This document presents a model for quantifying online "buzz" about consumer product launches by measuring word-of-mouth (WOM) equity. The authors developed a model that calculates WOM equity based on the influence of websites and relevance of content. They applied this model to measure iPhone's WOM equity from its 2007 launch through 2011. Key findings include: 1) WOM equity subsides over time at different rates for different iPhone versions; 2) there is a correlation between WOM equity, unit sales, and stock price; 3) when compared to competitor HTC, Apple's smaller increase in WOM equity corresponded to much larger sales increases.

1. Measuring Buzz in the context of Apple Inc’s
iPhone launch
Justin Thonzakhup, T Ginvanglian
IIM Bangalore
Abstract - This paper seeks to quantify online ‘buzz’
about a consumer product launch and measure its value
before, during or after the event. We developed a model
that enables us to measure ‘buzz’ (also called ‘word-ofmouth’ equity). We then used our model to measure
Iphone’s word-of-mouth equity over the years since its
first version was launch in 2007. It was found that wordof-mouth equity subsides over the course of months and
at different rates for different iphone versions like
iphone 3G, 3GS. There seem to be a correlation between
word-of-mouth equity and unit sales and also share
price. When compared to a competitor like HTC which
manufacture mobile phones before apple did, HTC’s
word-of-mouth equity increases 4.5 times during its
product launch (measured from one month prior and
one month post launch), with corresponding 32%
increase in sales within the next 3 quarters. By contrast
Apple’s WOM Equity increases only 1.7 times but the
corresponding sales increase within the next 3 quarters
was 750%. This strongly indicates about the significance
of the magnitude of the initial ‘buzz’ that is relevant to
consumer interest that can generate product purchase.
Index Terms – Word of mouth, iphone, News, Apple, HTC.
1. INTRODUCTION
Rumours are powerful agents that can convey powerful
information sometimes affecting our day to day life.
Rumours about war in the Gulf drive oil prices up; rumours
about a possible financial bankruptcy can de-value a
company’s stock price within a few seconds. With no
geographical boundaries, online rumours that spread at the
click of a mouse have gain more and more prominence and
more so increasing number of people beginning to have
access to the internet. As of March 2011, the number of
internet users stood at about 2.095 billion people (~30% of
the world’s population)1. Over the years, huge volumes of
content about a product, a service, a book, or an idea etc.
have been generated over the Internet. This paper deals with
the kind of rumour specifically about a particular consumer
product, the mobile phone. In the Internet medium, such
information is generated through online news, company
websites, discussion forums, blogs, product reviews. We
choose Apple Inc. because it has been traditionally
1
http://www.internetworldstats.com/stats.htm
associated with generating huge excitement in the media
when launching its latest products. Even before the
revolutionary iPhone was launched, a great deal of
information about the product was generated over the
Internet about the expectations, speculations, predictions etc.
These buzz existed in the form of speculations about
possible launch dates, the features that could be incorporated
in the product and so on.
Quantifying such buzz around a product is the first step
towards having a sensible discussion about it and its
significance. Giving a quantitative value to ‘buzz’ is always
a challenge faced by managers and marketers. This paper
present a novel approach of how to quantify the buzz about a
product based on a model which takes into account two
factors namely: the relevance of buzz and on how influential
the source is.
2. THEORY BACKGORUND
2.1 Concepts of buzz and WOM
Emmanuel Rosen in ‘The anatomy of buzz’ defines buzz as
simply the hum of everything that we tell each other about
products, movies, people, books, and ideas. Buzz is a result
of the Word-of-mouth marketing. Buzz can be either a
relatively commercial variant or a seemingly less
commercial kind of buzz, which is created by passionate
individuals with no expectation of commercial gains or
benefit in return (Larceneux, 2007). Several studies have
shown that word-of-mouth (WOM) communications often
exert a strong influence on judgments of products . For
example, consumers frequently rely on WOM when
considering the purchase of a new product or service (Arndt
1967; Brown and Reingen 1987; Reingen and Kernan 1986;
Richins 1983). Word-of-mouth (WOM) has a stronger
influence than those printed communications . Positive word
of mouth has higher influence than negative word of mouth .
