ArticlePDF Available

What's Not to "Like?" Can a Facebook Fan Base Give a Brand The Advertising Reach It Needs?


Abstract and Figures

A marketer with a Facebook Fan base has at least some ability to advertise to that audience. What quality of reach, however, does this sort of "earned media" deliver? The landmark discovery by Andrew Ehrenberg of the negative binomial distribution (NBD) implies that the most effective advertising requires media that reach across both heavy and light buyers of the brand. This article investigates the buying concentration of the Facebook Fan base of two different brands (both Fast Moving Consumer Goods (FMCG) categories) and compares it to the brands' actual buying bases. The buyer base of each of the brands is distributed in the typical NBD, whereas the Fan base delivered by Facebook is skewed in an opposite pattern-skewed toward the heaviest of the brands' buyers-making the quality of Facebook's reach appear rather unappealing.
Content may be subject to copyright.
DOI: 10.2501/JAR-52-2-000-000 June 2012 JOURNAL OF ADVERTISING RESEARCH 1
No other single media platform can boast the speed
of user uptake as Facebook. It is estimated that,
by 2015, social media will become a mainstream
mass-media platform that, in one form or another,
will engage one-third of the world’s population.
This penetration would offer advertisers access
to 80 percent of global consumer expenditures—
a potential $29 trillion market (Nuttney, 2010). In
light of such predictions, it is understandable that
many marketers are including social media in their
media mix. They do so, however, with limited
understanding of whether social media are more
effective than other platforms or of how they can
used most effectively (Nelson-Field and Klose, 2010).
Facebook is the dominant—and fastest-
growing—social medium, with more than 850
million active users. For marketers, the Facebook
platform offers a different kind of mechanism for
communicating with their potential audiences.
When it is compared to offline media, Facebook
often is reported as a cost-effective way of develop-
ing and communicating with actual (and/or poten-
tial) customers (comScore, 2011; Millward_Brown,
2011; Gibs and Bruich, 2010; Syncapse, 2010).
Advertisers create Facebook Fan pages for
their brands and then encourage Facebook users
to become “Fans” of these pages by clicking the
“like” button on the page. Once a user has “liked”
a brand’s page in this manner, they may receive
brand updates—and the observations of other
brand enthusiasts—in their personal newsfeeds.
The number of attracted and maintained Fans
typically are key metrics for evaluating the success
of Facebook marketing efforts (Millward_Brown,
2011; Sterne, 2010). How many Fans a brand has
obviously affects the breadth of a brand message,
but who these people are—not just how many
of them there may be—also is important. Some
industry studies have proposed that brand Fans
may spend more than non-Fans (Millward_Brown,
2011; Syncapse, 2010)—a finding that intuitively
would suggest that the Facebook enthusiasts are
heavy brand buyers. The concentration of these
“valuable” Fans across the entire Fan base, how-
ever, is unknown. Furthermore, whether these
valuable brand Fans have changed their buying
behavior after becoming a Fan also is unknown.
As such, the following three questions were the
focus of this research:
What’s Not to “Like?”
Can a Facebook Fan Base Give a Brand
the Advertising Reach It Needs?
Ehrenberg-Bass Institute
Ehrenberg-Bass Institute
Ehrenberg-Bass Institute
A marketer with a Facebook Fan base has at least some ability to advertise to that
audience. What quality of reach, however, does this sort of “earned media” deliver? The
landmark discovery by Andrew Ehrenberg of the negative binomial distribution (NBD)
implies that the most effective advertising requires media that reach across both heavy
and light buyers of the brand. This article investigates the buying concentration of the
Facebook Fan base of two different brands (both Fast Moving Consumer Goods (FMCG)
categories) and compares it to the brands’ actual buying bases. The buyer base of each of
the brands is distributed in the typical NBD, whereas the Fan base delivered by Facebook
is skewed in an opposite pattern—skewed toward the heaviest of the brands’ buyers—
making the quality of Facebook’s reach appear rather unappealing.
• Are Fans of brands on Facebook heavy
buyers of the brand?
• What is the concentration of these buy-
ers across the brand Fan base?
• Has the Fan recruitment profile
changed? Is the reach broadening?
Targeting strategies that focus on reaching
(and rewarding) heavy brand buyers long
have been popular in advertising practice.
For example, an analysis of advertising
effectiveness entries showed that this was
by far the most popular strategy, although
the opposite strategy (targeting light and
non-customers) was associated with far
higher sales and profit results (Binet and
Field, 2009).
