This study investigates the impact of paid search ads on organic traffic by analyzing a week where ads were paused on an e-commerce website. Using nonparametric statistical methods, the findings indicate a significant increase in organic traffic during the period without ads, with a confidence interval suggesting a daily increase of 2160 to 3060 users. The developed methodology can be applied to various websites, although specific results may differ based on the individual site's characteristics.

1. The impact of search ads on organic search traffic
A nonparametric statistical analysis based on a small size times series sample
Alexandros Papageorgiou
Advanced Business Data Analysis
National College of Ireland
Abstract—this study examines the impact of paid search engine
advertising on organic search engine traffic. In particular it is
concerned with ways of analysing the impact that pausing search
advertising can have on organic traffic. The main objective is to
develop a methodology that can be applied to individual websites to
help determine whether paid search clicks substitute traffic that
would have reached the website anyway. The study is based on a
small time series sample from an e-commerce website, with respect to
organic traffic, that includes a one week experimental period during
which search ads were disabled. A methodology for approaching the
problem was developed and nonparametric statistical techniques
were employed. The results for this particular experiment suggest
that pausing search ads does lead to an increase in organic search
engine traffic. A confidence interval for the change is provided too.
The methodology can be employed for different websites; however
the final results can vary depending on their specific characteristics.
I. INTRODUCTION
A. Background
Search engine traffic, in its two forms, i.e. paid and organic,
has been a rapidly evolving marketing channel for digital
properties. For many online businesses it is already the top
incoming traffic generator. Its importance is even higher with
the high relevancy of this type of traffic and the high
propensity for conversion that characterises it taken into
account.
There is an ongoing debate within the digital advertising
industry regarding the effect of the symbiosis between paid and
organic search engine traffic. Typical questions include: What
happens if a website is the top organic result for a given
keyword? Does it make sense to advertise in that case? What
would the repercussions be if its rank was third, fifth or 100th?
Companies spend considerable amounts on search
advertising in expectation of positive economic results;
however the possibility of traffic “cannibalisation” is a hidden
cost that is hard to quantify and integrate into the cost/benefit
equation.
Due to the importance of advertising for revenue
generation, pausing advertising for prolonged periods, as an
experiment, is undesirable from a business point of view. It can
result in lost sales or valuable customer traffic reaching
competitors' properties.
Therefore, the challenge is to develop a method of
approximating negative impact by minimising the exposure of
a company to the aforementioned risks.
B. Related Work
A number of research studies have addressed this question
from a macro level. These studies have covered a large number
of websites across several industries and involved disabling
search ads for some specific period of time. The collective
results provide a general conclusion, by industry, regarding the
impact of ads on organic search traffic. In particular, the
researchers suggest that for most industries paid search traffic
is almost entirely incremental to the organic one [1].
A follow up research reported that the final outcome can
vary based on the organic ranking of a website. The higher the
website ranks organically, the higher the likelihood that even in
the absence of an ad, users would find and click through to the
site [2].
It was also highlighted that while these findings provide
guidance on overall trends there was a lot of variability
between different advertisers and different search terms. The
authors encouraged advertisers to design their own
experiments.
Individual websites have particular characteristics related to
their industry or the degree of diversification of the product
they offer. Additionally, search rankings can vary greatly from
one page or one section of a site to another. It is therefore not
ideal to use those particular studies in order to determine the
precise effect that search ads can have on a given website.
C. Research statement
The objective of this study is to design a general framework
that enables individual online businesses with different
attributes to make inferences about the impact of paid search
advertising on organic traffic without the need to design
complex and costly longitudinal studies.
The method will provide the tools for a digital company to
establish if a change has taken place, and if so, to approximate
the estimated range of the change in organic traffic by using
suitable confidence intervals.

