Integrating Marketing Communications: New Findings, New Lessons and New Ideas

Abstract

With the challenges presented by new media, shifting media patterns, and divided consumer attention, the optimal integration of marketing communications takes on increasing importance. Drawing on a review of relevant academic research and guided by managerial priorities, the authors offer insights and advice as to how traditional and new media such as search, display, mobile, TV, and social media interact to affect consumer decision making. With an enhanced understanding of the consumer decision journey and how consumers process communications, the authors outline a comprehensive framework featuring two models designed to improve the effectiveness and efficiency of integrated marketing communication programs: a "bottom-up" communications matching model and a "top-down" communications optimizationmodel. The authors conclude by suggesting important future research priorities.

Full text

Rajeev Batra & Kevin Lane Keller
Integrating Marketing
Communications: New Findings, New
Lessons, and New Ideas
With the challenges presented by new media, shifting media patterns, and divided consumer attention, the optimal integration
of marketing communications takes on increasing importance. Drawing on a review of relevant academic research and
guided by managerial priorities, the authors offer insights and advice as to how traditional and new media such as search,
display, mobile, TV, and social media interact to affect consumer decision making. With an enhanced understanding of the
consumer decision journey and how consumers process communications, the authors outline a comprehensive framework
featuring two models designed to improve the effectiveness and efficiency of integrated marketing communication programs:
a“bottom-up”communications matching model and a “top-down”communications optimization model. The authors conclude
by suggesting important future research priorities.
Keywords: marketing communications, marketing integration, integrated marketing communications, traditional
media, digital media
In designing integrated marketing communication (IMC)
programs, marketers face many challenges as a result of the
fact that consumers, brands, and the media are funda-
mentally changing in profound ways. With the explosion of
new media, consumers are dramatically shifting both their
media usage patterns and how they utilize different media
sources to get the information they seek, which thus influences
when, where, and how they choose brands.1Perhaps more
than ever, their attention is divided, often due to multi-
tasking,andtheyareseeminglyinaperpetualstateofpartial
attention.
The consumer “path to purchase”is also fundamentally
different today—often shorter in length, less hierarchical, and
more complex (Court et al. 2009). Consumers do not necessarily
passively receive brand information strictly through legacy mass
media such as print or TV and store it in memory for later use.
Rather, they now actively seek it when needed, through search
engines, mobile browsers, blogs, and brand websites. Much more
consumer-to-firm, consumer-to-consumer, and consumer-about-
firm communication exists. Because of increased social influences
on purchase, word of mouth (WOM) and advocacy have become
especially important; brand messaging is even less under
the marketer’s control.
These new ways of communicating, however, also facilitate
greater personalization of message content, timing, and location,
enabling marketers to utilize more media types to accomplish
specific communication objectives. Previously, marketers had
only a small quiver of communication modalities at their dis-
posal to try to accomplish multiple brand communication ob-
jectives. They could, for instance, use broadcast television ads
to reach a large target audience and make them aware of the
brand and its offerings; use radio and outdoor ads to build
and maintain brand salience; use print ads in newspapers and
magazines to provide details about the brand’sfeaturesand
competitive superiority; generate public relations (PR) for third-
party credibility; use newspaper coupons and co-op ads to offer
limited promotional price discounts and communicate points
of purchase; and use frequently mailed newsletters and catalogs
to build long-term relationships and loyalty.
Today, in contrast, marketers are blessed with a much richer
array of communications possibilities. In addition to those tra-
ditional means, a brand can reach (and send reminders to) a large
number of consumers through mass or targeted Facebook
ads, banner or display ads on thousands of websites, or paid
and organic search ads. It can use its own website, third-
party websites, and bloggers to provide persuasive con-
tent to create brand preference; offer short-term promotions
through tweets and targeted e-coupons; and create online
brand communities through its own and social media–type
web properties. Second-by-second media-usage data allow
for micro and dynamic segmentation and targeting; media
and message changes can be made much more quickly.
By using the distinctive strengths of the greater number of
media choices available today, a marketer can potentially
Rajeev Batra is S.S. Kresge Professor of Marketing, Ross School of Business,
University of Michigan (e-mail: rajeevba@umich.edu). Kevin Lane Keller is
E.B. Osborn Professor of Marketing, Tuck School of Business, Dartmouth
College (e-mail: kevin.keller@dartmouth.edu). The authors thank the JM
review team for helpful feedback and suggestions.
1Throughout this article, we use the term “brand”in a broad sense;
it refers not only to commercially marketed products and services
but also to organizations, nonprofit causes, and any other offering
that consumers may seek information about so that they can make a
choice among alternative options.
©2016, American Marketing Association Journal of Marketing: AMA/MSI Special Issue
ISSN: 0022-2429 (print) Vol. 80 (November 2016), 122–145
1547-7185 (electronic) DOI: 10.1509/jm.15.0419122

sequence them in more powerful ways to move consumers
more quickly along their decision journey or funnel than was
ever possible before. Although these paths to purchase may
be faster and less linear today, there is still a beginning and an
end to them, with identifiable stages in between. To influence
prospective and existing consumers over this complex path,
multiple communication attempts are usually required to in-
form, persuade, or propel action or advocacy, pushing con-
sumers on to the next stage in the process.
Crucially, none of these individual communication attempts
is likely to accomplish the complete “sales”or “loyalty”job
by itself. One message often needs to “set up”or “build on”
another, such as prior trade advertising opening the door to
subsequent personal selling, as the famous McGraw-Hill “man
in the chair”trade ad (created in 1958) vividly showed. In other
words, what each communication attempt needs to do well
depends on the messages that came before and the ones that will
come after. Marketers thus must be concerned not just with
what each message can accomplish in isolation (its “main effect”)
but also with what it needs to accomplish in the context of this
entire sequence or stream of messages (its “interactive effects”).
Recent academic research has confirmed the existence of
interactions and cross-effects across new and old media options
such as search, display, mobile, TV, social media, offline
WOM, and so on (e.g., Joo et al. 2013; Mayzlin and Shin 2011).
However, although research has empirically established the
existence of media interactions and cross-effects, it has not yet
clearly explicated the relative strengths and weaknesses of
different media in influencing different communication out-
comes. Nor does the existing research really help marketers
decide the best sequence in which to use old and new media so
that they can deploy these media in a coherent and integrated
manner.
Successful integration of different marketing communica-
tions is critically important to drive short-term sales and long-
term brand building (Luo and Donthu 2006; Osinga et al. 2011;
Reid, Luxton, and Mavondo 2005). Not surprisingly, when the
Marketing Science Institute (MSI) recently surveyed its 70+
corporate members as to their most critical marketing ques-
tions, a set of questions emerged related to IMC, providing
useful insight into managerial priorities in the area. These
priorities included the development of better models and
frameworks to understand and integrate the full range of
consumer touch points for a brand to both generate short-term
purchases and build long-term loyalty and brand value. These
touch points included marketer-controlled and consumer-
driven media, offline and online media (particularly mobile),
and sales force efforts. The MSI priorities also called for ex-
plication of the organizational processes (including training)
required to achieve marketing integration within and across
marketing and sales.
The MSI priorities make clear that the always-existing
challenge of creating IMC is even more difficult today and thus
requires new tools and theories. Therefore, after reviewing the
existing literature, we argue that this integration challenge can
be met more easily through the use of a conceptual framework
that analytically considers consumers’most pressing brand-
related information needs at different points in their decision
journeys and then matches the particular media and messages
that are strongest in their ability to meet each of those different
specific needs. The planning and sequencing of multiple com-
munications must also account for the costs involved to create
the most effective and efficient IMC program possible (Briggs,
Krishnan, and Borin 2005; Madhavaran, Badrinarayanan, and
McDonald 2005, Naik, Raman, and Winer 2005).
Our article is organized as follows. We first review aca-
demic research onIMC related to the main effects as well as the
interaction effects that can be created by different communi-
cation options. Although this research is extremely insightful,
it does not provide enough overall guidance to address the
challenge of crafting a well-integrated marketing communi-
cations plan today. Toward that goal, we suggest that mar-
keters employ a set of two research-inspired (but empirically
untested) communication models as part of a comprehensive
IMC framework. We develop these models (with supporting
submodels) in depth subsequently. Next, we briefly introduce
the basic functioning of model:
1. A “bottom-up”communications matching model, which
identifies the communication options that have the greatest
ability to satisfy consumers’brand-related information needs
at different stages of the consumer decision journey.
2. A “top-down”communications optimization model that helps
marketers evaluate the overall design or makeup of a mar-
keting communication program with relevant criteria to judge
how well it is integrated to both drive shorter-term sales and
build longer-term brand equity.
Drawing on these learnings and models, we conclude by
considering the broad themes and managerial implications
that emerge from our analysis as well as develop key future
research priorities.
Prior Academic Research on IMC
Academic Approaches to IMC
Different disciplines have different views or approaches
toward IMC that have evolved through the years (see, e.g.,
Madhavaran, Badrinarayanan, and McDonald 2005; Naik
2007). These different viewpoints are reflected in how IMC
has been defined. Appendix A presents some notable defi-
nitions used by relevant organizations. There are some com-
mon elements in these definitions, however, that reflect our
approach to IMC. As we explain in greater detail next, we
take a consumer-centric approach and focus on how marketers
can optimally combine all available communication options.
In academia, researchers have put more emphasis on cer-
tain areas depending in part on their disciplinary background.
Communication and journalism academics have tended to place
more emphasis on internal factors, such as organizational pro-
cesses that promote or inhibit optimal coordination and
sequencing of message content (Schultz and Kitchen 1997). In
contrast, marketing academics have tended to place more
emphasis on factors external to the firm, such as the optimal
design of marketing communication programs based on their
effects on consumers. Two streams of research are notable within
marketing academia:
1. More micro approaches that use consumer psychology and
consumer information-processing principles to study multimedia
Integrating Marketing Communications / 123

campaigns (e.g., Edell and Keller 1989, 1999) and the strengths
and weaknesses of different media (new vs. old) in achieving
different communications goals (e.g., Chang and Thorson 2004;
Dijkstra, Buijtels, and Van Raaij 2005; Pfeiffer and Zinnbauer
2010).
2. More macro approaches that use econometric techniques to
assess multimedia effects at the brand level (e.g., Naik,
Raman, and Winer 2005). A topic of enduring importance in
this line of research is the allocation of resources across media
(Naik and Raman 2003; Raman and Naik 2004).
Our approach continues in the tradition of marketing
academics and their concern with the optimal design of
communication programs based largely on external, consumer-
driven factors (Pilotta et al. 2004; Voorveld, Neijens, and Smit
2011). Although the notions of IMC are applicable to all the
touch points through which a marketer communicates with the
targeted consumer—including the implicit communication that
takes place through product features and design, employee
service, and retail environments—we do not cover such non-
media-related consumer touch points for brands in this article.
As we detail next, several considerations come into play
in developing a well-integrated marketing communications
program for a brand. Three of the most important consid-
erations are consistency, complementarity, and cross-effects
among media and communication options, as the following
example for the Volvo automotive brand shows.
Consistency. To facilitate learning and induce action, the
exact same persuasive message can benefit from being re-
inforced in different ways across different communications. For
example, Volvo’skey“safety superiority”message is com-
municated consistently through print and TV ads, PR, and
corporate communications; on the company’s website and
through other digital means; and through sponsorships with the
American Trucking Associations to promote various key safety
outreach programs for the trucking industry.
Complementarity. At the same time, different communi-
cation options have varied strengths and weaknesses, which can
meet different brand-related information needs for consumers
and, thus, complement each other. Volvo sponsors golf, cul-
tural events, and an ocean race to help improve the brand’s
visibility and contemporary status, raising initial salience and
consideration. Their various sales promotions and financing
programs then push safety-concerned consumers to take action.
Each communication option addresses a different brand
objective, all of which are needed to successfully persuade
consumers and build brand equity and drive sales.
Cross-effects. Communication effects from consumer
exposure to one communication option can be enhanced
when consumers have had prior exposure to a different
communication option. For Volvo, the good feelings and
awareness engendered by one of its sports or arts spon-
sorships may later increase consumer predisposition to con-
sider the brand or color any impressions thata consumer forms
in evaluating a Volvo vehicle.
All three considerations are important. However, given
the substantial prior research reporting the benefits from
message consistency, that area would not seem to be a priority
for further investigation. We therefore focus more in this
article on the complementary main effects as well as the many
cross-effects and interactions that can exist in today’s digital
media environment—in which search, tweets, Facebook
feeds, and so on meet consumers’communication needs as
they move along their decision journey or path to purchase, a
process we describe next.
Consumer Decision Journey or Path to Purchase
In working to understand how different messages and media
can be optimally combined and sequenced, it is first neces-
sary to develop a model of the different stages or steps consu-
mers might go through in their evolving relationships with a
particular brand—before, during, and after purchase. The stages
in such a model can help suggest different communication
objectives and metrics depending on what stage the consumer is
in and, thus, which combination of media and messages might
be most appropriate there.
Building on classic early models from social and cog-
nitive psychology (McGuire 1978), many marketers have
founditusefultoportraythedifferent stages involved in
consumer decision making in terms of the well-known
“purchase funnel.”A simple version of the funnel is the
classic “hierarchy-of-effects”models (Wijaya 2012; e.g.,
AIDA [Awareness, Interest, Desire, and Action]). In today’s
rich communication environments, these more traditional
funnels, however, no longer adequately capture the different
stages involved in consumer decision making and the more
complex, nonlinear paths to purchase that consumers might
follow when choosing among multiple options.
Recently, researchers from McKinsey & Company (Court
et al. 2009) have suggested a “consumer decision journey
circle,”in which consumers begin by considering a prelimi-
nary set of brands to form an initial consideration set, modify
this consideration set (often adding more brands) as they gather
and evaluate more brand information, select a brand (at the
moment of purchase), and then utilize their postpurchase ex-
periences to shape their next decision. Consistent with that
reasoning, “attribution modeling”of actual consumer paths to
purchase has shown that consumers often go through com-
plex, long, and interacting steps before conversion and pur-
chase, with organic and paid search, retargeted display and
banner ads, price comparisons and e-mails, and visits to
marketer websites and physical stores all playing a part in
getting consumers from their initial starting points to the point
of conversion (Anderl et al. 2014; Smith, Gopalakrishna, and
Chatterjee 2006).
Although consumers today interact with many more media
types, and do so in more nonlinear and circular ways, it seems
reasonable that consumers still go through a sequence of steps
or stages as they engage with a particular brand. For instance,
consumers can go through a series of stages such as those
in Appendix B: they (1) feel a need or want for the overall
category of which the brand is a part; (2) recall the brands they
associate with meeting that category-level need; (3) further
evaluate a smaller subset of those brands not only with respect
to performance quality but also about their trustworthiness; (4)
develop a preference and make a tentative choice; (5) decide
how much they are willing to pay for the preferred brand; (6)
124 / Journal of Marketing: AMA/MSI Special Issue, November 2016

