87965639
Post on 03-Jun-2018
215 Views
Preview:
TRANSCRIPT
-
8/11/2019 87965639
1/14
An Integrated Marketing Communications
Perspective on Social Media Metrics
VictorA. Barger
University of Wisconsin-Whitewater
Lauren I. Labrecque
Loyola University Chicago
ABSTRACT: Marketers are being inundated with social tnedia metrics, but there is little consensus on what one
should be measuring, let alone how these measures inform marketing strategy. This article attempts to bring clar-
ity to the situation by adopting an integrated marketing communications perspective. By screening extant metrics
for alignment with social media communications objectives, seven key social media metrics are identified. These
metrics are then described and their application to social media marketing from an integrated marketing communi-
cations perspective is discussed. Finally, limitations of the m etrics are considered to arrive at suggestions for future
research.
Social media metrics are all the rage. In 2009just
three years after Twitter launched and Facebook
opened registration to the general publicBerkowitz
(2009) identified exactly 100 social media metrics.
Now, hardly a day goes by without a new article or
blog post proclaiming, "5 Social Media M etrics You
Should Be Monitoring"; "14 Social Media Metrics
You Can Use Right NOW "; or "50 Key Social Media
Metrics E very M arketer M ust Know." Yet for all the
new metrics, marketers maintain they are only slightly
more informed than they were at the beginning of the
social media revolution (Margiloff 2012). Calls for
standardization of metrics have gone unheeded
(Wurtzel 2009), and both practitioners and academics
lament the preponderance of "nice to know" (Fogel
2010) and "vanity" (Madison 2012) metrics over met-
rics that lead to meaningful action. Romaniuk (2012,
398) aptly sums up the state of social media metrics as
follows: "an available metric is not necessarily a use-
ful metric."
Metrics are necessary for the development and
evaluation of integrated marketing communications
programs (Kitchen, Kim, and Schultz 2008). As social
media continues to lure audiences away from mass
media, marketers can no longer rely on traditional au-
dience measurement (Kliatchko 2008; McDonald
2008).
The measurement challenge is further compli-
cated by the consumer-to-consumer interactions that
social media enables (Wind and Sharp 2009). Upper
management is also demanding more attention to mar-
keting metrics. Seggie, Cavusgil, and Phelan (2007)
suggest that this is due to three factors: (1) demand for
accountability from all units of a firm; (2) dissatisfac-
tion with subjective measures of performance; and (3)
the availability of technology for collecting data for
metrics. Rust, Ambler, Carpenter, Kumar, and Srivas-
tava (2004, 76) warn that "lack of accountability has
undermined marketers' credibility, threatened the
standing of the marketing inction within the firm,
and even threatened m arketing's existence as a distinct
capability within the firm."
This crisis in measurement comes at a time when
marketers are steadily increasing their social media
spending. In 201 2, a survey of marketers and advertis-
ing agencies revealed that 59% planned to increase
their spending on social media marketing (Del Rey
2012). Despite the increases, marketers may still be
underspending on social media (Briggs 2012).Without
informative metrics, however, efforts to optimally al-
locate advertising funds will continue to be impeded
(Schultz 2011).
This article attempts to bring clarity to the situation
by viewing social media metrics
fi om
he lens of inte-
grated marketing communications. We begin by de-
scribing the primary communications objectives for
social media. By screening extant metrics for align-
ment with communications objectives, we narrow the
list of social media metrics to seven key metrics.
64
-
8/11/2019 87965639
2/14
These metrics are then described and their application
to social media marketing from an integrated market-
ing communications perspective is discussed. Finally,
we conclude with sugg estions for future research.
SOCIAL MEDIA OBJECTIVES
At the core of an integrated marketing communica-
tions program are the communications objectives.
These are specific, measurable tasks that can be
achieved using adveriising and other forms of com mu-
nication (CoUey 1961). Metrics are employed to (1)
establish b aselines for comm unications objectives and
(2) track progress towards achieving each objective.
Since the selection of metrics depends on the objec-
tives,
we must first identify a set of potential social
media objectives before discussing social media met-
rics. In this section we consider two types of social
media objectives: short-term and long-term.
Short Term Objectives
The primary purpose of short-term social media ob-
jectives is to generate revenue. The three short-term
objectives that we consider are (1) gaining considera-
tion, (2) stimulating trial, and (3) encouraging repur-
chase.
Gaining consideration
Consumers are increasingly
turning to social media for product and service recom -
mendations. By monitoring and responding to re-
quests for advice, marketers can help ensure the
consideration of their products and services. For ex-
ample, a snowblower manufacturer might monitor
Twitter for tweets containing the keyword "snow-
blower"; when a match is found (e.g., "Can someone
recommend a good snowblower?"), the marketer could
reply with information. A more ambitious program
may involve monitoring social media for problems
that the marketer's product or service can solve. For
example, a retailer of ergonomie furniture could watch
for complaints related to workstation ergonomics
(e.g., "M y wrists are killing m e from typing all day "),
to which the retailer could respond with commisera-
tion and a link to a page on ergonomics on the re-
tailer's website.
Stimulating trial Marketers have long used consumer-
oriented sales promotions to stimulate trial of products
and services (Farris and Quelch 1987; Gupta 1988). It
is perhaps not surprising, then, that one of marketers'
primary uses of social media today is communicating
sales promotions (Schultz and Peltier 2013). Common
forms of online sales promotion include printable
coupons, discount codes, contests, sweepstakes, and
games. As an example, Starbucks frequently an-
nounces discounts on new coffee dritiks on Facebook
and Twitter to encourage followers to try the new bev-
erages.