Consumer networks exhibit decay, possibly due to the
dissipation of involvement. This phenomenon was noticed
by Holme (2003) in his study of dating networks. He noticed
that ties decay exponentially as time goes on because of
decreasing contact.
Bart et al. (2005) note that community features are a factor
driving trust in Web sites, especially those characterized by
information risk (the risk associated with revealing personal
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2. information). They propose that “shared consciousness and a
sense of moral responsibility and affinity enhance the
consumer’s level of trust” and may make consumers more
confident in acting on information gained from online
communities.
WOM Equitymedia
= ∑of all websites (Influence of website x Relevance of content)
= ∑of all websites (I x R)
2.2 WOM measurement
Where,
I = Influence of website
R= Relevance of the content to the context
Several attempts have been made in earlier research on how
to measure buzz. Schillewaert et al (2007) proposes an
approach for measuring buzz. Research shows that 59% of
communications about brands happen at home. There is a
high percentage of buzz at work or school, a good amount of
buzz at shops, food establishments, and public transport.
Online forums, company websites, blogs, chat sessions;
newspaper articles are some of the online media through
which consumers are informed about brands.
Google provides an option to choose the medium ‘News’
and also allow us to fix the range of dates say between Oct
1, 2007-Dec 31, 2007 (Q1-Financial year 2007). This is
shown below.
Figure 3a Google search is used with specific keyword for
the search along with sorting by date (not relevance)
Bughin et al (2010) proposes a new way to measure wordof- mouth marketing. The impact of word of mouth is called
word of mouth equity. The research proposed that word of
mouth equity is a product of the volume of buzz and the
level of impact.
3. METHODOLOGY AND MODEL
Our model is a direct descendant of the one proposed by
Bughin et al (2010) with the important difference being we
decided to take only two of the four factors they proposed
that influence consumer decision to purchase. Also, we
devise our own meanse to measure those corresponding
factors. The model is discussed below.
3.1 Factors influencing word-of-mouth
The following are the four factors influencing worth of
mouth equity namely2:
1.
2.
3.
4.
Network: Where are they talking?
(+) Close/trusted, (-) Large/dispersed
Message content: What are they saying?
(+)Relevant key buying factor,
(-)Irrelevant key buing factor
Sender: who is talking?
(+)Influential, (-) Non-influential
Message source: What is the trigger?
With regard to point 1, our measuremnet is based soley on
online ‘News’. Of the remaining, our model considers
message and sender as the most important factors and we
ignore the last point 4, since our interest is about quantifying
buzz and not necessarily about a specific type of buzz that
may necessarily result in consumer decision to purchase.
From now on we use ‘buzz’ and word-of-mouth equity also
called WOM Equity interchangeably.
According to our model, for N number of websites:
2
McKinsey Quarterly: A new way to measure word-of-mouth marketing,
April 2010, Bughin et al (2010)
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3. 3.2 Influence (I): We quantify the influence of a particular
website by its page rank. In our particular model we use the
website’s alexa ranking3. The highest rank being 1 will have
the maximum influence and greatest contribution to the
WOM Equity. We fix the lowest rank to be 1,000,000 and
convert the alexa rank for its contribution to WOM Equity
using the formula below.
I = 1-(alexa rank/1,000,000)
I varies between 0 and 1.
i.e., a website like ww.google.com which have alexa rank=1
is considered to have maximum influence since,
I = 1 – 1/1,000,000 ~ 1.
3.3 Relevance (R): A page analysed in the context of a
product launch like ‘iphone’ is considered irrelevant if its
falls in any of the following categories:
1.
2.
If the product is shown in negative light (see Appendix
Table A.1 for examples).
It is mentioned out of context e.g. in the context of other
product (see Appendix Table A.1 for examples).
