[Study] The Definitive Organic Click Through Rate Study

A tale of two studiesEstablishing Google & Bing Click-Through Rates
study by Digital Relevance™ using client data from Jan - june 2011
learn more

p3
overview
An organic click-through rate (CTR) on search engines is one of the best metrics
to measure user engagement on the Internet. Google and Bing have more influence
than ever before, controlling over 95% of all search engine queries. An organic
Page 1 ranking for a relevant, popular keyword can drive millions of dollars in
new business.
In search, it is not only the case that CTRs drive engagement, but it is speculated that search engines use click-
through metrics to help determine which results are relevant. That means if you are posting relevant content in
relevant ways, your organic search engine rankings will be, in a way, self-perpetuating. This helps users find the
content that answers their queries.
Digital Relevance™ conducted this study on user behavior to help define the impact of Page 1 search engine
rankings. However, for Bing and Google, the user experience is ever changing. There have been algorithm updates, a
new user interface, increased mobile search, the addition of social signals, and blended search engine results pages
(SERPs) with videos, news, places, images, and shopping results. As Bing and Google are constantly working to
improve the user experience, we expect even more changes that will have a significant impact on search behavior.
As SERPs continue to evolve, does a constant CTR behavioral pattern emerge? How many more organic visits can I
expect to receive as my website increases its position in the SERP for any given keyword? Our study attempts to
answer these questions by finding CTRs using actual client data.
This particular study was first designed by Paul Davison of Digital Relevance™, Director of Client Success, to serve
as a model to help make projections for our clients. It is our intent to make this study as transparent as possible so
that organic SEOs and marketers may see how we determined our results. This study will be an on-going project
that will be compared with future SERPs and other CTR studies.
what's inside:
Section 1 » Definition of Terms
Section 2 » Statement of Purpose
Section 3 » Assumptions
Section 4 » Limitations
Section 5 » Recent Studies
Section 6 » Methodology
» Participants
» Instruments
» Procedures
Section 7 » Results & Discussion
Section 8 » Descriptive statistics
Section 9 » Conclusions
A tale of two studies
Establishing Google & Bing Click-Through Rates

p4
definition of terms
click-through-rate (ctr): The percentage of users who click
on a given domain after entering a search query. In this study, CTR is
calculated as total visits divided by total searches for a given keyword
over a stable period.
Search Engine Results Page (SERP): The listing of web
pages returned by a search engine after a search query is made.
Above the Fold: Refers to the top half of a SERP, positions #1-5.
Below the Fold: Refers to the bottom half of a SERP, positions
#6-10.
Organic Results: The listing of web pages in the query results
that are not paid advertisements.
Paid Results: Advertisements on search engines that appear in the
query results, typically at the top or right side of the page.
Blended SERPs: A search engine results page with images, videos,
news, places, and/or shopping results included within the listings.
Branded Keywords: A keyword phrase that has a word directly
associated with the given company’s name. Example: “Gibson guitars”
is a branded keyword phrase while “guitars” is not.
Long-tail traffic: Visits to a given domain from all keyword
phrases that include the primary term except for the primary term by
itself. Example: if “cars” is the primary term, then the long-tail would be
traffic associated with terms like “fast cars,” “used cars,” “cars for sale,”
etc. The term “cars,” by itself, would not be included in long-tail traffic.
Halo-effect: The many long-tail or related keywords that rank
in SERPs in addition to the primary keyword. Also referred to as the
“umbrella effect” and “keyword association effect.”
= exact CTR
= long-tail ctr
Exact Visits
(from Google Analytics)
Phase Visits – Exact Visits
(from Google Analytics]
Exact Local Monthly
Searches (from Adwords)
Phrase – Exact Local
Monthly Searches
(from Adwords)
Section 1 »