Encouraging repurchase In addition to stimulating
trial, online sales promotions are effective at encour-
aging repeat purchases. Amazon.com, for example,
routinely provides limited-term discount codes to fol-
lowers on Facebook and Twitter. Of course, a sales
promotion is not always necessary; Panera Bread, for
example, relies on the appeal of new menu items an-
nounced on social media to draw customers back into
their restaurants. Social media also serve as a conven-
ient channel for encouraging followers to sign up for
loyalty programs and for communicating loyalty pro-
gram promotions.
Long Term Objectives
Long-term social media objectives are concerned less
with generating revenue and more with creating brand
equity and building brand relationships. The four
long-term objectives that we consider are (1) improv-
ing customer satisfaction, (2) creating awareness, (3)
building relationships, and (4) fostering commun ity.
Improving customer satisfaction Social media offer
brands a number of opportunities for improving cus-
tomer satisfaction. First, customers may contact a
company directly via social media to express dissatis-
faction with a product or service. If the company deals
with such complaints promptly and effectively, dissat-
isfied customers will be less likely to communicate
their dissatisfaction to others. Second, customers may
post messages about unsatisfactory experiences to so-
cial media. Although not ideal from a public relations
perspective, this at least gives the company a chance
to discover and address such posts before they are
widely shared. Third, a company can enhance cus-
tomer satisfaction by providing product support via
social media. Software developers, for instance, often
receive and reply to requests for technical support on
Twitter. Finally, by monitoring social media for posts
from recent customers, companies can reassure these
customers that they made a good choice and thereby
reduce cognitive dissonance.
Creating awareness
One of the primary functions of
social media is content sharing. As such, social media
is highly effective at propagating messages, particu-
larly when people find the messages entertaining, sur-
prising, and/or humorous. When a message is shared
Spring 2013 65
-
8/11/2019 87965639
3/14
widely within a relatively short period of time, it is
said to have gone viral. This leads to a rapid increase
in awareness of both the message and the message's
creator. Psy's Gangnam S tyle music video is a great
example of this. Within a matter of months, Psy's
video had received over one billion views, propelling
him from relative obscurity outside his home country
of South Korea to worldwide renown (Hall 2012;
Yang 2012). D r. Robert
Wagstaff
a retired dentist, ex-
perienced similar success with his invention, the
Orabrush. After eight years of unsuccessful market-
ing. Dr. Wagstaff enlisted the help of a student at
Bringham Young University to create a series of
YouTube videos promoting the Orabrush (Orabrush
2010).
Within two years, these videos had amassed
over 39 million views, prompting Walmart to decide
to carry the Orabrush at 3,500 of its stores (Wasser-
man 2011). Although social media can help create
awareness for any business, it is especially valuable
for startups and small businesses like Orabrush that
cannot afford the mass media buys traditionally used
to increase awareness.
Building relationships
Brands seek to build relation-
ships with customers to promote brand loyalty and
positive word-of-mouth (Hennig-Thurau, Gwinner,
and Gremler 2002). Since brand relationships develop
as a result of repeat positive interactions between cus-
tomer and brand (Duncan and Moriarty 1998), brands
must find ways to engage customers in such interac-
tions. Prior to the advent of social media, interactions
were primarily one-way (e.g., the viewing of a brand's
advertisements on mass media), with only occasional
two-way communication (e.g., contacting customer
service to resolve a problem). However, with social
media the options for personalized one-way and two-
way communications are greatly expanded. Marketers
now routinely stimulate interactions with consumers
on social media by posting interesting and relevant
content, such as news, articles, photos, videos, and
even games. Betty Crocker, for example, posts photos
of baked goods on Pinterest for customers to view,
comment on, and share.
Fostering commimity Building on the idea of brand
relationships, Muniz and O'Guinn (2001) proposed
the concept of the brand community. Here a brand's
customers interact not only with the brand but also
with one another. Brand communities may be organ-
ized by the company that owns the brand or they may
form autonomously (McAlexander, Schouten, and
Koenig 2002); either way, these communities carry
out imp ortant functions on behalf of the brand, such as
sharing information, perpetuating the history and cul-
ture of the brand, and providing assistance . . . [and]
exert[ing] pressure on members to remain loyal to the
collective and to the brand (Muniz and O'Guin n
2001,
427). Brand communities have also been shown
to increase revenue generated from community mem-
bers, both online and in stores (Manchanda, Packard,
and Pattabhiramaiah 2011). Furthermore, brand com-
munities can serve as a resource for idea generation
(e.g., crowdsourcing) and marketing research. Al-
though online brand communities have traditionally
been hosted on discussion forums, many have moved
to social media due to the lower overhead and the fact
that consumers are already active on social media.
SOCI L MEDI METRICS
Given the large number of social media metricsand
the costs involved in monitoring each metric^the first
step is to identify metrics that will be most informative
to the marketer (Fogel 2010). Fortunately, adopting an
integrated marketing communications perspective nar-
rows the list considerably, since a metric must provide
an indication of progress towards one or more cotnmu-
nications objectives to be considered. It is further help-
ful to make a distinction between metrics developed
specifically for social media analytics and metrics de-
veloped for web analytics. Although web metrics can be
helpftil in calculating social media metrics (e.g., social
media return on investment), they are by definition
more suited to analyzing website activity, and as such
provide an incomplete view of social media marketing
campaigns. This review thus focuses on social media
analytics. The marketer must also decide whether to uti-
lize both proprietary and nonproprietary metrics or only
nonproprietary metrics. Since the means of calculating
proprietary metrics is typically unavailable for public
scrutiny, we consider only nonproprietary metrics.