In contrast, a website which is relevant to ‘buzz’ about the
iphone is exclusively about the product. It may even be a
neutral article about the iphone, as long as it is not negative
about the product. If other products are mentioned, it should
be in the context of the iphone and not vice versa.
Table 3.4 Both rank information and content relevance is
collected per website
Oct
200x
to
Dec
200x
Google
Page
no.
Link for
each page
(From top)
Alexa
rank
I
R
1
1
1
10
6
2
10,000
70
4673
0.9900
0.9999
0.9953
1
N (results)=16,400
1
1
No. of
websites
547
547
547
WOM
Equity
541
547
544
The WOM Equity for ‘N’ results for media ‘News’ now
becomes:
WOM EquityNews
= ∑30 (I x R) x (N/30)
3.5 Data sources
For this paper, we sample a total of 49 quarters, 41 quarters
for Apple Inc. and 8 quarters for HTC Corp. i.e., we
individually-manually analysed a total of 49*30 = 1470
websites for its relevance and ranking. Each sample analysed
was documented giving the google page number and
position in the search page, keeping open the possibility for
third party verification. All data is attached in Appendix at
the end.
4 RESULTS
We give the weights,
R = 1, if the content is relevant to the product.
= 0, if the content is not relevant to the product.
3.4 WOM Equity for N websites:
The earlier example for ‘iphone’ News search returned
16,400 results. And it is impossible to analyze each
individual website for its influence and relevance of their
contents. So, we sample randomly 30 websites and took their
individual alexa rank and analyze each of them for relevance
based on the above framework.
For instance, if the 1st website analyzed is relevant and has
10,000 alexa rank, its WOM Equity is
=IxR= 1x (1-10,000/1,000,000) =0.99
We extrapolate the results of these 30 samples to the whole
16,400 results i.e., we assume that for every page like the
one we analyzed there are other 16,400/30 ~ 547 websites
which are both relevant and are equally influential. Such 547
website has WOM Equity = 0.99*547 = 541. We repeat the
same procedure for the remaining 29 samples.
3
Alexa has been crawling the web since early 1996. It currently gathers
approximately 1.6 terabytes (1600 gigabytes) of web content per day.
(http://www.alexa.com/company/technology)
The search item for analyzing Iphone’s Word-of-mouth
Equity is ‘iphone’. Data is analysed per quarter from FY
2007 Q1 till FY 2011 Q2. Also, with the release of other
Iphone versions like the Iphone 3G, Iphone 3GS and Iphone
4, we separately analyzed their WOM Equity just on the eve
of their launch spanning about 6 quarters from 2007 Q1.
Among others, we tried to compare the results with Apple’s
EPS (Earnings per share). This makes sense because by Q4
2008, the Iphone had become a significnat revenue earner
for Apple contribution to more than 30% of the company’s
gross margin. We also analyzed our findings from the
perspective of the unit sales of Iphones (and other related
products and services), the data being publicly available on
Apple’s website.
We continue our analysis by comparing Apple’s WOM
Equity with that of Apple and how their respective WOM
Equity changes with time. The idea behind choosing HTC
Corp. is not by accident but because HTC Corp. was an
already existing player selling smart phones and the launch
of HTC’s new smart phone HTC Touch was the same
month, June 2007. The search item for HTC Touch’ WOM
Equity is ‘htc touch’ with ‘News’ as the media for our
analysis, the same as for the ‘iphone’. Again, sales figures
for HTC Touch shipments are publicly available on the
company’s webstie.
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4. Figure 4.5. Iphone’s WOM Equity over the years
4.5 Apple’s iphone launch
Apple's long-awaited entry into the mobile phone market
was the biggest news of 2007. Apple’s CEO Steve Jobs
described the iPhone as a "magical device" that would
"revolutionize the industry". During the launch weekend of
29 June, more than 270,000 were sold in US stores. The
speculation about the iPhone generated an estimated $400m
free advertising for Apple4.
