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Today’s online customers can exchange opinions and experiences related to
companies, products and services with individuals outside of their personal
communication network of family, friends, acquaintances and col-
leagues. This ability to exchange opinions and experiences online
is known as online word of mouth and has become increas-
ingly popular in recent years. Traditional word of mouth is
described as informal communications that are exchanged
among consumers regarding sellers or the ownership, usage
or characteristics of goods and services from sellers. Thanks
to advances in both information technology and the Internet, the
power and potential impact of online word of mouth has substantially
increased. Not only are message boards (e.g., eOpinions), microblogging
sites (e.g., Twitter) and online communities (e.g., Facebook) available
for posting information and exchanging opinions, but many companies have
decided to include forums for exchanging word of mouth on their online product
pages (e.g., Amazon, Overstock, Walmart, Target).
20
Summer 2010
The importance of
BUZZ
By alanah mitchell and deepak khazanchi
Can online word of mouth
increase sales?
marketing research
21
Can online word of mouth
increase sales?
22
Summer 2010
Executive Summary that is communicated about companies, products or services
among consumers. In comparison to traditional advertising
(e.g., TV, newspapers) and other marketer-controlled sources,
word of mouth is perceived by consumers as being more
credible than private signals, more accessible through social
networks and more influential on consumer behavior. The no-
tions of volume and valence have been found to be two of the
most important attributes of word of mouth.
The first of these attributes, volume, measures the total
number of word of mouth interactions. The existence of
online word of mouth results in an increase in awareness and
a positive (or negative) attitude toward a product potentially
resulting in a change in sales. Thus, the more conversation
in the form of online comments there is about a product, the
more likely someone will be informed about it. This leads to
increased product awareness, which may in turn lead to high-
er product sales. Numerous previous studies have shown that
word of mouth volume significantly correlates with consumer
behavior and market outcome. In fact, rational consumers
still pay attention to anonymous online posts, even when it is
possible for firms to pose as online consumers. While many re-
searchers have concluded that online customer reviews have a
significant influence on the sale of products, some have argued
that only the volume of the reviews matter.
The second word of mouth attribute, valence, measures the
nature of the word of mouth message and whether it is posi-
tive or negative. Behavioral research has shown that it is un-
clear whether positive word of mouth leads to increased sales.
However, some research has suggested that online ratings have
become increasingly important because they provide custom-
ers with a community of knowledge prior to decision-making.
Indeed, a study of the influence of consumer ratings on video
game sales showed that a higher rating by only one point was
associated with a 4 percent increase in sales.
For our research, we evaluated the following questions:
• Doesthepresenceofonlinewordofmouthintheformof
review comments and ratings lead to higher product sales
on an e-commerce retail Web site?
• Howdotheonlinewordofmouthattributes(i.e.,volume
and/or valence) affect product sales?
Study Details
We conducted a quasi-experiment with a two-group ex-
perimental design using consumer review ratings, customer
comment reviews and real product sales data obtained from
a leading multi-product e-commerce company. The organiza-
tion employs nearly 300 people and has been in business since
2001. This online retailer currently owns and operates more
than 150 niche stores. To date, the e-commerce company has
served more than 1 million customers in the various catego-
ries of products it sells. This online retailer is similar to other
online retailers in that it markets its products on its own Web
site as well as the other major online marketing channels in-
cluding pay-per-click advertising, affiliates, eBay and Amazon.
Online word of mouth also may be used as a technique
for viral marketing in which a company uses its customers to
promote a product or service to prospective customers, which
is the case when companies include forums for exchanging
word of mouth on their product pages. The use of online
word of mouth has the potential to influence product sales for
e-commerce companies. However, research of word of mouth
using online review comments and ratings has received limited
attention despite existing research arguing that understanding
how word of mouth differs in an online environment is critical
for marketing managers.
Anecdotal evidence and prior empirical studies indicate a
high level of acceptance of consumers and reliance on online
word of mouth. More specifically, Forrester Research has
found that 50 percent of young Internet surfers rely on online
recommendations when purchasing CDs, movies and video
games. Additionally, 67 percent of consumer goods sales are
based on word of mouth, according to another study from
McKinsey and Company. Some researchers also have found
that positive customer reviews can have a great impact on
repurchase intention. Nielsen’s Global Online Consumer
Survey also has found that users are increasingly relying on
online word of mouth during the decision-making process.
Previous research also has shown that the influence of word of
mouth can be so strong that it overrides private signals (e.g.,
gut instinct or feelings) and results in individuals relying solely
on the information provided by others. In fact, some research-
ers have suggested that online word of mouth may eventually
take the place of traditional advertising. Managers also have
been encouraged to review the online product reviews for the
less popular because online reviews can be even more influen-
tial for these items.
