The Effect of Microdata on Search Engine Optimization

Running head: THE EFFECT OF MICRODATA ON SEARCH ENGINE OPTIMIZATION
THE EFFECT OF MICRODATA ON SEARCH ENGINE OPTIMIZATION
A Thesis
Presented by
Ben Griffiths
Submitted to the Graduate College of Stevens-Henager College in partial fulfillment of the
requirements for the degree of
MASTER OF BUSINESS ADMINISTRATION
June 2012
Committee:
Darren Adamson, Ph.D.
Cheryl McDowell, Ph.D.

THE EFFECT OF MICRODATA ON SEARCH ENGINE OPTIMIZATION 1
© 2012
Ben Griffiths
All Rights Reserved

Abstract
Microdata is a new development that is likely to have a significant impact on the search engine
optimization (SEO) industry. The objective of this study was to determine the effect of
microdata on search engine optimization. The attitudes and experiences of search engine
optimization professionals were explored to determine if, and how, microdata fits into their
overall search engine optimization strategy both now and in the future. The study also explored
the level of effort required and the payoff that was expected as a result of incorporating
microdata into web pages. The results of the study will provide search engine optimization
professionals with a better understanding of the importance of microdata to other industry
professionals and will help them determine the possible importance of microdata to their own
overall search engine optimization strategy.

TABLE OF CONTENTS
1. INTRODUCTION …………………………………………………………………… 5
Background ……………………………………………………………………. 5
Statement of the Problem ……………………………………………………… 6
Purpose Statement ……………………………………………………………... 7
Objectives of the Study ………………………………………………………... 7
Hypothesis ……………………………………………………………………... 7
Assumptions …………………………………………………………………… 8
Limitations ……………………………………………………………………... 8
Definition of Terms ……………………………………………………………. 9
2. LITERATURE REVIEW ……………………………………………………………. 11
Introduction ……………………………………………………………………. 11
Review …………………………………………………………………………. 11
Conclusion ……………………………………………………………………... 20
3. METHODOLOGY …………………………………………………………………... 21
Introduction ……………………………………………………………………. 21
Participants ……………………………………………………………………... 21
Materials ……………………………………………………………………….. 22
Design ………………………………………………………………………….. 23
Procedure ………………………………………………………………………. 25
4. RESULTS ……………………………………………………………………………. 26
Introduction ……………………………………………………………………. 26
Findings of the Study …………………………………………………………... 27

Summary ……………………………………………………………………….. 41
5. CONCLUSION AND RECOMMENDATIONS ……………………………………. 42
Introduction ……………………………………………………………………. 42
Conclusion ……………………………………………………………………... 43
Recommendations ……………………………………………………………… 44
Considerations for Future Research ……………………………………………. 44
Summary ……………………………………………………………………….. 45
6. REFERENCES ………………………………………………………………………. 46

Chapter I
Introduction
Background
Search engines have transformed the way we gather information about the world around
us. Research that used to take months or years to complete can now be conducted in hours or
minutes. With a few keystrokes we can access literally billions of pages of information about
nearly any topic in a fraction of a second through several web-based search engines.
While many smaller search engines serve niche groups of users, the three major search
engines that serve the greatest user base today are Google, Bing, and Yahoo!. Google, arguably
the most influential search engine, was founded in 1998 by Larry Page and Sergey Brin, two
students at Stanford University. Various search engines have come and gone over the years, but
their goal has largely remained unchanged: gather information from web pages from across the
Internet and help users find the ones that are most relevant to what they are searching for.
―Google’s mission is to organize the world’s information and make it universally accessible and
useful,‖ (Google, 2012).
Search engines are powered by ―robots‖ or ―spiders‖ that crawl the web accessing web
pages and indexing the content that they find. The web pages are then ranked so searchers may
be presented with the most relevant information at the top of the results. Properly ranking the
search results keeps users happy, ensuring that they return to their search engine of choice for
their next search—and that keeps search engines happy as they retain users, and continue to gain
new ones.
Low quality or irrelevant search results frustrate users, driving them to competing search
engines. To increase the relevance of search results, search engines constantly update their