The thinking behind such a strategy
seems, at least superficially, to be logical:
It would seem to make sense to spend
more money on customers who are worth
more to the brand. That logic is flawed,
however, because what matters is not
how much customers buy but rather how
they respond to advertising (Wright and
Esslemont , 1994; Watts, 2011). Heavy-loyal
customers are, of course, by definition
worth to the brand per customer, but it is
unlikely that any advertising could stimu-
late them to buy more than they already
do: Customer who already are 100-percent
loyal to a brand are unable to give
the brand any more of their category
Andrew Ehrenberg’s discovery that
a brand’s customer base is described by
a negative binomial distribution of buy-
ing rates (Ehrenberg, 1959) shows that
reaching all types of customers is para-
mount. The NBD accurately describes
the frequency distributions of purchase
rates across a population of consumers
for a single brand or category (Ehrenberg,
1988; Goodhardt, Ehrenberg, and Chat-
field, 1984; Morrison and Schmittlein,
1988; Uncles, Ehrenberg, and Hammond,
The distribution has been validated
in many repeat-purchase markets across
hundreds of categories and brands and
has become a well-established benchmark
of the buying concentration of a brand’s
customer base (Ehrenberg, 1988; Good-
hardt et al., 1984; Morrison and Schmit-
tlein, 1988; Uncles et al., 1995). The NBD
denotes that the heterogeneity in purchas-
ing rates (λ) follows a gamma distribution
in that under most conditions, it reflects a
high incidence of light buyers (shoppers
who have a low to close-to-zero purchas-
ing rate), fewer medium buyers, and very
few heavy buyers (Ehrenberg, 1988).
As a brand grows, it moves from one
NBD to another. As it moves, however, it
achieves higher penetration and higher
average purchase rates. Because of the
shape of the distribution, the bulk of
change is seen among the brand’s very
many light (and non-) buyers (McDonald
and Ehrenberg, 2003; Stern and Ehren-
berg, 2003). Growth-oriented advertising,
therefore, needs to reach light (and non-)
It may be argued that the bulk of work
that advertising accomplishes is not to
increase market share but to maintain it
(Ehrenberg, Barnard, and Scriven, 1998).
In this case, the sales effects of advertis-
ing are not to increase purchase rates but
to prevent sales erosion that would oth-
erwise occur. It, therefore, would seem
highly important to reach the heavy cus-
tomers who do the most buying of the
brand; after all, this audience has the high-
est potential for erosion.
The sales importance of heavy buyers,
however, often is overstated. Recent stud-
ies have hypothesized that, for grocery
brands, the 80/20 adage (the top 20 per-
cent of a brand’s customers are respon-
sible for 80 percent of sales) does not apply
and that the top 20 percent of customers,
in fact, contribute less than 60 percent of
sales (Sharp and Romaniuk, 2007; Sharp,
2010). The brand’s lightest 80 percent of
customers, therefore, are important for
maintenance and growth; they contribute
half of today’s sales—an advantage brand
manager would prefer to retain. Further-
more, these buyers rarely think of the
brand and rarely buy it. They are likely to
be lured away by competitors’ advertis-
ing, particularly if the brand’s own adver-
tising efforts fail to reach them.
The work by Ehrenberg and others in doc-
umenting the sources of a brand’s sales
leads to a succinct rejection of the target-
ing strategy. As Ehrenberg wrote, his dis-
covery ended “marketing’s pipedream of
just recruiting heavy buyers” (Ehrenberg,
2005). Instead of targeting heavy brand
buyers, communication strategies, it was
argued, should seek to communicate with
potential buyers from across the entire
customer base—including not just light
buyers of the brand but non-buyers (so
long as they are already engaged in the
category), many of whom are not non-
buyers at all but rather simply light buy-
ers who did not make a purchase this year
but may return next year (Goodhardt and
Ehrenberg, 1967; Anschuetz, 2002; Morri-
son and Schmittlein, 1988).
At this point, it would seem that a
case could be made for the conclusion
that the ideal medium (or at least mix of
media) should reach all category buy-
ers and, hence, find the brand’s heaviest
and very lightest of buyers. The heavier
buyers, however—the more loyal cus-
tomers—are easier to reach than light buy-
ers because they more easily notice and
mentally process the brand’s advertising
messages (Sharp, 2010). These heavier
buyers also receive more reinforcement
from the brand’s other marketing efforts,
including packaging. The implication is
that advertising media that skew toward
light buyers are particularly valuable. By
contrast, media that skews towards heav-
ier buyers offers something that is easily
achieved and therefore less valuable to an
And, it is from this heavy-user/light-
user perspective that the authors have
examined the influence of marketing on a
brand’s Facebook Fans.
To analyze the difference between a typi-
cal population of shoppers—a group that
typically would be NBD-distributed—and
the population of a Facebook Fan base, the
authors used two data sources:
• Self-reported purchase data from Face-
book Fans of two (unnamed) brands
from two different repeat-purchase cat-
egories (chocolate and soft drinks)
• These data was collected with a self-
completion Web-based survey link. One
of the data sets was collected via a link
on the brand’s Fan page, where only
Fans of the brand could respond; the
other was sourced from a probability-
based online panel—where respondents
could be Fans of any brand in the cat-
egory—with analysis restricted to Fans
of the one focal brand.
• Actual consumer panel data for the
same brands for direct comparison
To ensure consistency between the com-
parisons, the authors
• mined a 12-week rolling average and
• converted this continuous data into the
same categorical measures (i.e., grouped
into “never”; “once”; “two-three times”;
“4 or more times”).
For both data sets, the authors classified
these purchase categories:
• “non-buyers” (“never”);
• “light” (“once”);
• “moderate” (“two–three times”); and
• “heavy buyers” (“four or more times in
3 months”).
The use of retrospective self-reported
purchasing as the basis of frequency dis-
tributions introduced the possibility of
respondent error. For example, one 1979
study concluded that the average correct
classification rate (across seven brands
and three different purchase-related ques-
tions) was only 49 percent (Wind and
Learner, 1979).
