Here, there, and everywhere: Correlated online behaviors can lead to overesti...Ира Пустовит
Randall A. Lewis, Justin M. Rao, and David H. Reiley: “Here, there, and everywhere: Correlated online behaviors can lead to overestimates of the effects of advertising. Исследование, заставляющее усомниться во многих ранее принятых интернет-метриках
On April 18, 2016, The United States Supreme Court denied a petiti.docxvannagoforth
On April 18, 2016, The United States Supreme Court denied a petition for certiorari (refused to review the lower court’s ruling) in the case of Authors Guild v. Google, Inc., 804 F. 3d 202 - Court of Appeals, 2nd Circuit 2015.
Tell me what you would do if you were the Supreme Court.
That case let stand the ruling of the Court of Appeals, which can be found at the following website:
https://scholar.google.com/scholar_case?case=2220742578695593916&q=Authors+Guild+v.+Google+Inc&hl=en&as_sdt=4000006
Please write a 500-word summary of fair use as this court decision says it.
Running head: YOUR SHORTENED TITLE GOES HERE 1
SHORTENED TITLE GOES HERE (IN CAPS) 2
Plan
What is your plan for evaluation of the strategies using performance improvement data and tracers? What tracers will you use? Include necessary detail to deliver key points and requirements, such as specific data collection methods, timeframes for evaluation, and intended re-evaluation.
Tracer method is a unique technique used by the healthcare organizations, to obtain a real time picture of quality performance from point of entry to discharge. A key part of The Joint Commission’s on-site survey process is the tracer methodology (The Joint Commission, 2017).. Some traditional tracer tools can be used for quality and safety improvement. The focus of these tools is on ….. and the plan for the evaluation of this initiative for fall prevention will use tracers in the following manner….
OR
To evaluate the identified measure is the 30 day readmission rate for patients, data twill be racked by system tracers which will be completed monthly by the Assistant Director of Nursing.
Plan Evaluation
How effective and sustainable is your plan? In other words, evaluate the effectiveness and the ease of use, timeliness, and efficiency of your plan for the progress and success of your initiative.
The plan to prevent falls is effective and sustainable with the involvement and collaboration of all team members by implementing the following strategies… The initiative will be evaluated by the following methods, post implementation…….
OR
Every three months this data will be compiled and analyzed to determine what actions were effective and ineffective. The complete study will take place over a one year period with the desired result of an 15% or below hospital readmission rate.
Use of Tracers
Individual tracers make the most sense to utilize for this proposal because these tracers are designed to “trace” the care experiences that a patient had during hospitalization. For example: in case of fall prevention, these tracers help to track the patient’s experience regarding safety, satisfaction of personal needs, hygiene, compliance of staff during care….. System tracers can be utilized as well, for example….
OR
System tracers provide information by tracking where in an organizational process breakdowns occur or exist and are a valuable tool in identifying where changes needs to occur. ...
Competitive advantage for e-business requires more accurate information and precise decision to aid international companies to analyze and predict sales forecasting trend optimizing potential profits and reduce losses. We propose an improvement model to minimize the forecast error for the time series of daily sales information. We use real international restaurant data as the basis to show the performance of our sales prediction model. Multiple time series are considered in this model to vastly improve the forecasting outcome. Those data series are combined from EPARK Company and open data like weather, into a multi-series data, our forecast model extend the previous predecessor tremendously. Various residue computations during the process are compared and discussed. We applied the model for data from different area to compare the difference respectively. Result shows a proper selection of computation method is more dynamic than a fixed method for shops in different geographic area even within the same company. In addition, analysis shows significant error reduction in forecasting achieved when open data like weather information is included in the regression process. Thus international business can be more agile and flexible for just-in-time stock inventory and better resource allocation strategy.
Here, there, and everywhere: Correlated online behaviors can lead to overesti...Ира Пустовит
Randall A. Lewis, Justin M. Rao, and David H. Reiley: “Here, there, and everywhere: Correlated online behaviors can lead to overestimates of the effects of advertising. Исследование, заставляющее усомниться во многих ранее принятых интернет-метриках
On April 18, 2016, The United States Supreme Court denied a petiti.docxvannagoforth
On April 18, 2016, The United States Supreme Court denied a petition for certiorari (refused to review the lower court’s ruling) in the case of Authors Guild v. Google, Inc., 804 F. 3d 202 - Court of Appeals, 2nd Circuit 2015.
Tell me what you would do if you were the Supreme Court.