2. II. METHODS
A. The dataset
The study was based on organic traffic data from an
ecommerce website. This included three full weeks of data,
with traffic from both organic and paid search channels visiting
the site, and one experimental week during which the ads were
completely paused. The website in question receives both paid
and organic traffic from multiple search engines, however, for
this particular study the focus was on organic data, originating
from google.com and other country level google domains.
In general, paid traffic visits to the website are a fraction of
organic traffic visits. It is, however, much more targeted to the
desired audiences. Attention was paid towards ensuring that no
other factors (beyond the absence of ads), that could alter
normal organic traffic patterns were present before and during
the experiment, e.g. website upgrades, server downtime or
google search algorithm updates. The data was collected via
Google Analytics and its API. The 28 data points refer to total
organic users by date. Descriptive statistics for the data are
presented in Table 1.
Table 1 Descriptive statistics
The data represent s time series which is illustrated in
Figure 1. The effect of the weekly seasonal component in the
data is evident.
Figure 1 The data represented as a 4 week time series
The boxplot in Figure 2 provides further evidence of this
cyclicality. In particular, the first days of the week, starting
from Monday, exhibit stronger numbers with regard to organic
users. Then there is a gradual decline leading to the weekend
during which user numbers reach the lowest point.
Figure 2 Boxplots of traffic by day of week
B. Data pre-processing
This known cyclicality is not atypical for e-commerce
websites. It presents several challenges for the methodologies
than can be employed for the data analysis. In particular, given
that the data are auto-correlated, normality and their respective
tests cannot be applied. In order to perform a statistical test
some adjustments need to be considered.
As a first step the seasonality was removed to enable day to
day comparison on an equal basis. To accomplish this, the time
series was decomposed into its seasonal, trend and irregular
factors[3] as illustrated in Figure 3. Subsequently, the seasonal
component was extracted and then applied to every data point
in the dataset by division (due to the multiplicative nature of
the time series with respect to its composition).
The adjusted time series was used for the following steps of
the analysis.
Figure 3 Time series decomposed into its 4 main components
C. Statistical Plans
A histogram of the adjusted time series is illustrated in
Figure 4. The data set is relatively small in size. There are less
than 30 data points represented and there is not enough
evidence that the data follow the normality pattern.

3. Figure 4 Histogram of seasonally adjusted time series
A quantile-quantile plot is also illustrated in Figure 5. In
general the trend in both graphs suggests a bimodal
distribution, which can be an early sign that the experimental
week has exhibited different behaviour.
Figure 5 Quantile-Quantile plot for the adjusted time series
In addition to the previous observations the sample sizes
are very small (especially for the days of the experiment). In
this context standard parametric assumptions are not met and
therefore using such methods to test if there is a difference
between the first three weeks and the experiment would likely
lead to inaccurate conclusions.
To examine the hypothesis that organic traffic has increased
when the ads were paused, the nonparametric Mann Whitney U
test was used instead. This test is typically employed to
examine whether two independent samples of observations are
drawn from the same or identical distributions. An additional
reason for employing this test is that the two samples under
consideration may not necessarily contain the same number of
observations [4].
Another nonparametric technique, the bootstrap, will be
used to provide a confidence interval based on multiple re-
samplings with replacement from the original data[5].
III. RESULTS
1) Mann Whitney U test for the distributions
The null hypothesis of the Mann Whitney U test stated that
there is no difference in the location of the distributions for
organic traffic users between the two conditions: when search
ads are activated and when they are not. The alternative
hypothesis was that the organic user traffic grows when the
search ads are not active. The alpha value used was 0.05, a
value commonly used in statistical practice.
Table 2 Output of the Mann Whitney U test
The basic assumptions of the Mann Whitney U test were
that the samples are independent from each other and they are
random samples from the populations. The former assumption
is met since seasonal components were removed. Likewise the
latter assumption is met if we consider the samples as
representative of their underlying populations. This is an
assumption that has to be made given the fact that the cost of
the experiment can only allow for a limited number of days
without search ads and therefore there is no real opportunity for
sampling. Further assumptions regarding shape of distributions
and variances were not tested due to the small size of the data
sets, particularly the limited number of experiment days.
