase in
market capitalisation for equivalent earnings
to the cost of the campaign. The MROI
is the increase in market cap of US$115m
minus the cost of the advertising divided
by the cost of the advertising,
(US$115m US$100m)/US$100m =
15 per cent. Our analysis identified an
incremental 15 per cent MROI of the
advertising campaign using the marketing
assets valuation method for the year.
It should be clear from our discussion
that the computation of a baseline sales
performance is essential for the estimation of
MROI. In straight business terms, any time
we wish to assess the ROI of a marketing
activity, we need to know what would have
happened (to sales and any metrics derived
from sales) if said marketing activity had not
taken place. The answer to this important
question leads us into a discussion of relevant
marketing models and analytics, ie abstract
representations of demand for the brand in
the presence versus absence of marketing
activity, ie the estimation of marketing impact.
Indeed, we may find that marketing spending
occurs and there is no increase in sales. Yet,
to assess this fairly, it would be necessary
to assess what would have happened if the
marketing spending had not taken place. This
again requires the use of the aforementioned
marketing mix models.
Marketing impact that has financial
consequences comes in three forms: either
cost savings, unit sales impact or change in
margin impact, or some combination of the
three. The most straightforward method for
assessing impact is a simple experimental
design (A/B testing, where B is the control
group) in which some markets (eg regions,
or individual customers, or time periods)
are exposed to the marketing activity and
others are not. Such an A/B test reveals two
points on the demand curve, as shown in
Figure 1. In most applications the marketing
executive will make a linear interpolation
between the two, and derive the MROI as
[gross margin (condition A)
gross margin (condition B)
marketing spend (condition A)]
marketing spend (condition A)
The ease of interpretation of such test
results is offset by its limitation: two data
points are insufficient to characterise a
response function, and obtaining more data
points quickly becomes expensive in time
and execution cost. Of course, as marketers
move to incorporate more digital media into
the mix, the cost of marketing experiments
has declined and the value has increased.
New development such as programmatic
advertising can also help implement ever
more complex media buys.
Analytic measures can leverage the ever-
increasing marketing datasets to understand
the buyers’ journey and the incremental
sales, revenue and profits generated. Many
companies choose to assess their MROI by
building marketing mix models or market
response models (see Hanssens et al.
for an
elaboration). Such models should explicitly
incorporate marketing phenomena that
have important consequences for MROI,
nonlinear response effects, in particular
concave and S-shaped response;
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interactions among the marketing mix
sales impact that is distributed over time
(so-called carryover effects);
non-zero sales with zero marketing
Ideally, although rarely, these models should
also include competitive spending as well as
competitive reaction to changes in the firm’s
These considerations could result in
complex response models that may fit sales
data well, but are tedious to interpret for
marketing managers. Fortunately, relatively
simple response models, such as the
multiplicative (Cobb–Douglas) function
from economics, exist that can meet the
criteria above and still result in easy-to-
interpret measures of marketing lift. The
most common of those is the response
elasticity e:
% change in sales
% change in marketing spen
e =
So for example e(advertising) = 0.08 means
that a 10 per cent increase in advertising
spend results in a 0.8 per cent increase in
sales [(10) × (0.08)], all else being equal.
Elasticities can be shown to be estimated
directly from a multiplicative model. If
an S-shaped response is suspected (which
is common, but involves more than one
elasticity value), a model specification test
can be run on the data at hand (see, for
example, Hanssens and Dekimpe
for the
Response elasticity is a measure of top-line
lift due to marketing, which is the basis for
MROI calculation. Numerous studies in
marketing science have resulted in various
empirical generalisations; for example,
advertising elasticity averages 0.1, but is
much higher for new products relative to
established products, and sales calls have an
average elasticity of 0.35 (see, for example,
Importantly, marketing elasticity and
MROI are not the same, as one is a top-line
and the other a bottom-line impact measure.
They are, however, connected via the well-
known Dorfman–Steiner theorem (discussed
in Hanssens et al.