More recent studies have considered
purchase intention (Romaniuk, 2004;
Wright and MacRae, 2007); product con-
sumption (Stanton and Tucci, 1982); and
product usage (Hu, Toh, and Lee, 2000;
Ram and Hyung-Shik, 1990). The com-
parisons between recall and panel data
were consistent, with respondents tend-
ing to give responses for a typical period
rather than the specific period they were
questioned about. Those results mean
that self-report data potentially can out-
perform panel data in correctly classify-
ing consumers into light or heavy buyers.
Moreover, because of stochastic variation
in purchasing, panel data always will mis-
classify some normally heavy consumers
as light because they bought at less than
their usual rate during the particular anal-
ysis period (Schmittlein, Cooper, and Mor-
rison, 1993). And some “light” buyers also
will be misclassified as “heavy.”
Some recent research in the area is more
directly relevant to this research because
it investigated distributions of heavy to
light behavior, whereas most previous
research considered only overall averages,
ignoring the heterogeneity in respond-
ents’ behavior (Nenycz-Thiel, Roma-
niuk, Ludwichowski, and Beal, 2012). In
that instance, the authors conducted two
studies—one in the chocolate category
(using purchase recall) and the other at
brand level (using television-program
viewing). Their results suggested that
the main source of error occurred at the
light buying/using end, where respond-
ents slightly underestimated infrequent
events; there was less classification error
with heavy buyers/users. These findings
are consistent with respondents report-
ing on their typical behavior rather than
specific behavior during the questioned
Such biases in self-report data, unsur-
prisingly, did not prevent respondents
from correctly identifying whether they
were heavy or lighter buyers. Indeed, self-
report data may outperform panel data in
making correct individual classifications.
Both approaches should generate correct
distributions of purchase weight—which
is the focus of this article.
In recognition of recommendations on
how to minimize errors to increase overall
survey accuracy (Nenycz-Thiel et al, 2012),
the authors applied the following stand-
ards in the current research:
• Reduced memory decay through
brand/category choice
Memory decay is a key factor hinder-
ing accuracy and can result in under-
reporting. Fewer reporting errors
occur when the (buying) event is more
frequent and, thereby, less reliant on
long-term memory (Hu et al., 2000; Lee,
Hu, and Toh, 2000; Sudman, 1964). In
this research, the authors considered
two leading brands from two repeat-
purchase categories (chocolate and soft
• Decreased the timeframe to improve
To improve recollection and increase the
vividness of the event, the authors lim-
ited the reporting timeframe to 3 months
(typical panel data for the NBD are
12 months; Allison, 1985; Tourangeau,
Rips, and Rasinski, 2000).
• Provided context cues to reduce
Respondents tend to generalize the
responses to reflect an overall behav-
ioral pattern. Such shortcuts can result
in over- and underestimation of fre-
quency (Hu et al., 2000; Tourangeau
et al., 2000). Context cues can improve
accuracy. In the current research, the
authors provided respondents with a
full list of potential responses. Applying
this context cue disallowed the respond-
ents to write in their own number
and potentially overestimate frequency
(Nenycz-Thiel et al., 2012). Further-
more, the authors asked respondents
to report purchase behavior in a “typi-
cal” 3 months (before and after becom-
ing a Fan). Ideally, adding this context
should have assisted the mental averag-
ing process and reduced exaggeration
(Parfitt, 1967).
• Reduced the complexity of the recall
One goal of the current study was to
simplify the recall task (Parfitt, 1967).
The authors simply asked, “In a “typi-
cal” three months, how often would you
buy this brand? Never? Once? Two or
three times? Four or more times?”
These frequency classifications were
appropriate for these categories, given
the expectation that the distribution of the
buying rates in the wider customer base
would be NBD-distributed. The authors
acknowledge that these classifications may
need to be altered for different categories,
with longer inter-purchase intervals.
The current study produced consistent
findings for both categories examined,
and the differences in distributions, in
fact, were so stark that any small biases
in either the panel or the self-report were
The buying concentration of a brand’s
Facebook Fan base was extremely differ-
ent from that of a “typical” (i.e., not NBD-
distributed) population of shoppers. In
fact, the authors found two generalizable
patterns in describing the average brand’s
Facebook Fan base:
• Using the classification of buying rates
as outlined above to study the distribu-
tion of buying rates for a typical choco-
late brand (based on 2011 consumer
panel data with a 12-week rolling aver-
age), the results revealed a typical NBD-
distributed customer base, with high
numbers of light (and zero) buyers and
fewer medium and heavy buyers (See
Figure 1).
• By comparison, the distribution of
buying rates for Facebook Fans of the
same brand in the chocolate category
showed a strikingly different shape
(See Figure 2): The Fan base produced
a particularly high incidence of heavy
buyers (57 percent), and virtually no
non-buyers (1 percent).
The Facebook Fan base of this chocolate
brand was very skewed to heavy buyers—
the opposite of a typically distributed cus-
tomer base.