take the action step of trial or purchase; (7) form an assessment
of postconsumption satisfaction with the brand, which deter-
mines repurchase intentions and loyalty; and hopefully, over
time, (8) increase their usage amount or purchase frequency; (9)
engage in postpurchase interactions with the brand; and (10)
become a loyal and willing advocate for it.
Because each of these steps is probabilistic, a successful
consumer decision journey for a brand can be derailed by
failure at any stage (e.g., ignorance of the existence of the
brand, a negative product experience at trial). Across brands
and for any one brand, consumers may backtrack, skip steps,
or implicitly or explicitly choose to reject the brand. Figure 1
displays the dynamics involved in a consumer decision jour-
ney with multiple stages and the potential to move forward or
backward across stages or drop out of the decision process
altogether.
Each of these steps puts the consumer in a particular
information-processing state of mind, which makes any one
media type more or less appropriate for deployment by the
brand to satisfy that consumer’s information needs and ensure
movement to the next stage. Subsequently, we elaborate on
these likely “states of mind”and their IMC implications. First,
FIGURE 1
A Dynamic, Expanded Consumer Decision Journey
Needs/Wants
Is Aware/Knows
Considers/Examines
Searches/Learns
Likes/Trusts
Sees Value/Is Willing to Pay
Commits/Plans
Consumes
Is Satisfied
Is Loyal/Repeat Buyer
Is Engaged/Interacts
Act ively
Advocates
Rejection
Integrating Marketing Communications / 125

we reviewsome of the relevant research on main (direct) as well
as interactive (indirect) effects across different media.
Main Effects of Different Media: Research Findings
Much academic research has shown the effectiveness of dif-
ferent communication options and media, as well as the value
of a multimedia campaign (Koslow and Tellis 2011; Sethuraman,
Tellis, and Briesch 2011; Wind and Sharp 2009). For exam-
ple, Danaher and Dagger (2013) provide evidence of the
effectiveness of multimedia advertising for a chain of depart-
ment stores. Specifically, they found that among traditional
media options, catalogs most strongly influenced sales and
profit, followed by TV and direct mail. For digital media
options, only e-mail and sponsored Google search had an
influence on purchase outcomes. In their setting, magazines,
online display advertising, and social media had no significant
association with purchase incidence or outcomes.
Collectively, prior research has revealed some of the in-
herent strengths and weaknesses of different communication
options and how they can be most effective (Belch and Belch
2015; O’Guinn et al. 2015). Much research has examined the
short- and long-term effects of traditional advertising media
(Assmus, Farley, and Lehmann 1984; Hu, Lodish, and Krieger
2007; Lodish, Abraham, Kalmenson, et al. 1995, Lodish,
Abraham, Livensberger, et al. 1995; Stewart and Furse 1986;
Vakratsas and Ambler 1999) or has compared those effects
with those of promotion (Jedidi, Mela, and Gupta 1999; Mela,
Gupta, and Lehmann 1997). To illustrate some of the issues
involved, it is useful to briefly contrast some of what we have
learned from academic research on advertising in traditional
media (e.g., print, TV) with what we have learned from aca-
demic research on newer online media (for some empirical
comparisons between the two, see Draganska, Hartmann, and
Stanglein [2014] and Trusov, Bucklin, and Pauwels [2009]).
Traditional media. Even in today’s media environment,
the power of traditional advertising media endures (Nunes
and Merrihue 2007). Advertising research continues to address
many interesting, important topics—showing, for example, (1)
how informative advertising can create brand awareness and
increase knowledge of new products or new features of existing
products (Amaldoss and He 2010; Barroso and Llobet 2012),
(2) the role of ad creative in persuasion (Reinartz and Saffert
2013), (3) the effects of comparative advertising (Thompson
and Hamilton 2006) or competitive advertising (Danaher,
Bonfrer, and Dhar 2008), (4) estimates of response functions
(Freimer and Horsky 2012; Tellis, Chandy, and Thaivanich
2000; Vakratsas et al. 2004), and (5) the effects of ad rep-
etition (Malaviya 2007).
Concrete guidelines have emerged from this work (Hanssens
2015). For example, a review of academic research has found
that advertising elasticities were estimated to be higher for
new (.3) than for established products (.1) (Allenby and Hanssens
2005; see also Sethuraman, Tellis, and Briesch 2011; Van
Heerde et al. 2013). Research has also found that what a message
says (e.g., the emotional vs. attribute content of ad copy or of
online WOM) is typically more important than the number
of times a message is communicated (e.g., the volume of ad
exposures or online WOM conversation) (Gopinath, Thomas,
and Krishnamurthy 2014). Research has also explored newer
topics such as “open sourcing”or “crowdsourcing”and the use
of consumers as a creative team (Lawrence, Fournier, and Brunel
2013; Thompson and Malaviya 2013; see also Marketing Sci-
ence’s 2012 Special Issue on the Emergence and Impact of
User-Generated Content).
By identifying various factors that moderate advertising
effectiveness, this prior research also offers relevant insights
into how older and newer media interact with each other.
Advertising effects certainly vary by medium. For example,
consumers process broadcast (TV and radio) advertising
differently from print (newspapers and magazines) adver-
tising. Because of its pervasiveness and expressive nature,
TV advertising is highly effective at creating awareness,
interest, and consideration—the front end of the funnel or
consumer decision journey. In contrast, print advertising
allows for detailed exposition and is well-suited for mid-
funnel information provision (see, e.g., classic comparisons
of the “information content”of different media by Stern and
Resnick [1991] and Abernathy [1992]).
Newer online media. A company chooses which forms
of online communications will be most cost effective in
achieving sales and brand equity objectives (Katona, Zubcsek,
and Sarvary 2011; Risselada, Verhoef, and Bijmolt 2014). The
wide variety of available online communication options means
companies can offer tailored information or send messages that
engage consumers by reflecting their special interests and
behavior. We next highlight six key online communication
options that are receiving increased research attention and some
illustrative research findings that have emerged for each.
•Search ads (Berman and Katona 2013; Jerath, Ma, and Park
2014; Rutz and Bucklin 2011; Rutz, Bucklin, and Sonnier
2012). Because consumers w ho se arch for les s popular
keywords expend more effort in their search for information
and are closer to a purchase, they can be more easily targeted
through sponsored search advertising. Higher positions of
paid search ads can increase both the click-through rates and,
especially, the conversion rates.
•Display ads (Danaher, Lee, and Kerbache 2010; Hoban and
Bucklin 2015; Manchanda et al. 2006). Display advertising
can positively affect visitation to a firm’s website for users in
most stages of the purchase funnel but may not do so for those
who previously visited the site without creating an account. In
one application, expected visits increased by almost 10%
when display ad impressions were partially reallocated from
nonvisitors and visitors to authenticated users.
•Websites (Danaher, Mullarkey, and Essegaier 2006; Hauser
et al. 2009; Steenkamp and Geyskens 2006). Websites can be
better liked and can increase sales if their characteristics (e.g.,
more-detailed data) match customers’cognitive styles (e.g.,
more analytic). Differences in consumers’age, gender, and
geographical location can also affect successful website
characteristics.
•E-mail (Aufreiter, Boudet, and Weng 2014; Li and Kannan
2014). The rate at which e-mails prompt purchases has been
estimated to be at least three times that of social media, and
the average order value has been estimated to be 17% higher.
E-mail effectiveness has been shown to improve with per-
sonalized emails—though, even then, repeated e-mails can
backfire—as well as with customized landing pages when
someone clicks.
126 / Journal of Marketing: AMA/MSI Special Issue, November 2016

•Social media (Kumar et al. 2016; Naylor, Lamberton, and West
2012; Schweidel and Moe 2014). Three key characteristics of
firm-generated content—valence, receptivity, and customer
susceptibility—have been shown to positively affect customer
spending, cross-buying, and profitability, though the effect of
receptivity was shown to be the largest. Approaches to mon-
itoring social media that ignore the multiple social media
venues that exist for the brand and either focus on a single
venue or ignore differences across venues have been shown to
produce misleading brand sentiment metrics.
•Mobile (Bell 2014; Fang, Luo, and Keith 2014; Hwang,
McInerney, and Shin 2015). When and where consumers use a
mobile phone to shop has been shown to have important
implications for how they shop (e.g., proximity to physical
stores for a brand, physical presence of other brand users).
Mobileconsumershavebeenshowntobemorelikelytogo
directly to a retailer’s site or app than to use a search engine;
moreover, they deliberate less and make purchases more by
impulse than by product features. Research has found mobile
ads and coupons to be more effective when customized (to
reflect, e.g., a person’s tastes, geographic location, time of day).
Marketers can trace the effects of online marketing com-
munications in various ways (Bonfrer and Dr`
eze 2009), such as
by noting how many unique visitors click on a page or ad, how
long they spend with it, what they do on it, and where they
go afterward. Although some traditional media also allow for
placement (e.g., newspaper sections, special interest mag-
azines), online media offers even more targeted placement,
through the placement of ads on sites related to a company’s
offerings and the ability to place ads on the basis of search
engine keywords, to reach people who have started the buying
process. Marketers can also emphasize certain types of newer
media to signal that the firm has a particular competence or
personality, making the choice of medium itself the message
(McLuhan 2001; e.g., a firm such as Delta Airlines may use
Twitter for customer service in part to signal its utmost re-
sponsiveness to immediately solving customer problems).
Mixing and matching online media. As with all forms of
communication, each online option has pros and cons, suggesting
the need to mix and match. For example, given that Internet users
may spend only 5% of their time online searching for infor-
mation, display ads still hold great promise compared with search
ads. These display ads, however, need to be more attention-
getting and influential, better targeted, and more closely tracked
to fully realize that potential (Braun and Moe 2013; Lambrecht
and Tucker 2013; Schumann et al. 2014; Urban et al. 2014).
Social media has received much academic and practi-
tioner attention (Lamberton and Stephen 2016). Three main
social media platforms enable consumers to become engaged
with a brand at perhaps a deeper and broader level than ever
before (Van Den Bulte and Wuyts 2007): (1) online com-
munities and forums, (2) blogs (individual blogs and blog
networks such as PopSugar and TechCrunch), and (3) social
networks (e.g., Facebook, Twitter, YouTube).
Although marketers can encourage willing consumers to
engage productively with their brands on social media (Lee
and Bell 2013; Naylor, Lamberton, and West 2012), research
has shown that social media should rarely be the sole source
of marketing communications for a brand for the following
reasons:
•Social media may not be as effective as more traditional
forms of communications in attracting new users and driving
brand penetration.
•Research by DDB and others has suggested that brands and
products vary widely in how social they are online. Con-
sumers are most likely to engage with media, charities, and
fashion and least likely to engage with consumer goods
(Schulze, Sch¨
oler, and Skiera 2014).
•Although consumers may use social media to get useful
information or deals and promotions or to enjoy interesting or
entertaining brand-created content, a much smaller percentage
of consumer want to use social media to engage in two-way
“conversations”with brands.
Nevertheless, consumers can be influenced by the online
opinions and recommendations of others. The informal social
networks that arise among consumers complement the product
networks set up by the company (Goldenberg, Oestreicher-
Singer, and Reichman 2012). Online “influentials”who are
one of a few people (or maybe even the only person) to
influence certain consumers are particularly important and
valuable to companies (Katona 2014; Trusov, Bodapati, and
Bucklin 2010). Although online communications may be
more influential than mass communication for many con-
sumers, mass media might be the major means of stim-
ulating it (Bruce, Foutz, and Kolsarici 2012; Gopinath,
Thomas, and Krishnamurthy 2014).
Interaction and Cross-Effects Across Different
Media: Research Findings
Understanding the direct or main effects provides valuable
insight into possible components of an IMC program. To be
able to decide on exactly which communications to employ,
however, it is also necessary to understand the interaction
effects that may arise. However, very few academic articles
have taken a macro, “big picture”perspective to examine such
interactions. Before presenting our two models to help with
this goal, we first review some relevant academic research on
media interactions.
Several different academic works have provided useful
insight into the nature of cross-media effects, typically
focusing on just a few communication options at a time. As a
general rule, this research has contrasted cross-media syn-
ergies with corresponding within-media effects to find the
incremental value of coordinating communication strategies
across media in some fashion. Next, we next highlight some
representative cross-media research in three different areas.
Traditional media synergies. Some of the earliest re-
search on IMC showed how traditional advertising and
promotions—such as TV, radio, and print advertising; price
promotions; and others—interacted with each other to lead to
more favorable consumer response.
•Several researchers empirically demonstrate how coordi-
nated TV–radio and TV–print campaigns can improve recall
and lead to more favorable attitudes (Edell and Keller 1989,
1999; Jagpal 1981; Naik and Raman 2003).
•Lemon and Nowlis (2002) show that synergies between
displays and price promotions, as well as between feature
advertising and price promotions, were greater for low-tier
brands than high-tier brands.
Integrating Marketing Communications / 127

Sales force and personal selling interactions. Much research
has shown that personal selling efforts and the sales force are more
productive when preceded by or combined with other forms of
marketing communications.
•Smith, Gopalakrishna, and Smith (2004) show that follow-up
sales efforts generate higher sales productivity when firms
have already exposed customers to its product at a trade
show. Trade show exposure reduced sales force expenses by
approximately 50% to generate a given level of sales (p. 70).
•Gopalakrishna and Chatterjee (1992) find an interactive
effect of advertising and personal selling on sales of a mature
industrial product of low complexity. This interaction was
more than half the size of the main effect of advertising itself
(p. 192).
•Gatignon and Hanssens (1987) show how personal selling
effectiveness in Navy recruiting increased with local adver-
tising support (Mantrala 2002).
•Narayanan, Desiraju, and Chintagunta (2004) report that
direct-to-consumer advertising and detailing interacted to
affect brand shares in a pharmaceutical category.
•Naik and Raman (2003) demonstrate positive interactions
between advertising and several different marketing activities:
Advertising effectiveness increases with improved product
quality (Kuehn 1962), greater retail availability (Parsons 1974),
increased salesperson contact (Swinyard and Ray 1977), and a
larger sales force (Gatignon and Hanssens 1987).
Online and offline synergies. Marketers are increasingly
attempting to coordinate all their online and offline com-
munications activities. Recent research has provided much
evidence as to how online and offline communications can
interact by tapping into different stages of the consumer
decision journey (Chang and Thorson 2004; Havlena,
Cardarelli, and De Montigny 2007).
•For a major German car company, Naik and Peters (2009)
found synergies within the offline media types (TV, mag-
azines, radio and newspapers) for offline dealer visits, while
for online website visits, they found both these within-media
synergies and cross-media synergies with online media types
(banner and search ads). Direct mail only contributed to the
online visits, without any synergies.
•Wiesel, Pauwels, and Arts (2011) found multiple online–offline
synergies in a model that examined multiple funnel outcomes
and also utilized time delays (lags) and feedback relations. They
found the maximum profit impact for Google AdWords was
17 times higher than that for faxes, which were the highest-
impact offline medium studied. The sales elasticity for Google
AdWords was a sizable 4.35. Notably, in their study, 73% of the
effect of Google AdWords took place through offline orders.
•Dinner, Van Heerde, and Neslin (2014) found that, for a high-
end clothing and apparel retailer, paid search and online display
advertising had the greatest impact on both online and offline
sales, and both were more effective than traditional advertising
primarily because of strong cross-effects on the offline channel.
Clearance promotions had even higher effects on online sales.
They also found that traditional advertising decreased paid
search click-through rates, thus reducing the net cross-effect of
traditional advertising.
•Drawing on customers’path of visits and purchases at a travel
and hospitality firm’s website, Li and Kannan (2014) found
significant carryover and spillover effects across online channel
options. If customers visited the website through e-mail and
display channels (firm-initiated channels), it increased visits to
the website through customer-initiated channels (e.g., search,
direct, referrals) but increased purchase probabilities (con-
versions) only through paid search and direct visits (but not
through referrals).
•Stephen and Galak (2012), in studying the microlending
website Kiva, found that traditional earned media activity (e.g.,
press mentions in newspaper articles and on television pro-
grams) flowed from social media (e.g., posts made by con-
sumers on online communities, social networking websites, or
blogs), but not the other way around. The per-event sales
impact of traditional-media earned mentions was greater than
that for social-media-earned mentions—presumably because
of the former’s greater reach—but because the frequency of the
latter was much higher, social-media-earned mentions ended
up having greater overall impact on sales.
•Kumar et al. (2016) found that firm-generated content in
social media worked synergistically with both television
advertising and e-mail marketing.
•Gopinath, Thomas, and Krishnamurthy (2014) reported that
emotion-oriented advertising drove online WOM recom-
mendations and thus sales.
Development of a Conceptual
Framework
As the previous section’s review shows, prior academic re-
search has persuasively documented that significant cross-
effects do occur across media. At the same time, this prior
research has several limitations.
First, it has not illuminated the full range of outcomes that
may arise from consumer exposure to communications along
different stages of the consumer decision journey, as we show
in Appendix B and Figure 1. Many different dependent var-
iables have been identified and examined collectively across
studies, but in any one study, the focus has typically been on
only one or a few outcomes. Because marketers are potentially
interested in many of the outcomes shown in Appendix B and
Figure 1, this narrow focus of current research is clearly an
important limitation.
Second, current research has tended to study only message
effects that occur at the same point of time, rather than to also
incorporate lagged and downstream effects. Thus, it has not
really provided a fully dynamic, longitudinal view of when and
how cross-effects occur across different media and the tem-
poral sequences (downstream effects) involved. Consumers
view messages in sequence, and research should therefore
study the effects of multiple messages viewed in sequence,
with the ordering (sequence) potentially making a difference.
Some prior research has already demonstrated that media
effects can vary depending on the sequence or order in which
they are seen. For example, Kim, Yoon, and Lee (2010) ex-
perimentally show that the interactive effects of advertising
with positive or negative publicity vary depending on which is
seen first (see also Edell and Keller 1989, 1999; Stammerjohan
et al. 2005; Voorveld, Neijens, and Smit 2012).
A third drawback of prior research is that it generally pools
all consumers together during analysis, implicitly assuming that
they have the same response elasticities to every communi-
cation or media option. Because consumer needs vary by their
stage in the decision journey, their brand-related information
needs and response elasticities also likely vary with their stage
in the consumer decision journey; therefore, the stage of their
128 / Journal of Marketing: AMA/MSI Special Issue, November 2016

