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Marketing departments are under increased pressure to demonstrate their economic value to the firm. This challenge is exacerbated by the fact that marketing uses attitudinal (e.g., brand awareness), behavioral (e.g., brand loyalty), and financial (e.g., sales revenue) performancemetrics, which do not correlate highly with each other. Thus, onemetric could view marketing initiatives as successful, whereas another could interpret them as a waste of resources. The resulting ambiguity has several consequences for marketing practice. Among these are that the scope and objectives of marketing differ widely across organizations. There is confusion about the difference between marketing effectiveness and efficiency.Hard and softmetrics and offline and onlinemetrics are typically not integrated. The two dominant tools for marketing impact assessment, response models and experiments, are rarely combined. Risk inmarketing planning and execution receives little consideration, and analytic insights are not communicated effectively to drive decisions. The authors first examine how these factors affect both research and practice. They then discuss how the use of marketing analytics can improve marketing decision making at different levels of the organization. The authors identify gaps in marketing's knowledge base that set the stage for further research and enhanced practice in demonstratingmarketing's value.
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Dominique M. Hanssens & Koen H. Pauwels
Demonstrating the Value of Marketing
Marketing departments are under increased pressure to demonstrate their economic value to the rm. This challenge is
exacerbated by the fact that marketing uses attitudinal (e.g., brand awareness), behavioral (e.g., brand loyalty), and
nancial (e.g., sales revenue) performance metrics, which do not correlate highly with each other. Thus, one metric could
view marketing initiatives as successful, whereas another could interpret them as a waste of resources. The resulting
ambiguity has several consequences for marketing practice. Among these are that the scope and objectives of
marketing differ widely across organizations. There is confusion about the difference between marketing effectiveness
and efciency. Hard and soft metrics and ofineand online metrics are typically not integrated. The two dominant tools for
marketing impact assessment, response models and experiments, are rarely combined. Risk in marketing planning and
execution receives little consideration, and analytic insights are not communicated effectively to drive decisions. The
authors rst examine how these factors affect both research and practice. They then discuss how the use of marketing
analytics can improve marketing decision making at different levels of the organization. The authors identify gaps in
marketings knowledge base that set the stage for further researchand enhanced practice in demonstrating marketings
value.
Keywords: accountability, marketing effectiveness, efciency, return on marketing investment, marketing value
assessment
The Difculty of Marketing Value
Assessment
I want marketing to be viewed as a prot center, not a cost
center.
A chief executive ofcer
I have more data than ever, less staff than ever, and more
pressure to demonstrate marketing impact than ever.
A chief marketing ofcer
Marketing is at a crossroads. Managers are frustrated by
the gap between the promise and the practice of effect
measurement, big data, and online/ofine integration.
Caught between nancial accountability and creative exibility,
most chief marketing ofcers (CMOs) do not last long at their
companies (Nath and Mahajan 2011). Top management has
woken up to the fact that their companies make multimillion-
dollar marketing decisions on the basis of less data and analytics
than they devote to thousand-dollar operational changes. Cus-
tomerand market data management, product innovation and
launch, international budget allocation, online search opti-
mization, and the integration of social and traditional media
are just some of the protable growth drivers that greatly
benet from analytical insights and data-driven action. Yet
marketing value assessment, dened as the identication
and measurement of how marketing inuences business
performance as well as the accurate calculation of return on
marketing investment (ROMI), remains an elusive goal for
most companies, which are struggling to integrate big and
small data and marketing analytics into their marketing
decision and operations.
Why is marketing value assessment so challenging? To
begin with, the term marketingrefers to several things: a
management philosophy (customer centricity), an organiza-
tional function (the marketing department), and a set of specic
activities or programs (the marketing mix). However, regardless
of the intended use of the term, marketing aims to create and
stimulate favorable customer attitudes with the goal of ulti-
mately boosting customer demand. This demand, in turn,
generates sales and prots for the brand or rm, which can
enhance its market position and nancial value. This sequence
of inuences has been termed the chain of marketing pro-
ductivity(Rust et al. 2004), as depicted in Figure 1.
As a result, marketing has multiple facets, some attitu-
dinal, some behavioral, and some nancial. However, the
relation between the metrics that assess these facets is com-
plex and nonlinear (Gupta and Zeithaml 2006), and their
average correlations are below .5 (Katsikeas et al. 2016). For
example, product differentiation tends to be associated with
higher customer protability but lower acquisition and re-
tention rates (Stahl et al. 2012). Similarly, online behavior
and ofine surveys yield different information to explain and
predict brand sales (Pauwels and Van Ewijk 2013). Likewise,
some attitudinal brand metrics (esteem, relevance, and knowl-
edge) are associated with higher sales but not with higher prices,
while others (energized differentiation) show the opposite
pattern (Ailawadi and Van Heerde 2015).
This makes it difcult for researchers to synthesize
ndings across studies of marketing impact, and it makes it
difcult for organizations to choose which metrics to rely on
Dominique M. Hanssens is Distinguished Research Professor of Marketing,
Anderson School of Management, University of California, Los Angeles
(e-mail: dominique.hanssens@anderson.ucla.edu). Koen H. Pauwels is
Professor of Marketing, ¨
Ozye˘gin University (e-mail: koen.pauwels@ozyegin.
edu.tr).
©2016, American Marketing Association Journal of Marketing: AMA/MSI Special Issue
ISSN: 0022-2429 (print) Vol. 80 (November 2016), 173–190
1547-7185 (electronic) DOI: 10.1509/jm.15.0417173
when making resource allocation decisions. For example,
advertising is only deemed nancially successful if its ability
to increase awareness results in higher sales and/or prot
margins.
Current efforts in marketing measurement often do not go all
the way in connecting metrics to each other. For instance, many
balanced scoreboards and dashboards do not tell managers how
their marketing inputs relate to customer insight metrics and to
product market performance metrics. Consistent with this notion,
in a personal communication, Lehmann uses the term ow-
boardsfor dashboards connecting metrics, while Pauwels (2014)
denes analytic dashboards as a concise set of interconnected
metrics. Indeed, reconciling multiple perspectives on marketing
value requires causality to be shown among marketing actions
and multiple performance outcomes (e.g., customer attitudes,
product markets, nancial markets; i.e., quantifying the arrows in
Figure 1). Connecting the metrics is especially challenging if data
and decisions exist in silos within the organization. However,
theconsumerorcustomeristhetargetandrecipientofall these
actions, the combination of which will create the consumers
attitude toward the brand and, eventually, his or her purchase
behavior. In assessing marketings value, we therefore pay close
attentiontotheintegration of marketing activities as they affect
consumer behavior. In this context, Court et al. (2009) argue that
the critical task is to describe the process that generates sales for
the rm and to identify the bottlenecks that impede protable
business growth.
In addition to relating performance metrics to each other (the
metrics challenge), these metrics also need to be connected to
marketing activity. Indeed, assessing marketing value requires
various demand functions that quantify how changes in mar-
keting activity inuence changes in these dependent variables
(e.g., with response elasticities). Demand functions are often too
complex for senior managers to intuitively understand and
FIGURE 1
The Chain of Marketing Productivity
Source: Rust et al. (2004).
Notes: EVA =economic value analysis; MVA =marketing value analysis.
174 / Journal of Marketing: AMA/MSI Special Issue, November 2016
estimate. Consequently, marketing analytics expertise is needed,
either in-house or through specialized suppliers, which in turn
creates an organizational challenge because those who practice
marketing tend to be different from those who measure it. A nal
necessity in marketing value assessment is effective communi-
cation within the organization, including to decision makers who
may not be uent in the technical aspects of value measurement.
Despite the challenges,the benets of marketing smarter
are substantial, as both academic studies and business cases
demonstrate. Even a small improvement in using marketing
analytics creates, on average, 8% higher return on assets to the
companies, compared with their peers (Germann, Lilien, and
Rangaswamy 2013). This benet increases to 21% for rms
in highly competitive industries. Organizations of any size and
in any industry have had a sustainable competitive advantage
from using marketing analytics. However, even the large U.S.
companies that participated in the CMO Survey (2016) report
that marketing analytics are used in only 35% of all marketing
decisions. This percentage is expectedly even lower for small
and medium-sized rms across the world.
The causality implied by the chain of marketing productivity
increases the pressure for good performance metrics, causal links
between metrics and marketing actions, and effective communi-
cation to demonstrate the value of a rms marketing. This article
discusses the challenges of obtaining those three things. We rst
provide a general overview, critically examining the knowledge
base and practice of marketing value assessment in organizations.
We then discuss marketing objectives and how they determine
the choice of marketing metrics. Next, we turn our attention to the
research methods that drive marketing value assessmentnamely,
the use of models, surveys, and experiments. Those methods have
generated several important ndings about marketing value. Then,
because marketing analysts and marketing decision makers are
typically not the same people, we examine ways of improving
how marketing value is communicated within the organization.
We conclude with a brief summary of current knowledge and
important areas for further research.
The Inuence of Marketing
Objectives on Marketing Value
Metrics
As organizations grow and marketing technologies evolve, mar-
keting tasks become increasingly specialized and complex. A
vice president for sales and marketing may be replaced by two
vice presidents, one for sales and another for marketing. Within
marketing, separate departments may focus on advertising and
customer service. Advertising itself may be divided into brand
and direct, ofine and online. Each of these people or depart-
ments is held accountable for increasingly focused business
objectives and performance metrics. In customer service, the
performance measure may be the Net Promoter Score, while
brandrecognitionscoresmaybeusedtogaugetheperformance
of the brand advertising team, and CPM (cost per 1,000 pros-
pects touched) may be used for the direct advertising team.
The result is an increasingly siloed marketing department
in which each specialized function has its own objectives,
with little consistency across functions. Another consequence
may be the imposition of inappropriate efciency metrics that
make marketing less impactful. In some cases, marketing
may be treated as an expense rather than an investment.
What is needed are guidelines for (1) reconciling different
marketing objectives, (2) distinguishing between marketing
effectiveness and efciency, (3) dening the scope of mar-
keting, and (4) distinguishing between marketing budget set-
ting and budget allocation.
Reconciling Different Objectives for Marketing
Among the multitude of objectives marketing managers aim
to achieve are gains in sales volume and growth, market
share, prots, market penetration, brand equity, stock price,
and a variety of consumer mindset metrics, such as awareness
and consideration. Table 1 presents an overview of the focus
of different performance assessments, their benets, and their
drawbacks.