p5
Statement of Purpose / Main Research Questions
The main objective of this study, first and foremost, is to better understand user CTR behavior and to help quantify
ROI from SEO campagins. We attempted to do this by answering the following questions:
1» What is our observed CTR curve for organic U.S.
results for positions #1-10 in Google SERPs?
2» What is our observed CTR curve for organic U.S.
results for positions #1-10 in Bing SERPs?
3» How do the CTR curves for Google and Bing
compare with each other?
4» What amount of long-tail click-through can we
expect for a keyword that has a stable rank in
its SERP?
5» What impact do images, videos, news, places,
and shopping results have on user behavior?
Assumptions
1» The reporting instruments used were sufficiently
accurate to make valid conclusions.
2» Non-branded keywords provide a more accurate
measure of user engagement. Non-branded keywords
were used in these studies.
Limitations
1» The sample consisted of major retailers and
enterprise groups who are all clients of Digital
Relevance™. Therefore, the results may not
generalize to other populations.
2» The design of this study was behavioral, measuring
one period in time. Therefore, causal relationships
were not addressed and interpretations must
be qualified.
3» The sample of keywords had stable positions in
the SERP for a certain period of time. All keywords
in the Google study had stable positions for 30
days. The Bing study included keywords with
stable positions ranging from 7 to 30 days, due to
the fact that Bing Advertising Intelligence reports
daily search volumes, while Google Adwords
does not. This introduces the possibility of a
systematic difference in CTR for certain keywords
that are stable.
Non-branded keywords
provide a more accurate
measure of user engagement.
Non-branded keywords were
used in these studies.
Section 2 »
Section 3 »
Section 4 »

p6
Recent Studies
The last two major CTR studies were conducted by Optify and Enquiro. Optify’s study was conducted in the holiday
season of 2010 from a variety of business-to-business websites. The study published by Enquiro Search Solutions
was conducted in 2007 using survey data and eye-tracking research. It was the result of a business-to-business
focused survey of 1,084 pre-researched and pre-selected participants. Both studies observed Google as its search
engine; the CTR curves for each study are shown below.
Table 1:
It is important to note that in Optify’s curve, a click-through rate is defined as follows: Given that a user clicks on a
top 20 organic ranking, the click-through rate is the percentage of users that clicked on each position. Digital
Relevance™ defines its curve as follows: For any given search, the click-through rate is the percentage of users that
click on each position in the top 10 organic results. The key difference here is the given information in each
calculation. Optify’s top 20 curve sums to 100% and shows the CTR curve only considering those who clicked on a
top 20 organic result. Therefore, the curve must be adjusted because not all searches result in a click. Users often
re-query, click on a paid advertisement, exit the page, go on to page two, or click on one of the shopping, news,
places, videos, or images results.
It is also important to note that Optify’s study was conducted during the holiday season of December 2010. It is
possible that Google makes changes to its rankings during the holiday season that many believe have a significant
impact on user behavior as well as the inherent change in user intent. Therefore, one should not blindly compare
the CTR curves between these studies, but note their differences, especially in how they were calculated.
Section 5 »
google serp position
# 1 36.4% 27.1%
# 2 12.5% 11.7%
# 3 9.5% 8.7%
# 4 7.9% 5.1%
# 5 6.1% 4.0%
# 6 4.1% 4.1%
# 7 3.8% 4.1%
# 8 3.5% 3.2%
# 9 3.0% 2.8%
# 10 2.2% 3.6%
optify*
(Dec 2012) enquiro (2007)
*
distribution of clicks in top 20 organic positions

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Methodology
We can now calculate the Exact and Long-Tail click-through for that keyword.
PARTICIPANTS
To generate an accurate model, Digital Relevance™ analyzed the user behavior of more than 170,000 visitors
across 624 non-branded keywords (324 were used in the Google study and 300 in the Bing study). The sample set
that was used came from a databank of over 10,000 keywords and were chosen based on the strict criteria of a
stable ranking period. These keywords were derived from Slingshot’s group of over 200 major retailers and
enterprise clients.
The Google and Bing studies shared 108 keywords in common and utilized 91 of the same clients. This study
calculated CTR based on United States searches only.
INSTRUMENTS
» Authority Labs
» Google Adwords Keyword Tool
» Bing Advertising Intelligence Excel Add-in
» Google Analytics
PROCEDURES
In this section, we illustrate our research procedures so
that the study may be replicated and so the reader may
see how we determined our results.
Finding Stable Keywords
In order to isolate each keyword to a specific position in the top 10, a tool called Authority Labs was used to
identify keywords with stable positions in the SERP. As an example, the word “cars” held a stable rank at Position
2 for June 2011.
Digital Relevance™
analyzed the user behavior
of more than 170,000
visitors across 624
non-branded keywords.
Section 6 »