ranking algorithms—complex mathematical equations used to measure the relevance of a web
page to a specific search query and rank them against each other.
Business owners and marketers have recognized the change that the Internet has made to
the way consumers and businesses communicate and interact with each other. Consumers have
turned to the Internet, particularly through search engines, to gather information about companies
and products before making purchases, and even complete many of their purchases directly from
company websites. ―Getting found‖ on search engines has become an important and lucrative
business objective and has led to an entirely new industry called Search Engine Optimization
(SEO).
The goal of search engine optimization is to get a company’s or individual’s web pages to
outrank competitors’ for search terms that consumers are using to find relevant products,
services, or information. This is done by analyzing the behavior of search engines to determine
factors included in search engines’ ranking algorithms, and optimizing web pages to satisfy these
ranking factors.
Statement of the Problem
Over the years the major search engines, such as Google, Yahoo!, and Bing have
incorporated different types of information into search results. Instead of showing just a title,
brief description, and a hyperlink to searchers, they are now showing photos, videos, product
information, pricing, addresses, phone numbers, customer reviews, and more in their search
result pages. This information, known as structured data, provides useful information to
searchers and helps them find what they are looking for more quickly and efficiently.
While search engines may be able to identify, interpret, and gather some of this
information on their own, various schemas have been created to help communicate this type of

information to the search engines directly. Recently, Google, Yahoo!, and Bing have joined
together in an effort to promote a single schema for webmasters to use to communicate
structured data to the search engines—eliminating the need for multiple schemas to satisfy
multiple search engines. This single schema is called microdata.
Purpose Statement
Microdata represents a considerable change to the way search results are displayed and
how website owners and webmasters can communicate relevant information to search engines.
Microdata is now universally supported by the three major search engines: Google, Bing, and
Yahoo!. The purpose of this thesis is to examine the effect of microdata on search engine
optimization.
Objectives of the Study
Microdata is a new development that is likely to have a significant impact on the search
engine optimization industry. While the implementation of microdata into web pages is
relatively easy, the full effect has yet to be determined. The objective of the study is to
determine the effect of microdata on search engine optimization. The results of the study will
provide search engine optimization professionals with a better understanding of the importance
of microdata to industry professionals. It will also help them determine the possible importance
of microdata to their overall search engine optimization strategy.
Hypothesis
Early indications are that for a small investment of time, search engine optimizers may
see a large impact in how users interact with search results. While microdata is not expected to
be a direct ranking factor, it will likely be an indirect ranking factor because of changes in the
way users interact with search results. Particularly, search result click-through-rates are expected

to increase, sending a signal to search engines that the associated pages are relevant to search
queries. Having a single schema that applies to all three of the major search engines also makes
the job of a search engine optimizer that much easier.
Assumptions
The following assumptions have been made in relation to this study:
1. Search Engine Optimization is possible—that is, webmasters can take actions
that will directly influence search engine results.
2. Search engine optimization professionals have a desire to optimize their web
pages to the fullest extent possible.
3. Google, Bing, and Yahoo! have accurately represented the nature of microdata
in public communications, such as blog posts and announcements on company
web pages.
4. Relevant search results mutually benefit users, search engines, and
webmasters.
5. Search engine optimization professionals know what microdata is, and have
had at least limited experience with it.
Limitations
Due to limited time and resources this study will not attempt to demonstrate a statistically
significant change in rankings, click-through-rates, or web page performance as a result of
incorporating microdata into web pages. Rather, this study will be exploratory in natureand
measure the attitudes and experiences of search engine optimization professionals as they relate
to microdata and its effect on search engine optimization.

Definition of Terms
The following definitions will aid the reader in sharing the same meaning as the author:
Search Engine: A web-based service for retrieving web pages, documents, and
other information from the Internet.
Search Engine Optimization (SEO): The practice of optimizing web pages to
appear at the top of search results for relevant search queries with the intent of
generating traffic to web pages that ultimately results in revenue for an individual
or business.
Keywords: Words or phrases entered into a search engine by users when
searching for information on the Internet.
Search Engine Result Placements (SERPs): The results that are presented by a
search engine following a search.
Rankings: The sort order of search engine result placements (SERPs).
Ranking algorithms: Complex mathematical equations used to measure the
relevance of web pages to specific search queries.
Click-through-rate: The rate at which users click on a particular search engine
result placements (SERPs).
Structured data: Data such as photos, videos, product information, pricing,
addresses, phone numbers, customer reviews, etc. that is easily
distinguishable by humans, but not by machines.
Schema: The representation of a plan or theory in the form of a model.
Markup: A set of symbols used to annotate a web page that is syntactically
distinguishable from text.

Microdata: A simple schema for embedding semantic markup into HTML
documents.
Eye-tracking: A system of hardware and software used to measure and track the
movement of a subject’s eyes for analysis of user behavior.
Analytics: Analytical tools and software used to track and measure user actions
on web pages for analysis by an analyst with the intention of determining user
behavior and intent.