) for optimal marketing
spending, where optimal means profit
maximising. Illustrated here for the simple
case of two marketing spending categories,
say, television advertising (TV) and paid
search advertising (PS), the Dorfman–Steiner
theorem specifies that allocations that follow
the simple ratio
TV e(TV)
PS e(PS)
results in maximum profits. At that spending
level, the marginal MROI for the two
media will be equal to zero: at the margin,
spending fewer dollars on TV or PS will
result in the brand ‘leaving money on the
table’, and spending more will result in profit
loss (despite possible sales gain). Dorfman–
Steiner also show that the optimal budget
corresponding to these allocations will be
TV = e(TV) × gross margin
PS = e(PS) × gross margin
So, if the gross margin of a brand is
50 per cent and the TV elasticity is 0.08, the
optimal TV spend = (0.50) × (0.08) =
4 per cent of sales. At that spending level, the
marginal MROI will be zero.
Naturally, response elasticities can be
extended to represent long-term impact
rather than short-term sales impact. This can
be achieved in two different ways:
1. Change the performance metric to a
metric that is intrinsically long-term
oriented, such as brand equity and CE.
Some of the case studies in this paper
will use CE as a long-run brand health
metric. If reliable external estimates of
brand equity are available, then brand-
response elasticities may be derived as
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2. Infer the long-term impact of marketing
on sales econometrically. For example, if
a doubling of advertising lifts sales by
10 per cent in the short run (ie elasticity
= 0.1), and half of that increase becomes
permanent (eg due to newly gained
customers becoming brand loyal), then
the long-term response elasticity would
be 0.05. Various time-series methods
discussed in Hanssens et al.
may be used
for this purpose. Naturally, since the
time horizon now extends well into the
future, it is advisable to discount the future
sales lifts so as to obtain a net present value
estimation of marketing impact.
In conclusion, in many cases MROI is
derived from individual business events,
as illustrated in the scenarios above. So
long as the causal connection between
input (marketing) and output (sales and
the other components in the sales funnel
and the follow-on impact to the firm)
is unambiguous, this is fine, at least for
evaluating the ROI of historical campaigns.
When it comes to planning future marketing
efforts, however, we need sales projections
with and without the marketing investment,
and that requires either A/B testing (which is
a form of test marketing), or formal statistical
models of brand demand. The latter can be
used, not only for MROI estimation, but also
for sales forecasting and determining optimal
marketing allocations. As we shall see below,
profit maximisation is quite different from
chasing high MROI.
Companies need to maximise both short-
term profits and long-term value. The vast
majority of marketing spend is directed
toward driving profitable sales volume in
current and upcoming years, while a portion
is directed toward building long-term assets.
Questions and concerns on the use of ROI
have been raised by experts such as Ambler,
who stated that ‘ROI is a useful way to
choose the preferred options for the
marketing mix when the total budget is fixed …
but the concept is seriously misleading
when it is used more broadly’. Rust et al.
(p .79)
write ‘maximization of ROI as
a management tool is not recommended
(unless management’s goal is efficiency
rather than effectiveness), because it is
inconsistent with profit maximization –
a point that has long been noted in the
marketing literature (e.g., Kaplan and
Shocker 1971
)’. These shortcomings
can be overcome with the right approach
demonstrated here.
Marketing ROI provides a measure
of profit contribution relative to the
marketing amount invested. This ratio has
advantages over fixed-value outcomes such
as discounted cash flow or net present value,
which do not differentiate between a net
profit gain of US$500,000 generated from
a marketing investment of US$200,000
or US$1m. The missing step required
to make MROI measures relate to profit
maximisation is assessing incremental or
marginal ROI, as shown in the simple
example that follows.
In the example shown in Table 2a, a
company must decide if they should increase
their marketing spend from US$400,000 to
US$600,000, a level where measured profits
(after accounting for the marketing costs)
increase but ROI decreases. They have a
marketing ROI threshold (ie minimum ROI
required) of 50 per cent. When comparing
Option A to Option B, the additional
marketing spend shows an opportunity
to increase profits, even though total ROI
While total MROI cannot be set as the goal,
the ROI process can be used for maximising
profits based on using incremental ROI
measures, along with a total ROI threshold as
applied to other spending in the organisation.
This is accomplished by calculating the
incremental ROI as shown in Table 2b.
The ROI of the incremental US$200,000
investment shows a return of 100 per cent.