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Finding Keyword-Specific Search Volumes
To find the number of U.S. searches for a certain keyword, we consulted the webmaster tools for Google and Bing.
Google provides an Adwords Keyword Tool, which was used to report monthly search volumes. Using this tool, the
number of exact and phrase local monthly searches was found. It is important to note that all keywords have
different monthly trends. For example, a keyword such as “LCD TV” would typically spike in November, just before
the holiday season. If one looks at searches for that keyword in May, during a time when the search volume is not
as high, the monthly search average may be overstated. Therefore, it was necessary to download the .csv file from
Adwords, which separates the search data by month for more accuracy.
By extracting “Phrase” and [Exact] data from the Google Adwords Keyword Tool, we can calculate the long-tail
click-through for the keywords associated with a head term holding a stable rank in the SERP.
Bing provides an Advertising Intelligence Exel add-in, which breaks down the number of U.S. searches by day. Bing
does not provide search volumes for alternate query occurrences of a keyword used in all phrases as Google does.
Thus, we were unable to calculate the long-tail CTR for Bing.
Both tools are widely used by marketers and SEOs, and are the most accurate tools freely available for search volumes.
= Long-tailPhrase Exact

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= exact ctr
= long-tail ctr
# of visits
Phase Visits –
Exact Visits
(from Google Analytics)
Phase –
ExactU.S. Searches
(from Adwords)
# of searches
Finding Keyword-Specific Search Traffic
Using Google Analytics, we found the actual number of visits over the stable period for each keyword.
In Google Analytics, it is necessary to specify “non-paid” visits to include only organic results and to filter for U.S.
visits. This reflects the fact that the reported Google and Bing search volumes were for U.S. searches.
Calculations
The number of visits is obtained from Google Analytics. The number of searches, for Google, is obtained from
the Google Adwords Keyword Tool. The number of searches, for Bing, is obtained from the Bing Advertising
Intelligence Tool.
The number of Phrase and Exact visits was obtained from Google Analytics. The number of Phrase and Exact
searches was obtained from the Google Adwords Keyword Tool.
Long-tail CTR was not calculated for Bing.

p10
Results & Discussion
1. What is our observed CTR curve for organic U.S. results for positions #1-10
in Google SERPs?
Based on our sample set of 324 keywords, we have observed the following curve for Google Exact Keyword CTR:
google click-through rate
1
4%
0%
8%
12%
16%
20%
Click-throughrate
organic position in serp
2 4 6 7 8 9 105
18.20%
1.04%
We observed an 18.20% average CTR for a No. 1 rank and 10.05% for Position 2. Average CTR for each position
below the fold was observed to be below 4%.
An interesting implication from our CTR curve is that for any given SERP, the percentage of users who click on
an organic result in the top 10 was found to be 52.32%. This is typical user behavior, as many Google users will
window shop the SERP results and search again before clicking on a domain. These results can be used as a
baseline model and is most useful for showing the relative CTR for increases in each position.
1st
is approximately 4x position 4, 2nd
is approximately 2x position 4, and 3rd
is approximately 1.5x position 4.
Using these relative increases, marketers can better asses the value of an increase in rank from any position in the SERP.
3
Section 7 »
7.22%
4.81%
2.78%
1.75%
1.52%
10.05%
3.09%
1.88%
1
st
position
is approximately
4x position 4
2
nd
position
is approximately
2x position 4
3
rd
position
is approximately
1.5x position 4