The same results appeared in the authors’
analysis of the soft-drink category. Figure 3
is an example of a typical soft-drink cus-
tomer base. Using a 2007 consumer panel
data with a 12-week rolling average, the
typical soft-drink customer base showed
a typical NBD-distributed pattern. Con-
sistent with the findings for the chocolate
brand, the buying distribution among the
Buyer Groups
Proportion of shoppers
Figure 1 Buying
Concentration across the
Entire Customer Base for a
Chocolate Brand
Buyer Groups
Proportion of shoppers
Figure 3 Buying
Concentration across the
Entire Customer Base for a
Soft-Drink Brand
Buyer Groups
Proportion of fans
Figure 2 Buying
Concentration across the
Facebook Brand Fan Base for
the Same Chocolate Brand
soft-drink Facebook Fans was very differ-
ent and was skewed toward heavier buy-
ers (See Figure 4).
The findings were consistent between
two different brands in two different
categories: The Facebook Fan base was
strongly skewed to heavy buyers.
The analyses of both products dem-
onstrate that Facebook is a platform that
delivers an audience for advertising that is
skewed toward heavier brand buyers (See
Table 1). In more detail: The Facebook fan
base does not give the marketer access to
sufficient numbers of light buyers to main-
tain communication with a substantial
proportion of the customer base, particu-
larly if the desired outcome of communi-
cation efforts is to grow the brand (Sharp,
2010). Marketers who want to focus on
spending their resources on recruiting and
maintaining a Fan base, in essence, are
limiting their efforts to the small propor-
tion of the customer base who do not have
sufficient capacity to increase their buying
of the brand.
Given the use of self-report data in the
current study, the authors sought vali-
dation by conducting a third study that
examined self-report purchasing profiles
for a different media. Again, the authors
sourced the data from a probability-based
online panel (n = 397). In place of Face-
book Fans, however, in the third study,
the authors interviewed recent television
viewers—specifically, people who had
watched the 2012 Super Bowl (U.S. sample
weighted to the population).
The Super Bowl was chosen because it
delivers the sort of audience the brands
under study normally would like to
reach. (Had the authors chosen all tel-
evision viewers, the results might have
been skewed unduly toward light users
of the brands—an unfair comparison for
The authors asked the same purchase
frequency questions of the same two
brands in the original Facebook ques-
tionnaire. Distribution in the third study
clearly skewed to non- and light buyers
of the brand (See Figure 5)—consistent
with a typically distributed customer
base and diametrically opposite to the
same brands’ Facebook Fan bases (See
Figure 6).
The findings of buying concentration
from 2012 Super Bowl television audi-
ences were more consistent with a typical
population of a brand’s buyers. This dem-
onstrates that the Facebook Fan base gen-
erates a non NBD-distributed population,
not the nature of the claimed data.
In a side-by-side examination of audi-
ence concentration for both brands,
although the chocolate brand showed
a smaller skew to non-buyers than the
Buyer Groups
Proportion of fans
Figure 4 Buying
Concentration across the
Facebook Brand Fan Base for
the Same Soft-Drink Brand
Buyer Groups
Proportion of fans
Figure 6 Buying
Concentration across the
Facebook Brand Fan Base for
the Same Soft-Drink Brand
Buyer Groups
Proportion of viewers
Figure 5 Buying
Concentration of the 2012
Super Bowl Viewing Audience,
for the Same Soft-Drink Brand
Buying Concentration (%) across the Facebook Fan Base by
Light Buyers
Moderate Buyers
Heavy Buyers
Chocolate 1000 1 10 33 56
Soft Drink 520 4 9 24 63
Average 3 10 28 60
soft-drink brand, the direction of the dis-
tribution still followed an NBD and was
starkly different from the Facebook results
(See Table 2).
The act of “liking” brands on Facebook
has dramatically increased in popular-
ity in recent years (Cashmore, 2010). In
part, this is due to the natural growth of
the Facebook platform but also as a result
of its increasing cross-functionality with
other online media and portability with
other digital devices, making access to the
Facebook like button easier. That in turn
raises the question of whether brands’
Facebook brand Fan bases are becoming
more representative of their brands’ actual
buyer bases. If so, lighter buyers may have
been expected to have been Fans of brands
for shorter periods (i.e., they signed on as
Fans more recently). Similarly, heavy buy-
ers of a brand may have been Fans for a
longer period.
There is slight trend toward this split,
with “older” Fans (12–24 months) being
more likely to be heavier buyers than
“newer” Fans (See Table 3). Even the
newer Fan base, however, still was sig-
nificantly different from the brands’ actual
buyer base, demonstrating that Facebook
continues to attract heavy buyers of the
Newer Facebook Fans were highly simi-
lar to older Facebook Fans in that they
both skewed to heavy brand buyers.
This current study drew heavily on the
extensive work of Andrew Ehrenberg and
his colleagues. By discovering the statisti-
cal regularities in the buyer behavior of
individuals, they have highlighted the
important role that light buyers play for all
brands (Romaniuk, 2011). And, the authors
have relied on this insight to clarify one of
the disadvantages of “earned” media.
Specifically, the authors found that the
buying distribution of a brand’s Facebook
Fan base is opposite that of a typical popu-
lation of category buyers, with a signifi-
cantly higher incidence of heavy buyers
reached with this social-media vehicle.
This finding identifies a clear deficiency
of earned media and raises questions about
the value of the Facebook platform as a
stand-alone earned advertising medium.
One could argue that earned such as Face-
book are only parts of a multi-media mix
and that other media with better reach
profiles will reach the missing light buyers.