Marketing scholars can no longer assume that prot
maximization is the sole goal of marketing (see Keeney and
Raiffa 1993). When Natter et al. (2007) optimized dynamic
pricing and promotion planning for a retailing company,
having initially agreed to maximize prots, their recom-
mendation of higher prices met with substantial resistance
from the purchasing managers, whose supplier discounts
depend on sales volume, and from local branch managers,
whoinsistedonkeepingamarketleadershippositionin
their city. After further discussion, they decided to combine
prots, total sales volume, and local market share objec-
tives in an overall goal function for the model to optimize.
The resulting model yielded recommendations that were more
acceptable to the managers, who successfully implemented
them.
Despite individual contributions such as Natter et al. (2007),
marketing academia and practice have not produced a set of
generalizable weights for using different objectives under dif-
ferent conditions. Instead, marketing practice tends to focus on
case studies of each companys unique situation and, within the
rm, on individual executivessiloed departments.
Further research should attempt to bridge marketing ob-
jectives and metrics across functional, geographical, and life
cycle boundaries. Bronnenberg, Mahajan, and Vanhonacker
(2000) provide a good example: they demonstrate that, in one
product category, consumer liking and distribution are dominant
success metrics for brands in the early phases of the category life
cycle, with pricing and advertising becoming important only
later. Similarly, Pauwels, Erguncu, and Yildirim (2013) show
that brand liking matters more in mature markets, but brand
consideration is more important in emerging markets. Research
should also investigate the optimal weighting of objectives on the
basis of hard performance measures, along the lines of research
that combines model-based and managerial judgment (Blattberg
and Hoch 1990). Recently, the notion that models should not
ignore human decision makers has reemerged within a big-data
context as algorithmic accountability (Dwoskin 2014). The goal
is to tweak social media classication algorithms not for max-
imum efciency but to avoid human-relations mistakes (Lohr
2015). A widely shared example is that of Target, which sent out
pregnancy-related coupons to teenagers for whom its algorithm
Demonstrating the Value of Marketing / 175
TABLE 1
Types of Performance Outcomes
Aspect of
Performance Advantages Disadvantages Considerations
Customer
mindset
Causally close (often closest) to
marketing actions
May be unique to marketing
performance outcomes vs. other
business disciplines
Commonly used to set marketing-
specic goals and assess marketing
performance in practice
Primary data may be difcult and costly
to collect if direct from customers
Secondary data from research vendors
may not align well with theorized
constructs or data from other vendors
Sampling: current customers versus
past customers versus all potential
customers in the marketplace
Possible demographic effects on
measures
Noise in survey measures (primary
and secondary data)
Only allows for goal-based assessment
if collected with or supplemented by
primary data
Transaction-specic versus overall
evaluations
Customer
behaviors
Causally close to marketing actions
May be unique to marketing
performance outcomes versus other
business disciplines
Commonly used to set marketing-
specic goals and assess
performance in practice
Direct observation shows revealed
preferences
Primary data may be difcult and costly
to collect if direct self-reports from
customers
Observed behavior data may require
working with rms and can be difcult to
collect from multiple rms
Differences across rms in how
observed behaviors are dened and
calibrated
Noise in survey measures (primary
data)
Only allows for goal-based assessment
if collected or supplemented by primary
data
Customer-
level
outcomes
Causally close to marketing actions
May be unique to marketing
performance outcomes versus other
business disciplines
Commonly used to set marketing-
specic goals and assess
performance in practice
May require working directly with rms
and may be difcult to work with multiple
rms
Differences across rms in how
economic outcomes are determined
and calculated
Only allows for goal-based assessment
if collected or supplemented by primary
data
Noise in survey measures (primary
data)
Product-
market-
level
outcomes
Causally close to marketing actions
May be unique to marketing
performance outcomes versus other
business disciplines
Commonly used to set marketing-
specic goals and assess
performance in practice
Unit sales data are difcult to obtain
from secondary sources for most
industries
Even rms in the same industry may
differently dene the markets in which
they compete
Higher level of aggregation, so may be
less diagnostic
How to dene the market
Only allows for goal-based assessment
if collected or supplemented by primary
data
Noise in survey measures (primary
data)
Accounting Well-dened and standardized
measures
Revenue-related items commonly
used to set marketing-specic goals
and assess marketing performance
in practice
Secondary data availability
For primary survey data, specic
items likely to have the same
meaning across rms
Corporate level, so may be further away
from marketing actions and less
diagnostic
Not forward looking
May undervalue intangible assets
Mostly ignores risk
Treats most marketing expenditures as
an expense
Potential differences between rms and
industries in their accounting practices,
policies, and norms
Differences in measures across
countries
Only allows for goal-based assessment
if collected or supplemented by primary
data
Noise in survey measures (primary
data)
Financial
market
Investors (and analysts) are forward
looking
May better value intangible assets
Finance theory suggests that
investors may be more goal
agnostic (but time frames and even
criteria may be goal related from the
rms perspective)
Secondary data availability
Corporate level, so may be further away
from marketing actions and less
diagnostic
Publicly traded rms only, which tend to
be larger
Difculties in assessing rms across
different countries (and nancial
markets)
May be subject to short-term
uctuations unconnected with a rms
underlying performance
Risk adjustment
Public/larger rm sample-selection bias
Assumes primacy of shareholders
among stakeholders, but this may not
be true in some countries
Assumes the nancial market is efcient
and participants are well informed of the
marketing phenomena being studied
Only allows for goal-based assessment
if collected or supplemented with
primary data
Noise in survey measures (primary
data)
Source: Katsikeas et al. (2016).
176 / Journal of Marketing: AMA/MSI Special Issue, November 2016
predicted pregnancy (Hill 2012). Marketing is in a unique
position to contribute to the debate on the use of such algo-
rithmic predictions by applying the rich existing literature on
quantifying the consequences of loss in customer goodwill and
estimating the probabilities of these loss scenarios.
Effectiveness and Efciency
When we understand the target objectives of decision makers,
a key question is whether they give primacy to effectiveness or
efciency in reaching these goals. Effectiveness refers to the
ability to reach the goal; efciency refers to the ability to do so
with the lowest resource usage. For instance, mass media ad-
vertising may be effective in reaching the vast majority of pro-
spective customers, but it is not very efcient, whereas online
advertising may be very efcient but not as effective because
it reaches fewer prospective customers.
The value of marketing can be expressed in terms of either
effectiveness or efciency. Return on marketing investment
deals with efciency. When efciency is the goal, the result is
almost always a budget reduction through the elimination of
the least efcient marketing programs. However, the rm may
be more interested in the effectiveness of a marketing action,
which may be better expressed as return minus investment,
without dividing by the investment as in the standard return
on investment (ROI) formula from nance. As an illustration,
consider two mutually exclusive projects (e.g., alternative ad
messages aimed at the same segment), with returns of $100
million and $10 million, respectively, and investment costs of
$80 million and $2 million at the same level of risk. The rst
project has the larger net return ($20 million is greater than $8
million), but the second project has the larger ROI (25% is less
than 400%). Which project should a manager prefer?
The trade-off between effectiveness and efciency is par-
ticularly salient when there is a conict between short-term and
longer-term goals. Price promotional tactics, for example, may
be optimized for their short-term protability, but the repeated
use of such tactics is known to erode brand equity over a longer
time span (Mela, Gupta, and Lehmann 1997). Efciency-driven
marketing decisions should be supported only when they do not
jeopardize the long-term viability of the brand.
Ultimately, rms want to strike a balance between effec-
tiveness an d efciency goals. To accomplish this, beverage
company Diageo displays marketing actions on a 2 ·2matrix
that juxtaposes their effectiveness (on dened objectives) with
their efciency (ROMI). Actions without sufcient effective-
ness are likely to be canceled, no matter how high their ROMI,
while effective but inefcient actions are reexamined to improve
efciency in the future (Pauwels and Reibstein 2010). A
company may benet from instituting a threshold return value
that marketing programs must achieve to be supported. Ex-
amples of such thresholds are the rms cost of capital and
its economic prot (Biesdorf, Court, and Willmott 2013).
Research is needed to establish what the thresholds for impact
and efciency should be.
Beyond dening and relating multiple objectives, we
also need to conceptually and empirically relate effective-
ness and efciency in reaching these objectives. Measuring
the effectiveness or the efciency of marketing is not an easy
task. It is important to measure not only the percentage return
of any spending amount but also its magnitude. Conceptual and
empirical models of marketing effectiveness show diminishing
returns (e.g., Kireyev, Pauwels, and Gupta 2016; Little 1979),
implying that ROI (efciency) is maximized at levels of
marketing spending that are below protmaximizing(effec-
tiveness) (Pauwels and Reibstein 2010). We propose that the
goal should be to maximize the total effectiveness when a
certain threshold is achieved, even if that reduces the overall
efciency (Farris et al. 2015). However, our proposal may be
more applicable to large organizations, which have plenty of
resources and opportunities, than to small ones. Further re-
search is needed to determine the best mix of effectiveness
and efciency for smaller organizations and in dire times.
The Scope of Marketing Within the Organization
The scope of marketing is one of the key determinants of its
objectives and of the effectiveness/efciency decisions that the
marketing department makes (e.g., Webster, Malter, and
Ganesan 2003). In some organizations, the marketing depart-
ment is only responsible for a subset of the marketing mix, such
as executing advertising campaigns and running sales promo-
tions. Marketing decision makers are typically more junior in
such organizations. Pricing, distribution, and product decisions
are made elsewhere in the organization, by more senior decision
makers. In our experience, this situation is typical in emerging
countries, in engineering-dominated companies, and in
business-to-business industries.
At the other extreme, a few organizations consider the
marketing department to be the true protable growth driver
and both hold it accountable for protable growth and
provide it with the necessary resources and authority to
achieve it. Examples include Procter & Gamble and Diageo,
which are marketing-dominated companies in business-to-
consumer industries (Pauwels 2014). Most companies fall
somewhere between these extremes; they may hold mar-
keting responsible for pricing, promotion, and branding, but
not for creating successful new products (which is often the
domain of research and development or a new product de-
velopment group) or expanding distribution (which is often
the domain of the sales organization).
The scope of marketing also has a major impact on the data
collection that underlies marketing value assessment. The
broader the scope, the more variables are included in marketing
databases and, generally, the lower the level of granularity of
these databases. For example, digital attribution models have
a very narrow scope (determining which combination and
sequencing of digital media impressions produces the highest
consumer response) but can be executed daily or even hourly
(see, e.g., Li and Kannan 2014). In contrast, complete marketing-
mix models that include product innovation and sales call metrics
in addition to various marketing communication and sales pro-
motion variables are typically executed monthly or weekly. The
latter, however, assign a much broader responsibility to marketing
than do the former. At the same time, greater data granularity
necessitates more advanced econometrics. A detailed discussion
of econometric advances in market response modeling is beyond
the scope of this article and may be found in Hanssens (2014).