decision journey should be used as a moderator variable to
allow for heterogeneity in effects. For example, the high ef-
fectiveness of search advertising found in prior research
(e.g., Wiesel, Pauwels, and Arts 2011) may be because it
perfectly fits the needs of those consumers in that particular
stage of the decision journey. As Dinner, Van Heerde, and
Neslin (2014, p. 542) note: “The strong effects for search
advertising are probably due to the medium’s‘value pro-
position,’in that search advertising targets customers who
are already in the process of buying. As a result, paid search
expenditures translate more readily into purchases”(emphasis
added).
In one of the few studies that has attempted to address
these shortcomings, Smith, Gopalakrishna, and Chatterjee
(2006) developed a three-stage model of the communications
of a manufacturer of replacement windows that captured the
effects of their expenditures and the exposure sequence for
communications for nine different media on multiple out-
comes (e.g., lead generation, appointment conversions, sales
conversions). Their analysis incorporated main and inter-
active effects, individual-media time lags, and delayed decay
effects. They found that follow-up sales efforts were more
effective if consumers had already been exposed to media
communications, with media type and timing of exposure
making a difference; they state that “the indirect effects of
increased radio spending on the level of response to news-
paper ads and exhibition are substantial”(p. 570). Another
study that utilizes multiple outcome metrics and examines
time-delayed effects and feedback relationships is Wiesel,
Pauwels, and Arts (2011).
Such incorporation of many more outcomes as dependent
variables, lagged and downstream sequence effects, and effect
heterogeneity (varying with consumer stage in the decision
journey) is required not just in future empirical research but
also in the development of more relevant conceptual frame-
works. To guide managerial decision making and academic
research for IMC, an updated comprehensive, dynamic frame-
work is needed that captures the full range of outcomes of interest
as well as the various temporal sequences involved. With a goal
of providing insight into which combinations of media are best
used at different stages of the consumer journey, in the next
section we propose two different communication models as part
of such an improved conceptual framework. These models
address two specific questions:
1. What are the effects or outcomes created by different types of
communication options or platforms? Which of these com-
munication options best help achieve the communication
objectives associated with each stage in the consumer deci-
sion journey?
2. How do you ensure that those communication options are
well-integrated to collectively maximize the communication
effects created?
Combining these models provides useful guidance and
advice to answer academic and managerial questions as well
as to suggest propositions for further research. Before we
develop our models, however, it is helpful to first group the
different media possibilities. One broad distinction many mar-
keters and academic researchers have made (e.g., Stephen
and Galak 2012) is between communications that appear
in paid media (traditional outlets such as TV, print, and
direct mail), owned media (company-controlled options
such as websites, blogs, mobile apps, and social media),
and earned media (virtual or real-world WOM, press
coverage, etc.).
For our purposes, we focus more on paid and owned
media, given that those are the areas over which marketers
have the most control, though we also incorporate earned
media (e.g., WOM advocacy) in our analyses. The most
important examples of these different media types appear as
column headings in Table 1 and are described more com-
pletely in the next section.
Understanding Consumer
Processing of Communications
To develop a fully integrated marketing communication pro-
gram, it is first necessary to understand how communications
“work”over a consumer’s decision journey—specifically, the
resources and mindsets a consumer brings to the reception and
processing of different messages, as well as the outcomes these
TABLE 1
Research Propositions Concerning Likely Communication Outcomes from Different Communication Options
Communication Outcomes
Communication Options
TV Promos Events PR Social Media Website Search Display Mobile Direct Selling
Create awareness and
salience
+++ ++ ++ ++ +++ ++ +++ +++ +++ ++ +
Convey detailed information + + + + ++ +++ + + ++ +++ +++
Create brand imagery
and personality
+++ ++ ++ ++ +++ ++ + + ++ + +
Build trust + + + +++ +++ + + + ++ + +++
Elicit emotions +++ ++ +++ +++ +++ ++ + + ++ + +
Inspire action + +++ + + + ++ +++ ++ +++ +++ +++
Instill loyalty ++ + + + ++ ++ + + ++ ++ ++
Connect people + + ++ + +++ +++ + + +++ + +
Notes: +++ = greatest influence; ++ = medium influence; +=least influence.
Integrating Marketing Communications / 129

messages can lead to in terms of consumer knowledge, atti-
tudes, and action tendencies. Figure 2 organizes the key fac-
tors that interact to determine these outcomes into consumer
characteristics, (message reception) context, and content of the
communication itself. Figure 2 also makes clear that the out-
comes of interest are many (Keller 2003), ranging from creating
awareness, to conveying information or emotion and building
trust, to taking action and even engaging in brand advocacy.
The extent to which each of these outcomes occurs will depend
on the interactive effect of the consumer characteristics, pro-
cessing context, and message content factors, some which we
highlight in the following subsections.
Consumer Characteristics
Much research has shown how consumer motivation, ability,
and opportunity to process a communication determine the
intensity and direction of that processing and the resulting
outcomes that occur (Batra and Ray 1986; MacInnis and
Jaworski 1989; MacInnis, Moorman, and Jaworski 1991; Petty,
Cacioppo, and Schumann 1983). If motivation, ability, or
opportunity are lacking for any reason, consumer processing
of a communication will be impaired or may not even occur.
While motivation and ability to process information are
consumer-level factors, the opportunity to do so is determined
in large part by the nature of the medium itself, as well as the
processing conditions (context) under which the message is
received (which we discuss separately in a following
subsection).
The motivation, or desire, to process incoming infor-
mation varies with the extent to which the consumer views
it as potentially helping with the brand choice task at hand.
This increases with the level of perceived risk (Cox 1967;
Loewenstein et al. 2001; Mitchell 1999) and the degree to
which the category-level need is salient or pressing—such as
the geo-location of the consumer (affecting the processing of
mobile messages) or the degree to which the consumer is in
the search process (affecting the processing of retargeted
online advertising). Situational factors may also matter in
their potential to affect consumers’emotional state/mood and
willingness or desire to seek information.
The consumer ability to process information depends,
among other factors, on the amount of prior familiarity and
knowledge with the brand and category. Thus, consumers at
the earliest stages of category search might not be able to
process in-depth attribute-level brand comparison informa-
tion (Alba and Hutchinson 1987), though they should still be
able to understand which brands are more versus less relevant
for their brand choice decisions (Court et al. 2009).
Interactions across motivation, ability, and opportunity
factors can have important implications for IMC planning.
For example, mobile brand messages may tap into high-
motivation processing situations but be low in processing
opportunity; alternatively, social media messaging from
FIGURE 2
Factors That Affect Consumer Communication Processing
Consumer
Situation
Communication
Outcomes
Ability
Motivation
Modality
characteristics
Brand and
product
information
Executional
characteristics
Time
Place
Propensity to create
awareness and
salience
Propensity to
convey detailed
information
Propensity to create
imagery and
personality
Propensity to
build trust
Propensity to
inspire action
Propensity to
connect people
Source
credibility
characteristics
Propensity to
elicit emotions
Propensity to
instill
loyalty
Opportunity
130 / Journal of Marketing: AMA/MSI Special Issue, November 2016

trusted peers might be very high in credibility but low in its
ability to convey detailed information.
Characteristics of the Processing Context or
Situation
As mentioned previously, the consumer’s opportunity to process
information is affected by several aspects of the time and place of
message exposure, such as whether the consumer is under time
pressure or in a physical setting where (s)he can process the
communications in depth. Different media options vary in how
strong they are along these processing factors. Thus, certain
media, such as mobile or billboards, seem more suitable for
messages that remind or trigger action rather than for messages
that aim to persuade through detailed information. Other media,
such as online video or TV, naturally lend themselves to
messages that inject more emotion and imagery into brand
perceptions.
Table 1 shows some of these plausible relationships. In
doing so, it draws on prior research by Stern and Resnick
(1991) and others who compared the “information content”
across multiple media (e.g., TV ads typically having less
product information than print ads). While textbooks on
marketing communications provide useful descriptions
of practitioner beliefs about the qualitative strengths and
weaknesses of alternative media classes on various com-
munication goals (e.g., O’Guinn et al. 2015), most of these
relationships have, to our knowledge, not yet been sub-
jected to formal empirical investigation by academic
researchers.
Communication Content Characteristics
Research has also examined how various characteristics of
the communication itself (e.g., its modality, its executional
and message information, its source credibility) influence the
resulting outcomes from exposure (Stern and Resnick 1991).
Creative strategies such as music, celebrities, special effects,
sex appeals, and fear appeals are all examples of attention-
getting devices to increase consumer motivation to process a
communication and build awareness and salience, though they
may direct processing away from the brand or its message in
the process, hurting preference formation (McGuire 1978).
While much of the prior research using traditional media
should also apply to the newer media, marketers need to
think about communication content idiosyncrasies in today’s
digital environment, such as different processing implica-
tions and outcomes for different types of Facebook posts,
tweets, and so on (e.g., De Vries, Gensler, and Leeflang 2012;
Lee, Hosanagar, and Nair 2015; Stephen, Sciandra, and
Inman 2015).
The nature of the medium can itself affect opportunity to
process: the smaller screen size of a mobile message can limit
processing depth, while detailed information can be provided
on a website that permits extensive brand-related processing
by consumers, assuming that they are attracted to the website
to begin with. Yet this detailed information may lack cred-
ibility and may not motivate consumers unless it is attributed
to a trustworthy message source (McGuire 1978).
Communication Outcomes
As stated previously and reflected in Appendix B, the consumer
processing that results from exposure to a communication—
contingent on the characteristics of the communication, con-
sumer, and context involved (see Figure 2)—can create several
different possible communication outcomes. Depending on
the stage of the consumer’s processing journey, these multiple
possible outcomes vary in importance to a particular brand
marketer. These communication outcomes or goals include
the following:
•Create awareness and salience. The foundation of all brand
and marketing efforts is creating awareness and ensuring that
the brand is sufficiently salient and thought of in the right way
at the right times and right places. Salience occurs when the
brand is associated with a wide variety of cues—categories,
situations, need states, and so on—such that the brand is
recalled easily and often (Keller 2001a). In some cases (e.g.,
with really new products), awareness of both an unmet need
and the brand to satisfy that need may be required.
•Convey detailed information. After creating that basic aware-
ness, marketers must convince consumers of the advantages of
choosing the actual products or services identified with the
brand. Persuading consumers about brand performance requires
that they appreciate the benefits of the products or services and
understand why the brand is able to better deliver those benefits
in terms of supporting product attributes, features, or charac-
teristics that function as “proof points.”In today’scrowded,
limited-attention-span communication environment, effectively
conveying such detailed information to consumers is more and
more challenging.
•Create imagery and personality. Most successful brands have a
duality—what they offer in terms of tangible and intangible
benefits. All kinds of user and usage imagery may be created in
terms of the type of person who uses the brand, when and where
the brand could be used, and so on. Brand personality reflects the
human-like traits consumers attribute to a brand (e.g., sincer-
ity, competence, excitement; Aaker 1997) and influences how
consumers view themselves (Park and John 2010) and the brand
relationships/bonds that consumers form (Batra, Ahuvia, and
Bagozzi 2012; Fournier 1997; Swaminathan, Stilley, Ahluwalia
2009).
•Build trust. Even when the consumer receives and processes
the information in the message, (s)he might not use it in
judgment and decision making if it is not sufficiently credible
and trustworthy or “diagnostic”(Lynch, Marmorstein, and
Weigold 1988). There is a vast stream of literature on “source
effects”(Janis and Hovland 1959; Ohanian 1990), which has
highlighted the important role of source credibility, expertise,
likability, and similarity in facilitating the acceptance of
messages. Consumers are increasingly knowledgeable and
skeptical about marketer influence attempts (Campbell and
Kirmani 2008; Friestad and Wright 1994), and there is an
increasing desire for product and message “authenticity”
(Brown, Kozinets, and Sherry 2003). The increasing usage
of social media communications from friends and peers is
indicative of the greater importance of messages that come
from similar and trusted, rather than distant and motivated,
sources.
•Elicit emotions. The importance of emotional, social, and sym-
bolic benefits and purchase motivations for branded products has
been known for decades (Batra and Ahtola 1990; Belk 1988;
Holbrook and Hirschman 1982; Levy 1959). Brands can increase
their total perceived value by adding such benefits to the usual
functional/utilitarian ones. Brand messages can increase these
Integrating Marketing Communications / 131