That case let stand the ruling of the Court of Appeals, which can be found at the following website:
https://scholar.google.com/scholar_case?case=2220742578695593916&q=Authors+Guild+v.+Google+Inc&hl=en&as_sdt=4000006
Please write a 500-word summary of fair use as this court decision says it.
Running head: YOUR SHORTENED TITLE GOES HERE 1
SHORTENED TITLE GOES HERE (IN CAPS) 2
Plan
What is your plan for evaluation of the strategies using performance improvement data and tracers? What tracers will you use? Include necessary detail to deliver key points and requirements, such as specific data collection methods, timeframes for evaluation, and intended re-evaluation.
Tracer method is a unique technique used by the healthcare organizations, to obtain a real time picture of quality performance from point of entry to discharge. A key part of The Joint Commission’s on-site survey process is the tracer methodology (The Joint Commission, 2017).. Some traditional tracer tools can be used for quality and safety improvement. The focus of these tools is on ….. and the plan for the evaluation of this initiative for fall prevention will use tracers in the following manner….
OR
To evaluate the identified measure is the 30 day readmission rate for patients, data twill be racked by system tracers which will be completed monthly by the Assistant Director of Nursing.
Plan Evaluation
How effective and sustainable is your plan? In other words, evaluate the effectiveness and the ease of use, timeliness, and efficiency of your plan for the progress and success of your initiative.
The plan to prevent falls is effective and sustainable with the involvement and collaboration of all team members by implementing the following strategies… The initiative will be evaluated by the following methods, post implementation…….
OR
Every three months this data will be compiled and analyzed to determine what actions were effective and ineffective. The complete study will take place over a one year period with the desired result of an 15% or below hospital readmission rate.
Use of Tracers
Individual tracers make the most sense to utilize for this proposal because these tracers are designed to “trace” the care experiences that a patient had during hospitalization. For example: in case of fall prevention, these tracers help to track the patient’s experience regarding safety, satisfaction of personal needs, hygiene, compliance of staff during care….. System tracers can be utilized as well, for example….
OR
System tracers provide information by tracking where in an organizational process breakdowns occur or exist and are a valuable tool in identifying where changes needs to occur. ...
Competitive advantage for e-business requires more accurate information and precise decision to aid international companies to analyze and predict sales forecasting trend optimizing potential profits and reduce losses. We propose an improvement model to minimize the forecast error for the time series of daily sales information. We use real international restaurant data as the basis to show the performance of our sales prediction model. Multiple time series are considered in this model to vastly improve the forecasting outcome. Those data series are combined from EPARK Company and open data like weather, into a multi-series data, our forecast model extend the previous predecessor tremendously. Various residue computations during the process are compared and discussed. We applied the model for data from different area to compare the difference respectively. Result shows a proper selection of computation method is more dynamic than a fixed method for shops in different geographic area even within the same company. In addition, analysis shows significant error reduction in forecasting achieved when open data like weather information is included in the regression process. Thus international business can be more agile and flexible for just-in-time stock inventory and better resource allocation strategy.
A Novel Feature Engineering Framework in Digital Advertising Platformijaia
Digital advertising is growing massively all over the world, and, nowadays, is the best way to reach potential customers, where they spend the vast majority of their time on the Internet. While an advertisement is an announcement online about something such as a product or service, predicting the probability that a user do any action on the ads, is critical to many web applications. Due to over billions daily active users, and millions daily active advertisers, a typical model should provide predictions on billions events per day. So, the main challenge lies in the large design space to address issues of scale, where we need to rely on a subset of well-designed features. In this paper, we propose a novel feature engineering framework, specialized in feature selection using the efficient statistical approaches, which significantly outperform the state-of-the-art ones. To justify our claim, a large dataset of a running marketing campaign is used to evaluate the efficiency of the proposed approaches, where the results illustrate their benefits.
A Novel Feature Engineering Framework in Digital Advertising Platformgerogepatton
Digital advertising is growing massively all over the world, and, nowadays, is the best way to reach potential customers, where they spend the vast majority of their time on the Internet. While an advertisement is an announcement online about something such as a product or service, predicting the probability that a user do any action on the ads, is critical to many web applications. Due to over billions daily active users, and millions daily active advertisers, a typical model should provide predictions on billions events per day. So, the main challenge lies in the large design space to address issues of scale, where we need to rely on a subset of well-designed features. In this paper, we propose a novel feature engineering framework, specialized in feature selection using the efficient statistical approaches, which significantly outperform the state-of-the-art ones. To justify our claim, a large dataset of a running marketing campaign is used to evaluate the efficiency of the proposed approaches, where the results illustrate their benefits.