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This additional spend might be dedicated
to increasing media impressions, including
a financial offer or adding another tactic to
an integrated campaign. Based on the ROI
threshold of 50 per cent, this incremental
investment meets that objective and therefore
the ‘spend’ is justified. The evaluation process
can continue with an assessment of the next
increment of spend, as shown in Table 2c.
In this example, each increment of spend
increases profits while decreasing ROI. The
increment of spend from Option B to
Option C does not meet the ROI threshold
and is therefore rejected. The incremental
ROI measure indicates the point where the
ROI threshold is no longer being met (ie the
point of diminishing returns, as shown in
Figure 1).
Ironically, Option C spending from this
example might have proved very valuable
in producing an ROI far in excess of the
threshold, had Options A and B not already
occurred, as the response function would
have been at an earlier stage of the response
function (see Figure 1). If one considers
Option C as the next incremental spend,
however, its ROI would not be sufficient at
this stage.
Marketing ROI measures work well for the
various valuation methodologies presented,
where marketing impact can be captured
as the net present value of future profits (or
adapted to cost savings for the comparable
cost methodology). Companies should
standardise their own ROI calculation and set their
ROI threshold so there is clear agreement on when
marketing contribution achieves break even or the
amount that could be earned via other expenditures
and when marketing meets financial success criteria.
We recognise that calculating the incremental
return for the next marketing spend may be
difficult. NPV (net of marketing costs), on
the other hand, is a direct contributor to the
bottom line (ie not a percentage), and may be
more usable in practice.
Marketing that generates long-term assets
can use ROI measures by comparing future
asset value or the projected cash flow from
those assets to spend. As illustrated in our
previous scenarios, however, these ROI
measures include only costs that are directly
associated with the marketing activity
Table 2: Example of assessing incremental ROI
(a) Option A Option B
Marketing spend $400,000 $600,000
Incremental profi ts $1,000,000 $1,400,000
ROI 150% 133%
(b) Option A Option B Incremental (Option B–A)
Marketing spend $400,000 $600,000 $200,000
Incremental profi ts $1,000,000 $1,400,000 $400,000
ROI 150% 133% 100%
(c) Option B Option C Incremental (Option C–B)
Marketing spend $600,000 $800,000 $200,000
Incremental profi ts $1,400,000 $1,620,000 $220,000
ROI 133% 100% 10%
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© Henry Stewart Publications 2054-7544 (2015) Vol. 1, 3 267–282 Applied Marketing Analytics
to be evaluated. Furthermore, allocating
today for profits tomorrow always involves
assessing risk and the time value of money,
both of which require adequate returns.
These requirements are reflected in the
MROI hurdle rates that firms should set
and use. Economic profit metrics that take
into account shareholder value or balance
sheet assets will have advantages for asset
Specifically, brand and customer assets
generated by marketing will often require
other investments to convert them to sales,
revenue and profits. This could require new
product development, new infrastructure to
support a larger customer base, additional
marketing investment and perhaps more
sales people. Those investments increase the
hurdle rate, or return required of marketing,
and will almost always require a dialogue
with the CFO to align marketing spending
and the targeted return on marketing with
the company’s cost of capital.
The widely employed financial metric,
economic profit (EP) is one way this
alignment might be achieved. EP is defined
as follows:
EP = net operating profit after tax (NOPAT)
(capital employed
× weighted average cost of capital)
Importantly, EP is denominated
in currency, not percentages. Instead
of dividing profit by capital employed
(investment), a cost of capital is subtracted
from NOPAT. This focuses on operating
profit as opposed to extraordinary income.
The cost of capital reflects the company’s
financing strategy as well as the risks for
investors (cost of equity) and volatility
compared to the overall market. The
important point, however, is that economic
profit rewards growth as long as the rate of
profitability exceeds the capital investment
required to support that growth. McKinsey
recently singled out EP as the ‘strategic
yardstick you can’t afford to ignore’.
We propose that a similar EP metric is
appropriate for marketing, as follows:
Marketing EP = net dollar contribution
from marketing efforts
(marketing budgets
× targeted return on
In this way, the return measure considers
both direct marketing costs and opportunity
costs due to the use of firm capital. As an
example, suppose that a high-technology
firm such as General Electric, and a
consumer goods firm such as Procter &
Gamble each invest US$250m in brand
marketing that lifts their respective income
streams by the same amount, due to
increased brand equity. If GE’s cost of capital
is higher than that of P&G, due mainly to
the nature of their business sectors, then
their marketing EP would be lower.