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bing click-through rate
1
2%
0%
4%
6%
8%
10%
Click-throughrate
2 4 6 7 8 9 105
9.66%
0.45%
3
2.74%
1.88%
1.67%
0.65% 0.57%
5.51%
1.85%
1.34%
2. What is our observed CTR curve for organic U.S. results for positions #1-10
in Bing SERPs?
Based on our sample set of 300 keywords, we have observed the following curve for Bing Exact Keyword CTR:
We observed a 9.66% average CTR for a No. 1 rank and a 5.51% average CTR for Position 2. Average CTR for
each position below the fold was below 2%.
We observed a 9.66% average
CTR for a No. 1 rank and 5.51%
averge for Position 2.

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3. How do the CTR curves for Google and Bing compare?
Based on our sample set of 300 keywords, we have observed the following curve for Bing Exact Keyword CTR:
serp position
# 1 18.20% 9.66%
# 2 10.05% 5.51%
# 3 7.22% 2.74%
# 4 4.81% 1.88%
# 5 3.09% 1.85%
# 6 2.76% 1.67%
# 7 1.88% 1.34%
# 8 1.75% 0.65%
# 9 1.52% 0.57%
# 10 1.04% 0.45%
Google CTR Study bing ctr study Our R&D team was surprised
to see click-through rates
significantly lower for Bing,
especially since both studies
were conducted using data
over the same time period
(January 2011 to July 2011),
had 108 keywords in common,
and used the same group of
91 clients.
google vs bing click-through rate
1
4%
0%
8%
12%
16%
20%
Click-throughrate
2 4 6 7 8 9 105
18.20%
1.04%
3
7.22%
4.81%
2.78%
1.75%
1.52%
10.05%
3.09%
1.88%
9.66%
0.45%
2.74%
1.88% 1.67%
0.65% 0.57%
5.51%
1.85% 1.34%

p13
If so few users are clicking on Bing organic results after a search, where are they going? Possibilities include
“related searches” on the sidebar, re-queries, Page 2, one of the tabs at the top, a paid result, or the images,
videos, news, shopping, local listings results within the SERP.
Does this mean that Google’s results are more reliable? There is plenty of heated debate over this issue, but
whether or not Bing users actually abandon their searches more doesn’t necessarily mean that Google’s results
are more reliable; it just may be a behavioral issue. This is an implication from comparing the CTR curves, but
we must first acknowledge that the demographic of users for each search engine is very different.
Every month, roughly 117 million searches are made for “google” in
Bing. It’s hard to fathom why one would search for a search engine
while using a search engine, but let’s face it, users are aware that
Bing and Google provide very different results. To put this number in
perspective, consider the fact that, on average by month, only 55
million searches are made for “google” in Google and 4 million
searches are made for “bing” in Google. It’s an interesting behavioral
observation, to say the least, that even though Google controls 65%
of the search market, there are still an alarming number of users
who use Bing on a daily basis to get to Google.
Bing is the default search engine on Internet Explorer, which is still the world’s most popular web browser,
mainly because it comes standard on all Windows operating systems. This could explain why so many users
search “google” in Bing to get to Google’s homepage. They simply choose to stay with Internet Explorer instead
of using an alternative web browser, like Firefox or Chrome. Also, changing the default search engine on Internet
Explorer from Bing to Google is no easy task. The fact that Bing is the default search engine for over 38% of all
users significantly affects user behavior. The lower click-through rates could be attributed to Bing users
abandoning their results and heading to Google. Perhaps users are trying their searches and then re-querying
in Google.
Specifically optimizing for Bing is still important, especially since the demographic of users is different. Hit-wise
carried out a study, which showed that 58% of Bing users are female, whereas only 45% of Google users are
female. It also found that most Bing users are age of 35 or older. This is incredibly valuable information for
marketers because it affects the target audience. If you have a product geared towards females or an older
group, Bing may be more valuable to you.
Every month, roughly
117 million searches
are made for “google”
in Bing.