A/B Testing and Experimentation in Data ScienceUncodemy
In the ever-evolving landscape of data science, A/B testing and experimentation stand as pillars of empirical decision-making. These techniques allow businesses and analysts to validate hypotheses, optimize user experiences, and fine-tune products and services. Whether you are a budding data enthusiast or a seasoned analyst, understanding A/B testing and experimentation is essential in today’s data-driven world.
Optimizing Audience Buying on Facebook and InstagramBlitzMetrics
Running campaigns simultaneously across different digital platforms should provide advertisers with extended reach into new audiences as well as the ability to reach pre-existing audiences in a more cost-effective manner. This paper aims to measure the effectiveness of optimizing audience buying to deliver value to brand advertisers.
This research only implies marital condition is correlated to the duration of calls, but did not find the quantitative relationship between them. Besides, duration’s relationship with other dimensions of information is also important for us to predict duration and target at valuable customers, which needs further research such as regression analysis.
A Novel Feature Engineering Framework in Digital Advertising Platformijaia
Digital advertising is growing massively all over the world, and, nowadays, is the best way to reach potential customers, where they spend the vast majority of their time on the Internet. While an advertisement is an announcement online about something such as a product or service, predicting the probability that a user do any action on the ads, is critical to many web applications. Due to over billions daily active users, and millions daily active advertisers, a typical model should provide predictions on billions events per day. So, the main challenge lies in the large design space to address issues of scale, where we need to rely on a subset of well-designed features. In this paper, we propose a novel feature engineering framework, specialized in feature selection using the efficient statistical approaches, which significantly outperform the state-of-the-art ones. To justify our claim, a large dataset of a running marketing campaign is used to evaluate the efficiency of the proposed approaches, where the results illustrate their benefits.
A Novel Feature Engineering Framework in Digital Advertising Platformgerogepatton
Digital advertising is growing massively all over the world, and, nowadays, is the best way to reach potential customers, where they spend the vast majority of their time on the Internet. While an advertisement is an announcement online about something such as a product or service, predicting the probability that a user do any action on the ads, is critical to many web applications. Due to over billions daily active users, and millions daily active advertisers, a typical model should provide predictions on billions events per day. So, the main challenge lies in the large design space to address issues of scale, where we need to rely on a subset of well-designed features. In this paper, we propose a novel feature engineering framework, specialized in feature selection using the efficient statistical approaches, which significantly outperform the state-of-the-art ones. To justify our claim, a large dataset of a running marketing campaign is used to evaluate the efficiency of the proposed approaches, where the results illustrate their benefits.
A/B Testing and Experimentation in Data ScienceUncodemy
In the ever-evolving landscape of data science, A/B testing and experimentation stand as pillars of empirical decision-making. These techniques allow businesses and analysts to validate hypotheses, optimize user experiences, and fine-tune products and services. Whether you are a budding data enthusiast or a seasoned analyst, understanding A/B testing and experimentation is essential in today’s data-driven world.
Optimizing Audience Buying on Facebook and InstagramBlitzMetrics
Running campaigns simultaneously across different digital platforms should provide advertisers with extended reach into new audiences as well as the ability to reach pre-existing audiences in a more cost-effective manner. This paper aims to measure the effectiveness of optimizing audience buying to deliver value to brand advertisers.
This research only implies marital condition is correlated to the duration of calls, but did not find the quantitative relationship between them. Besides, duration’s relationship with other dimensions of information is also important for us to predict duration and target at valuable customers, which needs further research such as regression analysis.
"Growth Analytics: Evolution, Community and Tools" with emphasis on Google Analytics (and its API), including examples of how web analysts and data scientists can use this rich source of data for analysis and applications.
Customer analytics meetup in Dublin May '18
https://www.meetup.com/Customer-Analytics-Dublin-Meetup/events/250809233/
Covers key concepts of clickstream analysis and Markov Chains. Followed by 3 practical applications with the R language:
- Frequent path analysis
- Future click prediction
- Transition probabilities mapping
Niche bloggers up to multinational corporations, they are all interested in monitoring their web traffic and its patterns across time.
Google Analytics is the most widely used solution to keep track of this type of data. It provides a UI for a wide range of reports and possibilities for various types of visualizations.
Moreover, the availability of the Analytics API coupled with the corresponding R packages can now give more options for custom web analyses.
The plan for this talk is to cover the following :
• What is web analytics ? How it works ?
• Interfacing with the Analytics Reporting API via an R package (RGA)
• Practical analytics applications with R
• Discussion
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
The impact of search ads on organic search traffic
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.