In this article, our objective is to empirically address the
following question: Does the presence of online word of
mouth in the form of review comments and reviewer ratings
significantly influence product sales on an e-commerce multi-
product retail Web site? We assess this question through an
analysis of sales and online word of mouth data from a multi-
product e-commerce retail firm.
How It Works
Word of mouth (online or otherwise) relies on information
The power and potential impact of online word of mouth
has increased substantially. Consumers have come to ac-
cept and rely upon online word of mouth, so it is important
to understand how it works and what kind of impact it has on
online product sales. This article provides an assessment
of this question through an analysis of sales and online word
of mouth data from a multi-product e-commerce retail firm.
marketing research
23
The company also recognizes the impact of social networking
and alternative media marketing avenues and that is why it
has implemented online ratings and review comments. The
data for this study was collected before and after the Novem-
ber 2006 implementation of an online review system on the
company’s retail e-commerce Web site.
We use the number of review comments in the form of
review text (positive or negative) provided by consumers as a
measure for the volume attribute of word of mouth. We use
review ratings in the form of “star ratings,” 5 stars (best) to 1
(worst) as a proxy for the valence attribute of word of mouth.
Generally, for our subject firm, star ratings accompany review
comments.
Reviews are collected either from the
product page of the Web site or
through e-mail (usually a reply
e-mail from the customer
after they have received a
shipment notice by e-mail).
A review page includes a
“write a review” link at
the bottom of the page.
Reviews appear on the prod-
uct page with pros, cons and
other suggestions from previ-
ous customers. Additionally, the
reviews show whether the customer
is a verified purchaser, a registered reviewer
or neither. (A verified purchaser is a reviewer for
whom the company has record of purchasing the product
under review. A registered reviewer
has not necessarily purchased the
product from the company, but can
be identified by an e-mail account. A third
option exists for reviewers who do not fit either
category and want to remain anonymous.)
The online review system that was implemented by the
subject firm does allow the firm to filter product reviews. In
general, reviews are filtered to exclude any specific comments
related to price, customer service issues and other data that
does not speak to product quality. It is important to note here
that all reviews (negative or positive) related to product qual-
ity are posted.
In order to minimize the effect of the holiday shopping pe-
riod on online product sales, we deliberately chose to look at
sales and other data for January 2007 after the winter break.
The first timeframe chosen was the week of January 16, 2006,
before the implementation of the online review system. The
second time frame chosen was the week of January 15, 2007
after the implementation of the review system. By analyz-
ing data from the same week (in different years) we hope to
minimize additional confounding effects. However, we do
acknowledge the potential of other confounding variables
unrelated to online word of mouth that could affect product
sales. Examples of such variables include product brand,
perceived quality of product, featured items and advertising/
marketing campaigns among many others.
We attempted to manage any potential confounding effects
by ensuring that there were no special marketing campaigns
going on during the two time periods the data was collected.
Further, the comparison of similar time periods for the same
products before and after the introduction of online word
of mouth substantively reduced any potential confounding
caused by product category and product popularity.
Overall, data from 546 products and 73 categories was
collected both before and after the implementation of the new
customer review system. Most important, a product was com-
pared to its own performance over the two time periods.
Control Your Content
Our first research question asks whether or
not there is a significant change in the num-
ber of products sold following the addi-
tion of online word of mouth (in the
form of consumer ratings and reviews)
to product pages. A paired-samples
t-test was conducted in SPSS 10.0 to
evaluate the impact of the addition of
online word of mouth to product pages
on the number of products sold.
A paired-samples t-test allows us to test one
group of products on two different occasions
(i.e., January 16, 2006, and January 15,
2007). The dependent variable for this test
is the number of product units sold on the
two different periods, and the categorical
independent variable is time
or the two periods being
compared.
The results show that there
is a statistically significant
increase in the number of
products sold from January
2006 (M=5.57, SD=8.052)
to January 2007 (M=6.64,
SD=12.113, t(545)=-2.92,
p<.004). The eta squared
statistic (.015) indicates a
small effect size. Therefore,
we can conclude that simply
the presence of online word of
mouth in the form of consumer
review comments and ratings does
lead to higher product sales on an
e-commerce retail Web site.
Our second research question
asks whether there is a relationship
between online word of mouth
attributes—volume and valence—
and the number of products sold.
We measure volume in terms of
the number of online consumer
Simply the presence
of online word of mouth in
the form of consumer review
comments and ratings does lead
to higher product sales
24
Summer 2010
review comments and valence in terms of the average
consumer rating. We tested the data using standard multiple
regression in which the two continuous independent
predictor variables (i.e., number of online review comments
and average rating of products) were entered into the equa-
tion simultaneously and evaluated in terms of their predictive
power of the continuous dependent variable (i.e., number
of products sold).