p14
4. What amount of long-tail click-through can we expect for a keyword that has a
stable rank in its SERP?
For each keyword with a stable position in its given SERP, we found the percentage of click-through for all
long-tail terms that stem from the keyword over the same period. For example, if “cars” ranked at Position 2
for June 2011, then how much traffic could that domain expect to receive from “new cars,” “used cars,” or
“affordable cars”? The reasoning is that if you rank second for “cars,” you are likely to drive a lot of traffic for
those other keywords as well, even if those other positions are unstable. This is due, in part, to the “halo effect.”
We were hoping to find an elegant long-tail pattern, but we cannot prove that long-tail CTR is directly dependent
on the exact term’s position in the SERP. This suggests that the universal effect of having the primary term in
a stable rank is uncertain, but still relevant.
We observed an average long-tail range of 1.17% to 5.80% for each position. The average long-tail CTR
associated with each primary keyword with a stable rank (#1-10) was found to be 2.75%.
Long-tail CTR is one of the results of a dynamic campaign that should not be ignored. By creating relative
content, links that people will click, a dynamic site architecture, and social networking, your campaign can
have a powerful marketing strategy that is more than just click-through rates from primary keywords.
# 1 43 5.80%
# 2 32 3.19%
# 3 31 2.43%
# 4 34 4.58%
# 5 33 1.17%
# 6 30 1.49%
# 7 31 1.82%
# 8 30 2.27%
# 9 30 1.68%
# 10 30 3.46%
# of keyword phrases long-tail google
ctr study
The average long-tail
CTR associated with
each primary keyword
with a stable rank
(#1-10) was found
to be 2.75%.

p15
5. What impact do images, videos, news, places, and shopping results have
on user behavior?
Starting in May 2007, news, video, local, and book search engines were added to Google SERPs and now include
images, videos, shopping, and places results. Do blended SERPs have lower click-through rates? One would
think that because these results often push high-ranking domains towards the bottom of the page, CTR would
indeed be lower for blended SERPs. However, a counter-intuitive hypothesis would suggest that because certain
SERPs have these blended results inserted by Google, they are viewed as more credible results and that CTR
should be higher for those blended SERPs.
Our hypothesis was that Google click-through behavior would be significantly different for blended SERPs
versus non-blended SERPs. We analyzed our sample set and failed to prove that there are significant
differences in user behavior regarding blended versus non-blended results. The effect of blended results
on user behavior remains to be seen.
# 1 16.9% 20.9%
# 2 10.1% 10.0%
# 3 9.3% 5.4%
# 4 5.5% 3.6%
# 5 3.5% 1.5%
# 6 2.8% 2.4%
# 7 1.9% 1.5%
# 8 1.9% 0.8%
# 9 1.7% 1.0%
# 10 1.0% 0.7%
blended serp ctr non-blended
serp ctr

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Descriptive Statistics
Here are some additional statistics that describe the keywords used in our sample set:
bing serp position
# 1 18.2% (2.43% - 76.37%) 12.82%
# 1 9.66% (0.31% - 36.43%) 7.68%
# 2 10.1% (2.86% - 51.15%) 9.01%
# 2 5.51% (0.18% - 60.00%) 12.91%
# 3 7.2% (0.59% - 30.00%) 5.92%
# 3 2.74% (0.00% - 13.04%) 3.01%
# 4 4.8% (0.77% - 13.04%) 3.03%
# 4 1.88% (0.00% - 15.11%) 3.06%
# 5 3.1% (0.25% - 8.23%) 1.98%
# 5 1.85% (0.00% - 13.07%) 2.69%
# 6 2.8% (0.00% - 8.89%) 2.05%
# 6 1.67% (0.00% - 6.25%) 1.51%
# 7 1.9% (0.00% - 6.67%) 1.72%
# 7 1.34% (0.00% - 9.09%) 2.06%
# 8 1.8% (0.00% - 6.15%) 1.58%
# 8 0.65% (0.00% - 5.00%) 0.91%
# 9 1.5% (0.00% - 3.57%) 1.04%
# 9 0.57% (0.00% - 1.89%) 0.56%
# 10 1.0% (0.00% - 2.82%) 0.79%
# 10 0.45% (0.00% - 1.23%) 0.32%
average ctr (exact)
average ctr (exact)
range of values
range of values
standard deviation
standard deviation
exact keyword ctr - google study
exact keyword ctr - bing study
Section 8 »