With the above criteria in mind, seven social media
metrics were identified: volume, share of voice, en-
gagement, advocates, return on investment, leads gen-
erated, and response time. Each metric is applicable to
one or more social media channels (see Figure 1):
blogs (e.g., WordPress); social networks (e.g., Face-
book); photo/video sharing (e.g., YouTube); microblogs
(e.g.. Twitter); product review sites (e.g., Amazon); lo-
cation check-ins/reviews (e.g.. Foursquare); and social
bookmarking (e.g., Delicious). In the following para-
graphs, each metric is discussed in turn. (See Table 1
for an overview of the metrics and their corresponding
definitions.)
66 International Journal of Integrated Marketing Com munications
-
8/11/2019 87965639
4/14
-
8/11/2019 87965639
5/14
TABLE 1 Definitions of Common Social Media Marketing Metrics
Metric
Volume
Share of Voice (%)
Engagem ent (per post)
Engagement (overall %)
Advocates
Return on Investment (ROI)
Leads Generated
Response Time
Formula
The nu mber of mentions of
brand name over a specified
period of
time.
Often segmented into positive and negative
volume using sentiment an alysis.
Positive volume of brand
xlOO
Positive volum e of all brands in category
Th e num ber of comm ents on , replies to, likes of, and shares
of given post.
Engagement at time twith all posts to date
Num ber of views at timetof all posts to date
xlOO
or
Engagement at time
t
with all posts to date
Num ber of followers at time /
XlOO
The number of social media participants who write positive
posts about a brand during a specitled period of time.
Revenue from campaign - Cost of campaign
Cost of campaign
XlOO
The number of leads generated from social channels
(sometimes expressed as a percent of
ll
leads generated).
The amount of time elapsed between the receipt of an
inquiry or support request via social media and a response
from the company.
taking some action beyond viewing or reading (De-
lahaye Paine 2011, 60). This may include liking a
brand's post, commenting on or replying to a brand's
post, or sharing a brand's post witb others. Since eacb
platform employs its own terminology, we classify the
possible behaviors as expressing agreem ent, rat-
ing, voicing opinion, and sharing. The correspon-
ding terminology for each of the dominant social
med ia platforms is shown in Table 2.
Tracking engagement on a per-post basis enables
the marketer to gauge the audience's level of interest
in the content of each post, thereby informing the cre-
ation of future posts. As an aggregate measure, en-
gagement can also indicate the overall level of con-
sumer interest in a brand's message. To adjust for
differences in the viewership of each post, Lovett and
Owyang (2010) recommend tracking overall engage-
ment relative to the total number of views. Since the
num ber of views per po st can be difficult to ascertain,
others have advocated tracking engagement in pro-
portion to the number of followers at the time of the
posting. Even this must be viewed as a rough approx-
imation, however, as not all followers read every post,
and some followers may not even be human (Sterne
68 International Journal of Integrated Mark eting omm unications
-
8/11/2019 87965639
6/14
T A B L E 2 Use r Behaviors on Various Social Me dia Platform s
Platform
Posts
Primar} content
with which users
interact
Soc ia l B ookm ark i ng
Delicious
Stumbe Upon
Links
Links
Location Check ins/Reviews
Foursquare
Yelp
Product Reviews
Amazon
Goodreads
Microblogs
App.net
Pheed
Tumblr
Twitter
Locations
Businesses
Products
Books
Posts
Pheeds
Posts
Tweets
Photo/Video Sharing
Flickr_
lnstagram
Pinterest
YouTube
Social Networks
Facebook
Google+
Linkedin
Blogs
B logger
WordPress
Photos _ _
Photos
Pins
Videos
Status updates
Posts
Updates
Posts
Posts
Expressing
Agreement
Simple expression
ofagreemetilvith
a post
" l i k e " '
Like
' -
_ Sla r__ _ __
Love
Like
Favorite
Favorite
Like
Like
Like
Like
""+1 '^ "
"Tike""
+1
Like
Rating
Simple evaluation
of 1post
-
p i d n ' t j i k e
1 to 5 stars
1 to 5 stars
1 to 5 stars
Heartache
- - -
- "
Dislike
Dislike
' - - -
-
-
Voicmg
Opinion
Statementof one s
opinion\\ith
respect to apost
Comment
Til) . .
Review
Review
Review
Reply
Pheedback
Rebjojg _
Reply
C omment
Comment
C omment
Comment
Comment
C omment
Comment
Comment
Comment
Sharing
Sharing oa post
wit } others on the
same platjorm . .^^
Share link
Share
Share
Add to list
Add to bookshelf
Repost
Remix
Reblog
Retweet
Add to gallery
Repin
-
Share
Share
-
Reblog
2010) . Addi t iona l i ssues wi th engagement as a met r ic
are considered under Future Research.