perceived “added value”benefits, for example, through mech-
anisms such as the transfer of cultural meaning (Batra and Homer
2004; McCracken 1989; Torelli et al. 2012). A broad array of
benefits also helps elicit different types of emotions that affect
consumer decision making (Edell and Burke 1987; Holbrook and
Batra 1987; Olney, Holbrook, and Batra 1991; Richins 1997;
Westbrook and Oliver 1991).
•Inspire action. Brand message information that is received,
processed, and accepted may shape brand preference and
choice but still not lead to action and behavior because of the
inherent disconnect that can occur between cognition, affect,
and behavior (Bagozzi and Dholakia 1999; Johnson, Chang,
and Lord 2006). Therefore, particular kinds of messaging are
often required when the goal is the inducement of action and
behavior from consumers who are already favorably pre-
disposed to the brand.
•Instill loyalty. After actual consumption, consumers form an
assessment ofhow satisfied they are, in which their expectations
are compared with their interpreted consumption experience,
with the latter itself being potentially influenced by market-
ing communications (Bloemer and Kasper 1995; Chaudhuri
and Holbrook 2001; Hoch and Ha 1986; Oliver 2014). Shaping
satisfaction is part of a larger objective of reinforcing loyalty
and avoiding customer defections (Brakus, Schmitt, and
Zarantonello 2009; Johnson, Hermann, and Huber 2006;
Park et al. 2010).
•Connect people. High consumption satisfaction should lead
to brand repurchase behavior and loyalty, but this may not,
by itself, create brand advocacy. Such brand advocacy and
WOM are especially important for service brands, for which
the consumer cannot otherwise obtain tangible and credible
evidence of product quality and trustworthiness (Berry
2000). For WOM and advocacy to occur, the consumer might
need to engage and interact frequently with the brand and
develop a sense of “brand love”(Batra, Ahuvia, and Bagozzi
2012), arising in part from the brand becoming meaningful
in a symbolic and emotional (not just functional) manner.
Furthermore, the brand might need to send consumers the
types of brand messages that would motivate them to pass
these messages along to others (Berger 2014; Brown and
Reingen 1987; Chevalier and Mayzlin 2004; Godes and
Mayzlin 2004).
Rationale for the Communications Models
In developing an IMC program, the implication of the pre-
vious discussion is that marketers should be “media neutral”
and evaluate all communication options on the basis of
effectiveness (How many desired effects does a communi-
cation create?) and efficiency (At what cost are those out-
comes created?). In other words, marketers ultimately only
care about achieving their communication goals and moving
consumers along in their decision journey—any means of
communications that would facilitate those goals should be
considered.
For example, whether a consumer has a strong, favorable,
and unique brand association of Subaru with “outdoors,”
“active,”or “rugged”because of a TV ad that shows the car
driving over rough terrain or because Subaru sponsors ski,
kayak, and mountain bike events, the impact in terms of
Subaru’s brand equity should be similar unless the associ-
ations created are materially different in some ways. Research
has shown that “direct experience”communications (e.g.,
event-created ones) can create stronger associations than can
mass-mediated communications (e.g., through TV com-
mercials) (Smith and Swinyard 1983).
This kind of analysis is facilitated by our communications
matching model, a “bottom-up”approach to choosing com-
munications according to their effectiveness in achieving the
right communication effects at different stages of the con-
sumer decision journey. After reviewing that model, we then
present our communications optimization model, which takes
more of a “top-down”view of the communications program
as a whole to consider how efficiently it has been assembled
and how well it has been integrated overall.
Communications Matching Model
The communications matching model matches the expected
main and interactive effects of different media options with
the communications objectives for a brand, sequencing the
former to best match the dominant need at different stages
along the consumer decision journey. The communication
matching model thus considers the needs/gaps in consumer
knowledge and behaviors in terms of where different targeted
consumer segments are versus where they need to be, relative
to the brand’s needs, and then suggests the most appropriate
media combinations that should best meet these needs/gaps
at each stage along the journey.
When combined with prior research on media strengths,
our description of consumer processing factors (Figure 2)
suggests the kinds of processing effects or outcomes that
would likely be created by different types of communica-
tions (see Table 1). To develop the communications matching
model, it is also necessary to identify the communication
outcomes marketers actually desire at different stages of the
consumer decision journey. Table 2 offers such a character-
ization on the basis of the likely communication needs and
objectives along the consumer path to purchase.
By knowing both (1) the kinds of outcomes that different
communications create and (2) the kinds of outcomes that
marketers want at different stages of the consumer decision
journey, it is possible to derive the kinds of communications
that would be most helpful to marketers as consumers move
through their decision journey. Applying the logic and
analysis from Tables 1 and 2 thus suggests the communi-
cations that might be needed at different stages, as we sum-
marize in Table 3 (see also Young 2010).
Many of the linkages shown in these tables have not been
empirically established by academic research; they should
therefore be viewed as research propositions in need of
empirical testing. In particular, within the category of “new
media,”additional research is required to confirm that (1)
search advertising and display ads are the vehicles best suited
to raising brand awareness and salience; (2) own websites are
best for communicating brand benefits and features; (3) social
media is best for creating preference and, later, loyalty; and
(4) mobile is best for pushing persuaded consumers to action.
Although these strengths seem intuitively plausible, and
some are suggested in the research reviewed in the previous
section (e.g., Naylor, Lamberton, and West 2012), these are
important model assumptions that clearly require testing,
132 / Journal of Marketing: AMA/MSI Special Issue, November 2016

especially because the nature of the message and creative can
also shape the nature of these relationships.
To illustrate how such a model can operate, we next
examine several of the possible steps along the consumer
decision journey (see Appendix B). We discuss some of the
likely needs/gaps in consumer knowledge and behaviors at
each stage (combining a few) and propose media combina-
tions that might therefore be most useful as part of an IMC
plan for that stage.
Stage 1: The Consumer Recognizes That (S)he Has
an Unmet Need or Want and Begins to Think About
What Kinds of Products or Services Might Satisfy It
One communications implication is that larger-share brands
ought to consider the enhancement of overall category
demand as a possible communications objective. Appro-
priate media to utilize might be location-targeted mobile
apps and ads, paid search ads triggered by proximate search
keywords, or third-party website and blog native content
promoting generic category demand. Appropriate messag-
ing could make the category-level need conscious by
showing how it could solve a problem or increase quality
of life. Such messaging should then generate within the
consumer the logical desire to figure out which brands best
satisfy the now-recognized need. For example, the obstacle-
course brand “Tough Mudder”targeted Facebook ads to
consumers who were already into extreme sports, making
them aware that this kind of product could meet their need
for extreme sports.
Stage 2: The Consumer Then Begins to Consider
Which Specific Possible Brands Might Best Satisfy
That Need or Want
At this stage, the consumer is probably high on motivation
and desire, but not on cognitive ability. Thus, the consumer
will not be in a position to conduct extensive research but will
TABLE 2
Research Propositions Concerning Possible Communication Needs and Objectives at Different Stages of the
Consumer Decision Journey
Decision
Journey Stage
Communication Needs and Objectives
Awareness Information Imagery Trust Emotion Action Loyalty Connect
Needs +++ +++ + ++ ++ + + +
Is aware +++ +++ + + + + + +
Considers +++ +++ +++ ++ + + + +
Learns +++ +++ +++ +++ + + + +
Likes ++ +++ +++ +++ +++ + + +
Will pay ++ +++ +++ +++ +++ + + +
Commits ++ +++ +++ +++ +++ +++ + +
Consumes + ++ +++ +++ ++ +++ + +
Is satisfied + ++ +++ +++ ++ ++ +++ +
Is loyal + + + ++ ++ +++ +++ ++
Engages + + + ++ ++ +++ +++ +++
Advocates + + + ++ ++ +++ +++ +++
Notes: +++ = greatest influence; ++ = medium influence; +=least influence.
TABLE 3
Research Propositions Concerning the Relative Strengths of Different Communication Options Across the
Consumer Decision Journey
Decision
Journey Stage
Communication Options
TV Promos Events PR Social Media Website Search Display Mobile Direct Selling
Needs 111 1 1 11 11 1 111 111 11 111 111
Is aware 111 11 111 11 11 111 111 111 1 111 111
Examines 11 11 1 1 11 111 111 111 11 111 111
Learns 11 11 1 1 11 111 111 11 111 111 111
Likes 111 11 111 11 111 111 1 1 111 11 11
Will pay 1 11 1 11 1 11 1 1 11 111 111
Commits 1 111 1 1 1 11 1 1 111 111 111
Consumes 1 111 1 1 11 1 1 1 11 1 1
Is satisfied 11 11 1 11 11 11 1 1 11 1 1
Is loyal 11 111 111 1 111 11 1 1 111 111 111
Engages 1 111 111 111 111 111 1 1 111 111 1
Advocates 1 1 111 11 111 111 1 1 111 1 1
Notes = +++ = greatest influence; ++ = medium influence; +=least influence.
Integrating Marketing Communications / 133

simply be identifying the best few brands for further
investigation.2
During this initial brand consideration process, the mar-
keter’s key communication tasks would logically be to in-
crease the accessibility and salience of the brand’s existence
and its key reason for consideration. The marketer must thus
ensure that the brand is highly visible through organic search
and paid search ads, retargeted display and banner ads, targeted
Facebook posts and paid ads, (re)tweets, location-targeted
mobile apps and ads, high share of voice in targeted tradi-
tional advertising media, targeted events and sponsorships,
high presence in high-traffic third-party websites (e.g., via
“native content”), high and positive user-generated-content
presence (e.g., in blogs and reviews), the creation and
awareness-building of “thought leadership”–owned web-
sites, and so on. An example is a campaign by Papa John’s
Pizza, in which the company sent tweets with coupons to
Twitter users who were tweeting about pizza (“conquest-
ing”), thus making the brand more salient among consumers
demonstrating a high level of category need.
Stages 3 and 4: The Consumer Will Next Try to
Actively Learn More About the Various Brand
Options That Seem Capable of Satisfying the
Category-Level Need or Want
The consumer’s ability to process information will probably
increase over this stage of the decision journey, so that the
(s)he is likely to seek out more detailed and granular
information and rely less on heuristics and peripheral cues
such as endorsers or source/execution likability (Alba and
Hutchinson 1987). Such information search could include
visits to brand or third-party websites (e.g., Consumer
Reports, the automotive website Edmunds.com), search
engine queries (used to drive consumers to the brand’s
owned informative website), online and offline inquiries to
friends and acquaintances, visits to dealer and retailer outlets to
inspect options and talk to salespeople, and so on. Marketers
could also use persuasive content in informative long-
length TV ads or YouTube videos, informative online and
offline WOM and viral content, and placement of key brand
information on blogs and third-party websites.
For example, Lysol got mothers on Facebook to pass on
information to their networks about how they could take care
of cleaning tasks by using the Lysol brand instead of harsh
chemicals; Dollar Shave Club has used detailed YouTube
videos, and various law firms employ “thought leader”blogs.
During this information search process (which may be quick
and iterative), other brands may be added to the initial con-
sideration set (Court et al. 2009), leading to a fresh cycle of
information gathering.
Stage 5: The Consumer Then Decides Which Pieces
of Information Are Both Credible and Diagnostic
and, Thus, Relevant to Choice
In cases in which brands make specific performance claims,
the information-searching consumer may be looking for
credible evidence to substantiate the brand’s claims (i.e.,
“reasons to believe”). These could be in the form of objective,
third-party testing or endorsements and testimonials from
current and past customers (as in published reviews), friends
(via Facebook posts, e-mails, or personal conversations), or
perceived experts or celebrities (via ads, blog posts, tweets,
or in-store conversations with salespeople). Integrated mar-
keting communication plans also need to be in place to solicit
credible and positive reviews and ratings, endorsements, and
testimonials in the brand’s owned media as well as earned
and shared media (Chevalier and Mayzlin 2006; Trusov,
Bodapati, and Bucklin 2010).
Communications from a brand at this journey stage
should also engender a feeling of overall trust and confidence
in the brand and the organization and employees behind it.
More credible information helps reduce purchase anxiety and
build trust and confidence in the provider for service brands
in particular (Berry 2000). Credibility can be signaled through
the heightened prominence of the brand in the media, positive
and public reviews and ratings, publicized awards, linkages
with other trusted and high-quality brands and organizations,
and so on.
Stage 6: The Consumer Next Has to Decide How
Much the Preferred Brand Is Worth to Decide on
the Extent of His or Her Willingness to Pay the
Asked-For Price
Any brand communications for consumers at this stage needs
to communicate that the price being charged by the brand is
“fair,”competitively reasonable, and indeed perhaps less than
the actual “value”being delivered by the brand to its purchaser,
relative to competing brands. Conventional media campaigns
could do this (e.g., Jim Beam Black “8 Years Changes Every-
thing”campaign).
It is also likely that at this willingness-to-pay assessment
stage, the perceived value of the brand is influenced by its
perceived emotional benefits and social appeal. Associating
the brand with desired feelings and values through “meaning
transfer”and creating desired social image value and social
currency are thus important objectives for a brand at this
stage. Media that are stronger in their ability to “transfer in”
targeted brand associations, cultural meaning, and emotions
could include long-form TV ads, associated high-prestige
events, celebrity (re)tweets, YouTube brand videos (e.g., those
that were used by Dove), Facebook pages or posts (e.g.,
Procter & Gamble’s Cover Girl pages with Ellen DeGeneres),
and blogs (e.g., Iams).
Stage 7: Even if a Favorable Willingness-to-Pay
Judgment Is Rendered, Consumers Still Have to
Follow Through and Make the Purchase
Brand communications at this stage need to explicitly impel
action, letting ready-to-buy consumers know where the product
2Depending on the nature of the consumer’s consideration and
decision processes for that purchase, such higher awareness may or
may not be very important in driving eventual brand choice. So,
whereas brands in some categories (e.g., restaurants) may need to
expend maximum resources to lead in “top-of-mind awareness,”
brands in other categories (e.g., automobiles) might be adequately
served by ensuring that they are always one of the three or four that
are spontaneously considered for further evaluation.
134 / Journal of Marketing: AMA/MSI Special Issue, November 2016