Chapter II
Literature Review
Introduction
Microdata is a relatively new development within the Search Engine Optimization (SEO)
industry and thus, few studies have been conducted specific to microdata. Structured markup
and rich snippets have been around for years however, which has served as the genesis for
microdata. A brief history of the most popular rich snippet formats will be reviewed which led
up to microdata. The need for microdata will then be explored, along with a thorough
description of what it is and how it works.
Review
The Goal of Schemas
Humans and machines interpret data differently. Humans are able to distinguish between
different types of data and draw conclusions about them automatically. Machines, on the other
hand, have a difficult time distinguishing between different types of data. For example, a human
can read a testimonial from another user and understand that it represents a third-party opinion
about a product or service. The tone and word-choice of the review sends signals to humans that
one review is positive, while another is negative. The human reader then draws a conclusion
about the product or service based on the review that has been read and interpreted—which can
impact purchasing behavior.
For a machine, however, that same review is difficult to interpret as anything other than
more text on a web page. It is difficult for the machine to recognize that the text is a review,
measure the tone of the message, or draw conclusions based on that interpretation. As an affect,
no action may be taken by the machine as a result of that testimonial.

Schemas attempt to allow a webmaster to mark up the text on the web page in a way that
communicates to a search engine that a certain piece of text represents a particular type of data,
which it can then interpret and act on. A particular piece of text, for example, may be marked up
by a webmaster to indicate that it is a testimonial, that it pertains to a particular product, and that
it was rated 4 out of 5 stars by the user. The search engine may then recognize the favorable
review, its associated product, and not only display this information in the SERPs, but even rank
the highly-rated product page higher than the lower-rated product page.
This benefits the search engine because it can more easily recognize, interpret, and act
upon certain types of data. And, it benefits the human user because he or she can more easily
locate a product that has been highly-rated and view testimonials that will validate the product in
his or her mind.
Popular Schemas
The three most popular schemas are RDFa, microformats, and microdata. Each schema
allows webmasters to mark up structured data in a way that it is understood by both humans and
machines.
Adida and Birbeck (2008) have provided an overview of RDFa and described how to turn
existing ―human-visible‖ text and links into ―machine-readable‖ data without repeating content.
RDFa ―provides a set of XHTML attributes to augment visual data with machine-readable hints‖.
RDFa is highly extensible and easy for machines to understand, but can be difficult to implement
for humans.
Microformats.org (2012) outlines the proper use of microformats, giving a description of
what they are and what they are not. Microformats attempt to adapt to current behaviors and

usage patterns and are highly correlated with semantic XHTML. Microformats are human
friendly because of their simplicity, but are not as extensible as RDFa, making them less
impactful for machines.
Hickson (2012)has outlined the specification that defines the HTML microdata
mechanism. Microdata allow webmasters to embed ―machine-readable data‖ into HTML
documents in a simple format that may be parsed by machines. A balance of extensibility and
simplicity is reached by the microdata format, making it favorable to both machines as well as
humans.
Google (2011)has described the purpose of microdata and provided guidance on the use
of non-visible content. That is, Google generally will not display content that is not visible to
users on a web page. Google encourages webmasters to display the same information to search
engines as is shown to visitors, but mark up the data using microdata so that it can be interpreted
correctly (see Figure 2.1 and Figure 2.2).
Figure 2.1. HTML without microdata markup.

Figure 2.2. HTML with microdata markup.
In 2010, Chattopadhyay et al. officiallyannounced that Google had incorporated support
for microdata for rich snippets. According to Chattopadhyay, ―Microdata has the nice property
of balancing richness with simplicity‖ (para. 5). Google recognizes all three schemas, but
recommends the use of microdata.
Rich Snippets
According to Google (2012), if the search engine can understand the content on a web
page, it can include detailed snippets of information in its search results to help users with
specific queries. These detailed snippets of information are called rich snippets (see Figure 2.3).
Rich snippets are shown in search results to ―give users a sense for what’s on the page and why
it’s relevant to their query‖ (Google, 2012, para. 1).
―For example, the snippet for a restaurant might show the average review and price
range; the snippet for a recipe page might show the total preparation time, a photo, and

the recipe’s review rating; and the snippet for a music album could list songs along with a
link to play each song‖ (Google, 2012, para. 2).
Figure 2.3. Examples of Rich Snippets.
Google supports rich snippets for these content types:
Reviews
People
Products
Businesses and organizations
Recipes
Events
Music
Video content