Advocates
Consu mers can be viewed as progress ing through a se-
ries of four stages in their relat ionships with brands
on
social media (see Figure 2). Ini t ial ly each consumer
starts as a "bystan der" w ith respect to a specific brand ;
the consumer m ay see ment ions of the brand on soc ia l
media, but s/he does not act ively seek out posts by the
brand. In the second stage, the consumer adopts the
role of "fol lower"; here s/he seeks out the brand's mes-
sage by opt ing in to rece ive brand comm unica t ions on
social media. In the third stage, the consumer becomes
a par t ic ipant , inte rac t ing wi th the brand and the
brand's message on soc ia l media . The par t ic ipa t ion
continuum ranges fTom "passive" to "act ive" forms of
par t ic ipa t ion, depending on the type and purpose of
the act ion. Expressions of agreement (e.g. , cl icking a
"Like" but ton on a brand's Facebook post ) a re forms
of passive part icipat ion, whereas voicing an opinion
(e .g. , comment ing on a brand's Facebook post ) and
sharing (e.g. , sharing a brand's Facebook post with
one's Facebook friends) are more act ive forms of par-
t icipat ion. In the final stage, the consumer adopts the
role of brand advo cate , c rea t ing and uploading content
that act ively promotes the brand (e.g. , post ing a Face-
book s ta tus update tha t recommends or speaks favor-
ably of the brand).
Tracking the number of "fol lowers" or "fans" may
be ego-boost ing, but i t is unlikely to be helpful in en-
hancing the effect iveness of a brand's social media
marketing, due to the existence of fake and inact ive
fol lowers (Sterne 2010). More useful is t racking the
number of advocates of a brand. Not surprisingly, the
goal is to grow the number of advocates over t ime.
Spring 2013 69
-
8/11/2019 87965639
7/14
FIGURE
2
Levels
of
Consumer E ngagement
on
Social M edia
Bystander
Follower
Participant
Advocate
Bystanders may see mentions of a brand in social media, but they do
not actively seek out posts by the brand nor do they interact with the
brand.
Followers seek out a brand's message by opting-in to receive brand
communications (e.g., by following, friending, or subscribing), but
they do not interact with the brand or the brand's message.
Participants interact with a brand on social media. Participation
ranges from passive (e.g., liking) to active (e.g., commenting),
depending on the type and purpose of
the
action.
Advocates not only interact with the brand on social media, they
actively promote the brand by creating and uploading content
favorable to the brand (e.g., posting, reviewing).
This
is
particularly important when
a
marketer s
ob-
jective
is to
gain consideration, since friends
of
advo-
cates
are
more likely
to
consider
a
brand when
an
advocate speaks highly
of
it.
If the
number
of
advo-
cates decreases over time,
the
firm may need
to
estab-
lish
an
advocacy program
or
post more engaging
content (Lovett
and
Owyang 2010). Also
of
relevance
is
the
influence
of a
brand s advocates (Fogel 201 0).
An advocate with
a
large number
of
followers that
en-
gage with
the
advocate s postings
is
more influential
(and thus more valuable
to
the m arketer) than
an
advo-
cate with fewer followers that tend
not to
engage with
the advocate s posts.
ReturnonInvestment RO I)
Return
on
investment
is
defined
as the
revenue gained
from
a
social media marketing campaign minus
the
cost
of
the campaign divided
by the
cost
of
the
cam-
paign
(see
Table
1).
Return
on
investment
is
most
ef-
fective
at
evaluating short-term social media
objectives, such
as
stimulating trial
and
encouraging
repurchase.
For
example,
a
marketer might offer
a
printable coupon
or
communicate
a
discount code
as
part of a campaign to stimulate trial.To receivethe
discount,
the
customer must present
the
coupon
or
enter the code
at
the tim e
of
purchase. Since sales that
result from
the
campaign
are
directly attributable
to
the campaign
via the
coupon
or
discount code,
the
marketer
can
determine
the
revenue gained from
the
campaign
and
calculate
the
return
on
investment.
Caution
is
advised when using return
on
investment
as
a
measure
of
performance.
The
belief that every-
thing digital
is
measurable
is a
misconception that
can
easily lead marketers astray. Attributing sales
to
social
media
is
problema tic, particularly
for
campaigns that
do
not
offer
an
incentive. Even with cam paigns that
do
offer
an
incentive,
the
calculation
of
return
on
invest-
ment ignores potential synergies between incentive-
based
and
non-incentive-based campaigns. Return
on
investment
has
also been criticized
for its
overempha-
sis
of
shori-term returns over long-term brand build-
ing (Calkins
and
Rucker 2008). Hoffman
and
Fodor
(2010) warn that social media
is
still
in its
early stages
and that focusing
too
much
on
return
on
investment
could stifle experimentation, potentially creating
op-
portunities
for
competitors. They suggest that
mar-
keters instead view return
on
investment from
the
consum er s perspective; namely, what
the
consumer
gets
for
investing
his or her
time
and
energy
in
engag-
ing with
a
brand through social media.
Leads Generated
When a company seeks to gain consideration of its
products and services,
it
will often track the number
of
leads generated through social media. This
is
particu-
larly helpfiil when
the
firm
is
investing considerable
resources
in
monitoring and responding to requests
for
adviceonsocial m edia,asdescribed earlier un derSo-
cial M edia Objectives. Leads generated through social
media
can
also
be
expressed
as a
percentage
of
total
leads generated by the firm, in which case
it
provides
a
measure of the relative effectiveness
of
social media
at
70 International Journal of Integrated Ma rketing om mun ications
-
8/11/2019 87965639
8/14
generating leads. Although leads generated can be a
useful metric, it is important to note that it suffers
from many of the attribution problems that affect re-
turn on investment. This is discussed further in Future
Research.