Demonstrating the Value of Marketing / 177
How has academic research advanced the understanding
of the importance of marketing scope? Far too little, argue
Lee, Kozlenkova, and Palmatier (2015). In a recent review,
they call for structural marketing: explicit consideration of
organizational structure when assessing the value of mar-
keting. They hypothesize that moving to a customer-facing
structure increases effectiveness but reduces efciency in
obtaining data on how products perform. A few academic
articles have investigated whether a more customer-focused
organizational structure induces a market orientation, with
mixed ndings. Likewise, the 2015 Marketing Science Institute
conference on Frontiers in Marketingfeatured several man-
agement questions and comments on the costbenet trade-offs
of customer-focused teams.
Our recommendation is twofold: we agree with Lee,
Kozlenkova, and Palmatiers (2015) call for more research on
the impact of organizational structure on market-related out-
comes, but we would also like to see more attention paid to the
relationship between marketing performance and marketing
scope. To what extent does excellent performance help mar-
keting increaseits scopeand get it a seat at the table(Webster,
Malter, and Ganesan 2003)? Or is it the communication of such
performance (i.e., marketing the marketing department) that
matters most? Because the answer may depend on the industry
and company setting, we recommend further research on the
boundary conditions of the interplay between organizational
structure, marketing actions, and performance outcomes.
Marketing Decisions: Budgets or Allocations?
It is important to know whether marketing actions are con-
sidered tactical or strategic in assessing their value. Broadly
speaking, managerial decisions are either budget (investment)
or allocation (execution) decisions (Mantrala, Sinha, and
Zoltners 1992). For example, a CMO receives a $100 million
budget from his or her CEO, for whom this $100 million
represents an investment. The CMO allocates this budget to
traditional media, digital media, and sponsorships. The owners
of these three marketing groups make subsequent allocation
decisions for their respective (smaller) budgets, and so on.
Setting aside prevailing accounting standards that generally
force these allocations to be expensed in the spending period,
any marketing investment decision becomes an allocation
decision one level down in the hierarchy.
The deeper in the organizational hierarchy one goes, the
more tactical the allocation decisions become, and the more
junior the decision makers are. For example, the decision
to advertise on channel 4 rather than channel 7 is tactical
relative to the higher-order decision to allocate 40% of the
marketing budget to television advertising. At the same
time, the deeper one goes in the hierarchy, the more detailed
the available databases are and, therefore, the more opportu-
nity for analytics-enhanced decision making. Such tactical
decisions lend themselves to continuous data collection and
decision automation, which is a decentralizing force in the
organization (Bloom et al. 2014). However, analytics and
decision support systems should support the different decision-
making modes of optimizing (typical for very structured,
tactical marketing problems), reasoning, analogizing, and
creating (typical for more strategic marketing problems)
(Wierenga and Van Bruggen 2012).
Academic research in marketing has tended to focus
on tactical decisions rather than on strategy. For example,
product line and distribution elasticities are at least seven
times higher than advertising elasticities, which makes them
strategically more relevant (Ataman, Van Heerde, and Mela
2010; Shah, Kumar, and Zhao 2015), but the abundance
of data on the latter has resulted in many more academic
publications on advertising effects than on distribution or
product line effects on business performance. This tendency
is amplied by the increased availability of micro-level mar-
keting data, especially in digital marketing.
Academic research specically on strategy versus tactics
has focused mainly on the relative merits of setting the budget
size or allocating a given budget (e.g., Mantrala, Sinha, and
Zoltners 1992). More recently, Holtrop et al. (2015) show that
competitive reactions on a strategic level differ substantially
from reactions at a tactical level. Interestingly, strategic
actions (presumably by senior managers) follow marketing
theory expectations, whereas tactical actions (presumably by
junior managers) often violate research recommendations
by (1) retaliating when unwarranted and with an ineffective
marketing instrument and (2) accommodating with an effective
marketing instrument. Manchanda, Rossi, and Chintagunta
(2004) obtain similar ndings. Both articles focus on the
pharmaceuticals industry; their important results regarding
suboptimal marketing resource allocations are in need of
replication in different sectors.
In marketing practice, the focus on marketing tactics
benets the organizations accountability and protability
but rarely creates sustained business growth, which is a
more strategic objective. For business growth, product and
process innovation become more important, as evidenced
by empirical work demonstrating the positive impact of inno-
vation on rm value (e.g., Sorescu and Spanjol 2008).
Analytics in the product innovation area has focused
mainly on measuring consumer response to new product
offeringsin particular, using conjoint analysis. The internal
customer of such work is typically the product development
group, which is a separate entity from marketing, with a
separate budget. As a result, the insights from one function are
rarely incorporated in the other; for example, the results from
conjoint analyses (used by the product development group) are
typically not included in marketing-mix models (used by the
marketing group). The critical element of product appeal (e.g.,
conjoint utility) may therefore be missing from demand models,
resulting in lower-quality sales forecasts.
A powerful illustration of the strategic importance of in-
novation is in investor reactions to new product launches, as
measured by stock returns. Not only is investor reaction
typically positive, despite the costs and the risk involved, but
it occurs well ahead of the typical diffusion pattern of the new
product. As an example, when Honda introduced the sunken
third-row seatinnovation in its minivan, the Odyssey, the
innovation effect was fully absorbed in its stock price in
approximately 12 weeks, whereas the sales diffusion of the
product is much longer. One can surmise that investors realize
the nancial value of such an innovation after the rst few
178 / Journal of Marketing: AMA/MSI Special Issue, November 2016
weeks of positive consumer feedback and then assume that
the marketing of the innovation will be well executed, so
that the new product can reach its full market potential (Pauwels
et al. 2004).
We recommend a broad denition of marketing in the
organization and a commensurate broad inclusion of business
functions in the generation of demand models for marketing
resource allocation. This task can be complex because data
from a variety of sources need to be combined in an integrated
data and analytics platform. Importantly, such a platform can
become the much-needed integrator of intelligence for senior
management decisions and, as such, a centralizing force in the
modern enterprise (Bloom et al. 2014). This means that the
same strategic assetthe data and analytics platformserves
as both a centralizing (of intelligence) and a decentralizing
(of execution) force, whereby both directions offer tangible
advantages to the rm.
Methods and Findings About
Assessing Marketing Value
Marketing value measurement has both a methodological
and a knowledge component. We focus on these two here,
leaving the third component, communication of marketing
value, to the next section.
Methods: Models, Surveys, and Experiments
Marketing impact can be assessed empirically in two ways:
by modeling historical data (secondary data) and by running
surveys and experiments (primary data). Both methods have
their proponents and advantages; however, neither is typically
sufcient by itself to convince decision makers of the value of
marketing and to induce change in marketing decision making.
The use of historical data sources has beneted tremen-
dously from improvements in consumer and marketing
databases and from developments in statistics (mainly
econometrics) and computer science. On the data side, recent
history has seen the emergence of scanner databases; customer
relationship management databases; and digital search, social
media, and mobile-marketing databases. On the modeling side, a
steady stream of econometric and computer science advances
has delivered the improvements in estimation methodology
necessary to deal with these novel data (Hanssens 2014; Ilhan,
Pauwels, and K¨
ubler 2016; Murphy 2012).
Criticism of models estimated on historical data stems
mainly from their limitations in capturing reasons why(as
shown in surveys) or causal connections (as shown in exper-
imental manipulations). A survey may show that one consumer
visited the brands website for reasons of purchase interest,
whereas another visited to rationalize his or her choice for a
competing brandinformation not obtainable from models
estimated on historical data.
In particular, the two geneities(heterogeneity and endo-
geneity) are challenging for marketing modelers. Heterogeneity
(i.e., differences in response to marketing among consumers)
has been addressed successfully thanks to simulated Bayes es-
timators (for a comprehensive review, see Rossi, Allenby, and
McCulloch 2005). Endogeneity (i.e., the existence of decision
rules in marketing that may bias the results of statistical response
estimation) continues to pose major challenges, which are dis-
cussed in Rossi (2014). However, as marketing databases be-
come more granular (monthly data intervals become weekly,
daily, hourly, or even real time), the endogeneity challenge is
easier to handle because the response models become more
recursive in nature. In these higher-frequency databases, atten-
tion shifts to long-term impact measurement, in particular the
testing for persistent effects, for which modern time-series
techniques are readily available (see Hanssens, Parsons, and
Schultz 2001; Leeang et al. 2009).
Field experiments, by contrast, require customers and/
or managers to react to an intervention at the time of data
collection and allow for a direct comparison of treatment and
control conditions, thereby removing concerns about endo-
geneity. Unfortunately, eld experiments are often costly to
conduct, limited to changing only one or a few decision vari-
ables at a time, and require trust in the organization that dis-
appointing outcomes will not be held against the manager.
For example, managers and salespeople often object to being
part of the control group for a potentially impactful marketing
action. Even online, where experiments are relatively easy to
implement, companies often refuse to do so (Ariely 2010).
Finally, marketing experiments are run for a limited amount of
time and therefore are typically unable to detect long-term
effects of a particular marketing action. Exceptions include
longitudinal single-source eld experiments (e.g., Lodish
et al. 1995) and digital-marketing experiments in which,
under the right circumstances, subjects can be tracked dig-
itally after the experiment has concluded in order to infer
long-term effects.
The best insights on marketing value will come from the
combined use of secondary and primary data. Indeed, taken
together, models, surveys, and experiments provide the ben-
ets of highest decision impact at a moderate cost and risk. Yet
what is the best sequence? In our experience, a eld experi-
ment on a strategic decision is perceived as too risky without a
model or survey to justify the treatment proposal. For instance,
furniture company Inofec (Wiesel, Arts, and Pauwels 2011)
rst had analysts run a response model based on historical data.
After simulating potential scenarios based on the model output,
management decided to double spending on one marketing
channel (paid search) and to halve it on the other (direct mail).
In the ensuing eld experiment, the treatment condition earned
14 times the net prot earned by the control condition.
Modeling the data of the eld experiment revealed that paid
search continued to yield high returns but that the reduced
direct-mail budget began to break even. As a result, the
company further experimented with increasing paid search
butkeptdirectmailatitsnewlevel.
In situations in which both approaches are feasible,
we recommend the sequence of model, experiment, model,
experiment (MEME) to obtain the maximum impact of
analytics-driven decision making. At the same time, sur-
veys and other methods should be used to provide insight
into the whyand howof customer behavior. Further
research should analyze whether the MEME sequence is
the most productive across situations, consider other possible
sequences, and establish boundary conditions. Regardless of
Demonstrating the Value of Marketing / 179
the method used, a critical question for management is whether
market conditions will have changed by the time the actual
decision is made. The beliefs that change outpaces analytic
insights and that past patterns do not apply to the future hinder
the use of marketing analytics in many organizations.