Structured markup also helps Google present relevant information in its local search
results. When this structured markup is included in web pages, it allows webmasters to
communicate specific types of information, such as a business name, address, or a phone number
to Google, which is in turn presented to searchers in local results (Google, 2011).
The Case for Microdata
With three different schemas fighting for adoption, each of the search engines had to
decide which of the schemas to support, and webmasters were left needing to satisfy multiple
search engines by incorporating multiple schemas. The alternative was to choose only one
schema to support and only satisfy some of the search engines.
In 2012, Google announced the launch of Schema.org, which is an effort co-supported by
Google, Bing, and Yahoo!. Schema.org (2011) provides a collection of shared vocabularies
webmasters can use to mark up their pages in ways that can be understood by the major search
engines: Google, Microsoft, and Yahoo! The vocabularies found at Schema.org may be encoded
using the microdata format to add information to the HTML content of a web page.
Google chose to support Microdata as ―a single format [to] improve consistency across
search engines‖, and states that ―microdata strikes a balance between the extensibility of RDFa
and the simplicity of microformats,‖ (Google, 2012, n.p.). Google also states that this data is not
currently used as a ranking factor, but that it ―can make your web pages appear more
prominently in search results, so you may see an increase in traffic,‖ (Google, 2012, n.p.).
Google’sprovides an online testing tool that allows webmasters to check that Google can
correctly parse the structured data markup on their web pages and display it in search results
(2010). The tool is available at http://www.google.com/webmasters/tools/richsnippets.

The Effect of Microdata on SEO
In a study by González-Caro and Marcos (2010) researchers examined user behavior to
determine whether the intention behind search queries affects the way people browse the results
page. Eye tracking techniques were used to record eye fixations in title, snippet, URL and
images. Generally, the results demonstrated that a relationship exists between the users’
intention and their behavior when they browse the results page. In other words, the type of
information that searchers were looking for dictated the way they viewed and interpreted search
results.
Search engines pay special attention to the way searchers interact with search results. In
an interview with Enge (2011), Duane Forrester, a Sr. Product manager with Bing’s Webmaster
Program, described various ranking factors that Bing looks at, including the interaction of
searchers with search results. ―We are watching the user’s behavior to understand which result
we showed them seemed to be the most relevant in their opinion, and their opinion is voiced by
their actions‖ (n.p.).
Enge (2011)shared his experiences and opinions in relation to the impact of microdata on
click-through rates for search engine results. Enge said, ―The presence of the stars in the search
listings will tend to draw the human eye and increase the click-through rate for those results‖
(n.p.).
Meyers (2011)confirmed the behavior that Enge described above with the results of an
eye-tracking study that demonstrates the effect of rich snippets in local search results. Meyers’
summary of the results of the eye-tracking study describes how searchers’ eyes tend to fixate on

rich snippet data in search results, such as user reviews, addresses, photos, and videos even when
these results rank lower than non-rich snippet results (see Figure 2.4).
While the ranking of web pages in search results is not directly impacted by rich snippets,
the number of users clicking on the links tends to increase because their eyes are drawn to the
results.
Figure 2.4. Rich Snippets Eye-Tracking Study.

The inclusion of reviews in rich snippets is of particular interest given the results of a
study by Luca (2010) where he demonstrated that a one-star increase in Yelp rating lead to a 9%
increase in revenue for independent restaurants. Luca described how ―online consumer reviews
substitute for more traditional forms of reputation,‖ (p. 1).
Microdata represents an opportunity for webmasters to communicate reviews to search
engines and have those reviews displayed in SERPs, increasing click-through rates and
potentially increasing revenues. The rich snippets can include star ratings; number of votes,
price range, the date of the last review, the number of critic reviews vs. regular user reviews, and
the address with a link to a map of the location (see Figure 2.5). Presenting this level of data
directly in the search results helps users find relevant data more quickly, leading to higher click-
through rates and traffic for the site owners.
Figure 2.5. Example of a Rich Snippet – Local Restaurant Review.

Conclusion
While much material exists describing what rich snippets are and how they are
theoretically useful to machines (search engines) and humans (searchers), little has been done to
demonstrate the true impact of rich snippets on the practice of SEO. Initial eye-tracking studies
have shown that searchers are attracted to rich snippets that are presented in SERPs, leading to
higher click-through-rates. Star ratings and reviews, in particular, have been demonstrated to
impact buyer purchasing habits and directly impact revenues. Despite this, the full effect of rich
snippets, and in particular, microdata, has not been explored.
To better understand the effect of microdata on search engine optimization, further study
is needed. The attitudes and experiences of search engine optimization professionals need to be
explored. These are the professionals that determine if and how microdata fits into the overall
search engine optimization strategy. The level of effort required and the payoff expected will
determine if microdata is just a fad, or if it is here to stay.