Response Time
On the Internet, people expect quick responses, and
social media is no exception. In fact, a recent survey
showed that 32% of consumers who contact a brand
through social media expect a response within thirty
minutes (Baer 2012). To ensure customer satisfaction,
it is thus essential that brands respond promptly to in-
quiries and support requests submitted via social
media. Tracking and managing average response time,
which is the average amount oftimeit takes the brand
to reply to social media queries, can accomplish this.
In addition, it is recommended that brands routinely
follow up with a sample of recent contacts to ensure
that requests are being resolved satisfactorily.
Relationship to Traditional IMC Metrics
Traditional metrics for evaluating integrated market-
ing communications can be classified into three types
(Ewing 2009): attitudinal measures, behavioral meas-
ures,
and financial measures. Attitudinal measures are
commonly employed to ascertain the effects of adver-
tising (Schultz 2011 ). For example, in the Lavidge and
Steiner (1961) hierarchy of effects, exposure to adver-
tising is presumed to move consumers through a series
of
stages:
(1) awareness, (2) knowledge, (3) liking, (4)
preference, (5) conviction, and (6) purchase . A brand's
performance at each stage (with the exception of the
purchase stage) is determined by using survey re-
search and attitudinal measures. Behavioral measures,
in contrast, are based on actions taken by consumers
in response to marketing campaigns. For example, the
effectiveness of sales promotion could be established
by tracking the number of consumers who redeem a
coupon. Lastly, financial measures emphasize the rev-
enue generated by marketing communications. The
two most common financial measures of integrated
marketing communications are return on investment
and change in customer lifetime value (Schultz 2011).
Social media metrics span the three types of tradi-
tional metrics. With respect to attitudinal measures,
the social media metrics of volume, engagement, and
number of advocates correspond to the awareness, lik-
ing, and conviction stages of the hierarchy of effects.
Engagement can also be viewed as a behavioral meas-
ure, since it reflects specific actions that consumers
take in response to brand m essages. Similarly, num ber
of leads generated serves as a behavioral measure,
since it quantifies the number of consumers that take
action in response to a messagefi oma brand on social
media. In the financial category, the return on invest-
ment of social media mirrors traditional ROI. Finally,
although response time does not appear to correspond
to one of the traditional types, its outcomecustomer
satisfactionis clearly an attitudinal measure.
FUTURE RESE RCH
The metrics identified in this paper are conceptually
sound; however, associated w ith each are difficulties in
estimation. One source of error is sentiment analysis.
Although humans can determine the tone of a post
with relative ease, it is difficult to do so algorithmi-
cally. Unfortunately, given the huge number of posts
that are created daily, not to mention hourly, it is im-
possible to conduct sentiment analysis at scale without
automation. Sentiment analysis vendors have estimated
their systems to be 70-80% accurate (Wright 2009),
but a recent study by Schweidel, Moe, and Boudreaux
(2012) found almost no correlation (r = -.002) between
an automated sentiment analysis and a survey con-
ducted using traditional marketing research tech-
niques. Recognizing the growing importance of social
media monitoring and m etrics, compa nies offering so-
phisticated natural language processing to improve ac-
curacy and reliability have emerged (NetBase
Solutions 2012). Clearly, more research in the areas of
sentiment analysis, natural language processing, and
computational linguistics is needed.
Adding to the estimation issues associated with
sentiment analysis is the presence of robots (or
bots ), fake accounts, and inactive accounts on social
media. All three inflate counts of followers and activ-
ity. A recent study by Marco Camisani Calzolari esti-
mated that up to 46% of brand followers are bots
(Policschi 2012). The problem is compounded by the
ease with which companies and individuals can ac-
quire fake followers. For example, fake Twitter follow-
ers can be purchased for less than a cent per follower
(Considine 2012). Inactive accounts may be less of a
problem in that they do not generate posts, but they
still inflate follower and viewership estimates. Al-
though progress is being made at identifying fake ac-
counts, more research is needed in this area to ensure
accurate accounting of social media participants and
their online activity.
Current social media metrics also suffer from a
Spring 2013 71
-
8/11/2019 87965639
9/14
-
8/11/2019 87965639
10/14
should get credit for the conversion. Most web analyt-
ics program s employ a last touc h or last click
method of tracking, which assumes tbe marketing
channel most responsible for a consumer's behavior is
the channel that the consumer last touched before vis-
iting or making a purchase. In reality, consumers are
likely to have encountered multiple touchpoints across
an array of channels prior to conversion. Take for ex-
ample the consumer journey outlined in Figure 3.
Here, the consumer has enco untered the brand in a va-
riety of channels, including multiple social media
sites;
however, only the last touch, paid search, is cred-
ited for the conversion. Since the initial brand aware-
ness was driven (and subsequently reinforced) through
social media, social media should be credited for at
least a portion ofthe conversion.
There are a number of possible attribution models
(see Figure 4). The example just described corre-
sponds to last-click attribution, which is based on the
notion that the last marketing communication en-
countered by a customer is responsible for the con-
version. In this model, the value of prior interactions
is ignored and these channels do not receive any
credit. This model focuses solely on short-term ob-
jectives such as gaining consideration, stimulating
trial, and encouraging repurchase, and does not con-
sider long-term objectives. This is the most common
model and has been used since the early days of on-
line direct response.