Each commonly used ROI measure we
have identified offers unique insight. It is,
however, necessary to know the conditions
under which each ROI measure should be
used, and to understand that not all ROI
metrics are comparable across the different
types of measures. Some observations are:
1. The further we move from estimating
incremental sales and profits due to
marketing and attempt to forecast long-
term future returns, in general, the higher
will be the risk associated with those
forecasts. As shown in Figure 2, however,
there may be a different degree of
uncertainty associated with assessing the
degree of the marketing-wrought change
in the relevant metric than there is in
placing a value on the change. It would
make sense that, with higher degrees
of risk, the threshold that needs to be
exceeded grows as well.
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2. When the purpose of estimating
returns is not only to evaluate past
performance, but also to improve
marketing productivity, more granular
estimates will inform the shifting of
funds from less to more productive mix
elements. This includes changing the
focus from ‘total’ to ‘marginal’ effect.
The latter may allow scaling back or
increasing investment in individual mix
elements to improve marginal and total
3. Knowing the potential effect of MROI
measures on marketing decisions can help
inform the scope, granularity and type of
valuation that is most appropriate to the
Certainty, timing costs and returns. Most
executives cannot wait until all the data
are available before trying to estimate the
MROI. In the case of certain e-commerce
transactions, the timing of marketing outlays
and incremental revenues generated can
be virtually instantaneous. By contrast,
recruiting, training and deploying a sales
force may take years and the resulting
impact will, therefore, take longer. In many
cases the outlays of marketing expenditures
are separated by a considerable amount
of time from the results that the spending
generates. Feedback, carryover and issues
of momentum play more important roles
over longer periods. Depending on the
time frame considered, we can expect
MROI calculations to vary. Forecasting
the future always involves uncertainty, as
do attributions of the present rooted in
historical analyses of marketing efforts. The
degree of uncertainty typically increases as
the time horizon expands, but other sources
of uncertainty can be market turbulence,
technological disruptions, competitive
actions or reactions, or any number of other
factors that companies list in their annual
reports. Applying mathematically rigorous
estimation techniques cannot always produce
estimates that have a high degree of certainty.
Disclosing that uncertainty in ways that
are transparent to those who rely on those
estimates is as much an obligation as is
doing our utmost to estimate marketing’s
contributions accurately.
Estimates of uncertainty will likely remain
in eye of the beholder, but full transparency
will inform that estimate. The uncertainty
may have two important implications for
management application of MROI.
Figure 2: Metrics potentially affected by marketing spending
Notes: Example — the value of sales generated from marketing efforts is usually relatively easy to calculate, but
deciding what portion of sales is truly ‘incremental’ may be more diffi cult to measure with precision.
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First, the higher the uncertainty the higher
the required MROI is likely to be, as is the
case for all financial investments. Secondly,
uncertainty metrics of estimates, such as
standard deviations, are needed in assessing
the MROI. A full development is beyond
the scope of this paper, although we will
return to this issue when spelling out future
work needed.
Metrics that are used for estimating
MROI vary in their difficulty to measure
as well as to value. Increases on either
dimension grow the uncertainty in the
resulting MROI estimates.
There are good reasons why marketers
should focus on measuring and improving
MROI. Firms need budgets for many
reasons, but controlling and predicting cash
flows is one of the main ones. Once budgets
are established, strict limits on spending for
marketing are very often specified. At this
point, the job of marketing is not just to
spend the money, but to constantly look
for way to spend it more efficiently and
Of course, maximising long-term
profits is often not simply a matter of
shifting funds from low ROI to high ROI
activities, because there may well be strategic
considerations not fully captured in the ROI
measures themselves. Examples are brand
building and new customer acquisition
versus the need for short-term sales,
balancing push and pull efforts to support
distribution channels, and targeting market
segments that are of strategic importance.
Some of these issues may also be clarified by
our distinctions of the different methods for
estimating marketing ROI.