The question remains, however: In a mix
of media, is it ever a good idea to include a
medium that skews so strongly away from
light users? Almost any medium can reach
some part of the brand-loyal audience.
Consequently, advertisers should identify
media that can reach beyond their most
loyal customers. Media that skew as does
the Facebook Fan base are lower-quality
media and certainly should command low
Cost Per Thousand (CPMs).
There are a number of potential ben-
efits of a Facebook Fan base. Having direct
access to heavy buyers may provide a
useful research/insight opportunity by
providing a forum to listen to customers
(and competitor’s customers). Further-
more, Facebook may offer the potential for
Fans to become active brand advocates,
creating new networks that, in fact, include
light buyers. The ability of Facebook to lev-
erage such networks of brand Fans (and its
relative efficiency) is outside the scope of
this research and demands future research.
This current study should caution
marketers against using Facebook as
Buying Concentration (%) of the 2012 Super Bowl Viewing
Audience by Category
Soft Drink Chocolate
(n = 520)
Super Bowl
(n = 397)
(n = 1000)
Super Bowl
(n = 397)
Non-Buyers 4 47 1 28
Light 9 29 10 34
Moderate 24 11 33 23
Heavy 63 13 56 15
Average Buying Concentration (%)—All Categories
Purchase Classication
Length of time subscribed
<6 months 6–12 months 12–24 months Total
Non-buyers 4 2 0 2
Light 10 7 11 9
Moderate 27 31 26 29
Heavy 61 60 64 61
Total 100
a stand-alone medium to drive brand
growth. Furthermore, marketers should
be wary of over-investing in small rela-
tive pockets of heavy buyers if it comes
at the expense of overlooking light buyers
who may be the primary source of brand
Karen nelson-Field
is a post-doctoral research Fellow at
the Ehrenberg-Bass Institute for Marketing Science at
the University of South Australia. Her current research is
in the social media space, in particular whether existing
empirical generalizations in advertising, buyer behavior
and media hold in the social media context and how this
impacts on the ability of social media to assist brand
growth. Her ndings have been presented internationally
at the European Communications Symposium
(Barcelona), the London Business School, ESOMAR
World Media 3 (Berlin), ARF Rethink (New York), and
the prestigious Wharton Business School. Her industry
experience includes senior marketing roles in FMCG,
media, tourism and major retail over 16 years.
erica riebe
heads the Media Research Group at the
Ehrenberg-Bass Institute in the School of Marketing,
University of SA. Erica Riebe’s research focuses on the
following areas:
1) Determining effective and efcient media placement
2) Measuring the impact of changes in the media
environment on audiences;
3) Predicting the uptake of new products using
probabilistic scales;
4) Determining the impact of customer loss and gain on
the likely growth or decline of a brand.
byron sharp
is a professor of marketing science
and director of the Ehrenberg-Bass Institute at the
University of South Australia. His research is funded
by corporations around the world including Coca-Cola,
Mars, P&G, Kraft, Turner Broadcasting, CBS, and the
Australian Research Council. His book How Brands
Grow, Oxford University Press, 2010, presents a wide
variety of scientic laws and what they mean for
marketing strategy (see www.MarketingLawsofGrowth.
Allison, D.
“Survival Analysis of Backwards
Recurrence Times.” Journal of the American
Statistical Association 80, (1985): 315–322.
Anschuetz, n.
“Why a Brand’s Most Valuable
Customer Is the Next One it Adds.” Journal of
Advertising Research 42, 1 (2002): 15–21.
Binet, l.,
P. FielD .
“Empirical Gener-
alisations about Advertising Campaign Suc-
cess.” Journal of Advertising Research 49, 2 (2009):
cAshmore, P.
“Facebook ‘Likes’ World Domi-
nation [Online].” Mashable Social Media,
like-launch/%5D Retrieved on 3 November,
2011. “The Power of Like. How
Brands Reach and Influence Fans Through
Social Media Marketing.” comScore Inc.
ehrenBerg, A. s. c.
“The Pattern of Consumer
Purchases.” Applied Statistics 8, 1 (1959): 26–41.
ehrenBerg, A. s. c.
Repeat-buying: facts, theory
and applications, London, UK: Oxford University
Press, 1988.
ehrenBerg, A. s. c.
“My Research in Market-
ing.” Admap, 461 (2005): 6.
ehrenBerg, A. s. c., n. BArnArD,
and J.
“Justifying our Advertising Budgets.”
In Warc Conference paper, 1998, pp. 1–13.
giBs, J.,
s. Bruich.
2010. “Advertising
Effectiveness: Understanding the Value of a
social Media Impression. Nielsen and Facebook.
gooDhArDt, g. J.,
A. s. c. ehrenBerg.
“Conditional Trend Analysis: A Breakdown by
Initial Purchasing Level.” Journal of Marketing
Research 4, May (1967): 155–161.
gooDhArDt, g. J., A. s. c. ehrenBerg,
c. chAtFielD.
“The Dirichlet: A Com-
prehensive Model of Buying Behaviour.” Jour-
nal of the Royal Statistical Society 147, 5 (1984):
hu, m. Y., r. s. toh,
e. lee.
“Survey accu-
racy as a Function of Usage Rate.” Marketing
Letters 11, 4 (2000): 335–348.
lee, e., m. Y. hu,
r. s. toh.