First-click attribution takes the opposite view of
last-click attribution; namely, it gives full credit to the
initial interaction. This model places greater emphasis
on creating awareness, a long-term objective, and
views the first marketing communication as the most
valuable, disregarding the impact of other touchpoints.
Equal attribution views all interactions as valuable
and assigns identical weight to each touchpoint. In our
example, all four touchpoints would receive 25%
credit for the conversion. This model is advantageous
in that it incorporates both short-term and long-term
objectives; however, the assumption that each touch-
point is of equal value may not reflect reality. More-
over, as the number of touchpoints gro ws, the value of
each touchpoint decreases equally.
Fractional attribution recognizes that different
touchpoints play different roles and allows for each in-
teraction to be weighted accordingly. In theory this
method is ideal; not only does it incorporate both
short-term and long-term objectives, it accounts for
the respective value of each touchp oint in generating a
conversion. The assumption that marketers can deter-
FIGURE 4 Example Attribution Models
Last click Attribution
Go> sie
100%
First click Attribution
100% 0%
You fM
Co yle
Fractional Attribution
4 5 %
15%
15%
25
Equal Attribution
25
25 25
mine the appropriate weights for each touchpoint may
not be realistic, however, and technology may not be
in place to capture each touchpoint.
The point of this discussion is that marketers need
to consider the impact of multiple touchpoints when
measuring marketing success, especially in regards to
social media. Although tracking codes can be embed-
ded in links, thereby enabling the attribution of online
sales to social media sources, users of social media do
not always include these codes in their posts. Simi-
larly, many analytics programs rely on web browsers
to submit referrer information; since not all applica-
tions supply referrer data (e.g., mobile apps and e-mail
clients), these referrals may be misconstrued as direct
traffic. Even more problematic is the attribution of of-
fline sales to social media. Unless a customer men-
tions a social media cam paign w hen making an offline
purchase, the link between the sale and social media
not to mention a particular social media campaign
will not be made. The end result is an incomplete
picture of the marketing effort. Further advances in
methods of attribution are necessary to improve the re-
liability of social media metrics.
Spring 2013 73
-
8/11/2019 87965639
11/14
Social Med ia and Effective Integrated
Marketing ommunications
As of this writing, Wikipedia hsts 198 active social
networking websites, with a disclaimer that this list is
not exhaustive (Wikipedia 2013). Indeed, newer social
media sites, such as AppNet and Pheed, are conspicu-
ously absent from the list. As social media continue to
proliferate, effective integration of social media into
marketing communications will be increasingly diffi-
cult. We highlight some of the potential challenges
here as suggestions for areas of future research.
One issue concerns the number of social media
sites a brand should be active on. On the one hand,
there is clearly an opportunity cost associated with
each additional site; the time and resources required to
maintain a presence on a new social channel could be
spent enhancing the brand's presence on existing
channels or even dedicated to other aspects of market-
ing communications. On the other hand, a brand lack-
ing presence on a social channel may cause discord
among its customers who are engaged there. Even
worse, the brand might fall victim to a third party
masquerading as the brand on the new social channel,
potentially damaging the brand's reputation and its re-
lationship with its customers. Aside from having a
presence, how active does a brand need to be on each
social channel? Is it sufficient to maintain a minimal
presence on the less popular social sites? Or must a
brand be equally active on each site? Research is
needed to help brands answer these questions and
guide their social media strategy.
Marketers must also consider the demographics of
the social chann els. Pinterest, for exam ple, is currently
more popular among a female audience, while Twitter
is slightly more popular among males, and Pheed is
dominated by teenagers (Duggan and Brenner 2013;
Pozin 2013). These differences in demographics are
due in part to the design goals of the respective plat-
forms. Pinterest, for example, is designed to facilitate
sharing of photos found on the web. Twitter requires
users to communicate in short, primarily textual mes-
sages, and Pheed emphasizes the sharing of multime-
dia. There is also a growing number of niche social
networks that are dedicated to special topics, such as
knitting (Manjoo 2011). Just as a marketer would re-
search audience profiles of various magazines, mar-
keters will increasingly need to consider the user base
of each social channel when developing their social
media strategy.
Differences in design and user base are also re-
flected in the style of communication employed on
each social channel. Communications on Linkedin,
for example, are expected to be professional and well-
written, whereas posts on Pheed tend to be raw and
graphic. Moreover, when deciding whether or not to
participate in a social channel, brands must also con-
sider whether the style of communication is consistent
with the brand's image. A hip, modern brand targeted
at youth may find the style of communication on
Pheed appropriate for the brand, whereas a conserva-
tive,
high-end brand may not. In cases of overlap,
where multiple social channels match the brand's
image, the marketer may need to customize communi-
cations for each social channel. Twitter users, for ex-
ample, may be annoyed by brands that post truncated
versions of Facebook updates to Twitter.
Lastly, how should a brand deal w ith users who fol-
low the brand on multiple social channels? A current
practice of many brands is to post roughly the same
content on each social channel. Does this duplication
annoy consumers who see the same message across
multiple channels? Or does it simply reinforce the
message? Presumably, people who follow a brand on
multiple channels are fans, possibly even advocates.
Does posting similar messages on multiple social
channels help these users share the brand's message
with different sets of followers? Is there any advantage
to encouraging people to follow the brand on multiple
channels? Namely, can the brand post to multiple so-
cial charmels in such a way as to create cross-channel
synergies? Ifso ,how can the brand m easure the effec-
tiveness of its efforts, given the attribution problem
discussed earlier?