There is also a need to more formally
report and asses s marketing risk and match
the estimates of risk to the required return
for marketing spending. Breaking even is
not enough, but how much more is largely
a function of the company’s strategic stance
toward a market, the depth of it pockets,
and perceived risk. This required ROI
hurdle rate should be reflective of the risk
associated with the investment as well as the
expected timing of returns if the valuation
method does not include the time-cost
of funds. Alternatively, one could simply
estimate a risk-adjusted return rather than
have different ROI threshold levels to reflect
the risk of different marketing campaigns
or budgets. These are long-term goals,
however. Finance as a discipline still struggles
to standardise the implementation of the
‘cost of capital’ (see especially Jacobs and
) and the lack of a single method
means that transparency will be required for
both progress and achieving trust from the
other fields and disciplines.
The authors wish to thank Eric Bradlow,
Daniel Kehrer, Koen Pauwels and Jeff
Winsper for their careful reviews and useful
suggestions for improving earlier versions of
this paper.
References and Notes
1. Kotler, P. (1971) ‘Marketing Decision Making: A
Model Building Approach’, Holt, Rinehart and
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3. Lenskold, J. D. (2003) ‘Marketing ROI: The Path to
Campaign, Customer and Corporate Profitability’,
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4. Farris, P. W., Bendle, N. T., Pfeifer, P. E. and
Reibstein, D. J. (2010) ‘Marketing Metrics: The
Definitive Guide to Measuring Marketing
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6. Rogers, D. and Sexton, D. (2012) ‘Marketing ROI in
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9. Equivalent language for these three concepts is:
(1) return on marketing investment (ROMI):
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(3) return on marginal marketing investment (ROMMI):
return on marginal marketing investment.
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(2001) ‘Market Response Models: Econometric and
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The Marketing Metrics to Pump Up Cash Flow’,
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pp. 70–72.
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Applied Marketing Analytics
The Peer-Reviewed Journal
Volume 1 Number 3
Denis Malin 196
Practice papers
Measuring the success of social marketing campaigns with web/digital analytics 198
Vaibhav Gardé
Discovering discovery: Data discovery best practices 206
Jim Sterne
New tools and techniques for understanding non-conscious consumer decisions 214
Darren Bridger and Thom Noble
Using survival analytics to estimate lifetime value 221
Mike Grigsby
The unified log: What it is and how it is changing marketing analytics 226
Yali Sassoon
Mining the gold in customer data to uncover your competitive advantage 237
Dr Amy Shi-Nash
Planning and implementing conversation-led marketing 243
Hagen Wenzek and Paul Pangaro
Academic papers
Media usage patterns of social media users 252
Vijay Viswanathan, Don E Schultz and Martin Block
Marketing return on investment: Seeking clarity for concept and measurement 267
Paul W. Farris, Dominique M. Hanssens, James D. Lenskold and David J. Reibstein
Book review
Humanizing Big Data: Marketing at the Meeting of Data, Social Science
& Consumer Insight 283
Reviewed by Chris Johannessen
Henry Stewart Publications
Russell House
28/30 Little Russell Street
London WC1A 2HN, UK
Henry Stewart Publications
North American Subscriptions Office
PO Box 361
AL 35201-0361, USA
011_AMA0028_Hanssens_1_3.indd 284011_AMA0028_Hanssens_1_3.indd 284 20-07-2015 12:17:51 PM20-07-2015 12:17:51 PM
... They were proven to be associated with achieving better performance results, which, however, depend on the employee (manager), the company, the type of industry and, of course, the way of deciding on the marketing mix. There is also a consistency between the results of the current study and the findings of Farris et al. (2015), in which the return on investment has a positive impact on the present value of future profits and meets the criterion of financial success. ...