can be purchased conveniently, at an acceptable price, and with
confidence. A preference, or the intention to choose a brand,
does not automatically and immediately lead to a trial or pur-
chase action. Delay is possible, sometimes because the purchase
step is considered inconvenient or untrustworthy or because it is
not available at the best price; such delay can reduce the extent
to which brand purchases actually take place. Messages about
retail or e-shopping locations, bolstered by reassurances about
guarantees/warranties and return policies, can be combined with
“actual prices paid”data that show that the price the consumer
pays will indeed be “agooddeal.”“Limited-time”promotions
and deals could be offered to close the sale.
These messages might need to be delivered through paid
media, owned media (e.g., brand websites, Facebook pages,
Twitter feeds, location-triggered mobile apps), and/or earned
media (e.g., third-party sites offering shopping advice, cou-
pons, price comparison smartphone apps), as well as through
the message modalities of distribution (trade) partners, possibly
utilizing co-op advertising and linked-to-retailer websites. Ex-
amples of campaigns incentivizing quick action include the use
of Groupon and videos on YouTube and on brand websites that
reduce barriers to action, using client case studies and brand
specifics.
Stages 9 and 10: A Consumer Then Has to Weigh
Whether (S)he Wants to Repurchase the Brand
over Time
Because repeat purchases (increasing the lifetime value of the
customer) only occur if the consumer feels highly satisfied
with the product or service and his or her initial decision to
purchase it, the brand’s ongoing communications need to
convince the buyer that the brand is, in fact, performing well
when compared with “reasonable”expectations. To increase
this real and perceived customer satisfaction, companies can
also use many of the newer media for internal employee
communications that improve the quality of their customer
interactions, such as internal YouTube channels (e.g., Best
Buy’s“Blue Shirt Nation”).
Appropriate media at this stage for “purchase reinforce-
ment”messaging might be postpurchase direct mail, e-mail, and
outbound telemarketing; targeted traditional media ads and even
retargeted banner and display ads; social media reinforcement;
Facebook pages and Twitter direct communications for cus-
tomer service support (e.g., as with Zappos, Delta, and UPS);
and online sentiment and review tracking. Brands can also
increase the frequency and amount of consumption, as well as
cross-selling and up-selling, through targeted discount offers to
existing customers from their loyalty program communications.
Stages 11 and 12: Some Consumers May Then
Choose to Engage in Positive WOM for the Brand
or Even Become Brand “Advocates”and
“Missionaries”for It
Here,brandsneedtomakeiteasytogettheirconsumers
to recommend the brand to others, through Facebook likes,
(re)tweets, viral branded content, Instagram comments, and
so on (e.g., Duane Reade got its loyal followers to retweet its
women’s hosiery stories using the hashtag #DRLegwear).
Through such media tactics, a brand not only can reach
many more potential consumers but also can do so in
credible, low-key ways that are less likely to evoke hostility
and skepticism.
Consumers are more likely to take advantage of these
WOM opportunities if they genuinely love the brand—that is,
believe that the brand reflects their deeply held life values and
helps them communicate “who they are”and “who they want
to be,”among many other things. Brand communications are
therefore needed that touch consumers in deeply meaningful
ways and make the brand symbolic of the targeted consumers’
life values, identities, and aspirations (Batra, Ahuvia, and
Bagozzi 2012). Creating such communications requires the
utilization of emotional, cultural, and symbolic cues—delivered
through “authentic”messages and “close-to-consumer”media
(e.g., Four Seasons tweeted about wine tastings to some of its
top customers).
To facilitate brand love, marketers also need to create
easy and attractive ways for consumers to engage and interact
with the brand in multiple and frequent ways, through Twitter
hashtag events, Facebook pages, online and offline brand
communities, the presence of helpful and inspiring native
content on websites and blogs, and voting-type contests (e.g.,
Electronic Arts asked fans to pick a player for the cover of a
game; American Express had members vote on charitable
projects it should support).
Summary
Utilizing the previous analysis and other research inputs,
Table 3 offers an illustration of how different types of media
vary in their effectiveness at different stages of the consumer
decision journey. A brand thus needs to form an overall or
segment-level assessment of where its target consumers are
on this decision journey. It needs to quickly and automati-
cally aggregate data from various sources to decide how its
media dollars should be allocated at that moment, what kinds
of messages and tonality should be used, and at which
locations and times these messages should be sent.
After these decisions have been made and a communi-
cation plan has been tentatively developed, the communi-
cation optimization model provides a top-down evaluation of
the proposed communication program in terms of how effi-
ciently it maximizes main and interaction effects. It helps fine-
tune the communication program to ensure optimal integration
by helping marketers answer the question: “How do I know if I
have a fully integrated marketing communications program?”
Communications Optimization
Model
Although several criteria may be relevant to assess integra-
tion, the following seven criteria (“the 7Cs”) have been found
to be particularly helpful in terms of capturing the key
considerations raised by much prior IMC research (e.g.,
Keller 2001b, 2013). The first two criteria are especially
relevant to the financial efficiency of the communication
plan. Some of these criteria have been addressed in previous
sections and, thus, are not elaborated in great detail.
Integrating Marketing Communications / 135

Coverage
Coverage is the proportion of the target audience reached by
each communication option employed, as well as the amount
of overlap among those options. In other words, to what extent
do different communication options reach the designated tar-
get market and the same versus different consumers who make
up that market?
Cost
Marketers must evaluate marketing communications on all
these criteria against their cost to arrive at the most effective
and most efficient communications program. To increase the
cost efficiency of an IMC program, marketers can conduct
A/B split (multivariate design) field experiments, using track-
ing metrics on business metrics that feed into return-on-
investment assessments. For instance, do those customers
who are fans on a firm’s Facebook page or followers of its
Twitter account have a sufficiently higher purchase fre-
quency? Do they spend more, with fewer discounts? Ideally,
such tests would use a randomized test versus control design
to obviate issues of causality (e.g., Facebook page/Twitter
followers could otherwise be consumers who already feel
high brand affinity).
There are several brands that already conduct such tests.
U.K. retailer Marks & Spencer found that its YouTube
channel video watchers (vs. nonwatchers) had a 25% higher
basket size, had higher conversion rates, had fewer returns,
and spent twice as much time on the company website, which
they visited twice as often. Volvo compared the percentage of
times that it was in people’s consideration set of prospects in
areas where it ran (vs. did not run) a Facebook campaign, with
controlled equality in the other-media campaigns in both
areas.
The coverage and cost criteria thus reflect the efficiency of
the media plan to cost effectively reach the right consumers
(Lin, Venkataraman, and Jap 2013). The next five criteria home
in on the success that the IMC program has in actually influ-
encing those consumers along their decision journey to drive
sales in the short run and build brand equity in the long run.
Contribution
Contribution reflects direct “main effects”and the inherent ability
of a marketing communication to create the desired response
and communication effects from consumers in the absence of
exposure to any other communication option. How much does
a communication—by itself—affect consumer processing and
build awareness, enhance image, elicit responses, induce sales, or
affect any other stage along the consumer journey?
Commonality
Commonality is the extent to which common associations are
reinforced across communication options; that is, the extent
to which different communication options share the same
meaning. Information processing research has found that a
repeated message is more effective when it is presented in two
different modes rather than in one (Unnava and Burnkrant
1991; Young and Bellezza 1982). McGrath (2005) empirically
shows how communication programs with a common theme
executed across multiple media in a visually consistent manner
can induce stronger attitudes toward the brand than the same
executions with less visual consistency.
Complementarity
As noted in previous sections, communication options are
more effective when used in tandem, ideally in the most
appropriate sequence. Complementarity is the extent to
which different associations and linkages are emphasized
across communication options. For effective competitive
positioning, brands typically need to establish multiple
brand associations. Different marketing communication
options may be better suited to establishing a particular
brand association (e.g., sponsorship of a cause may improve
perceptions of a brand’s trust and credibility, but TV and
print advertising may be needed to communicate its per-
formance advantages).
Cross-Effects
Communications used in tandem are more powerful when
they interact and create synergistic cross-effects with other
communications through proper sequencing. Our review has
illustrated many such effects within and between online and
offline media. For example, promotions and online solic-
itations can be more effective when combined with adver-
tising (Neslin 2002).The awareness and attitudes created by
advertising campaigns can increase the success of more direct
sales pitches. Advertising can convey the positioning of a
brand and benefit from online display advertising or search
engine marketing that sends a stronger call to action (Pfeiffer
and Zinnbauer 2010).
Conformability
In any IMC program, consumers will encounter communi-
cations in different orders or sequences, or perhaps not at all.
Any particular message may be new to some consumers, but
not to others, and, for any one consumer, it may be preceded
or followed by a completely different set or sequence of
communications from the brand. Conformability refers to
communication versatility and the extent to which a particular
marketing communication “works”for many target con-
sumers in many times and places. In other words, how well
does the communication conform to the different charac-
teristics and communication needs of different consumers?
The ability to work at two levels—effectively communicating
to consumers who have or have not seen a communication
and will or will not see other communications—is critically
important. A highly conformable or versatile communication
achieves its communication goals regardless of the com-
munications path consumers have been on or will be on. Note
that one of the benefits of new media and programmatic
advertising efforts is that there may be a more accurate record
of prior communication exposures for consumers and, thus,
greater ability to adapt a communication accordingly.
A fully integrated communications program should perform
well on each of these seven integration criteria (coverage, cost,
contribution, commonality, complementarity, cross-effects, and
conformability). Several points about the specific criteria are
136 / Journal of Marketing: AMA/MSI Special Issue, November 2016

worth noting. First, there are not necessarily inherent dif-
ferences across communication types for contribution and
complementarity because, in theory, any communication, if
properly designed, can play a critical and unique role in
achieving certain communication objectives. Communication
types vary, however, in their breadth and depth of audience
coverage and in terms of commonality and conformability
according to the number of modalities they employ: the more
modalities available with a communication type, the greater
its potential commonality and conformability. In other words,
the more a communication employs multiple modalities (e.g.,
audio, video, images), the more opportunities there are to
match that communication with another communication and
to appeal to different audiences.
Summary
Conclusions
Integrated marketing communications are the coordinated,
consistent means by which firms attempt to inform, incent,
persuade, and remind consumers—directly or indirectly—
about the products and brands they sell. Technological
advances and other factors have transformed the marketing
communications environment and present new challenges and
opportunities to marketers. Digital media, in particular, offer
tremendous potential through their greater versatility and pre-
cision, but they also create greater integration challenges.
To capitalize on these new media opportunities, marketers
need new tools and ways of thinking that can provide structure
to an increasingly complex communications environment. To
better understand how to develop and deliver fully integrated
communication programs in today’s changing marketing world,
we reviewed academic research and substantive findings to
date. Appendix C displays five main themes that emerged from
this review.
With these main themes in mind, we propose two com-
munication models as part of a broad conceptual framework to
address key IMC questions of interest to both academics and
marketing managers and researchers. Figure 3 shows how the
two conceptual models combine to assist in the development
and delivery of IMC programs.
•Building on an understanding of the consumer, contextual,
and content factors that shape communication outcomes, the
communications matching model considers the specific
objectives and desired outcomes at different stages of the
consumer decision journey and the characteristics of dif-
ferent media types to recommend the best-aligned media
and messaging options.
•The communication optimization model evaluates all pro-
posed communication options for effectiveness and effi-
ciency to ensure that maximal collective effects result.
FIGURE 3
IMC Conceptual Framework
Online and
Social Media
Marketing
Mobile
Marketing
Direct and
Database
Marketing
Personal
Selling
Advertising
Sales
Promotion
Events and
Experiences
PR and
Publicity
Needs/
Wants
Knows Considers Searches/
Learns
Likes/
Trusts
WTP Commits Consumes Satisfied Loyal Engages Advocates
Top-Down Communications
Optimization Model
Bottom-Up Communications
Matching Model
Major
Communication
Platforms
Stages of
Consumer
Decision
Journey
Propensity
to create
awareness
and
salience
Propensity
to convey
detailed
information
Propensity to
create brand
imagery and
personality
Propensity
to elicit
emotions
Propensity
to instill
loyalty
Propensity
to connect
people
Propensity
to build
trust
Propensity
to inspire
action
Communication
Outcomes and
Objectives
Integrating Marketing Communications / 137

By considering communication matching and integration
in detail, the two communications models offer a compre-
hensive top-to-bottom view of marketing communications,
helping address some of MSI’s research priorities in this IMC
domain. By virtue of its broad nature and media neutrality,
the framework we offer can help guide managerial thinking
and academic research in several specificareas,suchasin
understanding online versus offline, marketer-controlled
versus consumer-driven, and digital versus nondigital
differences.
Managerial Implications
There are several important managerial guidelines that follow
from this research that can help marketers better integrate
their marketing communications.
1. There must be as complete an understanding as possible of the
consumer paths to purchase and decision funnels for the
specific product categories and consumer segments of most
interest to the brand being managed. There is no single path to
purchase shared by all consumers. However, there can be a
general sense of the kinds of decision journeys taken by
different sets of consumers and their likely attitudes and
behaviors at different stages along the way. In formulat-
ing these paths to purchase, it is important to adopt a model
that reflects and can account for the more complex and non-
linear decision journeys that characterize many of today’s
consumers.
2. For every communication option under consideration or
currently being used, it is critical that marketers assess:
a. What is the direct (“main”or independent) effect of the
communication on consumers? In what ways does it
change consumer knowledge or behaviors? What does it
make consumers think, feel, and do? Does it reinforce the
messages conveyed by other communications in any way?
b. What does the communication uniquely contribute that
complements other communications? What is the unique role
played by the communication that makes it indispensable?
c. In what ways, if at all, does the communication interact with
other communications and enhance their effects? How does
the communication improve the ability of other communi-
cations to influence consumer knowledge and behaviors?
Consequently, what is the best role for that communication
in the necessary sequence of multiple messages?
3. More broadly, marketers must routinely conduct a commu-
nications audit of any proposed communications program to
evaluate how well it is integrated and the extent to which the
whole exceeds the sum of its parts. The seven criteria from
the communication integration model—coverage, cost, con-
tribution, commonality, complementarity, cross-effects, and
conformability—can provide a useful foundation to assist
in such a critique. Marketers must also conduct in-depth
communication reviews for ongoing or already executed
communications programs. Such a review should attempt
to empirically assess both the direct and indirect con-
tributions to brand equity and sales of each marketing
communication. Applying the proper measurement models
is critical here.
Future Research Directions
Given what we have learned about the state of IMCs, what
pending issues still require additional academic research? We
summarize some of the remaining key methodological and
substantive questions in Appendix D. As we discuss in more
detail next, we organize these questions into three main areas
concerning (1) our conceptual framework, (2) how to model
communication effects, and (3) other key priorities.
Our conceptual framework. Certainly one important fu-
ture research area is to validate the usefulness and logic of
the general approach suggested by the proposed communi-
cations framework. Given that there are several issues in
operationalizing the two communication models, it is crucially
important to further develop and refine each model and
empirically test the resulting formulations.
In particular, the depicted linkages in Tables 1, 2, and 3,
which support the foundation of the communications
matching model, are all research propositions that require
careful further investigation and testing. As part of the val-
idation of Table 1, several specific links should be explored.
For example, are brand websites necessarily poor in build-
ing trust and eliciting emotions, or can such outcomes be
obtained with careful use of certain types of endorsers/
sources and audio/video material? If so, which specific types?
To test the propositions in Table 2, research should explore all
possible communication needs for consumers at different
stages of the decision journey, as well as all possible out-
comes created by communications with consumers, to ensure
that a comprehensive inventory of all meaningful commu-
nication effects exists.
Many of the posited linkages in Table 3 also call for
additional research. What, for example, are the types of
communications content and formats that increase a con-
sumer’s willingness to pay or lead a consumer to purchase
action? For both Tables 1 and 3, researchers may find it easier
to focus their study on individual columns to assess the full
range of processing effects and communication outcomes
associated with particular communication platforms.
In terms of the communications optimization model, the
seven IMC criteria should be investigated in greater depth.
Are they sufficiently exhaustive and mutually exclusive? What
kinds of specific checklists and quantifiable metrics can be
applied to help both the design of IMC programs a priori and
the assessment of their effects after the fact?
How to model communication effects. In motivating the
logic behind our conceptual framework, we noted the need
in researching IMC to (1) incorporate multiple dependent
variables, (2) move beyond contemporaneous effects into
lagged and sequencing effects, and (3) measure effects con-
ditional on the stage of the consumer decision journey. We
elaborate on these three suggestions next.
First, there clearly is a need for more research using a
broader set of dependent variables more closely linked to
expanded funnel metrics (as in Appendix B and Table 2).
Ideally, this research should be conducted in field settings
(as was done by, e.g., Ataman et al. 2010; Naik and Peters 2009;
Smith, Gopalakrishna and Chatterjee 2006; Smith, Gopalakrishna
and Smith 2004; Wie sel e t al. 2 011). Such research would be
more likely to provide insights that tie more closely with how
managers use them to make decisions.
Second, there is a definite need for the development of
dynamic models that recognize the longitudinal, time-delayed
138 / Journal of Marketing: AMA/MSI Special Issue, November 2016