Findings on Marketing Investments and Allocations
Previously, we discussed investments and allocations in
terms of their relationship to strategy and tactics. Next,
we discuss ndings more broadly. Table 2 shows dif-
ferences between allocation and investment decisions on
several fronts. Managers and academics are keenly interested
in decision rules for both, as is evident from the fact that this
topic appears frequently among the biennial research priori-
ties disseminated by the Marketing Science Institute.
Notably, most applications in marketing analytics (includ-
ing analytics exploiting big data) focus on the deep dive for
tactical allocations (see Table 2). Insofar as these contributions
overemphasize areas in which good data are readily available,
they run the risk of being bogged down in details and failing
to see the forest for the trees. In contrast, when complete
marketing-mix data are used along with econometric methods
for inferring long-term impact, marketing analytics can also be
very helpful for strategic investment decisions and for quan-
tifying risk in such decisions (e.g., Leeang et al. 2009).
In academic research, empirical generalizations on sales
response functions provide valuable guidance for marketing
spending (Hanssens 2015). Table 3 provides a quantitative
overview, expressed as sales or market value elasticity esti-
mates. These relate directly to marketing spending rules by
virtue of the fact that, at optimality, a rm should allocate re-
sources in proportion to its response elasticities (Dorfman and
Steiner 1954). Table 3 also indicates the extent to which the
marketing variable is an organic growth driver (i.e., its impact
on sales is sustained rather than temporary). This is an im-
portant distinction because it identies the strategic nature of
marketing activities. Although price promotions and adver-
tising for existing brands (which often consume the majority of
marketings budget and effort) are not major organic growth
drivers of company performance, marketing assets (e.g., cus-
tomer satisfaction, brand equity) and actions (e.g., distribution,
innovation) have a strong impact on long-term company value.
In an example from the French market, Ataman, Van Heerde,
and Mela (2010) demonstrated across 70 brands in 25 con-
sumer product categories that only breadth of distribution (.61)
and length of product line (1.29) had strong long-term sales
elasticities. By contrast, long-term elasticities of advertising
(.12) and sales promotion (.04) were small or negative.
At this point, generalizationsexpressed as response
elasticitiesexist for many quantiable marketing inputs,
TABLE 2
A Comparison of Allocating and Investing Marketing Resources
Allocating Investing
Resources Budget is received from senior management Budget is created for junior management
Objectives Efciency, accountability of resource use Stimulating protable growth for the brand or rm
Use of analytics Detailed analysis of (typically) one marketing-mix
element
Integration across the marketing mix
Key challenges/risks Exaggerated belief in the strategic importance of
ones own silo
Large nancial consequences
Examples Media-mix allocations
Dynamic pricing
Product portfolio decisions across international
markets
TABLE 3
Response Elasticities Summaries
Typical
Elasticity Range Drivers (1)
Organic Growth
Driver?
Advertising .1 0 to .3 Product newness, durables Minor
Sales calls .35 .27 to .54 Early life cycle, European markets Major
Distribution >1 .6 to 1.7 Brand concentration, high-revenue categories,
bulky items
Major
Price -2.6 -2.5 to 5.4 Stockkeeping unit level versus brand level, sales
versus market share, early life cycle, durables
Minor
Price promotion -3.6 -2to12 Storables versus perishables No
E-word of mouth Positive .24 (volume) Low trialability, private consumption, independent
review sites, less competitive categories
Possibly
.42 (valence)
InnovationaPositive N.A. Radical versus incremental innovations Major
Brand and customer
assetsa
.33 (brand) Major
.72 (customer)
aOn rm value.
Source: Hanssens (2015).
Notes: N.A. =not applicable.
180 / Journal of Marketing: AMA/MSI Special Issue, November 2016
along with expected ranges and distinctions between short-
term and long-term effects on sales. It is also apparent that
rms generally deviate from optimal (prot-maximizing)
spending in the marketing mix (i.e., they either over- or
underspend). However, because the spending objectives of a
rm or brand at any point in time are typically not known to
the researcher, this conclusion about apparent suboptimality
in spending remains tentative. One important conclusion that
can be drawn from Table 3 is that marketing communications
(i.e., advertising and sales calls) have the lowest elasticities.
Their relatively at response curves imply that they are un-
likely to be the sole drivers of major performance change.
However, when combined with one or more of the other
marketing-mix elements, their impact can be substantial. For
example, a recent study of high-level digital cameras dem-
onstrated that when a camera brand receives highly positive
reviews, advertising can have positive trend-setting effects
on brand sales (Hanssens, Wang, and Zhang 2016). During
these eeting windows of opportunity, the combination of
high perceived product quality and advertising produces
long-lasting impact that neither driver can achieve by itself.
Such ndings illustrate that the timing and sequencing of
marketing initiatives can be determining factors of their
impact.
Recent research has identied conditions in which the
most value is generated, such as distribution in emerging
countries (e.g., Pauwels, Erguncu, and Yildirim 2013), new
product launch during recessions (e.g., Talay, Pauwels, and
Seggie 2012), and owned (vs. paid online) media for lesser-
known products and for services (Demirci et al. 2014). We
call for further research on these and other inuential market
conditions.
Researchers should not only help companies identify their
response functions but also derive where on the function
companiescurrent spending lies. This enables rms to deter-
mine whether to allocate more or less to various marketing
activities than in previous years. Mantrala et al. (2007) demon-
strate this for the publishing industry. An alternative approach
is to run marketing experiments to assess alternative levels of
expenditure and different programs and their resulting impact.
This was done, for example, by the U.S. Navy to determine
optimal levels of recruiters and advertising support to reach its
manpower goals (Morey and McCann 1980). More recently,
the advent of the digital marketing era has allowed for a more
extended use of experimental designs to make advertising more
effective. This is achieved principally through an improved
understanding of the consumer journey (i.e., What are pros-
pectsindividual propensities to buy and how can they be
increased through various targeted marketing efforts?; see, e.g.,
Li and Kannan 2014).
Connecting and Integrating Soft Metrics and
Hard Metrics
Whereas nance practice is the domain of hard, monetary
performance metrics, marketing practice has traditionally been
the domain of soft, attitudinal metrics. The marketing literature
has discussed attitude metrics at least since Colleys (1961)
work on the effect of advertising on how targeted customers
think and feel. Recent literature has demonstrated that includ-
ing such attitude (or purchase funnel) metrics in market re-
sponse models increases their predictive and diagnostic power
(Hanssens et al. 2014; Pauwels, Erguncu, and Yildirim 2013;
Srinivasan, Vanhuele, and Pauwels 2010). Furthermore, the
digital age has provided even more metrics of (prospective)
customer behavior in customersonline decision journey (Court
et al. 2009; Lecinski 2011). A key question is how to integrate
soft (attitude) and hard (behavior) metrics, both conceptually
and in empirical models (Marketing Science Institute 2014).
A recent study by Pauwels and Van Ewijk (2013) ad-
dresses this question both conceptually and empirically for
36 brands in 15 categories, including services, durables, and
fast-moving consumer goods. They observe that survey-based
attitude metrics typically move more slowly (i.e., have a lower
variance) than weekly sales, while online behavior metrics
move faster than weekly sales. Thus, attitudes and online
actions represent, respectively, slow and fast lanes on the
road to purchase. Dynamic system models reveal dual cau-
sality among survey-based attitudes and online actions, leading
to the framework in Figure 2.
Although this road-to-purchase framework is inspired by
the classical ThinkFeelDo distinction, it recognizes that the
digital age provides many more metrics regarding customer
behavior, including online search, clicks, website visits, and
(social media) expressions of consumption and (dis)sat-
isfaction. Online behavior does not simply reect underlying
attitudes (e.g., a known brand obtains higher click-through on
its ads), it also shapes them. For instance, consumers shop-
ping for their next smartphone may begin with a few brands in
mind but then discover new ones online through reviews,
(price) comparison sites, and social media, which increase their
thoughts and feelings about those new brands (Court et al.
2009). This zero moment of truth(Lecinski 2011) of online
FIGURE 2
Integrative Model of Attitudes and Actions on the
Consumer Road to Purchase
Source: Pauwels and Van Ewijk (2013).
Demonstrating the Value of Marketing / 181
discovery now precedes consumersobserving the brand at
retail in the rstmomentoftruthandconsumingitinthe
second moment of truth.
Only a few studies to date have quantied the connection
between soft and hard metrics in ways that managers can use.
Srinivasan, Vanhuele, and Pauwels (2010) analyze a large
number of consumer products and report strong cumulative
sales elasticities for advertising awareness (.29), consumer
consideration (.37), and consumer liking (.59). A recent
meta-analysis in digital marketing reveals that the sales elastici-
ty of electronic word of mouth averages .42 for valence
(sentiment) and .24 for volume (You, Vadakkepatt, and Joshi
2015). These elasticity results compare favorably with those
in Table 3.
Although recent studies have provided some guidance on
integrating soft metrics and online behavior into marketing
analytics, more research is needed to learn the best ways to
model the consumer decision journey and shed light on
whether there are models that are more appropriate than the
decision funnel (Marketing Science Institute 2014, p. 4). The
ndings are likely to be nuanced and to vary depending on
the category (high involvement or low involvement) and
existing brand strength (Demirci et al. 2014). This is an
important agenda because attitudinal and transactional met-
rics are not highly correlated, and thus brands run the risk on
focusing on the wrong performance metric in conducting their
marketing valuations.
Dealing with Risk
Risk considerations have had little systematic coverage in mar-
keting academia or practice. Studies of the relationship
between marketing and rm value (the bottom box in Figure 1)
have discussed risk factors because they are critical in investor
valuation of assets or future income streams. Whereas the -
nance literature has focused mainly on systemic risk (i.e., risk
faced by all companies in the market), the marketing literature
offers insights into idiosyncratic risk (i.e., risk tied to unique
circumstances of the specic company). For example, Rao
and Bharadwaj (2008, 2016) demonstrate that effective mar-
keting not only generates future cash ows but also lowers
the working capital that is required to accommodate different
scenarios in the economic environment. These authors argue
convincingly that demonstrating the connection between mar-
keting and rm value is essential if marketing is to be a
part of strategic planning in the enterprise. An empowered
CMOdened as a procient demand forecaster and
marketing decision makeris uniquely able to do this
because of his or her outside-in viewand knowledge
about likely consumer response to different business ini-
tiatives. Drawing on that knowledge, the CMO can project
cash ows and required working capital (both of which
drive rm value) under different economic scenarios and
then advise top management on the best course of action for
the rms shareholders. As such, marketings ability to man-
age business risk is an integral part of its value creation for
the rm.