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Marketing is one of the key elements of the success of all companies, including the wine sector. Given the importance of wine producers for agriculture, it is important to define and monitor key performance indicators in marketing (KPIs) for a successful stay in the market and a competitive position at home and abroad. Today, the increase in competitive advantage includes mainly marketing, innovation and information and communication technologies. New digital tools and innovations have changed the way we approached data and decisions. A modernly adapted and effective strategic marketing strategy represents for wine companies an understanding mainly of their possibilities as well as the possibilities to influence the customer. This article evaluates the key performance indicators in marketing (KPI) and its relationship and impact on the financial situation of wine producers in Slovakia. The research sample includes 80 respondents. We obtained the primary data through a questionnaire, which was filled in by the leaders of wine companies. We verified the accuracy by means of descriptive statistics and multiple linear regression and Kruskall-Wallis test. We have verified the reliability of the data with the Cronbach alpha test. We have formulated scientific assumptions for in-depth analysis: hypothesis 1 – assumes that key performance indicators have a significant influence on financial situation of selected companies, hypothesis 2 – the use of ICT in marketing is statistically related to the key performance indicators. The results showed a statistically significant impact of KPIs on the financial situation of companies. We have identified significance in customer satisfaction and loyalty, brand awareness and return of investment. However, we were unable to statistically confirm the impact of other indicators (sales growth, market share, gaining new customers). We also identified significant differences in the use of ICT in marketing with key performance indicators in hiring new customers and return on investment. This research contributes positively to the importance of brand building in the eyes of customers as well as customer service, building loyalty and satisfaction, which returns to the loyal approach of customers to the repurchase of wine products and provides advice for professionals. Return on investment helps in more accurate business decisions that can be used when purchasing new equipment (technology), hiring employees, or properly assessing the profitability of marketing strategies.
... Expressing sales as a function of spending variables with diminishing marginal returns [9] is one of the fundamental properties in an attribution or marketing response model. In practice, Han and Gabor [11], and Lewis and Wong [19] adopted a similar strategy for their budget allocation, bidding, and attribution. ...
Both Bayesian and varying coefficient models are very useful tools in practice as they can be used to model parameter heterogeneity in a generalizable way. Motivated by the need of enhancing Marketing Mix Modeling at Uber, we propose a Bayesian Time Varying Coefficient model, equipped with a hierarchical Bayesian structure. This model is different from other time varying coefficient models in the sense that the coefficients are weighted over a set of local latent variables following certain probabilistic distributions. Stochastic Variational Inference is used to approximate the posteriors of latent variables and dynamic coefficients. The proposed model also helps address many challenges faced by traditional MMM approaches. We used simulations as well as real world marketing datasets to demonstrate our model superior performance in terms of both accuracy and interpretability.
... To account for the direct and indirect value contributions of a content piece as well as for the dynamics involved in indirect value contributions, we follow mainstream ROI literature in marketing (e.g., Farris et al. 2015;Srinivasan and Hanssens 2009) and decompose the potential impact of content during both processes into attributional and performance metrics. Thus, we not only gain a more elaborate view of the value creation chain leading to the three revenue streams but also are able to plan and forecast the cash flow in relation to the platform portfolio. ...
Full-text available
For digital video subscription platforms, creating and managing content portfolios are critical to acquire new customers, retain existing customers, leverage cross-sales, and generate advertising revenues. We treat content portfolios as a form of pure bundling which may vary in composition and attractiveness over time. Therefore, evaluating the value contribution of each content piece is essential to manage a platform’s portfolio efficiently and to understand how a specific content piece contributes to the bundle’s attractiveness. In this article, we develop an ROI content-valuation framework for a digital film subscription platform. This framework describes how a single piece of content diffuses through consumers’ journeys and influences subscription fees through acquisition and retention as well as revenues from cross-sales and advertising. This conceptual approach allows us to address the heterogeneity across content and platform contingencies such as exclusive availability and platform specifics, and link them to revenue streams. Building on this framework, we offer avenues for future research andprovide potential lead performance indicators together with their operationalization, enablingall parties involved in the production, marketing, distribution, and sales of contentto determine the platform-specific value of a content piece.
... Los KPI's del marketing digital surgen como una necesidad de controlar y monitorear el gasto o su crecimiento producido en campañas de marketing, siendo capaz de desarrollar medidas confiables de la contribución del marketing en la rentabilidad de la empresa (Farris, Hanssens, Lenskold, & Reibstein, 2015), tienen cuatro propósitos que se encuentran ligados a generar información sobre el mercado, dichos propósitos son: establecer una marca, difundir información crítica, generar un mayor alcance entre la audiencia y crear un compromiso con el público u otras empresas (Neiger, y otros, 2012). ...