Chapter III
Methodology
Introduction
Microdata is a new development within the Search Engine Optimization (SEO) industry,
and thus few studies have been conducted specific to microdata. The purpose of thisstudy was to
examine the effect of microdata on search engine optimization. In particular, the attitudes and
experiences of search engine optimization professionals were explored.
This chapter describes the participants of the study and how they were selected,the
materials, measures, equipment and organizational procedures followed, the type of design used
in the study, the variables that were measured, and a detail of the procedures that were followed.
Participants
The participants in the studywereSearch Engine Optimization (SEO) professionals who
were selected based on their membership in various online professional networking groups. The
professional networking groups included: Inbound Marketers LinkedIn group, Inbound
Marketing University Alumni LinkedIn group, Market Motive LinkedIn group, Triiibes
Members LinkedIn group, and SEOmoz LinkedIn group. Members of these groups were invited
to participate in an online survey. A total of twelve SEO professionals were included in the
study.
The Inbound Marketers LinkedIn group is an online group for marketing professionals.
The group was created on September 21, 2007 and consists of 79,702 members. The group
forms a community of marketers who are interested in online techniques like ―inbound
marketing, search engine optimization (SEO and social media,‖ (Inbound Marketers, n.p., 2012).

The Inbound Marketing University Alumni LinkedIn group is a group of certified
inbound marketing professionals. The group was created on June 20, 2009 and consists of 2,268
members. The group is a place for graduates of the Inbound Marketing University certification
program to connect and share ideas (Inbound Marketing University Alumni, 2012).
Market Motive is a subscription service that provides weekly workshops, tutorials,
courses, and certifications to online marketing professionals. The Market Motive LinkedIn
group is a place for Market Motive subscribers to communicate about conversion optimization,
online PR, paid search, social media, web analytics, SEO, and email marketing. The group was
created on March 31, 2009 and includes 230 members (Market Motive, 2012).
The Triiibes Members LinkedIn group is a place for members of Seth Godin’s Triiibes
network. Seth Godin’s Triiibes network is a by-invitation-only group of marketing
professionals. The group was created on August 7, 2008 and consists of 328 members (Triiibes
Members, 2012).
The SEOmoz LinkedIn group is a place for search engine optimization professionals
(SEOs) to connect, find resources, and network. The group is run by SEOmoz, a provider of
SEO tools and tutorials. The group was created on April 20, 2010 and consists of 8,140
members (SEOmoz, 2012).
Materials
A survey of search engine optimization (SEO) industry professionals was conducted to
examine their experiences and opinions regarding microdata. The study was conducted using
SurveyMonkey’s online survey tool. Participants were invited to participate in the study via
various professional LinkedIn groups where they were encouraged to click on a hyperlink and

answer 14 survey questions using the online survey tool. The survey included nine multiple
choice questions allowing a single answer, four multiple choice questions allowing multiple
answers, and one optional essay question. Access to the Internet was required to complete the
survey.
Design
The research design was a quantitative, cross-sectional, descriptive survey. The purpose
of a quantitative study is to ―quantify data and generalize results from a sample to the population
of interest‖ (Snap Surveys, n.p., 2012). The survey questions examined the opinions and
experiences of SEO professionals as they relate to microdata with the purpose of quantifying the
data and generalizing the results to the SEO industry.
Given the requirement of participants to be an SEO professional, a probability sampling
proved too difficult. A nonprobability sampling method was instead used, which still allowed
for generalization about the culture of SEO professionals as it relates to microdata (Bernard,
2000). The survey was conducted at a single point in time, making it cross-sectional in nature
(Creswell, 2002).
The survey questions were designed to gather quantitative, descriptive data regarding the
following areas of interest:
1. Identify the current role of the SEO professional
2. Identify the amount of SEO experience the professional has acquired
3. Identify the schemas the SEO professional has used in the previous 12 months
4. Identify which types of structured data the SEO professional has attempted to
communicate to the search engines within the previous 12 months