In practical terms, an empowered CMO needs to show-
case his or her ability to manage marketing-induced risk, given
uncertainty about consumer, retailer, and competitive reactions
and the timing ofthese responses (Pauwels 2014). Most studies
that have examined the consequences of risk for marketing
planning, execution, and results monitoring have performed
scenario analyses that contrast best and worstcases on the basis
of estimated standard errors of response coefcients. Only one
academic article to date, by Albers (1998), has formalized
this process. After specifying the response functions dis-
cussed in the previous section, Albers decomposes the devi-
ation between actual andpredictedperformance as (1) incorrect
market response assumptions (planning variance), (2) devia-
tions of actual marketing actions from planned ones (execu-
tion variance), and (3) misanticipation of competitive reactions
(reaction variance). Each of these variances can be decomposed
further into the separate effects of single marketing instruments.
Planning variance. Incorrect market response assump-
tions can stem from faulty predictions of market size (driven
by business cycle or other consumption trends that affect
the entire sector) or market share (driven by brand-specic
actions such as advertising messaging or relative price).
Understanding the extent of deviation that results from each
factor helps companies adjust future predictions and also
assign accountability to the proper party (industry forecasters
or brand managers). Although benchmarks exist for mar-
keting effect size (see Table 3), the timing of marketing wear-
in and wear-out effects remains uncertain in practice and is
relatively underresearched.
While early research (Little 1970) has suggested the pos-
sibility of wear-in times for marketing campaigns, empirical
evidence has mainly covered sales effects of advertising, new
product introductions, and point-of-purchase actions. The peak
sales effect of advertising occurs relatively quickly, typically
within two months (Pauwels 2004; Tellis 2004), and the wear-
in times for mindset metrics (e.g., awareness, liking, consid-
eration) are just over two months (Srinivasan, Vanhuele, and
Pauwels 2010).In contrast, new product introductions typically
take several months or years to take off (Golder and Tellis
1997). As can be expected, point-of-purchase actions work
either immediately or not at all (Pauwels 2004), with price
promotions standing out as the most studied marketing action
(Srinivasan et al. 2004). The effect of distribution changes
seems to take longer (2.1 months on average in Srinivasan,
Vanhuele, and Pauwels [2010]). Further investigation of dis-
tribution is important because distribution stands out as the
most impactful marketing action (Ataman, Van Heerde, and
Mela 2010; Bronnenberg, Mahajan, and Vanhonacker 2000).
Finally, we know very little about the timing of ROIs in new
(digital) media such as paid search, banner ads, and word-of-
mouth referrals. Notable exceptions include DeHaan, Wiesel,
and Pauwelss (2015) study of 11 online and 3 ofine adver-
tising forms for an online retailer and Trusov, Bucklin, and
Pauwelss (2009) report that wear-out times are substan-
tially higher for word-of-mouth referrals than for traditional
marketing actions for a social networking site.
Similarly, we know little about the impact and temporal
effects of marketing spending on brand and customer value, as
opposed to sales response. In modeling terms, marketing brand
value effects are generally captured by state-space models with
182 / Journal of Marketing: AMA/MSI Special Issue, November 2016
Kalman lters (e.g., Naik, Prasad, and Sethi 2008) or by
Bayesian dynamic linear models (e.g., Ataman, Van Heerde,
and Mela 2010). The idea is that insofar as marketing induces
purchases that yield satisfactory consumer associations
with the brand, future purchases may occur without marketing
support, thus increasing baseline demand for the brand. Like-
wise, marketing actions may decrease price sensitivity and
thus increase the price premium (Ataman et al. 2016). Other
researchers have tracked the connection between marketing
spending, customer acquisition, and the value these actions
bring to the rm (Rust et al. 2004). Despite these methodo-
logical developments, we do not yet have a strong empirical
knowledge base on how marketing creates brand and customer
value over time.
Empirical generalizations on wear-in and wear-out effects
are necessary for managerial advice in cases in which data are
missing (Lehmann 2006). We need studies analyzing return
timing for investments in new media and new (emerging)
markets. Moreover, the timing of returns may systematically
vary by medium and target audience, a possibility that should
be taken into consideration when deciding on campaigns.
Considerable research is still required to determine the con-
tribution of marketing spending to a brands value as well as
when the rm realizes this value. Conversely, more research is
needed on the impact of cessation or reduction of marketing,
especially its long-term consequences. On that topic, Sloot,
Fok, and Verhoef (2006) nd that assortment reductions lower
category sales in the short run, but less so in the long run.
Although Li and Kannan (2014) nd virtually no short-term
sales loss from stopping paid search for a well-known brand,
Kireyev, Pauwels, and Gupta (2016) show substantial long-
term sales loss from reducing display and search ads for a
lesser-known brand. Finally, Ailawadi, Lehmann, and Neslin
(2001) report that Procter & Gambles strategic decision to
reduce price-promotional spending across 24 product cate-
gories resulted in a drop in long-term market shares but a gain
in protability. More research of this type will help the CMO
project the impact of alternative marketing plans.
Execution variance and reaction variance. Execution
variance is very important in practice but has had virtually no
research in academia (Albers 1998). Marketing executions
often stray from their plan because of third-party factors (e.g.,
the ad agency did not place billboards in time because local
regulations and insufcient temporary employees) or for in-
ternal reasons, such as lower-level managers reacting more
strongly to competitive moves than necessary (Holtrop et al.
2015). Albers (1998) provides the illustrative example of a
product manager decreasing the price more than planned and
switching the allocation away from distribution to adver-
tising. Because such occurrences are widespread, execution
variance and its consequences require further academic
research.
In contrast, academic research on competitive reaction is
plentiful, including research on its nature (aggressive, accom-
modating, or neutral), its speed, and its absence as a result of
competitorsunawareness or inability to react (Chen 1996).
Notably, managers often overestimate the incidence of com-
petitive reaction (e.g.,Holtrop et al. 2015) because research has
shown that lack of reaction is the dominant response, at least
for advertising and price promotions (Steenkamp et al. 2005).
Even when there is a retaliatory competitive reaction, it typ-
ically decreases the sales benet from price promotions across
fast-moving consumer goods categories by only 10% (Pauwels
2007). Competitive response has a similarly small impact on
the sales benets of new product introductions, advertising, and
distribution activity (Pauwels 2004). Further research is needed
to determine the boundary condition of reaction size and var-
iance. If competitive response variance is high, the rm may
want to start a competitive intelligenceinitiative.
Beyond competitors, other market players (e.g., retailers)
also inuence the ROMI, as does the marketing organiza-
tion itselffor example, through decision rules that favor
repeating past successes (Dekimpe and Hanssens 1999). The
marketing literature has focused thus far on estimating cus-
tomer and competitor response to marketing actions, but
much less so on the sector ecosystem response that includes
other players and the companys own decision rules and
heuristics (Dekimpe and Hanssens 1999). A few notable
exceptions include studies on retailer pricing showing, for
example, that retailers tend to increase a promoted price back
to its regular level slowly rather than abruptly (Pauwels 2004;
Srinivasan et al. 2004; Tsiros and Hardesty 2010). Company
decision rules/heuristics include the managerial tendency to
weigh past prices when setting future prices (Krishna, Mela,
and Urbany 2000). Managers should be aware of such ten-
dencies in their companys decision making and investigate
whether it is appropriate to continue such habits in the current
market environment.
The reaction of market players in ofine environments has
been assessed by dynamic system modeling in data-rich envi-
ronments (e.g., Pauwels 2004) and by role playing in data-scarce
environments, such as one-shot negotiations (Armstrong 2001).
Further research is needed on the role of market player reac-
tions in worldwide competition in online environments. As for
research on the marketingnance interface, more insights are
needed to assess whether investors react appropriately to mar-
keting actions and, thus, how valuable information about
investor reaction is for marketing decision making.
A key research priority is to go beyond documenting
reactions toward understanding the impact of that reaction
on the ROI of the initiating action. For marketing-mix actions,
is it really the case that the majority of the net sales impact
derives not from customer reaction but from support from
other marketing actions (Pauwels 2004)? For strategic mar-
keting actions, how does one assess likely competitive reac-
tion in deciding on location, product quality, and regular price
level?
In conclusion, when marketing plans do not materialize
as anticipated, the reasons can be various, as formalized by
Albers (1998). Only when an organization can identify the
reasons that apply to its own history can it take the right
corrective actions. Risk analysis in marketing planning
is more important to organizations than the paucity of prior
research suggests and, as such, it is one of the most promis-
ingareas for further research. This is especially important
if marketing is to become an integral part of strategic and
nancial planning.
Demonstrating the Value of Marketing / 183
Communicating Marketing Value
Within the Organization
After dening and measuring marketing value, it is important
to properly communicate this value within the organization.
This creates closed-loop learning (see the feedback loops in
Figure 1), which both justies future marketing activities and
examines them for increased effectiveness and/or efciency.
Internally communicating the value of marketing requires
(1) communicating multiple objectives in marketing dash-
boards, (2) adapting communication to the style of the deci-
sion maker, and (3) adapting communication to the marketing
organization.
Communicating Multiple Objectives in
Marketing Dashboards
In addition to their stated objectives, decision makers also
have personal objectives such as retaining their jobs and
growing their career prospects. The use of marketing ana-
lytics is often impeded by a perception that analytics compete
with people in the organization. Some managers may be
fearful that the spread of analytics in decision making will
eventually make them redundant in the organization. This
need not be the case, as people and data (including models)
have distinct competencies and weaknesses (e.g., Blattberg
and Hoch 1990), which we summarize in Table 4.
Managers tend to excel at diagnosing new situations on
the basis of their experience and integrating a variety of cues,
especially so-called broken-leg cues(unusual situations
that may not have prior history but are intuitively known
to be important). However, human decision makers are also
subjective in their judgment and tend to rush to a decision
overcondently, without properly accounting for uncertainty
and risk. These weaknesses are well addressed by models,
which account for uncertainty and weigh different cues on the
basis of past data and optimalrules. However, the rules
may be too rigid for a new situation, and the output of a model
inevitably depends on human inputs, which the model is not
designed to question.
Given those strengths and weaknesses, organizations
should design decision support systems that take advantage
of the distinctive competencies of managers while using tech-
nology to compensate for managersinherent weaknesses.