Full-text available
After the arrival of social networks and the interconnection that it caused among its users, the generation of businesses within these platforms was inevitable, the main one of them selling advertising, in Ecuador the sale of advertising through social networks amounts to a amount exceeding thirty million dollars only for companies in the commerce sector, therefore, this paper analyzes the efficiency of advertising spending through the condition of Dorfman-Steiner for eighty-eight companies in the commerce sector during 2018, generating indicators of Management for digital marketing (KPI's) using Machine Learning techniques for the processing of data from the social network twitter and relating them to the financial results of these companies during the same period, through a multiple linear regression. In the analysis performed, a significant effect was found by the indicators towards the Dorfman-Steiner condition for companies with a small number of tweets, the greatest effects found were given through the interaction between the indicators concluding that, to reduce the level Advertising spending should be aimed at the propagation and popularity of the content that is published, taking into account the quality of the content that is disclosed.
The perception of investors depends on the different behavioral traits. The stock market seems to be an exciting and exciting platform for investors to perform their investment activities (frydrych et al., 2014). The market has continuously grown over time in these years. However, investors are sometimes skeptical about investing because of the uncertainties and risks associated with the stock market. After going through many hurdles, the money earned over a long time is meant to be safe and risk-free. Therefore, the investors feel the risk in the stock market is high and fear investing in platforms like these. Awareness of the stock market is also essential for investing in suitable firms. There must be a fair understanding of the return on investments (roi) before investing a large sum of money into the firms (farris et al., 2015). As this is a hot topic among businesses, the study of the behavior of small investors concerning the stock market must be carried out to understand their perspective. Financial behavior is an important concept to understand if one wishes to analyze the perception of individual investors. It has been observed that some of the investors are dependent on self-made decisions while investing, whereas some are made aware by the different financial seminars or workshops. Therefore, this paper deals with empirical findings to establish a more profound understanding of the behavior of investors following the stock market. A sample of 117 small retail investors was surveyed through a structured questionnaire to know their perception of investing in the stock market and its impact on the investment process in India. The study concludes that there is a significant impact on investors' perception of investing in the stock market.
The article develops the topic of managing the marketing capability (MC) of business organizations (BO) in present radically transforming environment. The authors examine methodological problems of analysis and assessment of MC including gaps between the accelerating complexity of markets and the limited ability of business organizations to respond. The relationship between MC and actual market position of a company—its competitiveness—is considered. It was found that these methods need to be developed further taking into account new trends and circumstances, as well as the requirement for greater integrativity and consideration of the relevant factors that form the capabilities of business organizations. The proposed methodology for assessing the level of MC is based on 8 marketing sub-capabilities (MSCs): (1) the core concept of a BO marketing; (2) the functionality complex; (3) the type of marketing behavior; (4) the scope of digital marketing technologies of a BO; (5) the breadth of the marketing contour; (6) qualification of marketers and sales managers; (7) branding, incl. HR brand strength; (8) distribution network, availability of own retail chains, logistics system. Approbation of the methodology was carried out on the group of transport enterprises in Russia. The relationship between MC and target market parameters has been identified. Weak MCSs causing the capability gaps had been revealed.
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Aim: substantiation of methodical approaches to assessing the effectiveness of marketing developments to determine the quality of service to pharmacy visitors, the formation and evaluation of their loyalty and conducting research on the effectiveness of the implementation of these developments in the activities of pharmacies. Materials and methods: pharmacy reporting, logical, comparative analysis, grouping, content analysis, economic and statistical methods. Results. Methodical approaches to the evaluation of the effectiveness of marketing developments to determine the quality of service to pharmacy visitors, the formation and evaluation of their loyalty are substantiated. It is proposed to determine the results of marketing measures of social and psychological orientation taking into account the time lag (one year after implementation), and the interpretation of indicators through the prism of the relevant results of the control pharmacy, where no marketing developments were implemented (to eliminate seasonal fluctuations and other external factors). The average value of a check per year, the number of checks per year and the average value of a check in the month of the beginning of the implementation of these developments were chosen as indicators for assessing the effectiveness of marketing developments. It is shown that in pharmacies where marketing developments were implemented, the improvement of the researched indicators grew at a faster rate compared to the control pharmacy. Conclusions. Researches have shown the presence of socio-economic effect in pharmacies in the implementation of marketing developments to determine the quality of service to pharmacy visitors, the formation and evaluation of their loyalty. Key words: efficiency of marketing developments; measures of social and psychological orientation; time lag; control pharmacy; socio-economic effect.