5. Identify which types of structured data the SEO professional has successfully
incorporated into search results (i.e. rich snippets)
6. Identify which search engines the SEO professional has been successful with at
incorporating structured data in search results (i.e. rich snippets)
7. Identify how many times the SEO professional has visited Schema.org in the previous 12
months
8. Learn the SEO professional’s opinion regarding the effectiveness of schemas to increase
search engine rankings
9. Learn the SEO professional’s opinion regarding the effectiveness of rich snippets to
increase click-through-rates of search results
10. Learn the SEO professional’s opinion regarding the effectiveness of higher click-through-
rates to increase rankings with the search engines
11. Learn the SEO professional’s opinion regarding the difficulty of incorporating Microdata
into web pages
12. Learn the SEO professional’s opinion regarding the effort required to incorporate
Microdata into web pages
13. Identify the likelihood that the SEO professional will include Microdata in his or her
search engine optimization strategy during the following 12 months
14. Collect any other thoughts, opinions, or experiences that the SEO professional would like
to share about Microdata

Procedure
A brief introduction to the survey was posted on search engine optimization (SEO)
industry LinkedIn groups requesting participation. Participants from the groups were self-
selected by clicking on a hyperlink that was included in the LinkedIn discussion posts. The
participants then completed the survey using SurveyMonkey’s online survey software. The data
was then analyzed using SurveyMonkey’s online survey software.

Chapter IV
Results
Introduction
This chapter includes a description of the data that was collected during the course of the
study. Survey responses were collected, compiled, and analyzed using Survey Monkey’s online
survey tool. The charts below were generated by the software and represent all of the survey
responses as they were entered by the study participants. Counts and percentages are
representational of the number of responses received for each survey question, and not all survey
questions received an answer from all participants of the study. This chapter does not include an
interpretation of the data. The interpretation of the data will appear in Chapter V.

Findings of the Study
Figure 4.1. Current Search Engine Optimization (SEO) Role.
This question received a total of twelve responses. Three respondents, 25.0%, selected
that they were currently in a role as an in-house search marketer. One respondent, 8.3%, selected
that he or she was currently in a role as an agency search marketer. Three respondents, 25.0%,
selected that they were currently an independent consultant. Five respondents, 41.7%, selected
that they did not currently fulfill any of the roles stated above.

Figure 4.2. Years of SEO Experience.
This question received a total of twelve responses. Four respondents, 33.3%, selected
that they had less than a year of SEO experience. One respondent, 8.3%, selected that he or she
had 1-2 years of SEO experience. One respondent, 8.3%, selected that he or she had 2-3 years of
SEO experience. Three respondents, 25%, selected that they had 4-5 years of SEO experience.
Three respondents, 25%, selected that they had 5-10 years of SEO experience. None of the
respondents had more than 10 years of SEO experience. The respondents of the study had a
wide range of SEO experience.

Figure 4.3. Schemas Used by Respondents within the Previous 12 Months.
This question received a total of eleven responses. One respondent, 9.1%, selected that
he or she had used RDFa within the previous 12 months. None of the respondents selected that
they had used Microformats within the previous 12 months. Two respondents, 18.2%, had
selected that they had used Microdata within the previous 12 months. Eight respondents, 72.7%,
selected that they had not used RDFa, Microformats, or Microdata within the previous 12
months.

Figure 4.4. Types of Structured Data that was Attempted within the Previous 12 Months.
This question received a total of ten responses. Four respondents had attempted to
communicate reviews to the search engines within the previous 12 months, three had attempted
to communicate People, five had attempted to communicate Products, five had attempted to
communicate Businesses and organizations, three had attempted to communicate Recipes, two
had attempted to communicate Events, none had attempted to communicate Music, and four had
attempted to communicate Video content. Three respondents had not attempted to communicate
any of the types of structured data that were listed within the previous 12 months.

Figure 4.5. Types of Structured Data that was Successfully Incorporated into Search Results
within the Previous 12 Months.
This question received a total of ten responses. Three respondents had successfully
incorporated reviews into search results (i.e. rich snippets) within the previous 12 months, three
had incorporated People, four had incorporated Products, three had incorporated Businesses and
organizations, two had incorporated Recipes, three had incorporated Events, none had
incorporated Music, and two had incorporated Video content. Four respondents had not
successfully incorporated into search results (i.e. rich snippets) any of the types of structured data
that were listed within the previous 12 months.

Figure 4.6. Search Engines Where Structured Data was Successfully Incorporated.
This question received a total of ten responses. Seven respondents, 70%, had
successfully incorporated structured data into Google’s search results (i.e. rich snippets) within
the previous 12 months. One respondent, 10%, had successfully incorporated structured data
into Bing’s search results (i.e. rich snippets) within the previous 12 months. Two respondents,
20%, had successfully incorporated structured data into Yahoo!’s search results (i.e. rich
snippets) within the previous 12 months. Three respondents, 30%, had not successfully
incorporated structured data into Google, Bing, or Yahoo’s search results.