“Are Con-
sumer Survey Results Distorted? Systematic
Impact of Behavioral Frequency and Duration
on Survey Response Errors.” Journal of Market-
ing Research 37, 1 (2000): 125–133.
mcDonAlD, c.,
A. s. c. ehrenBerg
“What Happens when Brands Gain or Lose
Share? Customer Acquisition or Increased Loy-
alty? Report 31 for Corporate Members. Adelaide,
Australia: Ehrenberg-Bass Institute for Market-
ing Science, 2003.
2011. “The Value of a Fan.”
morrison, D. g.,
D. c . schmittlein.
“Generalizing the NBD Model for Customer
Purchases: What Are the Implications and Is It
Worth the Effort?” Journal of Business and Eco-
nomic Statistics 6, 2 (1988): 145–159.
nelson-Fie lD, K.,
g. Klose.
“The Social
Media Leap: Integrating Social Media into
Marketing Strategy.” In WM3 Your Audience =
Media Consumer + Generator, Berlin, 2010.
nenYcz-thiel, m., J. romAniuK, g.
v. BeAl.
“Improving the
Accuracy of Consumer’s Self-Reported Brand
Usage Behaviour.” Journal of Business Research,
forthcoming (2012).
nuttneY, A.
The Social Networking Opportunity.
Business Insights, 2010.
PArFitt, J.
“A Comparison of Purchase Recall
with Diary Panel Records.” Journal of Advertis-
ing Research 7, 3 (1967): 16–31.
rAm, s.,
J. hYung-shiK.
“The Conceptu-
alization and Measurement of Product Usage.”
Journal of the Academy of Marketing Science 18, 1
(1990): 67–76.
romAniuK, J.
“Testing the Accuracy of Ver-
bal Probability Scale for Predicting Short-Term
Brand Choice.” Marketing Bulletin 15, (2004):
romAniuK, J.
“Are You Blinded by the Heavy
(Buyer)…Or Are You Seeing the Light?” Journal
of Advertising Research 51, 4 (2011): 561–563.
schmittlein, D. c., l. g. cooPer,
and D. g.
“Truth in Concentration in the Land
of (80/20) Laws.” Marketing Science 12, 2 (1993):
shArP, B.
How Brands Grow. Melbourne, Aus-
tralia: Oxford University Press, 2010.
shArP, B.,
J. romAniuK.
There is a Pareto
Law—but Not As You Know It. Report 42 for
Corporate Sponsors. Adelaide, Australia:
Ehrenberg-Bass Institute for Marketing Science,
stAnton, J. l.,
l. A.tucci.
“The Measure-
ment of Consumption: A Comparison of Sur-
veys and Diaries.” Journal of Marketing Research
19, May (1982): 274–277.
stern, P.,
A. s. c. ehrenBerg.
tions vs. Reality.” Marketing Insights, Marketing
Research Spring, (2003): 40–43.
sterne, J.
Social Media Metrics: How to Measure
and Optimize Your Marketing Investment. New
York, NY: John Wiley & Sons, 2010.
suDmAn, s.
“On the Accuracy of Recording
of Consumer Panels: II.” Journal of Marketing
Research 1, August (1964): 69–83.
The Value of a Facebook Fan: An Empir-
ical Review. London, UK: Hotspex, 2010.
tourAngeAu, r., l. J. riPs,
K. rAsinsKi.
The Psychology of Survey Response, 10th ed. Cam-
bridge, UK: Cambridge University Press, 2000.
uncles, m., A. s. c. ehrenBerg,
and K.
hAmmonD, K.
“Patterns of Buyer Behavior:
Regularities, Models, and Extensions.” Market-
ing Science 14, 3-2 (1995): G61–G70.
wAtts, D. J.
Everything is Obvious: Once You
Know the Answer. New York, NY: Crown Busi-
ness, 2011.
winD, Y.,
D. lerner.
“On the Measure-
ment of Purchase Data: Surveys Versus Pur-
chase Diaries.” Journal of Marketing Research 16,
February (1979): 39–47.
wright, m.,
D. esslemont.
“The Logi-
cal Limitations of Target Marketing.” Marketing
Bulletin 5, (1994): 133–120.
wright, m.,
m. mAcrAe.
”Bias and Vari-
ability in Purchase Intention Scales.” Journal
of the Academy of Marketing Science 35, 4 (2007):
wright, m.,
l. stocchi.
Temporal Stability of a Stochastic Model.” In
ANZMAC Conference Proceedings (2010),
... Overall, the better targeted and more attractive ads help online retailers reach consumers optimally and ultimately gain higher revenues, and the higher relevance and frictionless customer journeys are beneficial to customers as well (Malthouse et al., 2019). However, there are several critical voices that warn that targeting in general may lead to suboptimal spending, where customers who are already loyal are targeted (Nelson-Field et al., 2012;Sharp et al., 2009). This implies that retailers need to go beyond current fan-based targeting methods (e.g. ...
... Although targeting customers with the help of PA and AI is attractive due to cost-effectiveness, it may lead to negative outcomes. On the one hand, precision may entail a drastic drop in reach (Fulgoni, 2018;Nelson-Field et al., 2012). If only current customers receive the brand messages, the targeting may lead to stagnation of the customer base and reduce sales (Nelson-Field et al., 2012). ...