The standard multiple regression results show that
the number of online consumer reviews had a significant
(p<.0005) impact on the number of products sold. However,
average consumer ratings did not have a significant effect
on the number of products sold. Additionally, we added the
“number of product views” as an independent predictor
variable in the model. We found that this did not change the
resulting model. This is important to the extent that it affirms
our findings and shows that the number of product views (a
surrogate measure of the popularity of a product) does not
have any impact on sales as it relates to our research question.
Overall, the final model was able to account for 40 percent
of the variance in the number of products sold. Therefore, we
can conclude that the volume of online ratings is more impor-
tant than whether or not the ratings are positive or negative
in predicting an increase in product sales. This outcome is in
line with previous research, which has shown that it is unclear
whether positive word of mouth in terms of average reviewer
ratings (i.e., valence) leads to increased sales.
Because of the high acceptance of consumers and their ap-
parent reliance on online word of mouth, it is important for
marketing managers and organizations to understand mecha-
nisms for dealing with negative reviews and ratings. Practices
that can help in coping with unfair ratings and discriminatory
behavior related to a company’s reporting system suggest both
using controlled anonymity to avoid unfairly low ratings and
using filtering to reduce the effect of unfairly high ratings.
Some authors have also suggested that firms try to “en-
gineer” word of mouth communications among customers.
Example companies such as Picador Press, Lee Dungarees,
and Hasbro have been taking action to increase the number
of conversations taking place related to their products, rather
than hoping that satisfied customers and opinion leaders
would eventually start talking about their products with
people in their social networks. This idea is similar to the idea
of the retail site in this study, where site customers have access
to review products while they are on the site.
Furthermore, the research suggests that the managers’
motivation for taking advantage of online word of mouth is
a new tool that grew out of the idea that the effectiveness of
traditional media advertising is declining, particularly among
younger demographics. However, despite many researchers
arguing that online word of mouth is important, few research-
ers provide recommendations for organizations trying to
manage online word of mouth. This research suggests that
e-commerce organizations should include product reviews on
their product pages so customers can input their opinions. A
detailed list of guidelines for online retailer managers based
on our results is outlined above.
Words Matter
The overarching aim of this study was to empirically study
whether the presence of online word of mouth in the form of
review comments and ratings leads to higher product sales on
an e-commerce retail Web site. An empirical analysis of real
data collected from a multi-product retail e-commerce firm
was conducted. We found that there is a significant increase in
the number of products sold following the addition of online
1 Encourage customers to write reviews by using incentive programs
2 Build or license a recommendation system based on strategic interest in online word of mouth
3 Develop the rules for filtering and posting reviews; an option could be to exclude reviews that
are not tied to a product
4 Show both the positive and negative comments to customers
5 Invite different types of reviews to review products; for example include reviews from both experts
and registered reviewers
FIVE GUIDELINES
For e-commerce managers thinking about online word of mouth
marketing research
25
word of mouth. We also found that the volume of online
word of mouth can significantly influence product sales while
valence (or consumer ratings) did not matter at all.
From a practical perspective, our results suggest that hav-
ing word of mouth on an e-commerce site is important for in-
creasing product sales. There is clear empirical support in our
study for the contention that sales of reviewed products in-
creased at a higher rate. Our research findings also imply that
it is important for companies to focus on creating an online
buzz about their products and not be concerned with whether
the buzz is positive or negative (i.e., valence). As mentioned
above, this outcome is in line with previous research showing
that it is unclear whether positive word of mouth in terms of
average reviewer ratings (i.e., valence) leads to increased sales.
A limitation of this study is the potential of confounding
variables other than online word of mouth that affect prod-
uct sales. Examples of such variables could include product
brand, perceived quality of product, featured items and ad-
vertising/marketing campaigns among many others. However,
as mentioned earlier, we attempted to manage any potential
confounding effects by ensuring that there were no special
marketing campaigns going on during the two time periods
the data was collected. Additionally, our decision to compare
similar time periods for the same products before and after
the introduction of online word of mouth substantively re-
duced any potential confounding caused by product category
and product popularity. Further research should be conducted
into these and other explanatory factors that complement on-
line word of mouth to affect product sales or potentially affect
sales via increased word of mouth. l
Alanah Mitchell is an assistant professor of computer
information systems in the John A. Walker College of
Business at Appalachian State University. She may be reached
at mitchellaj@appstate.edu. Deepak Khazanchi is professor of
information systems and quantitative analysis and associate
dean for academic affairs in the College of Information
Science and Technology at the University of Nebraska at
Omaha. He may be reached at khazanchi@unomaha.edu.
Authors’ Note
All references related to this article will be available until
31 December 2011 on the author’s Web site: www.appstate.
edu/~mitchellaj/. Also, interested readers are welcome to re-
quest a complete reference list from the authors via e-mail.
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ResearchGate has not been able to resolve any references for this publication.