effects of marketing communications (i.e., capture sequential
effects instead of just simultaneous effects, as was done by,
e.g., Smith, Gopalakrishna, and Chatterjee 2006; see also
Assael 2011 and Raman et al. 2012). As part of this re-
search thrust, there is a need to study optimal sequence issues
(differential carryover and decay rates, lags and delays, etc.).
Although these inquiries may often involve analytical models
and empirical research of large databases, there is also a need
for individual-level analyses to explore changes in knowl-
edge and behavior in more detail.
Finally, there is a need to recognize that consumers’
brand-related needs vary as they move through various stages
in their decision journey. Consequently, their response
elasticities should also be expected to vary by stage. To
accurately reflect this heterogeneity in consumer response,
the stage of the consumer decision journey should therefore
be used as a moderator variable in analyses. Incorporating
these three changes in future IMC research modeling and
analyses should produce richer, more valid insights into how
exactly consumers are affected by different communication
options, either singularly or in combination.
Other key priorities. Our review also identified several
specific areas that we also believe deserve significant research
attention in this domain. First, there seems to be a paucity of
scholarly research on the changing nature of how consumers
seek, acquire, and integrate brand-relevant information in
today’s dramatically new media environment. Court et al.
(2009) claim that the purchase funnel model no longer applies
and that their model of the consumer decision journey “loop”
should replace it. Academic research is needed to test this
very consequential assertion. We find it quite surprising that
behaviorally oriented academic research has not attempted to
study how consumers gather and integrate their brand-related
information in ways that are potentially very different from
how they did so more than 30 years ago, when behavioral
research last pursued this question (e.g., Punj and Staelin
1983). Some more recent research has suggested propositions
but does has not conducted empirical tests (e.g., Peterson and
Merino 2003).
Second, individual-level IMC research must recognize
that communications coordinated in some fashion may pro-
duce differences in the levels of awareness, beliefs, and atti-
tudes, as well as qualitative differences in the actual knowledge
created, compared with uncoordinated messaging. Thus, a
well-designed and executed IMC program may form new
associations about the scope and meaning of the brand, as
well as the nature of its communications program as a whole.
Behaviorally oriented researchers also need to conduct
individual-level “mediators and moderators”analyses to
explore the full range of possible changes in consumer thoughts,
feelings, and actions about the brand resulting from IMC
programs versus single-medium or uncoordinated media pro-
grams. A deeper understanding of all these different possible
consumer behavior changes would help both agencies develop
better IMC programs and marketers better track their effects.
A third priority area concerns organizational issues and
the internal processes that will improve communication
integration. Several related topics, such as training and
alignment of sales and marketing, were identified as part
of the MSI research priority setting. In this area, progress
may be made by collaborating with academics from com-
munication and journalism schools, which, as we have noted,
have generally placed greater emphasis on organizational
issues with IMC. Blending their thinking with some of the
aforementioned marketing tools and insights may spark new
ideas as to how to strategically and tactically develop well-
integrated communication programs within organizations.
Finally, the MSI research priorities pose several ques-
tions that our article does not address, which require more
thorough examination to identify what we know and do not
know from academic research. These include improved integra-
tion of marketing and sales and more holistic development of
messages and creative across communication options. One
clearly emerging area of tremendous importance is mobile
marketing. Given its recent emergence due to the explosion of
smartphones, academics are just now beginning to develop
concerted research on the topic (e.g., Andrews et al. 2016;
Fang, Luo, and Keith 2014; Gupta 2013). Like any new com-
munication medium, although some general communication
principles will certainly apply, there will undoubtedly be a
number of uniquely original considerations that firms will have
to factor in (Bell 2014; Ericson, Herring, and Ungerman 2014;
Ghose 2015; Hwang, McInerney, and Shin 2015).
Appendix A: Some Definitions
of IMC3
American Academy of Advertising Agencies
“Integrated Marketing Communications recognizes the value
of a comprehensive plan that evaluates the strategic roles of
a variety of communication disciplines—advertising, public
relations, personal selling, and sales promotion—and com-
bines them to provide clarity, consistency, and maximum
communication impact.”
American Marketing Association
“A planning process designed to assure that all brand contacts
received by a customer or prospect for a product, service, or
organization are relevant to that person and consistent over
time.”
Journal of Integrated Marketing Communications
“IMC is a strategic marketing process specifically designed
to ensure that all messaging and communications strategies
are unified across all channels and are centered around
the customer. The IMC process emphasizes identifying and
assessing customer prospects, tailoring messaging to cus-
tomers and prospects that are both serviceable and profit-
able, and evaluating the success of these efforts to minimize
waste and transform marketing from an expense into a
profit-center.”
3Summarized at https://thedma.org/membership/member-groups-
communities/integrated-marketing-community/integrated-marketing-
definitions/.
Integrating Marketing Communications / 139

Appendix B: Potential Stages in
an Expanded Consumer
Decision Journey
1. Feels a need or want for it (at a category level)
2. Knows about it (sufficient brand awareness in terms of recall
and recognition)
3. Actively considers it (examines attributes and benefits)
4. Searches for more information, learns more about it, and
begins to critically evaluate it (builds brand knowledge)
5. Likes it and has trust/confidence in it (has functional and
nonfunctional brand associations that are strong, favorable,
and unique)
6. Is willing to pay (more) for it (high perceived brand value
based on functional, emotional, social, and symbolic
benefits)
7. Chooses to try it soon and knows where, when, and how to
get it (high desire to act)
8. Consumes it (timing, frequency, and amount of consumption)
9. Is satisfied with it (has positive thoughts, feelings, and
experiences)
10. Is loyal repeat buyer of it (both attitudinal and behavioral
loyalty)
11. Is engaged and interacts with it (participates in both online
and offline brand-related activities)
12. Is an active advocate for it (both offline and online with
social media)
Appendix C: Five Major Themes
Reflected in Prior IMC and
Related Research
1. The current consumer decision journey is significantly dif-
ferent from what it was in the past, in that it requires much
more carefully designed and implemented IMC programs
to ensure maximal results, with the deployment of stage-
appropriate media and messages in optimal sequence.
2. Different communication options have different strengths and
weaknesses and generate different types of effects on con-
sumer knowledge and behavior along consumers’decision
journeys. New media options greatly increase the marketer’s
ability to target specific outcomes with greater precision.
3. Much interaction and synergy exists across communications.
Different types of information in different communications
interact and combine to change consumer knowledge and
behavior in meaningful ways. These interactions often vary
depending on the sequence in which the different commu-
nications are processed.
4. By recognizing the direct and indirect changes in knowledge
and behavior engendered by different communications,
marketers can choose those communication options that are
most likely to collectively achieve brand objectives.
5. Digital communication options, in particular, offer robust
effects that influence consumers directly as well as indirectly
by how they interact with many other digital or nondigital
communication options.
Appendix D: Future Research
Imperatives in IMC and
Recommendations to Marketers
1. To bette r validate the communicati ons matching model,
gain a deeper understanding of both the full range of possible
outcomes created by different types of communications as
well as the specific communication needs for consumers at
different stages of the decision journey.
2. To improve the insights and recommendations from the
communications optimization model, at the macro level,
consider the scope and relevance of the seven IMC criteria
and, at the micro level, develop more specific and quantifiable
considerations for each of the seven criteria.
3. To better reflect the full range of outcomes that may arise
from consumer exposure to communications along different
stages of their decision journeys, employ multiple dependent
variables.
4. To capture the dynamic nature of marketing communication
effects, incorporate differential carryover and decay rates,
lags and delays, and the effects of sequencing in theoretical
models and empirical tests.
5. To better reflect the complexity of consumer paths to pur-
chase, use the message recipient’s current stage in the
decision journey as a moderator variable in assessing the
heterogeneous effectiveness of different marketing media
effects (main and interaction effects).
6. To provide stronger grounding to the proposed conceptual
framework, gain a richer, deeper understanding of the con-
sumer decision journey and how consumers choose to seek,
acquire, and integrate brand-relevant information in today’s
new communications environment.
7. To improve the impact of the proposed conceptual frame-
work, examine the optimal organizational structures and
processes in employing the proposed models and con-
cepts to craft a well-integrated marketing communica-
tions plan.
REFERENCES
Aaker, Jennifer (1997), “Dimensions of Brand Personality,”Journal
of Marketing Research, 34 (August), 347–57.
Abernathy, Avery M. (1992), “The Information Content of News-
paper Advertising,”Journal of Current Issues and Research in
Advertising,14(2),63–68.
Alba, Joseph W. and J. Wesley Hutchinson (1987), “Dimensions
of Consumer Expertise,”Journal of Consumer Research, 13 (4),
411–54.
Allenby, Greg and Dominique Hanssens (2005), “Advertising
Response,”Special Report No. 05-200, Marketing Science
Institute.
Amaldoss, Wilfred and Chuan He (2010), “Product Variety, In-
formative Advertising, and Price Competition,”Journal of
Marketing Research, 47 (February), 146–56.
Anderl, Eva, Ingo Becker, Florian von Wangenheim, Jan Schumann
(2014), “Mapping the Customer Journey: A Graph-Based Frame-
work for Online Attribution Modeling,”working paper, [DOI:
10.2139/ssrn.2343077].
Andrews, Michelle, Xueming Luo, Zheng Fang, and Anindya
Ghose (2016), “Mobile Ad Effectiveness: Hyper-Contextual
TargetingwithCrowdedness,”Marketing Science,35(2),
218–33.
140 / Journal of Marketing: AMA/MSI Special Issue, November 2016

Assael, Henry (2011), “From Silos to Synergy: A Fifty-Year Review
of Cross-Media Research Shows Synergy Has Yet to Achieve its
Full Potential,”Journal of Advertising Research, 51 (1), 42–48.
Assmus, Gert, John U. Farley, and Donald R. Lehmann (1984),
“How Advertising Affects Sales: Meta-analysis of Econometric
Results,”Journal of Marketing Research, 21 (February), 65–74.
Ataman, M. Berk, Harald J. Van Heerde, and Carl F. Mela (2010),
“The Long-Term Effect of Marketing Strategy on Brand Sales,”
Journal of Marketing Research, 47 (October), 866–82.
Aufreiter, Nora, Julien Boudet, and Vivian Weng (2014), “Why
Marketers Keep Sending You E-Mails,”McKinsey Quarterly,
(January), [available at http://www.mckinsey.com/business-
functions/marketing-and-sales/our-insights/why-marketers-
should-keep-sending-you-emails].
Bagozzi, Richard P. and Utpal Dholakia (1999), “Goal Setting and
Goal Striving in Consumer Behavior,”Journal of Marketing,
63 (Special Issue), 19–32.
Barroso, Alicia and Gerard Llobet (2012), “Advertising and Con-
sumer Awareness of New, Differentiated Products,”Journal of
Marketing Research, 49 (December), 773–92.
Batra, Rajeev and Olli T. Ahtola (1990), “Measuring the Hedonic
and Utilitarian Sources of Consumer Attitudes,”Marketing
Letters, 2 (2), 159–70.
———, Aaron Ahuvia, and Richard P. Bagozzi (2012), “Brand
Love,”Journal of Marketing, 76 (March), 1–16.
——— and Pamela M. Homer (2004), “The Situational Impact of
Brand Image Beliefs,”Journal of Consumer Psychology, 14 (3),
318–30.
——— and Michael L. Ray (1986), “Situational Effects of Ad-
vertising Repetition: The Moderating Influence of Motivation,
Ability, and Opportunity to Respond,”Journal of Consumer
Research, 12 (4), 432–45.
Belch, George E. and Michael A. Belch (2015), Advertising and
Promotions: An Integrated Marketing Communications Per-
spective, 10th ed. New York: McGraw-Hill.
Belk, Russell W. (1988), “Possessions and the Extended Self,”
Journal of Consumer Research, 15 (September), 139–68.
Bell, David (2014), Location Is (Still) Everything: The Surprising
Influence of the Real World on How We Search, Shop, and Sell in
the Virtual One. Boston: Houghton Mifflin Harcourt.
Berger, Jonah (2014), “Word-of-Mouth and Interpersonal Com-
munication: A Review and Directions for Future Research,”
Journal of Consumer Psychology, 24 (4), 586–607.
Berman, Ron and Zsolt Katona (2013), “The Role of Search Engine
Optimization in Search Marketing,”Marketing Science, 32 (4),
644–51.
Berry, Leonard L. (2000), “Cultivating Service Brand Equity,”
Journal of the Academy of Marketing Science, 28 (1), 128–37.
Bloemer, Jos´
e M.M. and Hans D.P. Kasper (1995), “The Complex
Relationship Between Consumer Satisfaction and Brand Loy-
alty,”Journal of Economic Psychology, 16 (2), 311–29.
Bonfrer, Andr´
e and Xavier Dr`
eze (2009), “Real-Time Evaluation
of Email Campaign Performance,”Marketing Science, 28 (2),
251–63.
Brakus, J. Joˇ
sko, Bernd H. Schmitt, and Lia Zarantonello (2009),
“Brand Experience: What Is It? How Is It Measured? Does It
Affect Loyalty?”Journal of Marketing, 73 (May), 52–68.
Braun, Michael and Wendy Moe (2013), “Online Display Adver-
tising: Modeling the Effects of Multiple Creatives and Individual
Impression Histories,”Marketing Science, 32 (5), 753–67.
Briggs, Rex, R. Krishnan, and Norm Borin (2005), “Integrated
Multichannel Communication Strategies: Evaluating the Return
on Marketing Objectives: The Case of the 2004 Ford F-150
Launch,”Journal of Interactive Marketing, 19 (3), 81–90.
Brown, Jacqueline Johnson and Peter H. Reingen (1987), “Social
Ties and Word-of-Mouth Referral Behavior,”Journal of Con-
sumer Research, 14 (3), 350–62.
Brown, Stephen, Robert V. Kozinets, and John F. Sherry Jr. (2003),
“Teaching Old Brands New Tricks: Retro Branding and the Re-
vival of Brand Meaning,”Journal of Marketing,67(July),19–33.
Bruce, Norris I., Natasha Zhang Foutz, and Ceren Kolsarici (2012),
“Dynamic Effectiveness of Advertising and Word of Mouth in
Sequential Distribution of New Products,”Journal of Marketing
Research, 49 (August), 469–86.
Campbell, Margaret C. and Amna Kirmani (2008), “I Know What
You’re Doing and Why You’re Doing it: The Use of the Per-
suasion Knowledge Model in Consumer Research,”in Hand-
book of Consumer Psychology, C. Haugtvedt, P. Herr, and F.
Kardes, eds. New York: Psychology Press, 549–74.
Chang, Yuhmiin and Esther Thorson (2004), “Television and Web
Advertising Synergies,”Journal of Advertising, 33 (2), 75–84.
Chaudhuri, Arjun and Morris B. Holbrook (2001), “The Chain of
Effects From Brand Trust and Brand Affect to Brand Per-
formance: The Role of Brand Loyalty,”Journal of Marketing,
65 (April), 81–93.
Chevalier, Judith A. and Dina Mayzlin (2006), “The Effect of Word
of Mouth on Sales: Online Book Reviews,”Journal of Marketing
Research, 43 (August), 345–54.
Court, David, David Elzinga, Susan Mulder, and Ole Jorgen
Vetvik (2009), “The Consumer Decision Journey,”McKinsey
Quarterly, (June), [available at http://www.mckinsey.com/business-
functions/marketing-and-sales/our-insights/the-consumer-decision-
journey].
Cox, Daniel F. (1967), Risk-Taking and Information-Handling in
Consumer Behavior. Boston: Harvard University Press.
Danaher, Peter J., Andr´
e Bonfrer, and Sanjay Dhar (2008), “The
Effect of Competitive Advertising,”Journal of Marketing
Research, 45 (April), 211–25.
——— and Tracey S. Dagger (2013), “Comparing the Relative
Effectiveness of Advertising Channels: A Case Study of a Mul-
timedia Blitz Campaign,”Journal of Marketing Research,
50 (August), 517–34.
———, Janghyuk Lee, and Laoucine Kerbache (2010), “Optimal
Internet Media Selection,”Marketing Science, 29 (2), 336–47.
———, Guy W. Mullarkey, and Skander Essegaier (2006), “Factors
Affecting Web Site Visit Duration: A Cross-Domain Analysis,”
Journal of Marketing Research, 43 (May), 182–94.
De Vries, Lisette, Sonja Gensler, and Peter S.H. Leeflang (2012),
“Popularity of Brand Posts on Brand Fan Pages: An Investigation
of the Effects of Social Media Marketing,”Journal of Interactive
Marketing, 26 (2), 83–91.
Dijkstra, Majorie, Heidi E.J.J.M. Buijtels, and W. Fred van Raaij
(2005), “Separate and Joint Effects of Medium Type on Con-
sumer Responses: A Comparison of Television, Print, and the
Internet,”Journal of Business Research, 58 (March), 377–86.
Dinner, Isaac M., Harald J. Van Heerde, and Scott A. Neslin (2014),
“Driving Online and Offline Sales: The Cross-Channel Effects
of Traditional, Online Display, and Paid Search Advertising,”
Journal of Marketing Research, 51 (October), 527–45.
Draganska, Michaela, Wesley R. Hartmann, and Gena Stanglein (2014),
“Internet Versus Television Advertising: A Brand-Building Com-
parison,”JournalofMarketingResearch, 51 (October), 578–90.
Edell, Julie A. and Marian Chapman Burke (1987), “The Power of
Feelings in Understanding Advertising Effects,”Journal of Con-
sumer Research, 14 (3), 421–33.
——— and Kevin Lane Keller (1989), “The Information Processing
of Coordinated Media Campaigns,”Journal of Marketing Re-
search, 26 (May), 149–63.
Integrating Marketing Communications / 141