... On the one hand, precision may entail a drastic drop in reach (Fulgoni, 2018;Nelson-Field et al., 2012). If only current customers receive the brand messages, the targeting may lead to stagnation of the customer base and reduce sales (Nelson-Field et al., 2012). On the other hand, microtargeting that increasingly predicts personal needs and wants entails ethical challenges, both in restricting customer choice and in terms of (over-)collection, use and potential sharing of data. ...
Full-text available
Purpose-Digital advertising enables retailers to rely on large volumes of data on consumers and even leverage artificial intelligence (AI) to target consumers online with personalised and context-aware advertisements. One recent example of such advertisements is programmatic advertising (PA), which is facilitated by automatic bidding systems. Given that retailers are expected to increase their use of PA in the future, further insights on the pros and cons of PA are required. This paper aims to enhance the understanding of the implications of PA use for retailers. Design/methodology/approach-A theoretical overview is conducted that compares PA to traditional advertising, with an empirical investigation into consumer attitudes towards PA (an online survey of 189 consumers using an experimental design) and a research agenda. Findings-Consumer attitudes towards PA are positively related to attitudes towards the retailer. Further, perceived ad relevance is positively related to attitudes towards PA, which is moderated by (1) consumer perceptions of risks related to sharing their data with retailers online and (2) consumer perceptions of AI's positive potential. Surprisingly, the disclosed use of AI for PA does not significantly influence consumer attitudes towards PA. Originality/value-This paper contributes to the literature on technology-enabled services by empirically demonstrating that ad relevance drives consumer attitudes towards PA. This paper further examines two contingencies: risk beliefs related to data (i.e. the source of PA) and perceptions of AI (i.e. the somewhat nebulous technology associated with PA) as beneficial. A research agenda illuminates central topics to guide future research on PA in retailing.
... Only a few of the tweets and posts (2%-3%) by the four corporate microbloggers greeted their fans. Since a large proportion of the fans following a company/brand page are loyal customers and heavy buyers (Nelson-Field et al., 2012), these corporate microbloggers tended to address their followers informally. For example, they used some teasing or informal nicknames to address their fans. ...
To investigate the generic features of firm-generated advertisements (FGAs) in cross-cultural contexts, this study analyzed 327 FGAs by Dell Technologies and the Lenovo Group on Twitter and Sina Weibo. Integrating affordances and multimodality into genre analysis, the study showed that the FGAs were characterized by (a) flexible move structure, (b) persuasive language, (c) visual illustration, and (d) hyperlinks, hashtagging (#), and mentioning (@) functions. The FGAs on Sina Weibo, compared with those on Twitter, tended to use more language play, emojis, and contextual product pictures and show more emphasis on the niche of products, incentives, and celebrity endorsement.
... Social media is facilitated by Web 2.0 technologies and is based on user-generated content. This is also known as user-generated media since it enables active participation amongst online users allowing them to communicate with, and respond to, promotional content that they interact with on social media (Nelson-Field et al. 2012). User-generated content has changed the layout of social media which helps in shaping the behaviour of customers by making social media a prevalent information source which, as a result, creates positive Electronic Word of Mouth (EWOM) . ...
Full-text available
Online behavioral tailoring has become an integral part of online marketing strategies. Contemporary marketers increasingly seek to create an influential environment on social media to empower online users to participate in online brand communities. By interacting in this way, online communities hosted by brands marketers can enhance the nature of the complex interactions that occur amongst those that participate. Such online interactions lead to three different types of social influence compliance, internalization, and identity, which develop the consumers' purchase intentions. This chapter explains how the social influence support the change in beliefs, attitude, and intentions of the online consumers in the user-generated social media networking sites (SNSs). Furthermore, it discusses the functional impact of such online social influence that enables companies to understand the perceptions and needs of online users making sense of how multiple levels of social influence phenomenon on social media impact on consumers purchase intentions.
Social media-induced tourism happens when a traveller visits a destination/attraction after being exposed to certain social media content. A user-generated content (UGC) provider, such as a social media influencer, has been identified as the initial motivator in social media-induced tourism. Social media influencers generate persuasive messages for their followers and are typically sources of credibility. In destination marketing and tourism destination studies, the UGC of social media influencers is significantly related to the destination image, destination brand, tourist trust, and tourist expectations. Of particular interest for Instagram influencers, this chapter proposes a conceptual framework to describe the role of the Instagram influencer in inducing his/her followers to travel and suggests a guide for future research.
Since the last decade, online gaming has become a trend of entertainment. In this study, investigates the reason beyond purchasing virtual goods in online games and the degree of satisfaction of the gamers after their purchase through the qualitative method of the focus group. Fourteen participants who are divided into nine males and five females have participated in the discussion. This study uses semi-structured to proceed with the question in order to find out the research questions. Three major reasons are found to be significant and influence the gamers of their purchase intention towards virtual goods in online games, namely achievement, socialization and immersion. Moreover, the study attempts to find out if they feel satisfied after their purchase. This study also provides a significant study for the online gaming developers to understand the consumer’s needs and wants, in order to satisfy and fulfil the gamers' demands.