For example, after a rms business goals for the next
quarter or year are set, marketing planning should start with
analytics or dashboard input. Then, decision makers need to
judge the extent to which unique circumstances require some
of the model outputs to be adjusted. Cross-functional input
is paramount in this exercise, and there needs to be a sense
of internal ownership of the analytics platform across the
business functions. Finally, business objectives need to be
tied to resource allocations. Corstjens and Merrihue (2003)
give the example of global marketing resource allocation
at Samsung: when a model-inspired reduction in marketing
budget for product category Z in country X was enacted, the
sales quota for the manager in charge of ZX was lowered as
well, and vice versa for marketing spending increases. Such
coordinated actions help create a culture in which managers
view models and dashboards as their friends, not their nem-
eses. Automation of marketing decisions is likely to in-
crease for tactical decisions in stable markets, but less so
for strategic decisions, such as choosing new organic growth
options, setting the rules for automation, and reacting to
unexpected changes in turbulent markets (Bucklin, Lehmann,
and Little 1998).
To combine the best of model-based and human-based
strengths, researchers have proposed the use of an analytic
marketing dashboard (Pauwels et al. 2009). Like the dash-
board of a car, a marketing analytics dashboard brings the
main multiple objectives and their metrics into a single display.
It provides a concise set of interconnected performance
drivers to be viewed in common throughout the organization
(Pauwels 2014, p. 7). Figure 3 shows the dashboard that Inofec
managers used to project the expected prots expected from
changes to price discounts and to ofine and online mar-
keting communication, which created the organizational
buy-in to run experiments demonstrating actual prothikes
(Wiesel, Arts, and Pauwels 2011).
Such communication tools make it possible to integrate
diverse business activities (some of them qualitative) with
performance outcomes. This helps managers in at least ve
ways (Pauwels 2014). First, a dashboard enforces con-
sistency in measures and measurement procedures across
departments and business units. For example, Avaya pro-
vides business communication solutions in over 50 coun-
tries and diverse markets, with varying marketing tactics.
Before the dashboard project, the company had no com-
monality of systems around the globe, it used different
denitions of what constituted a qualied lead(a key
performance metric in the handoff from marketing to sales
for business-to-business companies), and there was a lack of
regional interest in gathering metrics.
Second, a dashboard helps monitor performance. Mon-
itoring may be both evaluative (who or what performed
well?) and developmental (what have we learned?). Google
provides a good example: dashboard metrics are early indi-
cators of performance, and if a dip occurs in, for example,
the trust-and-privacy metric, the company takes corrective
action.
Third, a dashboard may be used to plan goals and strat-
egies. For example, TD Ameritrades corporate scorecards,
developed by the strategic planning department, led to a
dashboard that plugs into the planning cycle and is tied to
quarterly compensation.
TABLE 4
Advice for Communication in Analytic and
Intuitive Companies
Analytic Decision
Making
Intuitive Decision
Making
Present estimates Visualize effectiveness
Discuss assumptions Focus on main insights
Optimize allocation Adjust allocation
Optimize budget Adjust budget
Examples: Procter &
Gamble, Allstate
Examples: Campbells,
Inofec
184 / Journal of Marketing: AMA/MSI Special Issue, November 2016
Fourth, a dashboard may be used to communicate to
important stakeholders. The dashboard communicates not
only performance but also, through the choice of metrics, the
things an organization values. Vanguardsdashboard,forex-
ample, enabled it to share with its corporate board its focus
on customer loyalty, feedback, and word of mouth.
Finally, a dashboard offers a good starting point for im-
portant discussions, such as when management sets stretch
targets without providing additional resources. For instance,
the U.S. division of an automotive company was instructed
to increase prots despite longer innovation cycles and lower
advertising budgets. Analytics and dashboard tools helped
the division present what-if scenarios and make its case to
headquarters that trade-offs were necessary by quantifying
the relation between marketing actions and prots.
Dashboards also allow for more effective communication
with marketing partners, especially as companies move to
performance-based compensation of agency work. As the
sales impact of performance metrics may differ across countries,
managers should use dashboard insights to set specic
metric targets (Pauwels, Erguncu, and Yildirim 2013). In
the case of the U.S. division of the aforementioned auto-
motive company, brand consideration was a more important
performance metric in an emerging market, while brand liking
was more important in a mature market. Further research is
needed to generate empirical generalizations and boundary
conditions in this regard.
Adapting Communication to the Style of the
Decision Maker
In their review of ISMS-MSI Practice Prize nalists, Lilien,
Roberts, and Shankar (2013) detail the characteristics of success-
ful marketing science applications. They advocate estimat-
ing simple, easy-to-use models and obtaining organizational
buy-in through, among other things, speaking the same
language as inuential executives. Marketing analytics
customers strongly differ in their decision-making lan-
guage, with some companies favoring a more analytic style
and others using a more intuitive style. We recommend
communicating marketing analytics according to the com-
panysstyle.
When decision makers have a more analytical style,
presenting estimates and elasticities straight from the ana-
lytics helps them understand exactly what is going on and
how decision optimality is affectedfor example, when
deciding how to allocate marketing budgets by drawing on
their relative elasticities. Even in such cases, though, it is best
to provide the proper contextfor example, by comparing
the effects that television advertising elasticities and online
advertising elasticities have on online performance metrics,
as Figure 4 shows.
Decision makers with a more analytical style require more
information on the analytics assumptions and the uncertainty
around the performance projections. Academic researchers are
typically well versed in such explanations. In contrast, decision
makers with a more intuitive decision style may be averse to
discussions on condence intervals, functional form, and error
distribution assumptions. Communicating analytics insights
in such environments requires more visualization, such as the
heat map of the projected prot consequences of changes to
marketing actions shown in Figure 5.
Figure 5 shows the highest prot (8.51; units disguised)
as a specic combination of price ($45) and advertising
budget ($3.25 million) but also communicates how close
other combinations are to this maximum projected prot. For
instance, at a current price level of $35, the decision maker
may feel uncomfortable with prices over $40, perhaps fear-
ing a customer backlash not included as a model variable.
The decision maker can look up the highest possible prot
and associated marketing actions for prices below $40. After
adjusting the price in this model-suggested direction, more
FIGURE 3
Marketing Analytic Dashboard for Inofec
Source: Pauwels (2014).
Demonstrating the Value of Marketing / 185
data and insights will then be available for recalibration
of analytics and intuition. Alternatively, the decision maker
might decide to allocate only the $2 million communica-
tion budget provided by his or her superior, the investor (see
Table 1). The heat map provides the decision maker with not
only the best outcome under the given budget (a projected
prot of 7.79) but also a quantitative argument for why prots
can be increased (up to) 9% if the advertising budget is increased
toward its optimal level. As such, the heat map enables deci-
sion makers to tweak model-derived optimal allocations, which
provides a level of decision comfort. Decision comfort has been
shown to be an important contributor to managerswillingness
FIGURE 4
Comparison of Television and Online Marketing Elasticities on Online Performance Metrics
Source: Pauwels and Van Ewijk (2013).
FIGURE 5
Prot Heat Map of the Interaction of Price and Advertising
Source: Pauwels (2014).
186 / Journal of Marketing: AMA/MSI Special Issue, November 2016
to adopt analytics in decision making (Parker, Lehmann, and
Xie 2016).
As for the danger of analytics users misunderstanding
the models assumptions, note that the heat map in Figure 5
restricts the decision makers range of potential price and
advertising levels. Contacts at the company preferred this
restriction on the range of the past data rather than show in-
creasing condence intervals as users consider options far-
ther away from the mean(s) of past marketing level(s). The
contacts felt that although the latter might be appropriate for
decisions makers with an analytical style and background, it
would confuse other decision makers to the point that they
might not trust or use the model.
Empirical studies have shown that intuition may be better
than analysis in certain conditionsfor example, for novices
under time pressure to make complex decisions (Wierenga
2011). Further research is needed to specify such conditions
for marketing decisions and to show how intuition and
analysis interact.
Adapting Communication to the Marketing
Organization
Beyond decision-making styles, the structure and organ-
ization of the marketing team matters in communication
about marketing analytics. At least one analyst should be
included in a decision-making team. During discussions
about, for example, increasing spending on a marketing
action, the analyst could remind others that it has a small
sales elasticity. Such early inclusion of analytics insights
may reduce decision makersresistance to model-based
objections to proposals in which they are emotionally invested;
moreover, it may help companies guard against the tendency
of decision makers to cherry-pick the data and models
that generate results supporting a priori beliefs (Soyer and
Hogarth 2015). An example at a high strategic level is the
appointment of an algorithm to the board of directors of a
venture capital company (Wile 2014). In this way, analytics
has an independent vote in deciding which new venture
proposals to fund and can break the tie when the human
voters are split.
Conclusions
The multidimensional nature of marketing is expressed in
a variety of performance metricsattitudinal, behavioral,
and nancialthat turn out to be weakly interrelated. This
makes it difcult to assess marketings value and often re-
sults in skepticism about marketings contributions and a
reduction in the role of marketing at senior levels of decision
making. As the digital age marches on, new marketing ap-
plications are created (e.g., mobile targeting), which may
enable marketing to occupy an increasingly tactical function
in organizations.
This has led us to study marketing value assessment from
three perspectives: metrics, models, and communication. Fol-
lowing the chain of marketing productivity (Figure 1), we
postulate that successful marketing value assessment needs to
reconcile the different performance metrics that are available,
combine historical data analysis with marketing experiments,
and signicantly enhance the communication of analytical
results to an audience of decision makers who are not ana-
lytically oriented. Marketing educators can help bridge
this gap by integrating the assessment and the communication
of marketing value in their teaching. The current growth in
marketing and business analytics programs offers a clear op-
portunity in this regard.
We offer a brief review of what is currently known about
metrics, models, and communication, along with suggestions
for specic avenues for further research. First, we know that
market orientation and the use of marketing metrics improve
marketing performance, but we do not yet know how this
marketing performance (as opposed to marketing commu-
nication) drives the scope of marketing in the organization.
Second, we have rules for optimizing prots and sales, but
not for weighting different marketing objectives. Third, we
know how to measure effectiveness and efciency, but not the
conditions under which each is most appropriately pursued,
nor do we know when it is best to use automated marketing
programs (which focus on efciency). Fourth, empirical gene-
ralizations regarding response elasticities enable us to optimize
marketing allocations in the short run, but not yet to quantify
marketing synergies for organic growth, nor to identify
which conditions favor top-down allocations and which favor
bottom-up allocations.