Figure 4.7. How Many Times Respondents had Visited Schema.org in the Previous 12 Months.
This question received a total of ten responses. Seven respondents, 70%, had never
visited Schema.org. One respondent, 10%, had visited Schema.org 1-3 times in the previous 12
months, and two respondents, 20% had visited Schema.org more than 10 times in the previous 12
months.

Figure 4.8. Likelihood of RDFa, Microformats, or Microdata to Increase Rankings.
This question received a total of ten responses. 60% of respondents were not sure if the
use of RDFa, Microformats, or Microdata would increase rankings with the search engines. 20%
believed that it would increase rankings somewhat, and 20% believed that it would not increase
rankings.

Figure 4.9. Likelihood of Rich Snippets to Increase Click-Through-Rates.
This question received a total of ten responses. 50% of respondents believed that rich
snippets either somewhat or definitely increase click-through-rates of search results. 40% of
respondents were unsure if rich snippets increase click-through-rates, and only 10% believed that
rich snippets do not increase click-through-rates of search results.

Figure 4.10. Likelihood of Higher Click-Through-Rates to Increase Rankings.
This question received a total of ten responses. 40% of respondents believed that higher
click-through-rates would increase rankings with the search engines. 60% of respondents were
unsure if higher click-through-rates would increase rankings. None of the respondents indicated
that they believed that higher click-through-rates would not increase rankings.

Figure 4.11. Difficulty of Incorporating Microdata into Web Pages.
This question received a total of ten responses. 60% of respondents believed that
incorporating microdata into web pages was neither easy nor difficult. 10% of respondents
believed that incorporating microdata was somewhat easy, and 30% believed that it was
somewhat difficult.

Figure 4.12. Worth of Effort to Incorporate Microdata into Web Pages.
This question received a total of ten responses. 50% of respondents were unsure if it was
worth the effort to incorporate microdata into web pages. 30% of respondents believed that it
was either somewhat or definitely worth the effort to incorporate microdata into web pages, and
only 20% believed that it was not really worth the effort.

Figure 4.13. Likelihood of Including Microdata in the SEO Strategy During the Next 12 Months.
This question received a total of nine responses. 55.5% of respondents were either
somewhat likely or very likely to include microdata in their search engine optimization (SEO)
strategy in the next 12 months. 22.2% of respondents were unsure, and 22.2% were either
somewhat unlikely or very unlikely to include microdata in their search engine optimization
(SEO) strategy in the next 12 months.

Do you have any other thoughts, opinions, or experiences you'd like to
share about Microdata?
RDFa Lite has incorporated the design of microdata, and my feeling is that RDFa Lite is
just as easy to integrate in HTML as microdata. see the announcement:
http://blog.schema.org/2011/11/using-rdfa-11-lite-with-schemaorg.html
Haven't done much with microdata but plan on it in the future
Figure 4.13. Additional Thoughts, Opinions, or Experiences about Microdata.
This optional, open-ended question received a total of two responses. One respondent
indicated that he or she believed that RDFa Lite, a new development, was similar to microdata
and just as easy to integrate. One respondent indicated that he or she had not done much with
microdata, but planned on it in the future.

Summary
This chapter included a description of the data that was collected during the course of the
study. This description included data regarding the current role of the respondents, the number
of years of SEO experience they held, the schemas they had used within the previous 12 months,
the types of structured data that they had attempted to communicate to the search engines within
the previous 12 months, the types of structured data that they weresuccessful at incorporating
into search results within the previous 12 months, the search engines where they were successful
at incorporating structured data, and how many times they had visited schema.org in the previous
12 months.
The opinions of respondents regarding the likelihood of RDFa, microformats, or
microdata to increase rankings in search results were described, along with the likelihood of rich
snippets to increase click-through-rates, and the likelihood of higher click-through-rates to
increase rankings.
The respondents’ opinions regarding the difficulty of incorporating microdata into web
pages, and the worthiness of the effort required to incorporate microdata into web pages was
described. The likelihood that respondents would include microdata in theirSEO strategy during
the next 12 months was also described with additional thoughts, opinions, and experiences of
respondents regarding microdata.An analysis of the data was not included, but will appear in the
next chapter.