Full-text available
A avaliação da performance de marketing é um tema vital em muitas empresas. Nesse contexto, uma questão fundamental consiste em como conceptualizar e operacionalizar, com rigor, os instrumentos que conduzam a formas mais holísticas e equilibradas de avaliar a performance de marketing, não apenas para efeitos de monitorização e melhoria, mas também para comunicar estratégias. Infelizmente, apesar da existência de um número crescente de métricas individualiza­das, os estudos de modelização do fenómeno apresentados na literatura são escassos e a grande maioria não é suportada empiricamente. O presente trabalho desenvolve e testa um modelo inte­grado de avaliação da performance de marketing baseado na filosofia e nos princípios do Balan­ced Scorecard. Os dados foram obtidos através de um questionário respondido por 107 empresas portuguesas. Os resultados, analisados com base no método de equações estruturais, confirmam a robustez teórica do modelo e sugerem a sua viabilidade para avaliar a atuação de marketing. Nesse sentido, este estudo constitui um aprofundamento da temática em causa e fornece um instrumento destinado a orientar a seleção de um conjunto relevante de indicadores que auxilie as empresas a basearem análises de tipo avaliativo e decisões sobre alocação de recursos.
This study addresses the following question: For a given managerial, firm, and industry setting, which individual metrics are effective for making marketing-mix decisions that improve perceived performance outcomes? We articulate the key managerial takeaways based on testing a multi-stage behavioral framework that links decision context, metrics selection, and performance outcomes. Our statistical model adjusts for potential endogeneity bias in estimating metric effectiveness due to selection effects and differs from past literature in that managers can strategically choose metrics based on their ex-ante expected effectiveness. The key findings of our analysis of 439 managers making 1287 decisions are that customer-mindset marketing metrics such as awareness and willingness to recommend are the most effective metrics for managers to employ while financial metrics such as target volume and net present value are the least effective. However, relative to financial metrics, managers are more uncertain about the ex-ante effectiveness of customer-mindset marketing metrics, which attenuates their use. A second study on 142 managers helps provide detailed underlying rationale for these key results. The implications of metric effectiveness for dashboards and automated decision systems based on machine learning systems are discussed.
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For too long, marketers have not been held accountable for showing how marketing expenditures add to share-holder value. As time has gone by, this lack of accountability has undermined marketers' credibility, threatened the standing of the marketing function within the firm, and even threatened marketing's existence as a distinct capa-bility within the firm. This article proposes a broad framework for assessing marketing productivity, cataloging what is already known, and suggesting areas for further research. The authors conclude that it is possible to show how marketing expenditures add to shareholder value. The effective dissemination of new methods of assessing mar-keting productivity to the business community will be a major step toward raising marketing's vitality in the firm and, more important, toward raising the performance of the firm itself. The authors also suggest many areas in which further research is essential to making methods of evaluating marketing productivity increasingly valid, reli-able, and practical.
Probably not, if your company is like most
To increase marketing's accountability, Journal of Marketing, Marketing Science Institute, and the Institute for the Study of Business Markets have advocated development of marketing metrics and linking marketing-mix activities with financial metrics. Although the marketing field has made progress, researchers have paid less attention to what drives managerial use of marketing and financial metrics and whether metric use is associated with marketing-mix performance. The authors propose a conceptual model that links firm strategy, metric orientation, type of marketing-mix activity, and managerial, firm, and environmental characteristics to marketing and financial metric use, which in turn are linked to performance of marketing-mix activities. An analysis of 1287 marketing-mix activities reported by 439 U.S. managers reveals that firm strategy, metric orientation, type of marketing-mix activity, and firm and environmental characteristics are more useful than managerial characteristics in explaining use of marketing and financial metrics and that use of metrics is positively associated with marketing-mix performance. The results help identify conditions under which managers use fewer metrics and how metric use can be increased to improve marketing-mix performance.
Marketing ROI: The Path to Campaign, Customer and Corporate Profitability
  • J D Lenskold
Lenskold, J. D. (2003) 'Marketing ROI: The Path to Campaign, Customer and Corporate Profitability', McGraw Hill, New York.