Fifth, we know a lot about marketing elasticities on hard
performance metrics but know little about how marketing
affects soft performance metrics and how these relate to hard
performance under different conditions. Still unknown is
whether the complicated relation between soft performance
metrics and sales is better characterized by strong average
effects with large condence intervals (high elasticity with
high noise) or by small average effects with tight condence
intervals (low, precise elasticity). Furthermore, we need
to detect and explain outlier brands that buck the average
relationship among metrics (Ailawadi and Van Heerde 2015).
Sixth, risk has been decomposed in terms of performance
variance but is not yet quantied in the timing of these
performance returns. Moreover, we are limited in the advice
we can provide on the risk of stopping marketing activities
and optimal competitive reaction. Finally, we know several
generalities about communicating marketing value (e.g., visu-
alizations), but we have little insight into success factors
for different communication methods and for intuitive and
analytical decision making.
Our overall conclusions are as follows. First, marketing
value assessment is essential if marketing as a discipline wants
to exert an inuence at the highest levels of the organization.
Its inuence will also determine the scope of its role in the
organization, which could range from tactical execution of
advertising and promotion policies to being a fundamental
driver of organic growth.
Second, signicant advances in data quality and quantity,
along with new analytical methods, have served marketing
value assessment well both in academia and in industry. Most
of these advances have occurred at the tactical level. In
particular, digitization allows for a much improved under-
standing of the connection between soft (attitudinal) and hard
(transactional) metrics.
Demonstrating the Value of Marketing / 187
Third, marketing analytics technology has been used mainly
for resource allocation decisions, not investment decisions.
Media mix and digital attribution models, for example, are
widely accepted and used. This evolution pushes market-
ingpracticeinanautomated,programmatic direction, not
unlike the automated trading of securities on Wall Street. It
also necessitates the use of visualization methods to suc-
cessfully communicate the complexities of marketing value
creation.
Finally, to better serve the strategic aspect of marketing,
which is the key interest of senior management in the orga-
nization, databases will need to be better integrated across
the elements of the marketing mix, broadly dened. This
presents an opportunity for providers of enterprise resource-
planning solutions: by including customer and marketing data
in their systems, they can provide a unied data platform that
will allow for a cross-functional view of marketing and the
value of marketing in the organization.
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... Marketing investments build intangible assets that typically generate returns over the long term. Accounting standards do not allow marketing resources to be recorded as intangible assets on the balance sheet because of the difficulty of measuring their economic benefits (Hanssens & Pauwels, 2016). Consequently, investments in marketing are recorded as expenses, normally reducing the company's profit before generating the expected effects (Wies et al., 2019). ...
... While a high level of AT improves the prospects for profitability, it also requires continued investments in resources and capabilities that enable firms to extract value efficiently from their assets (Dickinson 2011, Houmes, Jun, Capriotti, & Wang, 2018, Kraft et al., 2018. We posit that marketing investments represent one such critical investment in this regard given marketing's role in effectively and efficiently generating revenue (Hanssens & Pauwels, 2016;Nath & Mahajan, 2017;Rust et al., 2002). Consequently, when firms have high AT and demonstrate an increase in profits, the expectation is of an accompanying or commensurate increase in investment in capabilities such as those related to marketing (Amir et al., 2011;Patin et al., 2020aPatin et al., , 2020b. ...
... Marketing investments stand out when firms look for options to reduce expenses as they build intangible assets that are not accounted in the balance sheet. Moreover, marketing investments generate expenses in the short term, and firms do not have established and readily available metrics to assess their contribution to firm value (Hanssens & Pauwels, 2016). ...
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The literature on marketing myopia suggests the need for exploring conditions under which myopic marketing management differs in its impact on stock performance. To that end, the authors investigate the role of asset turnover (AT), which is the ratio of the firm’s sales or revenues to its assets, indicating the effectiveness with which a firm is using its assets to generate incomes. AT suggests a persistent signal of the firm’s potential for future cash flows. The authors test their model on data from 210 publicly traded Brazilian companies from 2001 to 2017. Results show that when there is high AT, the stock performance in the short term (i) is higher in non-myopic firms with positive earnings surprise compared to myopic firms and (ii) does not significantly differ between non-myopic firms with negative earnings surprise and myopic firms. The study demonstrates that AT contributes to the identification, signalization, and evaluation of marketing myopia.
... The integration of big data, intelligent automation and digital transformation has significantly impacted business environments, driving organizations towards innovation, agility, and sustainable growth (Gabrys, 2014). The digital revolution has not only transformed business processes but has also created new prospects, enhanced customer experiences and fostered competitive advantages in an ever-changing digital landscape (Hanssens & Pauwels, 2016). This emphasis on digital advancement and expansion, commonly known as "growth hacking (GH)," has emerged as a key strategy in today's business world (Bohnsack & Liesner, 2019). ...
... Subtheme 2.4: Tailored customer experience through digital interfaces. (Hanssens & Pauwels, 2016) identify the close relationship between customer experience and the digital interfaces that are used to manage the relationship between the business and the customer. ...
Article
The Growth Hacking process brings new opportunities for business which are achieved through new strategies such as hyper-scalability, hyperspecialization and human-based competitive advantage. However this is a field which has not been deeply studied. In order to bridge this gap, by applying the lens of resource-based theory of the digital firm, this study is based on the findings from 20 semi-structured interviews with growth orientated entrepreneurs from a diverse range of sectors based in the UK. The study applies a content analysis and inductive approach and the results show that in the competitive marketplace new digital skills are needed to grow a successful business and these skills should be balanced with the introduction of any new technologies. The requirement for human skills and engagement is necessity for the development and leverage of unique capabilities and competencies to drive growth hacking strategies. As a result, these new strategies allow entrepreneurs to exploit new opportunities and overcome business challenges.
... Email and social media marketing are two ways through which digital marketing enhances consumer engagement (Mishra, 2019). Pauwels et al. (2020) suggest that the impact of digital marketing on financial performance may be more nuanced and mediated by factors such as the firm's digital maturity, market competition, and integration of digital efforts with traditional marketing strategies. Businesses that successfully integrate digital marketing into a holistic marketing strategy often see better long-term financial outcomes, as they can leverage data analytics and customer insights more effectively. ...
... This discrepancy can be due to the time lag between marketing and observable financial results, as well as the necessity for complementary business strategies to fully capitalize on digital marketing efforts. The finding is supported by Pauwels et al. (2020), the impact of digital marketing on financial performance may be affected by some factors such as the digital maturity, market competition, and the digital efforts. By which this does not mean that digital marketing strategies are ineffective, there are benefits that are not captured by financial metrics. ...
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... This last aspect is perhaps what has caused a representative focus on financial measures in the initial studies of the last decade, as can be seen in Table N° 7; however, many studies have recognized the relevance of contemplating diverse approaches with both financial and non-financial measurements of BP and offer a greater understanding. Assessing BP is impacted by the nature of business and the nature of marketing; since their multidimensional nature is expressed in a variety of performance metrics -attitudinal, behavioral, and financial -that turn out to be weakly interrelated, which often produces skepticism about its contributions (Hanssens and Pauwels, 2016). ...
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This study aimed to analyze the existing literature between 2010-2022 that covers brand performance as a highly relevant issue in the field of Brand Management and provide an accurate mapping of theoretical contributions by studying research outcomes, methods, approaches, and measurements, across empirical studies (MacInnis, 2011). Considering the potential influence of internal and external factors of the organization on brand performance, there needs to be more conceptual development and a systematic examination of how researchers in brand management should conceptualize and measure brand performance properly. The findings and future research agenda are presented under the TCCM Framework proposed by Paul and Rosado-Serrano (2019). The results suggest that 1) analyzing the characteristics and context of the market in which the brand is present before the definition of its evaluation is of the utmost importance, due to brand performance is the result of synergistic relationships between different internal and external factors, and stakeholders of the organization; 2) previous studies had focused on addressing multinational brands in developed markets, which make necessary to build new knowledge by considering the smaller brands, different types of markets and economies; and 3) identifying determinants of brand performance is as relevant as measuring it. Finally, we contributed with additional and actionable steps for researchers to systematically improve research and managerial practice in the future.
... Consumer feedback is an essential building block of the customer-supplier relationship (Gremyr et al. 2022;Hanssens -Pauwels 2016;Kohtamäki et al. 2021). As a significant source of information (Mou et al. 2019), it plays an essential role in the innovation capability of firms (Nathai-Balkissoon et al. 2017), and a vast amount of input is generated daily, especially by digital tools. ...
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This paper aims to enhance the understanding of the influencing factors and consequences of feedback, with a particular focus on brand outputs and co-creation, and to identify future research areas related to feedback. First, we propose to clarify definitions by introducing actionable customer feedback and drawing clear distinctions among synonymic concepts used in the literature. Then, we conduct a systematic literature review of 73 journal articles from the past two decades and synthesize their findings in the feedback, brand, and co-creation intercept. We also introduce a structure for feedback-related antecedents, moderators, mediators, and performance outputs. As a main contribution, we offer a visual representation of the findings of the systematic literature review to support scholars of customer behavior who are discovering their own directions according to their expertise. Through the use of visual tools such as tables and figures, we provide summary statistics reflecting the methodologies used in the literature, the industries involved, the geographical spread, and adjacent theories used. We also summarize the different positions of feedback within conceptual frameworks. We contribute to the literature by proposing and visually demonstrating new grouping dimensions of the antecedents, mediators, moderators and performance outcomes of the feedback literature. Finally, we recommend directions for future research on actionable feedback. We recommend studying the mediating and moderating impacts of demographics, gender, environmental characteristics, geography (especially developing economies), and B2B businesses on actionable feedback. The roles of trust and feedback in brand outputs, for example, brand value and brand equity, requires further investigation. Finally, we recommend exploring constructs in which feedback plays multiple roles in different positions.
... To highlight this dichotomy Hanssens (2016) cites the conflict in marketing between mass media advertising which is effective in reaching vast numbers of prospective customers but is not very efficient in terms of cost and online advertising which maybe efficient but not effective as it reaches less prospective consumers, nonetheless, the value of marketing can either be expressed in terms of efficiency or effectiveness depending on the objectives of the organisation. ...
Thesis
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This cross-sectional mixed-methods research explores whether Afro-Caribbean Entrepreneurs (ACE) in South London, specifically in the boroughs of Lewisham and Lambeth, are adhering to best practices in marketing effectiveness. A sample of 100 ACEs was surveyed, revealing that most were not engaging in optimal marketing practices. Key findings showed that 76% of campaigns lasted less than six months, with 50% running between 1-4 weeks. Social media was the primary communication channel for 51% of respondents, driven largely by resource constraints, particularly financial limitations. These marketing practices placed ACE campaigns at the lowest level on the Creative Effectiveness Ladder, resulting in a Creative Commitment score of 4, which is considered poor. The research revealed that many entrepreneurs would prefer using traditional media such as television and radio if not for financial barriers. This study fills a gap in the literature by focusing on the under-researched group of Afro-Caribbean entrepreneurs, offering valuable insights into their marketing behaviors and suggesting areas for future research and policy interventions aimed at improving marketing effectiveness for minority-owned businesses.