R E F E R E N C E S
Baer, Jay. 2013. 42% of
onsumers
complaining in social
media expect 60 minute response time
September 27
2012 [cited M arch 1 201 3]. Available from http://
www.convinceandconvert.com/the-social-habit/42-per-
cent-of-consumers-complaining-in-social-media-expect-
60-minute-response-time/.
Belch, George E., and Michael A. Belch. 2012.Advertising
and promotion: An integrated marketing communica-
tionsperspective. 9th ed. NewYork McGraw-Hill/Irwin.
Berkowitz, David. 2013.10 0 ways to m easure social media
2009 [cited F ebruary 15, 2013 2 013 ]. Available from
http://www.marketersstudio.eom/2009/l 1/100-ways-to-
measure-social-media-.html.
Briggs, Rex. 2012. SIRFs-Up: Catching the next wave in
marketing.
North Charleston, South Carolina: Create-
Space.
Calkins, Tim, and Derek D. Rucker. 2008. Don't overem-
74 International Journal of Integrated Marketing Com munications
-
8/11/2019 87965639
12/14
phasize ROI as single measure of success.
Advertising
Age,
February 4, 2008.
Colley, Russell H. 1961.
Defining advertising goals for
measured advertising results.
New York: Association of
National Advertisers.
Considine, Austin. 2012. "Buying their way to Twitter
fame." The New
York
Times,August 22, 2012.
Del Rey, Jason. 2012. Advertisers say what we're all think-
ing: Social-media spending is going to explode.
Adver-
tising Age,
March 6, 2012.
Delahaye Paine, Katie.
2011.Mea sure what matters: Online
tools for understanding cu stomers, social m edia, en-
gagement, and key relationships.Hoboken, New Jersey:
John Wiley & Sons.
Duggan, Maeve, and Joanna Brenner. 2013. The demo-
graphics of social media users2012. Washington,
D.C.: Pew Research Center.
Dunean, Tom, and Sandra E. Moriarty. 1998. "A communi-
cation-based marketing model for managing relation-
ships."
Tbi/rwa/o/MarAre/wg no. 62 (2):1 -13.
Ewing, Michael T. 2009. "Integrated marketing communi-
cations measurement and evaluation." Journal of Mar-
keting Communications
no. 15
(2-3):
103-117.
Farris,
Paul W., and John A. Quelch. 1987. In defense of
price promotion.Sloan Management Review,Fall, 63-72.
Fogel, Suzanne. 2010. "Issues in measurement of word of
mouth in social media marketing."
International Journal
of Integrated Marketing Communications
no. 2 (2):54-
60 .
Gupta, Sunil. 1988. "Impact of sales promotions on when,
what, and how much to buy."Journal of
arketing
Re -
search
no. 25 (4):342-355.
Hall, Emma. 2012. 'Gan gnam ' close to billion views: Most-
watched YouTube video has life of its own as parodies
continue.
Advertising Age,
November 28.
Hennig-Thurau, Thorsten, Kevin P. Gwinner, and Dwayne
D. Gremien 2002. "Understanding relationship market-
ing outcomes."
Journal of Service Research
no. 4
(3):230-247.
Hoffman, Donna L., and Marek Fodor. 2010. Can you
measure the ROI of your social media marketing?
MIT
Sloan M anagement Review,41-49.
Kitehen, Philip I, Ilchul Kim, and Don E. Schultz. 2008.
"Integrated marketing communications: Practice leads
theory."Journal of Advertising Researchno. 48
(4):531-
546.
Kliatchko, Jerry. 2008. "Revisiting the IMC construct: A re-
vised definition and four pillars."International Journal
ofAdvertising no.
27
(1):
133-160.
Lavidge, R obert J., and Gary A. Steiner. 1 961. "A model for
predictive measurements of advertising effectiveness."
Journal of M arketing no.
25 (6):59-62.
Lovett, John, and Jeremiah Owyang. 2010. Social market-
ing analytics: A new framework for measuring results
in social media, http://john.webanalyticsdemystified.com
/2010/04/22/new-research-on-social-marketing-analyt-
ics/.
Madison, Ivory. 2013.Whyyour social media m etrics are a
waste of time.
Harvard Business Review, December 18
2012 [cited February 15 2013]. Available from
http://blogs.hbr org/cs/2012/12/why_your_social_media
_metrics.html.
Madrigal, Alexis C. 2012. Dark social: We have the whole
history ofth web wrong.
TheAtlantic,
http://www.theat-
lantic.com/teehnology/archive/2012/10/dark-social-we-
have-the-whole-history-of-the-web-wrong/263523/.
Manchanda, Puneet, Grant Packard, and Adithya Pattabhira-
maiah. 2011. Social dollars: The conomie impact of
customer participation in a firm-sponsored online com-
munity. In Marketing Science Institute Working Paper
Series.
Cambridge, Massachusetts: Marketing Science
Institute.
Mangold, W. Glynn, and David J. Faulds. 2009. "Soeial
media: The new hybrid element of the promotion mix."
Business Horizons
no. 52 (4):357-365.
Manjoo, Farhad. 2013. A tight-knit community: Why Face-
book can't match
Ravelry,
the social network for knitters.
Slate, July 6 2011 [cited March 6 2013]. Available from
ht tp : / /www .s l a t e .com/a r t i c l e s /t echno logy / t echno l -
ogy/201 l/07/a_tightknit_community.html.
argiloffWill. 2012. Seven years in, it's time for social to
grow up.Advertising Age, July 16, 2012.