——— and ——— (1999), “Analyzing Media Interactions: The
Effects of Coordinated Print-TV Advertising Campaigns,”Re-
port No. 99–120, Marketing Science Institute.
Ericson, Liz, Louise Herring, and Kelly Ungerman (2014), “Busting
Mobile Shopping Myths,”McKinsey Quarterly, (December),
[available at http://www.mckinsey.com/industries/retail/our-insights/
busting-mobile-shopping-myths].
Fang, Zheng, Xueming Luo, and Megan E. Keith (2014), “How
Effective is Location-Targeted Mobile Advertising,”MIT Sloan
Management Review, (October), 14–15.
Fournier, Susan (1997), “Consumers and Their Brands: Developing
Relationship Theory in Consumer Research,”Journal of Con-
sumer Research, 24 (3), 343–73.
Freimer, Marshall and Dan Horsky (2012), “Periodic Advertising Pulsing
in a Competitive Market,”Marketing Science,31(4),637–48.
Friestad, Marian and Peter Wright (1994), “The Persuasion Knowledge
Model: How People Cope with Persuasion Attempts,”Journal of
Consumer Research,21(1),1–31.
Gatignon, Hubert and Dominique M. Hanssens (1987), “Modeling
Marketing Interactions with Application to Sales Force Effec-
tiveness,”Journal of Marketing Research, 24 (August), 247–57.
Ghose, Anindya (2015), “Cynicism About Mobile Advertising Is
Greatly Misplaced,”The Conversation, (August 19), [available at
http://theconversation.com/cynicism-about-mobile-advertising-is-
greatly-misplaced-45673].
Godes, David and Dina Mayzlin (2004), “Using Online Con-
versations to Study Word-of-Mouth Communication,”Mar-
keting Science, 23 (4), 545–60.
Goldenberg, Jacob, Gal Oestreicher-Singer, and Shachar Reichman
(2012), “The Quest for Content: How User-Generated Links Can
Facilitate Online Exploration,”Journal of Marketing Research,
49 (August), 452–68.
Gopalakrishna, Srinath and Rubikar Chatterjee (1992), “A Com-
munications Response Model for a Mature Industrial Product:
Applications and Implications,”Journal of Marketing Research,
29 (May), 189–200.
Gopinath, Shyam, Jacquelyn S. Thomas, and Lakshman Krishna-
murthi (2014), “Investigating the Relationship Between the
Content of Online Word of Mouth, Advertising, and Brand
Performance,”Marketing Science, 33 (2), 241–58.
Gupta, Sunil (2013), “For Mobile Devices, Think Apps, Not Ads,”
Harvard Business Review, 91 (March), 70–75.
Hanssens, Dominique M., ed. (2015), Empirical Generalizations
About Marketing Impact. Cambridge, MA: Marketing Science
Institute.
Hauser, John R., Glen L. Urban, Guilherme Liberali, and Michael Braun
(2009), “Website Morphing,”Marketing Science, 28 (2), 202–23.
Havlena, William, Robert Cardarelli, and Michelle De Montigny
(2007), “Quantifying the Isolated and Synergistic Effects of
Exposure Frequency for TV, Print, and Internet Advertising,”
Journal of Advertising Research, 47 (3), 215–21.
Hoban, Paul R. and Randolph E. Bucklin (2015), “Effects of Internet
Display Advertising in the Purchase Funnel: Model-Based
Insights from a Randomized Field Experiment,”Journal of Mar-
keting Research, 52 (June), 375–93.
Hoch, Stephen J. and Young-Won Ha (1986), “Consumer Learning:
Advertising and the Ambiguity of Product Experience,”Journal
of Consumer Research, 13 (2), 221–33.
Holbrook, Morris B. and Rajeev Batra (1987), “Assessing the Role
of Emotions as Mediators of Consumer Responses to Adver-
tising,”Journal of Consumer Research, 14 (3), 404–20.
——— and Elizabeth C. Hirschman (1982), “The Experiential
Aspects of Consumption: Consumer Fantasies, Feelings, and
Fun,”Journal of Consumer Research, 9 (2), 132–40.
Hu, Ye, Leonard M. Lodish, and Abba M. Krieger (2007), “An
Analysis of Real World TV Advertising Tests: A 15 Year
Update,”Journal of Advertising Research,47(3),341–53.
Hwang, Heeyoung, Paul McInerney, and Jun Shin (2015), “Learning
from South Korea’s Mobile-Retailing Boom, McKinsey Quar-
terly, (May), [available at http://www.mckinsey.com/industries/
retail/our-insights/learning-from-south-koreas-mobile-retailing-
boom].
Jagpal, Harsharanjeet S. (1981), “Measuring Joint Advertising Ef-
fects in Multiproduct Firms,”Journal of Advertising Research,
21 (1), 65–69.
Janis, Irving L. and Carl I. Hovland (1959), “An Overview of
Persuasibility Research,”in Personality and Persuasibility, C.I.
Hovland and I.L. Janis, eds. New Haven, CT: Yale University
Press, 1–28.
Jedidi, Kamel, Carl F. Mela, and Sunil Gupta (1999), “Managing
Advertising and Promotion for Long-Run Profitability,”Mar-
keting Science, 18 (1), 1–22.
Jerath, Kinshuk, Liye Ma, and Young-Hoon Park (2014), “Con-
sumer Click Behavior at a Search Engine: The Role of Key-
word Popularity,”Journal of Marketing Research, 51 (August),
480–86.
Johnson, Michael D., Andreas Herrmann, and Frank Huber (2006),
“The Evolution of Loyalty Intentions,”Journal of Marketing,
70 (April), 122–32.
Johnson, Russell E., Chu-Hsiang Chang, and Robert G. Lord (2006),
“Moving From Cognition to Behavior: What the Research Says,”
Psychological Bulletin, 132 (3), 381–415.
Joo, Mingyu, Kenneth C. Wilbur, Bo Cowgill, and Yi Zhu (2013),
“Television Advertising and Online Search,”Management
Science,60(1),56–73.
Katona, Zsolt (2014), “Competing for Influencers in a Social
Network,”working paper, Haas School of Business, University
of California, Berkeley.
———, Peter Pal Zubcsek, and Miklos Sarvary (2011), “Network
Effects and Personal Influences: The Diffusion of an Online
Social Network,”Journal of Marketing Research, 48 (June),
425–43.
Keller, Kevin Lane (2001a), “Building Customer-Based Brand
Equity: A Blueprint for Creating Strong Brands,”Marketing
Management, (July/August), 15–19.
——— (2001b), “Mastering the Marketing Communications Mix:
Micro and Macro Perspectives on Integrated Marketing Com-
munication Programs,”Journal of Marketing Management,
17 (7/8), 819–48.
——— (2003), “Brand Synthesis: The Multi-Dimensionality of
Brand Knowledge,”Journal of Consumer Research,29(4),
595–600.
——— (2013), Strategic Brand Management, 4th ed. Upper Saddle
River, NJ: Pearson Prentice-Hall.
Kim, Jooyoung, Hye Jin Yoon, and Sun Young Lee (2010),
“Integrating Advertising and Publicity,”Journal of Advertising,
39 (1), 97–114.
Koslow, Scott and Gerard J. Tellis (2011), “What Scanner Panel
Data Tell Us About Advertising: A Detective Story with a Dark
Twist,”Journal of Advertising Research, 51 (March), 87–100.
Kuehn, Alfred (1962), “Consumer Brand Choice as a Learning
Process,”Journal of Advertising Research, 2 (4), 10–17.
Kumar, Ashish, Ram Bezawada, Rishika, Ramkumar Janakiraman,
and P.K. Kannan (2016), “From Social to Sale: The Effects of
Firm-Generated Content in Social Media on Customer Behav-
ior,”Journal of Marketing, 80 (January), 7–25.
Lamberton, Cait and Andrew T. Stephen (2016), “AThematic
Exploration of Digital, Social Media, and Mobile Marketing:
142 / Journal of Marketing: AMA/MSI Special Issue, November 2016

Research Evolution from 2000 to 2015 and an Agenda for Future
Inquiry,”Journal of Marketing, 80 (November), 146–72.
Lambrecht, Anja and Catherine Tucker (2013), “When Does
Retargeting Work? Information Specificity in Online Adver-
tising,”Journal of Marketing Research, 50 (October), 561–76.
Lawrence, Benjamin, Susan Fournier, and Frederic Brunel (2013),
“When Companies Don’t Make the Ad: A Multi-Method In-
quiry into the Differential Effectiveness of Consumer-Generated
Advertising,”Journal of Advertising, 42 (4), 292–307.
Lee, Dokyun, Kartik Hosanagar, and Harikesh S. Nair (2015),
“Advertising Content and Consumer Engagement on Social
Media: Evidence from Facebook,”working paper, Tepper
School of Business, Carnegie-Mellon University.
Lee, Jae Young and David R. Bell (2013), “Neighborhood Social
Capital and Social Learning for Experience Attributes of Products,”
Marketing Science, 32 (6), 960–76.
Lemon, Katherine N. and Stephen M. Nowlis (2002), “Developing
Synergies Between Promotions and Brands in Different Price–
Quality Tiers,”Journal of Marketing Research,39(May),171–85.
Levy, Sydney J. (1959), “Symbols for Sale,”Harvard Business
Review, 37 (July/August), 117–24.
Li, Hongshuang (Alice) and P.K. Kannan (2014), “Attributing
Conversions in a Multichannel Online Marketing Environment:
An Empirical Model and a Field Experiment,”JournalofMar-
keting Research, 51 (February), 40–56.
Lin, Chen, Sriram Venkataraman, and Sandy Jap (2013), “Media
Multiplexing Behavior: Implications,”Marketing Science,32(2),
310–24.
Lodish, Leonard M., Magid M. Abraham, Stuart Kalmenson, Jeanne
Livelsberger, Beth Lubetkin, Bruce Richardson, et al. (1995),
“How TV Advertising Works: A Meta-Analysis of 389 Real
World Split Cable TV Advertising Experiments,”Journal of
Marketing Research, 32 (May), 125–39.
———,———, Jeanne Livelsberger, Beth Lubetkin, Bruce
Richardson, and Mary Ellen Stevens (1995), “A Summary of
Fifty-Five In-Market Experimental Estimates of the Long-Term
Effect of TV Advertising,”Marketing Science, 14 (3, part 2),
G133–40.
Loewenstein, George F., Elke U. Weber, Christopher K. Hsee, and
Ned Welch (2001), “Risk as Feelings,”Psychological Bulletin,
127 (2), 267–86.
Luo, Xueming and Naveen Donthu (2006), “Marketing’s Credi-
bility: A Longitudinal Investigation of Marketing Communication
Productivity and Shareholder Value,”Journal of Marketing,
70 (October), 70–91.
Lynch, John G., Jr., Howard Marmorstein, and Michael F. Weigold
(1988), “Choices from Sets Including Remembered Brands: Use
of Recalled Attributes and Prior Overall Evaluations,”Journal of
Consumer Research, 15 (2), 169–84.
MacInnis, Deborah and Bernard J. Jaworski (1989), “Information
Processing from Advertisements: Toward an Integrative Frame-
work,”Journal of Marketing, 53 (October), 1–23.
———, Christine Moorman, and Bernard J. Jaworski (1991),
“Enhancing and Measuring Consumers’Motivation, Oppor-
tunity, and Ability to Process Brand Information from Ads,”
Journal of Marketing, 55 (October), 32–53.
Madhavaram, Sreedhar, Vishag Badrinarayanan, and Robert E.
McDonald (2005), “Integrated Marketing Communication (IMC)
and Brand Identity as Critical Components of Brand Equity Strat-
egy,”Journal of Advertising,34(4),69–80.
Malaviya, Prashant (2007), “The Moderating Influence of Adver-
tising Context on Ad Repetition Effects: The Role of Amount
and Type of Elaboration,”Journal of Consumer Research,
34 (1), 32–40.
Manchanda, Puneet, Jean-Pierre Dub´
e, Khim Yong Goh, and
Pradeep K. Chintagunta (2006), “The Effects of Banner Adver-
tising on Internet Purchasing,”Journal of Marketing Research,
43 (February), 98–108.
Mantrala, Murali K. (2002), “Allocating Marketing Resources,”
Chapter 16, Handbook of Marketing,B.A.WeitzandR.Wensley,
eds. Thousand Oaks, CA: Sage Publications, 409–35.
Marketing Science (2012), “Special Issue on the Emergence and
Impact of User-Generated Content,”31 (3), 369–547.
Mayzlin, Dina and Jiwoong Shin (2011), “Uninformative Adver-
tising as an Invitation to Search,”Marketing Science, 30 (4),
666–85.
McCracken, Grant (1989), “Who Is the Celebrity Endorser? Cultural
Foundations of the Endorsement Process,”Journal of Consumer
Research, 16 (3), 310–21.
McGrath, John M. (2005), “A Pilot Study Testing Aspects of the
Integrated Marketing Communications Concept,”Journal of
Marketing Communications, 11 (3), 191–214.
McGuire, William J. (1978), “An Information Processing Model of
Advertising Effectiveness,”in Behavioral and Management
Science in Marketing, H.L. Davis and A.J. Silk, eds. New York:
Ronald Press, 156–80.
McLuhan, Marshall (2001), “The Medium Is the Message,”in
Media and Cultural Studies: Keyworks, M.G. Durham and D.M.
Kellner, eds. Malden, MA: Blackwell Publishers, 129–38.
Mela, Carl F., Sunil Gupta, and Donald R. Lehmann (1997), “The
Long-Term Impact of Promotion and Advertising on Consumer
Brand Choice,”Journal of Marketing Research,34(May),248–61.
Mitchell, Vincent-Wayne (1999), “Consumer Perceived Risk: Con-
ceptualisations and Models,”European Journal of Marketing,
33 (1/2), 163–95.
Naik, Prasad A. (2007), “Integrated Marketing Communications:
Provenance, Practice and Principles,”in Handbook of Adver-
tising, G.J. Tellis and T. Ambler, eds. Thousand Oaks, CA: Sage
Publications, 35–53.
——— and Kay Peters (2009), “A Hierarchical Marketing Com-
munications Model of Online and Offline Media Synergies,”
Journal of Interactive Marketing, 23 (4), 288–99.
——— and Kalyan Raman (2003), “Understanding the Impact of
Synergy in Multimedia Communications,”Journal of Marketing
Research, 40 (November), 375–88.
———,———, and Russ Winer (2005), “Planning Marketing-Mix
Strategies in the Presence of Interactions,”Marketing Science,
24 (1), 25–34.
Narayanan, Sridhar, Ramarao Desiraju, and Pradeep K. Chintagunta
(2004), “Return on Investment Implications for Pharmaceutical
Promotional Expenditures: The Role of Marketing-Mix Inter-
actions,”Journal of Marketing, 68 (October), 90–105.
Naylor, Rebecca Walker, Cait Poynor Lamberton, and Patricia M.
West (2012), “Beyond the “Like”Button: The Impact of Mere
Virtual Presence on Brand Evaluations and Purchase Intentions
in Social Media Settings,”Journal of Marketing, 76 (November),
105–20.
Neslin, Scott (2002), Sales Promotion: MSI Relevant Knowledge
Series. Cambridge, MA: Marketing Science Institute.
Nunes, Paul F. and Jeffrey Merrihue (2007), “The Continuing Power
of Mass Advertising,”MIT Sloan Management Review, (Win-
ter), 63–69.
O’Guinn, Thomas, Chris Allen, Richard J. Seminik, and Angeline
Close (2015), Advertising and Integrated Brand Promotion, 7th
ed. Stamford, CT: Cengage Learning.
Ohanian, Roobina (1990), “Construction and Validation of a
Scale to Measure Celebrity Endorsers’Perceived Expertise,
Integrating Marketing Communications / 143

