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 nature and

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. officially announced 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’s provides 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 this study 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 study were Search 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 were successful 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 their SEO 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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The Effect of Microdata on Search Engine Optimization

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