The aim of this chapter is to analyse young voter engagement in modern Western democracies. Why young voters? Young voters are disengaged from the political process. In order to complete the analysis, the author adapts an engagement model from social media marketing. The adapted model consists of three parts: consumption, contribution, and (co) creation of brand related materials. The author hypothesises that each aspect of the model is related to the other and that all three aspects of the model are positively related to loyalty to the political party brand. The aim of this conceptual adaptation is to investigate a new way to re-engage young voters with the political party brand, thereby strengthening one pillar of modern democracy.
Essential commodities are always on demand by people at a nominal rate during normal operations but due to COVID-19 and various other reasons, there is a sudden rise in demand for essentials goods. Every marketer who was not associated with essential goods tried to reposition their marketing strategy and tried to incorporate essential goods. During COVID-19, many people have turned towards essential goods sector for a living, and because of the rising demand and supply challenge, the government has acted to make essential products available to even the poorest parts of society. Artificial Intelligence (AI) would be essential to the supply chains of the next decade, effortlessly combining long haul to last-mile capabilities with commodity production, availability, and markets. Supply chains facilitated by AI can also help to respond to emergencies and pandemics quicker and faster, saving lives. The present study determines the important key points to be considered for formulating the utility and traceability of Artificial Intelligence in making the availability of essential goods.
Through examination has been conducted on the effects of messaging strategies on consumers’ online engagement, yet has neglected to assess the additional role brand characteristics play. This study employs content analysis to analyze the interaction effect of brand characteristics and messaging strategies on consumer responses. A total of 714 Facebook posts were analyzed. The results showed that: (1) High and low-involvement brands manage Facebook fan pages differently. High-involvement brands use a direct messaging strategy more than an indirect messaging strategy on their Facebook fan pages. In contract, low-involvement brands use an indirect messaging strategy more frequently; (2) High and low-involvement brands provide different types of content in their Facebook posts. The results partially are consistent with the findings from previous research that a direct messaging strategy is effective for high-involvement brands, whereas an indirect messaging strategy is effective for low-involvement brands, However, for low-involvement brands, there was no difference in user responses depending on the types of messaging strategy; (3) There is an interaction between types of messaging strategies and levels of brand involvement in the number of shares.
This chapter explores how one company leveraged motorsports to build brand credibility, establish powerful marketing relationships, and connect with distinctly different consumer groups via virtual brand communities. Companies with strong virtual communities may benefit from the case study suggestions that are provided and discussed based on the theoretical perspective of brand equity. Marketing scholars and practitioners alike may find this case study of interest due to the growing desire by companies to develop strong bonds with consumers and their interest in effectively leveraging virtual brand communities as a tool. Several practice recommendations for leveraging virtual communities to enhance brand equity are discussed.
Full-text available
The authors link consumers’ consumption behavior as reported by a 24-hour recall method and two-day diaries for 39 different food groups. Both frequency of ingestion and average serving size are compared across both methods. The study is based on the current U.S. Department of Agriculture Nationwide Food Consumption Survey, spring quarter, including 7945 individuals. The data suggest that the 24-hour recall measurements did not differ significantly from the two-day diaries.
The first large-scale marketing study in which individual consumers’ survey responses could be linked to their panel diary recordings is reported. The results, for the margarine category only, indicate correspondence between the two data sets at the aggregate brand share level but great discrepancies at the individual consumer level. Analysis of this discrepancy calls into question the use of survey reports as an indicator of individual purchase in product positioning, segmentation, advertising media and copy research, and concept/product testing.
The authors link consumers' consumption behavior as reported by a 24-hour recall method and two-day diaries for 39 different food groups. Both frequency of ingestion and average serving size are compared across both methods. The study is based on the current U.S. Department of Agriculture Nationwide Food Consumption Survey, spring quarter, including 7945 individuals. The data suggest that the 24-hour recall measurements did not differ significantly from the two-day diaries.
Some marketers spend a great deal of time, money, and effort trying to define and target a brand's most valuable consumers among current buyers. "Less valuable" consumers, however, are essential to brand health and growth. A larger percent of profitable consumers among all brand buyers is characteristic of smaller rather than larger brands. Because the average consumer buys more as a brand's franchise grows, the next consumer the brand adds will be its most valuable. Brands need to target inclusively and stand for a vivid, clear but broadly appealing benefit. A narrow, exclusive focus on the "most profitable" households is a recipe for stagnation and decline, not for brand health.
In this article Mr Ehrenberg shows how data on purchases of non-durable consumer goods can be fitted by the negative binomial distribution, and discusses applications of this finding. He also considers a simple model for purchases made in different periods of time and some quick and easy methods for calculating standard errors.
The purpose of this research is to identify the key conceptual dimensions of product usage, and to develop reliable and valid measures of product usage. Two different methods (a self-report questionnaire and a diary study), two samples, and four consumer durables have been used to develop the measures of usage. The results suggest that usage frequency and usage variety are two critical dimensions of product usage, and that the measures developed in this study for each dimension have high convergent and discriminant validity. The study highlights the importance of investigating usage in the post-purchase context, and helps to identify issues for future research.
A method of analyzing the components of a trend in consumer purchasing is described. An empirically-based mathematical model is first used to predict the purchasing pattern in the absence of a trend. Comparison between the observed data and these predicted norms permits a detailed evaluation of the trend. Two examples of practical applications of the technique are presented.