Trustworthiness, and Attractiveness,”Journal of Advertising,
19 (3), 39–52.
Oliver, Richard L. (2014), Satisfaction: A Behavioral Perspective on
the Consumer. New York: Routledge.
Olney, Thomas J., Morris B. Holbrook, and Rajeev Batra (1991),
“Consumer Responses to Advertising: The Effects of Ad
Content, Emotions, and Attitude Toward the Ad on Viewing
Time,”Journal of Consumer Research, 17 (4), 440–53.
Osinga, Ernst C., Peter S.H. Leeflang, Shuba Srinivasan, and
Jaap E. Wieringa (2011), “Why Do Firms Invest in Consumer
Advertising with Limited Sales Response? A Shareholder
Perspective,”Journal of Marketing, 75 (January), 109–24.
Park, C. Whan, Deborah J. MacInnis, Joseph Priester, Andreas B.
Eisingerich, and Dawn Iacobucci (2010), “Brand Attachment
and Brand Attitude Strength: Conceptual and Empirical Dif-
ferentiation of Two Critical Brand Equity Drivers,”Journal of
Marketing, 74 (November), 1–17.
Park, Ji Kyung and Deborah Roedder John (2010), “Got to Get You
into My Life: Do Brand Personalities Rub Off on Consumers?”
Journal of Consumer Research, 37 (4), 655–69.
Parsons, Leonard J. (1974), “An Econometric Analysis of Adver-
tising, Retail Availability, and Sales of a New Brand,”Man-
agement Science, 20 (6), 938–47.
Peterson, Robert A. and Maria C. Merino (2003), “Consumer In-
formation Search Behavior and the Internet,”Psychology and
Marketing, 20 (2), 99–121.
Petty, Richard E., John T. Cacioppo, and David Schumann (1983),
“Central and Peripheral Routes to Advertising Effectiveness:
The Moderating Role of Involvement,”Journal of Consumer
Research, 10 (2), 135–46.
Pfeiffer, Markus and Markus Zinnbauer (2010), “Can Old Media
Enhance New Media?”Journal of Advertising Research, 50 (1),
42–49.
Pilotta, Joseph J., Donald E. Schultz, Gary Drenik, and Philip Rist
(2004), “Simultaneous Media Usage: A Critical Consumer
Orientation to Media Planning,”Journal of Consumer Behav-
iour, 3 (3), 285–92.
Punj, Girish N. and Richard Staelin (1983), “A Model of Consumer
Information Search Behavior for New Automobiles,”Journal of
Consumer Research, 9 (4), 366–80.
Raman, Kalyan, Murali K. Mantrala, Shrihari Sridhar, and Yihui
Elina Tang (2012), “Optimal Resource Allocation with Time-
Varying Marketing Effectiveness, Margins and Costs,”Journal
of Interactive Marketing, 26 (10), 43–52.
——— and Prasaid A. Naik (2004), “Long-Term Profit Impact
of Integrated Marketing Communications Program,”Review of
Marketing Science, 2 (1), 1–23.
Reid, Mike, Sandra Luxton, and Felix Mavondo (2005), “The
Relationship Between Integrated Marketing Communication,
Market Orientation, and Brand Orientation,”Journal of Ad-
vertising, 34 (4), 11–23.
Reinartz, Werner and Peter Saffert (2013), “Creativity in Adver-
tising: When It Works and When It Doesn’t,”Harvard Business
Review, (June), 107–12.
Richins, Marsha L. (1997), “Measuring Emotions in the Con-
sumption Experience,”Journal of Consumer Research, 24 (2),
127–46.
Risselada, Hans, Peter C. Verhoef, and Tammo H.A. Bijmolt (2014),
“Dynamic Effects of Social Influence and Direct Marketing on
the Adoption of High-Technology Products,”Journal of Mar-
keting, 78 (March), 52–68.
Rutz, Oliver J. and Randolph E. Bucklin (2011), “From Generic to
Branded: A Model of Spillover in Paid Search Advertising,”
Journal of Marketing Research, 48 (February), 87–102.
———,———, and Garrett P. Sonnier (2012), “A Latent In-
strumental Variables Approach to Modeling Keyword Con-
version in Paid Search Advertising,”Journal of Marketing
Research, 49 (June), 306–19.
Schultz, Don E. and Philip J. Kitchen (1997), “Integrated Mar-
keting Communications in U.S. Advertising Agencies: An Ex-
ploratory Study,”Journal of Advertising Research,37(5),7–18.
Schulze, Christian, Lisa Sch¨
oler, and Bernd Skiera (2014), “Not All
Fun and Games: Viral Marketing for Utilitarian Products,”
Journal of Marketing, 78 (January), 1–19.
Schumann, Jan H., Florian von Wangenheim, and Nicole Groene
(2014), “Targeted Online Advertising: Using Reciprocity Appeals
to Increase Acceptance Among Users of Free Web Services,”
Journal of Marketing, 78 (January), 59–75.
Schweidel, David A. and Wendy W. Moe (2014), “Listening In on
Social Media: A Joint Model of Sentiment and Venue Format
Choice,”Journal of Marketing Research, 51 (August), 387–402.
Sethuraman, Raj, Gerard J. Tellis, and Richard A. Briesch (2011),
“How Well Does Advertising Work? Generalizations from
Meta-Analysis of Brand Advertising Elasticities,”Journal of
Marketing Research, 48 (June), 457–71.
Smith, Robert E. and William R. Swinyard (1983), “Attitude-
Behavior Consistency: The Impact of Product Trial Versus
Advertising,”Journal of Marketing Research, 20 (August),
257–67.
Smith, Timothy M., Srinath Gopalakrishna, and Rubikar Chatterjee
(2006), “A Three-Stage Model of Integrated Marketing Com-
munications at the Marketing–Sales Interface,”Journal of Mar-
keting Research, 43 (November), 546–79.
———,———, and Paul M. Smith (2004), “The Complementary
Effect of Trade Shows on Personal Selling,”International
Journal of Research in Marketing,21(1),61–76.
Stammerjohan, Claire, Charles M. Wood, Yuhmiin Chang, and
Esther Thorson (2005), “An Empirical Investigation of the
Interaction Between Publicity, Advertising, and Previous Brand
Attitudes and Knowledge,”Journal of Advertising,34(4),
55–67.
Steenkamp, Jan-Benedict E.M. and Inge Geyskens (2006), “How
Country Characteristics Affect the Perceived Value of Web
Sites,”Journal of Marketing, 70 (July), 136–50.
Stephen, Andrew T. and Jeff Galak (2012), “The Effects of Tra-
ditional and Social Earned Media on Sales: A Study of a
Microlending Marketplace,”Journal of Marketing Research,
49 (October), 624–39.
———, Michael R. Sciandra, and J. Jeffrey Inman (2015), “Is It
What You Say or How You Say It That Matters? The Effects of
Branded Content on Consumer Engagement with Brands on
Facebook,”working paper, Sa¨
ıd Business School, University of
Oxford.
Stern, Bruce L. and Alan J. Resnick (1991), “Information Content in
Television Advertising: A Replication and Extension,”Journal
of Advertising Research, 31 (3), 36–48.
Stewart, David A. and David H. Furse (1986), Effective Television
Advertising: A Study of 1000 Commercials. Lexington, MA:
Lexington Books.
Swaminathan, Vanitha, Karen Stilley, and Rohini Ahluwalia (2009),
“When Brand Personality Matters: The Moderating Role of
Attachment Styles,”Journal of Consumer Research, 35 (6),
985–1002.
Swinyard, William R. and Michael R. Ray (1977), “Advertising-
Selling Interactions: An Attribution Theory Experiment,”
Journal of Marketing Research, 14 (November), 509–16.
Tellis, Rajesh K. Chandy, and Pattana Thaivanich (2000), “Which
Ad Works, When, Where, and How Often? Modeling the Effects
144 / Journal of Marketing: AMA/MSI Special Issue, November 2016

of Direct Television Advertising,”Journal of Marketing Re-
search, 37 (February), 32–46.
Thompson, Debora V. and Rebecca W. Hamilton (2006), “The
Effects of Information Processing Mode on Consumers’Re-
sponses to Comparative Advertising,”Journal of Consumer
Research, 32 (March), 530–40.
——— and Prashant Malaviya (2013), “Consumer-Generated
Ads: Does Awareness of Advertising Co-Creation Help or
Hurt Persuasion?”Journal of Marketing, 77 (May), 33–47.
Torelli, Carlos J., Ayseg¨
ul ¨
Ozsomer, Sergio W. Carvalho, Hean Tat
Keh, and Natalia Maehle (2012), “Brand Concepts as Repre-
sentations of Human Values: Do Cultural Congruity and
Compatibility Between Values Matter?”Journal of Marketing,
76 (July), 92–108.
Trusov, Michael, Anand V. Bodapati, and Randolph E. Bucklin
(2010), “Determining Influential Users in Internet Social Net-
works,”Journal of Marketing Research, 47 (August), 643–58.
———, Randolph E. Bucklin, and Koen Pauwels (2009), “Effects
of Word-of-Mouth Versus Traditional Marketing: Findings from
an Internet Social Networking Site,”Journal of Marketing,
73 (September), 90–102.
Unnava, H. Rao and Robert E. Burnkrant (1991), “Effects of Re-
peating Varied Ad Executions on Brand Name Memory,”
Journal of Marketing Research, 28 (November), 406–16.
Urban, Glen, Guilherme (Gui) Liberali, Erin Macdonald, Robert
Bordley, and John Hauser (2014), “Morphing Banner Adver-
tising,”Marketing Science, 33 (1), 27–46.
Vakratsas, Demetrios and Tim Ambler (1999), “How Advertising
Works: What Do We Really Know?”Journal of Marketing,
63 (January), 26–43.
———, Fred M. Feinberg, Frank M. Bass, and Gurumurthy Kalyanaram
(2004), “The Shape of Advertising Response Functions Revisited: A
Model of Dynamic Probabilistic Thresholds,”Marketing Science,
23 (1), 109–19.
Van Den Bulte, Christophe and Stefan Wuyts (2007), Social Net-
works and Marketing. Cambridge, MA: Marketing Science
Institute Relevant Knowledge Series.
Van Heerde, Harald J., Maarten J. Gijsenberg, Marnik G. Dekimpe,
and Jan-Benedict E.M. Steenkamp (2013), “Price and Adver-
tising Effectiveness over the Business Cycle,”Journal of
Marketing Research, 50 (April), 177–93.
Voorveld, Hilde A.M., Peter C. Neijens, and Edith G. Smit (2011),
“Opening the Black Box: Understanding Cross-Media Effects,”
Journal of Marketing Communications, 17 (2), 69–85.
———,———, and ——— (2012), “The Interacting Role of
Media Sequence and Product Involvement in Cross-Media
Campaigns,”Journal of Marketing Communications, 18 (3),
203–16.
Westbrook, Robert A. and Richard L. Oliver (1991), “The
Dimensionality of Consumption Emotion Patterns and Con-
sumer Satisfaction,”Journal of Consumer Research,18(1),
84–91.
Wiesel, Thorsten, Koen Pauwels, and Joep Arts (2011), “Market-
ing’s Profit Impact: Quantifying Online and Off-line Funnel
Progression,”Marketing Science, 30 (4), 604–11.
Wijaya, Bambang Sukma (2012), “The Development of Hierarchy
of Effects Model in Advertising,”International Research
Journal of Business Studies, 5 (1), 73–85.
Wind, Yoram and Byron Sharp (2009), “Advertising Empirical
Generalizations: Implications for Research and Action,”Journal
of Advertising Research, 49 (2), 246–52.
Young, Antony (2010), Brand Media Strategy: Integrated Com-
munications Planning in the Digital Era. New York: Palgrave
MacMillan.
Young, Daniel R. and Francis S. Bellezza (1982), “Encoding
Variability, Memory Organization and the Repetition Effect,”
Journal of Experimental Psychology. Learning, Memory, and
Cognition, 8 (6), 545–59.
Integrating Marketing Communications / 145