Table 4.5 Product launch dates: typically a 1 yr. gap
Launch dates
June 29, 2007
July 11, 2008
June 19, 2009
June 24, 2010
Products
iPhone
iPhone 3G
iPhone 3G
iPhone 3GS
iPhone 3GS
iPhone 3GS
iPhone 4
4.6 WOM decay: Iphone 3G
Figure X cleary shows how the WOM Equity of Iphone 3G
subsides over the years. Launched on July 2008, its WOM
Equity continues to subside during FY 2009 down to about
500 and it is not affected by the ‘buzz’ of the Iphone 4
launch during 2010 Q3. This decay in WOM Equity
Figure 4.6 Iphone 3G 5 years after it was launch
Variations
8 GB
16 GB
8 GB
16 GB
8 GB
16 GB
32 GB
In fact the WOM Equity of Iphone increased from 4067 in
Jan 2008 to 11088 by September the same year; a 1700%
increases. It reached its peak by December reaching a value
of 13386. Refer Appendix for exact values. Also from
4
Figure 4.5, there appears to be a correlation between unit
sales and WOM Equity.
(http://goliath.ecnext.com/coms2/gi_0199-7029582/Non-food-Apple-siPhone.html)
This decay is analogous to what Holme (2003) recognized in
his study of dating networks mentioned earlier. Whether the
decay is exponential or not is left to speculation but like in
exponential decays, Iphone 3G’s WOM Equity hardly
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5. reaches zero, floating around a steady value of around 500.
In fact during our website analysis, such remainder WOM
Equity is contributed by countries like China where ‘buzz’
happens even at somewhat at a later stage far from the time
of its initial public launch.
correlation between a particular quarter’s WOM Equity and
the next quarter’s EPS had been observed, we decided to
check the correlation of all WOM Equity (relevant +
irrelevant) and the next quarter’s EPS. The two resulting
plots are shown in Figure x and figure y.
4.7 Iphone 4
Figure 4.8a WOM Equity vs. Next quarter’s EPS where
irrelevant content is not ignored
Figure 4.7 Huge spikes on Iphone’s WOM Equity just prior
to launch of the Iphone 4
Figure 4.8b WOM Equity vs Next quarter’s EPS where
irrelevant content is ignored
IPhone 4 has abnormally high buzz. In fact iPhone 4,
released on June 24, 2010 in the U.S., Britain, France,
Germany and Japan, sold a record 1.7 million in just three
days after the launch, making it the most successful start in
the company’s history in terms of single product sales5. This
corresponds to a dramatic shoot in WOM Equity by 4.6
times from 8447 in Q2 to a peak 39482 in Q3 2010. What is
interesting though is the significant decrease in WOM
Equity from the last quarter of 2009 to a very low value
8447 prior to the launch of the Iphone 4. In fact, the last time
Iphone’s WOM Equity went lower than this was way back
in Q3 2007 (a value of 6426). There is no clear cut
explanation for this.One strong possibility is due to the
launch of the Ipad in January 2010. Many search results
during this period often return the Iphone in the context of
the newly introduced Ipad.
4.8 Earnings per share and WOM Equity
Till this time, in our discussion about WOM Equity, w e had
incorporated in our model only relevant factors i.e., we give
zero weightage to irrelevant news. To check the significance
of this we decided to change all weightages to 1, whether the
information is relevant or not. Since some kind of
The resulting plot seems to suggest a closer relationship
between WOM Equity and EPS when the weightage of
irrelevant websites is given zero (which was followed in our
model). This is not to say that a one to one correlaton exists
between the WOM Equity of our model and EPS but it gives
some justification for bothering to distinguish between
relevant and irreleant contents out of the hundredes of
websites we analyzed.
5
(http://www.zacks.com/stock/news/36222/Apple%92s+iPhone+4+Sets+Rec
ord)
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6. 4.9 HTC Touch vs. Iphone: Unit sales
Launched the same month as the Iphone, shipped units of
HTC Touch was soon overtaken by the Iphone’s sales as
shown in figure below.
with corresponding increase in its units sales within the next
three quarteres being 750%. However, the initial January
‘buzz’ for HTC Touch was a mere 250 compared to
Iphone’s 4067.