The test statistic value was 132 and the associated p-value
of a one-tailed Mann Whitney U test for the location of the
distributions was 0.00047 as illustrated in Table 2. This
indicated that under a true null hypothesis, the probability is -
order of magnitude- less than 5% that the difference between
the two distribution locations is this or more extreme. Based on
the above observations, it was concluded that there is indeed
some significant increase in the organic traffic when search
advertising is paused.
B. The Bootstrap for the Confidence Intervals
The next question to address is about the range of the
possible change. To address this question the bootstrap method
was selected. It allows the generation of confidence intervals
and testing of statistical hypotheses without having to assume a
specific underlying theoretical distribution[6]. It was therefore
employed in order to construct a suitable confidence interval
around the difference in the medians of the two samples. The
median was preferred due to small number of data points for
the experiment dates.
Using the bootstrap’s resampling with replacement
technique, the difference in the medians between the two
groups was recorded for each of the 10000 iterations and a
95% confidence interval was subsequently constructed.

4. The Bias Corrected and Accelerated (BCa) confidence
interval for the difference in medians was (2160, 3060) which
suggests that the number of users reaching the website
organically on a daily basis, in the absence of search ads, is not
insignificant.
IV. DISCUSSION
A. Conclusions
The previous methodology can be applied in an
experimental setting enabling advertisers to evaluate the impact
of search advertising to the organic traffic using suitable
nonparametric statistical methods. A key feature of this
methodology is that it only requires that search ads be paused
for seven days only.
For the specific website under study it was found that the
act of pausing the ad campaigns had a positive impact on the
number of organic users visiting the website. A 95%
confidence interval was built to provide a better understanding
of the possible range of variation in the difference. This
methodology can be applied to any website but naturally the
results are likely to vary based on particular website
characteristics.
B. Future Work
In the present study the number of users was the primary
metric examined. However, it might be more meaningful from
a business point of view to instead examine differences in
organic search revenue or organic search users that complete a
transaction. An explicit ROAS (Return on Advertising Spend)
analysis in the light of the experiment results would be the final
verdict as to whether and to what extent search advertising is
beneficial for each advertiser.
As a consequence of natural variation of traffic it is always
likely that events that go beyond the experiment design can
play a role in changing traffic patterns, often without being
easily identifiable in order to be appropriately evaluated. An
approach that addressed this concern could be based on the
concept of geographically structured randomised experiments.
Additionally, not all sections of a website are impacted in
the same way by the presence or absence of ads. In fact it is
likely that different pages can have very different organic
search rankings. It would therefore be valuable to apply the
present or alternative methods of analysis to distinct sets of
pages on a website and report separately for each set in order to
achieve more focused results.
V. REFERENCES
[1] D. X. Chan, Y. Yuan, J. Koehler, and D. Kumar,
“Incremental Clicks: The Impact of Search Advertising,”
2011.
[2] D. Chan, D. Kumar, S. Ma, and J. Koehler, “Impact Of
Ranking Of Organic Search Results On The
Incrementality Of Search Ads,” 2012.
[3] A. Coghlan, “A Little Book of R For Time Series,”
Release 02, 2014.
[4] “Mann-Whitney U-test / Mann-Whitney-Wilcoxon.”
[Online]. Available: https://explorable.com/mann-
whitney-u-test. [Accessed: 03-Aug-2016].
[5] E. S. Banjanovic and J. W. Osborne, “Confidence
Intervals for Effect Sizes: Applying Bootstrap
Resampling.,” Pract. Assess. Res. Eval., vol. 21, no. 5, p.
2, 2016.
[6] R. Kabacoff, R in Action: Data Analysis and Graphics
with R, 2 edition. Shelter Island: Manning Publications,
2015.