McAlexander, James H., John W. Schouten, and Harold F.
Koenig. 2002. "Building brand community."
Journal of
Marketing
no. 66 (l):38-54.
McDonald, Scott. 2008. "The long tail and its implications
for media audience measurement."
Journal of Advertis-
ing Researchno. 48 (3):313-319.
Muniz, Albert M., Jr., and Thomas C. O'Guinn. 2001.
"Brand community."
Journal of Consumer Research
no.
27 (4):412-432.
NetBase Solutions, Inc. 2013.
Sentiment analysis with NLP
leads to more accurate understanding2012 [cited Febru-
ary 28 2013]. Available from http://www.netbase.com/
social-intelligence/the-nlp-advantage/.
Orabrush. 2013.
Story ofO rabrush
2010 [cited February 25
2013].
Available from http://ww w.orabrush.com/story.
Polleschi, Ilaria. 2013.
Robots crowd
Twitter brand
profiles.
Reuters, June 8 2012 [cited February 15 2013].
Pozin Ilya. 2013. Teens drive Pheed to 1 social app.
Forbes, March 6 2013 [cited March 6 2013]. Available
from http://www.forbes.com/sites/ilyapozin/2013/02/20/
teens-drive-pheed-to-1 -social-app/.
Romaniuk, Jenni. 2012. "Are you ready for the next big
thing? New media is dead Long live new m edia "Jour-
nal ofAdvertising Research
no. 52 (4):397-399.
Rust, Roland T., Tim Ambler, Gregory S. Carpenter, V.
Kumar, and Rajendra K. Srivastava. 2004. "Measuring
marketing productivity: Current knowledge and nature
directions."
Journal of M arketing
no. 68 (4):76-89.
Spring 2013 75
-
8/11/2019 87965639
13/14
Schultz, Don E. 201 1. IMC measurem ent: The challenges
of an interactive marketplace. International Journal of
Integrated Marketing Communicationsno. 3 (l ):7-24.
Schultz, Don E., Martin P. Block, and K aylan Raman. 2012 .
Understanding consumer-created media synergy.
Jour-
nal of Marketing Communicationsno. 18 (3): 173-187.
Schultz, Don E., and Jimmy W. Peltier. 2013 . Social
media's slippery slope: Challenges, opp ortunities and fu-
ture research directions.
Journal of Interactive Market-
ing (Forthcoming).
Schweidel, David A., WendyW Moe, and Chris Boudreaux.
2012. Social media intelligence: Measuring brand senti-
ment from online conversations. In
Marketing Science
Institute
Working
Paper Series. Cambridge, Massachu-
setts: Marketing Science Institute.
Seggie, Steven H., Erin Cavusgil, and Steven E. Phelan.
2007.
Measurem ent of return on marketing investment:
A conceptual framework and the future of marketing
metrics. Industrial Marketing Management no. 36
(6):834-841.
Sterne, Jim. 2010.
Social media metrics: How to measure
and optimize your marketing investment.Hoboken, New
Jersey: John Wiley & Sons.
Truong, Yann, Rod McCoU, and Philip J. Kitchen. 2010.
Practitioners' perceptions of advertising strategies for
digital media. International Journal of Advertising no.
29 (5):709-725.
Wasserman, Todd. 2013.
rabrush
parlays
YouTube
success
into
Walmart
deal. Mashable, September 20 2011 [cited
February 25 201 3]. Available from http://mashable.
com/2011/09/20/orabrush-walmar/.
Wikipedia. 2013.List of social networking w ebsites Febru-
ary 27 2013 [cited March 6 2013]. Available from
http: / /en.wikipedia.org/wiki/List_of_social_network-
ing_websites.
Wind, Yoram (Jerry), and Byron Sharp. 2009. Advertising
empirical generalizations: Implications for research and
action. Journal of Advertising Research no. 49 (2):
246-252.
Wright, Alex. 2009 . Mining the web for feelings, not
facts. The New
York
Times
August 23 , 2009.
W urtzel, Alan. 2009. Now. Or never: An urgent call to ac-
tion for consensus on new media metrics.
Journal of
Advertising Research
no. 49 (3):263-265.
Yang,Jeff 2012 . Gangn am Style's U.S. popularity has Ko-
reans puzzled, gratified.
TheWallStreet Journal
August
28 .
VIC TOR
A.
BARGER
is Associate Professor at Univer-
sity of WisconsinWhitewater.
LAUREN
I.
LAB R EC QUE
is Associate Professor at Loy-
ola University Chicago.
76 International Journal of Integrated Marketing Comm unications
-
8/11/2019 87965639
14/14
C o p y r i g h t o f I n t e r n a t i o n a l J o u r n a l o f I n t e g r a t e d M a r k e t i n g C o m m u n i c a t i o n s i s t h e p r o p e r t y o f
R a c o m C o m m u n i c a t i o n s a n d i t s c o n t e n t m a y n o t b e c o p i e d o r e m a i l e d t o m u l t i p l e s i t e s o r
p o s t e d t o a l i s t s e r v w i t h o u t t h e c o p y r i g h t h o l d e r ' s e x p r e s s w r i t t e n p e r m i s s i o n . H o w e v e r , u s e r s
m a y p r i n t , d o w n l o a d , o r e m a i l a r t i c l e s f o r i n d i v i d u a l u s e .