Figure 4.10b Huge ‘buzz’ build-up for the Iphone prior to
its launch
Figure 5. Quarterly Units shipped in thousands
4.10 HTC Touch vs. Iphone: WOM Equity
HTC Touch was launched globally on June 5, 20076.
5 Concluding remarks
Figure 4.10a Weak buzz before launch HTC Touch’s launch
date
5.1 Summary of findings & implications for marketing
management
Our goal for this pape was to quantify ‘buzz’ in the context
of product launch. To summarize.
(1) There appears to be a correlation between unit sales
of iphone and the buzz around it. How are they
actually correlated is beyond the scope of this paper
but examined against HTC Corp., bigger ‘buzz’
seem to favour better sales.
‘Buzz’/WOM Equity of HTC Touch during June, the month
of its launch was less than WOM Equity of Iphone 3G 1
year after its launch (see Figure 4.6), a value of 912 for
Iphone 3G against 902 for HTC Touch.
Also, during the first three quarters of 2007 covering before
and after its launch, HTC Touch’s WOM Equity increases
4.5 times and corresponding increase in unit’s shipped
within the next three quarters was 32%. In contrast, within
the same time, Iphone’s WOM Equity increases 1.7 times
6
HTC Corp. (press release), 5 June 2007
(2) ‘Buzz’/WOM Equity decays over time. Hence the
need to launch new versions. It is interesting though
to note that ‘Buzz’ of Iphone 3G after 1 year could
still be greater than that of HTC Crop one month
inside its launch date. ‘Buzz’ decays very slowly
over
time,
perhaps
made
possible
by
promotion/launch in other geographical locations.
However, the sacrifice of magnitude of ‘buzz’ by not
launching in simultaneious geographical locations
has to be examined against possible future launch
opportunity. And there seem to be a strong case
about the significance of the magnitude of the initial
‘buzz’ in relation to initial product sales.
(3) Prior to product launch, it is not the percentage
increase in ‘buzz’. What counts is the magnitude of
‘buzz’.
(4) With the advent of newer technologies and fast
changing fads in consumer products like the iphone,
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7. companies like Apple Inc. have only a small window
of opportunity to make the greatest sales. Launch of
Ipad and Iphone 4 around the same time could affect
the buzz around either of the product. This in turn
could significantly affect the huge initial sales of
either of the two products that are fed by the huge
buzz around it. Companies have to strategize
accordingly.
5.2 Future research opportunities
Of particular interest is the strong correlation between ‘buzz’
and the next quarter’s EPS. To dig deep into this topic
further, a thorough regresssion analysis could be the way
forward. The same could also be done between ‘buzz’ and
unit sales with different time periods like weeks, months
apart from the present quarterly analysis. Also, the huge data
collected amounting to 1470 websites with their rankings
and relevance to a particular product could be leverage from
another research perspective.
6 ACKNOWLEDGMENT
The authors thank Dr. Manaswini Bhalla, Assistant
Professor, Economics & Social Science area for the
guidance and support that they received for completing this
project. It would not have been possible without the
continuous feedback and guidance from her. The motivation
and encouragement that they received during the course of
the project was of immense help.
REFERENCES
[1]
Herr, Paul, M; Kardes, Frank, R; Kim, John "Effects of Word-ofMouth and Product-Attribute Information on Persuasion: An
Accessibility-Diagnosticity Perspective ", Journal of consumer
research. Inc. vol. 17 March 1991, pp. 454-462
[2]
East, Robert; Hammond, Kathy; Lomax, Wendy, " Measuring the
impact of positive and negative word of mouth on brand purchase
probability ", Intern. J. of Research in Marketing 25 (2008) pp. 215–
224
[3]
Bughin, Jacques; Doogan, Jonathan; Vetvik, Jorgen, Ole, “A new way
to measure word-of- mouth marketing”, McKinsey Quarterly, April
2010
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