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Sponsored content allows brands to partner with creators to reach creators’ audiences on digital platforms. However, both creators’ and brands’ incomplete understanding of this object generates two critical ambiguities: how to determine the value of sponsored content and how to effectively co-produce it. To better understand these ambiguities, we theorize sponsored content as an epistemic market object: an object that facilitates marketing functions but is only partially understood by the actors who use it . We analyze a data set of interviews, podcasts, media articles, and third-party platform reviews about—and by—content creators, brands, and intermediaries. Our findings show that brands, creators, and intermediaries create and apply knowledge to address valuation and co-production ambiguities. However, this knowledge work is incomplete, creating asymmetries in value outcomes and power relationships in a brand-creator partnership. Our paper contributes to marketing literature and practice by highlighting the role of epistemic market objects in transformative market disruptions that alter the roles of, and the relationships between, market actors. Our findings are transferable to other substantive areas such as Generative AI, Metaverse, NFTs, online news, and the sharing economy.
Presentation
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Research Focus: The document summarises and presents the investigation findings into whether Afro-Caribbean Entrepreneurs (ACEs) operating in South London are utilising marketing effectiveness best practices. Key Findings: 76% of surveyed entrepreneurs run campaigns lasting 6 months or less, with many campaigns lasting 1-4 weeks. 51% primarily rely on social media as their main communication channel. Financial constraints are a primary reason ACEs limit the duration and scope of their marketing campaigns. Campaigns rank low on the Creative Effectiveness Ladder with a poor Creative Commitment score (4). Keywords: Marketing effectiveness Afro-Caribbean entrepreneurs Black-owned businesses Marketing strategies Creative Effectiveness Ladder Marketing best practices Marketing campaign duration Traditional vs digital media Marketing communication channels Financial constraints in marketing Black entrepreneurs in London Black business challenges Afro-Caribbean business growth Minority-owned businesses Cultural impact on marketing Black-owned SMEs South London Afro-Caribbean marketing practices Ethnic minority business strategies Socio-economic factors in Black businesses Marketing barriers for Afro-Caribbean entrepreneurs
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How do competitors react to each other's price-promotion and advertising attacks? What are the reasons for the observed reaction behavior? We answer these questions by performing a large-scale empirical study on the short-run and long-run reactions to promotion and advertising shocks in over 400 consumer product categories over a four-year time span. Our results clearly show that the most predominant form of competitive response is passive in nature. When a reaction does occur, it is usually retaliatory in the same instrument, i.e., promotion attacks are countered with promotions, and advertising attacks are countered with advertising. There are very few long-run consequences of any type of reaction behavior. By linking reaction behavior to both cross- and own-effectiveness, we further demonstrate that passive behavior is often a sound strategy, while firms that do opt to retaliate often use ineffective instruments, resulting in “spoiled arms.” Accommodating behavior is observed in only a minority of cases, and often results in a missed sales opportunity when promotional support is reduced. The ultimate impact of most promotion and advertising campaigns depends primarily on the nature of consumer response, not the vigilance of competitors.
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It is widely accepted that marketing initiatives help firms acquire and retain customers. However, the link between the cash flows generated by customers’ purchases and shareholders’ wealth is not fully understood, and the literature is increasingly advocating the need for greater “marketing accountability.” In this interdisciplinary research, the authors adapt the theory of firm valuation from finance and show that a marketing action can affect the shareholders’ wealth by (1) determining the firm's net present value (the “stock price effect”) and (2) potentially reducing the firm's cash needs (the “released working capital effect”). In demonstrating these results, the authors advance marketing theory by answering calls made in the literature to link marketing actions to the investors’ cash flows, the firm's working capital needs, and the owners’ wealth. The approach also shows that by reducing the firm's cash needs, marketing can increase the firm's productivity (operating efficiency). This imputes a critically important role for marketing. By increasing productivity, marketing can increase the firm's competitive posture and, thus, its long-term viability.
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When deciding about changes in levels of multiple marketing investments aimed at improving profits, managers need to know whether they are on the uphill side or the downhill side of the profit function with respect to each investment variable. Spending errors made by an uphill-located firm that believes that it is a downhill-located firm can have serious consequences. This article offers an econometric “diagnostic tool” to infer a company's current location on a multivariable profit function and to determine the changes in investments that will take the company to the neighborhood of the maximum profit. The authors apply the proposed approach to daily newspaper industry data and investigate the optimality of companies' allocation behaviors with respect to three marketing efforts: investments in quality, distribution, and advertising space sales effort. A novel feature of this problem setting is that it involves a “dual-revenue” market with possibly interrelated demands for subscriptions and advertising space. The authors derive normative rules for marketing investments in four dual-revenue market types. The empirical analysis finds that daily newspapers' dual revenues are positively interrelated and that a majority of these companies are located near the optimal level of spending for quality, a surprising finding considering that previous studies have characterized marketing managers as overspenders. Indeed, when these companies are suboptimal, they are much more likely to be underspending (i.e., they are located on the uphill side of the profit function) than overspending. In addition, the research furnishes estimates of sales elasticities with respect to quality, personal selling, and distribution investments, which are sparsely available in the extant literature.
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In many organizations, marketing investment-level decisions precede the associated resource allocation decisions and are based on market-level sales response data, often with no attention to the impact of rules used to allocate resources to submarkets. Such top-down budgeting is commonly based on a perception that aggregate sales and profitability are affected much more by the level than by the allocation of the investment. The authors analyze the effects of different resource allocation rules assuming alternative specifications of submarket sales response functions and show that allocation decisions significantly influence aggregate sales response functions, investment-level decisions based on these functions, and realized profit. The authors also show aggregate sales and profit are usually more sensitive to improvements in allocation rules than to increases in investment levels and conclude that resource allocation decisions warrant more attention in marketing budgeting.
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The authors examine the long-term effects of promotion and advertising on consumers’ brand choice behavior. They use 8 1/4 years of panel data for a frequently purchased packaged good to address two questions: (1) Do consumers’ responses to marketing mix variables, such as price, change over a long period of time? (2) If yes, are these changes associated with changes in manufacturers’ advertising and retailers’ promotional policies? Using these results, the authors draw implications for manufacturers’ pricing, advertising, and promotion policies. The authors use a two-stage approach, which permits them to assess the medium-term (quarterly) effects of advertising and promotion as well as their long-term (i.e., over an infinite horizon) effects. Their results are consistent with the hypotheses that consumers become more price and promotion sensitive over time because of reduced advertising and increased promotions.
Article
The authors analyze results of 389 BehaviorScan® matched household, consumer panel, split cable, real world T.V. advertising weight, and copy tests. Additionally, study sponsors—packaged goods advertisers, T.V. networks, and advertising agencies—filled out questionnaires on 140 of the tests, which could test common beliefs about how T.V. advertising works, to evaluate strategic, media, and copy variables unavailable from the BehaviorScan® results. Although some of the variables did indeed identify T.V. advertising that positively affected sales, many of the variables did not differentiate among the sales effects of different advertising treatments. For example, increasing advertising budgets in relation to competitors does not increase sales in general. However, changing brand, copy, and media strategy in categories with many purchase occasions in which in-store merchandising is low increases the likelihood of T.V. advertising positively affecting sales. The authors’ data do not show a strong relationship between standard recall and persuasion copy test measures and sales effectiveness. The data also suggest different variable formulations for choice and market response models that include advertising.
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An intuitively appealing decision rule is to allocate a company's scarce marketing resources to where they have the greatest long-term benefit. This principle, however, is easier to accept than it is to execute, because long-run effects of marketing spending are difficult to estimate. The authors address this problem by examining the behavior of market response and marketing spending over time and identify four common strategic scenarios: business as usual, hysteresis in response, escalation, and evolving business practice. The authors explain and illustrate why each scenario can occur in practice and describe its positive and negative consequences for long-term profitability. The authors propose to use multivariate persistence measures to identify which of the four strategic scenarios is taking place and illustrate this approach in the pharmaceutical and packaged-food industries. The results substantiate the authors’ proposition that the strategic scenario is a major determinant of marketing effectiveness and long-term profitability. This conclusion sets up a substantial agenda for further research.
Book
Principles of Forecasting: A Handbook for Researchers and Practitioners summarizes knowledge from experts and from empirical studies. It provides guidelines that can be applied in fields such as economics, sociology, and psychology. It applies to problems such as those in finance (How much is this company worth?), marketing (Will a new product be successful?), personnel (How can we identify the best job candidates?), and production (What level of inventories should be kept?). The book is edited by Professor J. Scott Armstrong of the Wharton School, University of Pennsylvania. Contributions were written by 40 leading experts in forecasting, and the 30 chapters cover all types of forecasting methods. There are judgmental methods such as Delphi, role-playing, and intentions studies. Quantitative methods include econometric methods, expert systems, and extrapolation. Some methods, such as conjoint analysis, analogies, and rule-based forecasting, integrate quantitative and judgmental procedures. In each area, the authors identify what is known in the form of `if-then principles', and they summarize evidence on these principles. The project, developed over a four-year period, represents the first book to summarize all that is known about forecasting and to present it so that it can be used by researchers and practitioners. To ensure that the principles are correct, the authors reviewed one another's papers. In addition, external reviews were provided by more than 120 experts, some of whom reviewed many of the papers. The book includes the first comprehensive forecasting dictionary.
Article
Advertising often aims at creating and reinforcing brand differentiation, which should translate into reduced price competition. Currently unknown are the boundary conditions for long-term advertising benefits, the route through which advertising effects materialize, and the role of competitive advertising in the category. The authors develop a Hierarchical Dynamic Linear Model that links own and others’ advertising in the category to brand price elasticity directly and indirectly through their impact on own and competitive mindset metrics. The model accommodates dynamic dependencies in mindset metrics, controls for endogeneity in marketing, captures competitive reactions and performance feedback in marketing, and explains cross-sectional variation as a function of brand and category characteristics. Model estimation on seven years of data for 350 brands in 39 categories shows that both own and all competitive advertising in the category lower price sensitivity for the average brand, both directly and through advertising awareness. The attenuation of price sensitivity is more pronounced for niche brands in complex and more expensive categories, with higher concentration and purchase frequency. A financial simulation based on the estimates shows that while the price elasticity effect is positive and substantial for high-price brands, it hurts the advertising returns for low-price brands.