Chapter V
Conclusion and Recommendations
Introduction
The purpose of the study was to examine the effect of microdata on search engine
optimization. Rich snippets represent an important change to the way search results are
displayed to users and Microdata facilitates how website owners and webmasters can
communicate relevant information to search engines for display in search results.
Microdata is a relatively new development that has the potential to have a significant
impact on the search engine optimization industry because it is now universally supported by the
three major search engines: Google, Bing, and Yahoo!.. The results of the study can provide
search engine optimization professionals with a better understanding of the importance that other
SEO professionals are placing on microdata in their overall SEO efforts. This data can then help
SEO professionals to better determine the possible importance of microdata in their own overall
search engine optimization strategy.
Early indications were that for a small investment of time, search engine optimizers may
see a large impact in how users interact with search results. While microdata was not expected
to be a direct ranking factor, it was likely be an indirect ranking factor because of changes in the
way users interact with search results. Particularly, search result click-through-rates were
expected to increase, sending a signal to search engines that the associated pages are relevant to
search queries. Having a single schema that applies to all three of the major search engines also
was expected to make the job of a search engine optimizer that much easier.

Conclusion
While the purpose of the study was to examine the effect of microdata on search engine
optimization, the results show that it may still be too early to decide. Rich snippets do in fact
represent an important change to the way search results are displayed to users, and Microdata
does facilitates the communication of relevant information to the search engines, but microdata
has not yet reached widespread adoption by search engine optimization professionals.
Even though microdata is now universally supported by the three major search engines,
very few participants in the study had ever attempted to utilize it. The participants of the study
had attempted to communicate nearly every type of structured data that microdata is equipped to
handle, but had failed to use microdata in those attempts. While some of the participants were
successful in their attempts, their lack of experience with microdata likely affected their ability to
succeed in all of those efforts. In fact, 70% of the participants in the study had never visited
Schema.org, the site created by the three major search engines to outline and describe the schema
to website owners and webmasters.
The hypothesis of the study was that for a small investment of time, search engine
optimizers could see a large impact in how users interact with search results, increasing click-
through-rates, and indirectly increasing rankings. Participants of the study were relatively
confident that rich snippets could increase click-through-rates, and that higher click-through-
rates could lead to higher rankings, but were unconvinced that schemas such as microdata could
increase rankings. It is unclear if participants merely failed to link the use of microdata with the
display of rich snippets, and therefore higher click-through-rates and rankings, or if they simply
did not believe that microdata was effective at influencing search engines to display rich
snippets.

Participants in the study did not perceive the incorporation of microdata as easy, and
were generally unsure if the effort to incorporate microdata into web pages was worth the effort.
The participants did expect to include microdata in their search engine optimization strategy in
the next 12 months, however.
Recommendations
The participants’ lack of experience with microdata clearly prevented them from drawing
conclusions as to the effect it could have on their SEO efforts. Even though they were not sure if
incorporating microdata would be worth the effort, or that it would have an impact on rankings,
they expressed a willingness to test it within the next 12 months. Search engine optimization
professionals who are considering the inclusion of microdata in their overall SEO strategy should
recognize that their competitors are likely to do so soon and that if microdata does eventually
prove to be effective SEOs who wait to incorporate it will be at a disadvantage.
While the use of microdata may lead to higher click-through-rates, the increase may only
affect results that have already achieved first-page rankings. Rankings on lower pages receive
less traffic, causing click-through-rates to become a less important factor. It may be wise,
therefore, to focus first on getting to the first page of results, then on the incorporation of
microdata with the intention of increasing click-through-rates, and ultimately even higher
rankings.
Considerations for Future Research
Microdata is relatively new to the SEO industry and future research on this topic is still
needed. This study was limited in scope and only included 12 participants, and participants were
selected using a nonprobability sampling method. The following recommendations would
improve future studies:

1. A larger sample size is recommended, and a probability sampling method could
provide results that more closely represent the opinions, views, and experiences of
the SEO industry as a whole.
2. Does the amount of experience that a search engine optimization professional
holds affect the decision to use microdata?
3. Do those who have used microdata in the past plan to continue to use it in the
future?
4. Where does microdata rank in terms of importance with other possible
optimizations that can be performed (such as on page factors, link building, etc.)?
5. Does the industry of the business influence the importance of microdata (service
providers, restaurants, ecommerce, informational, etc.)?
Summary
The purpose of the study was to examine the effect of microdata on search engine
optimization. Rich snippets represent an important change to the way search results are
displayed to users, and microdata facilitates the communication of relevant information to the
search engines. Microdata has not yet reached widespread adoption by search engine
optimization professionals, but is expected to increase over the next 12 months. Future studies
are needed to measure the impact that the adoption of microdata will have on search engine
optimization.

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