p17
The Exact CTR tables suggest that click-through rates for higher positions are more volatile than lower positions.
The CTR range for positions above the fold (1-4) were much wider and had a higher standard deviation than those
below the fold. This speaks to the enormous advantage of ranking in the top four positions and how user attention
is focused less towards the bottom of the results page.
long-tail ctr - google study
bing serp position
# 1 5.80% (0.00% - 30.16%) 7.24%
# 2 3.19% (0.00% - 23.08%) 5.04%
# 3 2.43% (0.00% - 11.30%) 3.07%
# 4 4.58% (0.00% - 44.55%) 8.23%
# 5 1.17% (0.00% - 6.80%) 1.36%
# 6 1.49% (0.00% - 10.08%) 2.14%
# 7 1.82% (0.00% - 11.87%) 2.63%
# 8 2.27% (0.00% - 19.77%) 4.20%
# 9 1.68% (0.00% - 19.99%) 3.67%
# 10 3.46% (0.00% - 34.83%) 6.92&
average ctr (exact) range of values standard deviation
The long-tail CTR table suggests that a clear pattern for terms associated with the primary keyword phrase is very
difficult to determine, which speaks to the importance but uncertainty of the “halo effect.”

p18
CONCLUSIONS
The key takeaways from the two studies are:
1. By studying user behavior through click-through rates, we emphasize the importance of ranking in the top ten
positions in search engines. Higher Rankings = Higher Click-Through Rates.
2. For Google SERPs, the observed CTR was 18.20% for a No. 1 rank and 10.05% for a No. 2 rank.
3. For Bing SERPs, the observed CTR was 9.66% for a No. 1 rank and 5.51% for a No. 2 rank.
4. Relative CTRs across each position in the SERP reveal the importance of an increase in rank. With an average
CTR of 18.20% for position 1 and 1.04% for position 10, this staggering difference shows that a change in rank
from 10 to 1 will generate approximately 1650% more traffic and associated sales.
5. We observed a significantly higher CTR curve for Google than for Bing, which suggests that Google’s organic
results are more reliable, as many users abandon searches. However, this is simply an interesting implication
from our CTR studies, as we do not have actual bounce rates for the search engines.
6. The “halo effect” of long-tail CTR associated with primary terms is difficult to quantify, but should be considered
when estimating traffic to your website.
7. An interesting implication of the Google CTR curve is that, on average, 52.32% of Google users click on an
organic search result on page 1. This means that while a large amount of search engine users are engaging
with the organic rankings on the first page, searchers are also clicking on ads, traveling to subsequent pages,
or entering another search phrase to refine their searches.
8. Rank is not everything. It is important to implement a holistic SEO campaign that incorporates and executes
effective Content, Link-graph Optimization, Site Architecture, Social Push, and Strategy (CLASS).
Section 9 »

Connect with us!
Contact Aaron Aders, Market Research Director & Co-Founder at Digital Relevance™, at
aaron@relevance.com if you have questions on Digital Relevance™ services.
Contact research@relevance.com if you have questions about this study.
Office » 888.603.7337
Facebook » www.facebook.com/digitalrelevance
Twitter » @drelevance
©2013 Digital Relevance™. All rights reserved.
v.1, January 2013
8900 Keystone Crossing, Suite 100
Indianapolis, IN 46240
888.603.7337 toll-free
317.575.8852 local
317.575.8904 fax
info@relevance.com
relevance.com

[Study] The Definitive Organic Click Through Rate Study

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