• Web Experimentation Rely on graphic and code editors to optimize and personalize web experiences
  • Feature Experimentation Manage features and make feature experimentation easier with Kameleoon
  • AI-Driven Personalization Predict behavior and trigger 1:1 experiences in real-time with advanced AI
  • A single platform
  • Ease of use
  • AI-powered conversion
  • Focused on security
  • Powerful integrations

demo-request

  • Experimentation Leaders
  • Product Managers
  • Mobile App Testing
  • Hybrid Experimentation
  • Data Accuracy
  • Data Privacy & Security
  • Single Page Application
  • E-commerce & Retail
  • Travel & Tourism
  • Financial Services
  • Media & Entertainment

Kameleoon Free Trial

  • Developer Documentation
  • User Manual

CUPED

  • Expert Directory
  • Success Stories
  • A/B testing
  • Personalization
  • Feature Management
  • Feature Flags
  • Testing Server-Side

From client-side to server-side guide

  • Tech Partners & Integrations Kameleoon integrates with your existing analytics and marketing technology ecosystem.
  • Agency Partners We work with a range of Kameleoon-certified agencies
  • Web Experimentation

Dynamic traffic allocation: optimize your A/B tests

Dynamic traffic allocation

Dynamic traffic allocation allows you to optimize your A/B tests by distributing the traffic on your website to the most efficient variations before the test phase has even finished. This post explains how this technique works and the benefits it delivers if you follow best practice.

Dynamic traffic allocation is sometimes presented as a revolutionary alternative to A/B testing , or even as a competitor to predictive targeting. The reality is more straightforward: it is an optimized testing approach which does not address the same issues as “classic” A/B testing.

1  The multi-armed bandit problem

Bandit manchot - allocation dynamique de trafic

Dynamic traffic allocation can resolve what is known as the multi-armed bandit (MAB) problem. This is a mathematical problem posed in probability theory, with the name coming from the nickname of old-fashioned slot machines with an arm on the side that you pull. It is easier to show by giving an example.

Imagine that you are in a casino, looking at several slot machines. Some machines distribute big payouts, others smaller ones. How do you choose which machines to play on in order to maximize your winnings?

There are two main approaches:

  • Quickly test some of the machines, identify one that pays out a lot, and decide to stay with that machine: this is exploitation.
  • Continue testing all the machines, one by one, hoping that the next one will let you win more: this is exploration.

What solution should you choose between exploitation and exploration? Multi-armed bandit algorithms can resolve this problem by simultaneously combining a test period and a winning period, i.e. exploiting and exploring at the same time.

What strategy should you use to optimize your website?

Let’s change the example to optimizing your website. You want to integrate a new element and you have two possible options:

  • Continue using an existing element that works well with your visitors
  • Choosing a new element and A/B testing its efficiency in the hope of obtaining results that are better than your current practice.

You are going to have to choose between exploitation and exploration approaches.

If you choose to exploit the resources whose performance you know, you’ll get adequate results but you won’t know if alternatives work better.

If you base your approach on exploration, then you’re taking the risk of testing an element that could be unprofitable, but at the same time could also surpass all your other results.

How A/B testing addresses the multi-armed bandit problem

A/B testing compares different versions of an element on your website with the aim of discovering which is the most efficient with your visitors. This technique allows marketing teams to make informed decisions so as to optimize the website user experience.

However, with A/B tests, you need to implement a strict process and learning period to gather data before deciding to make changes.

Dynamic Traffic Allocation

So A/B testing lets you analyze (exploration) the performance of different elements on your website, but you have to wait for the end of the test before you can draw any conclusions from it and put these into action (exploitation).

This is where dynamic traffic allocation comes into play, aiming to try to combine exploration and exploitation.

2  What is dynamic traffic allocation?

It’s a simple concept: dynamic traffic allocation can modify the distribution of traffic based on the first results observed during the test period. A multi-armed bandit algorithm progressively redirects visitors towards the variation that works best.

So dynamic traffic allocation lets you exploit your results while continuing to carry out tests.

Let’s take the example of a brand that would like to determine what type of CTA converts most efficiently on its website. In a traditional test, the traffic is distributed equally between each variation until the end of the test. So, if two types of CTA are tested, 50% of the visitors will be exposed to variation A, and the rest to variation B.

With dynamic traffic allocation, the distribution can change depending on the performance of the element being tested. If variation B shows a high conversion rate, then the traffic distribution can be adjusted accordingly.

Dynamic traffic allocation

3  Why should you use dynamic traffic allocation?

To maximize conversions.

Thanks to dynamic traffic allocation, visitors are directed towards the variation that works best. Consequently, you minimize the losses due to A/B testing while continuing to explore new possibilities.

To minimize risk 

Conversely, you minimize risk when you test new elements since, if they don’t work, the traffic allocated to this variation will be gradually reduced.

4  When should you use bandit tests instead of A/B tests?

Multi-armed bandit algorithms are useful as they minimize the cost of lost opportunities during the exploratory phase of an A/B test by combining exploration and exploitation.

When time is limited

Dynamic traffic allocation is very useful when the exploration period and the exploitation period have to take place over a short time. This is particularly the case when you test headings for articles or for promotional campaigns with a limited timespan (such as holidays, events).

If you don’t have much time to analyze results

Dynamic traffic allocation can also be useful when you want to run tests without having to closely follow their progress and results. By putting the bandit test in place, the learning time will be longer but the traffic will be directed towards the best variation without ending the exploratory period.

5  Is dynamic traffic allocation suitable for all websites?

If this practice can combine the exploration period and the exploitation period, then why not make it more widespread?

Website traffic

With the pure exploration period reduced, your website needs to have high traffic in order to obtain significant results. Indeed, if you only have very few visitors on your website, the first results obtained will not represent the general trend and you run the risk of drawing bad conclusions from them.

Dynamic traffic allocation

Dynamic traffic allocation is not therefore suitable for everyone, which is why we only recommend it to our customers with very sizeable traffic (over 5 million visitors per month).

Significant results

The multi-armed bandit technique takes more time than an A/B test to obtain significant statistical results since the algorithms distribute the traffic according to the results obtained. This means many more visitors need to be tested to get conclusive results.

If the test isn’t performed for long enough, you run the risk of obtaining false results. For example, if a variation is tested right at the start of the test and is initially declared a losing one because traffic is not yet large enough, then more time will be needed to understand its true benefits.

Loss of conversionS over the long term

You can use multi-armed bandit tests to continuously optimize your website, but in this case you will miss out on a certain number of conversions in the long term.

In fact, with this technique, you’ll need much more time to obtain significant results and, during this period, a part of your traffic will always be directed towards the least efficient variations.

In order to maximize conversions in the long term, the best solution is to quickly obtain a winning variation with an A/B test and then to implement it on your website.

Dynamic traffic allocation

6  Can you personalize with multi-armed bandit algorithms?

With a classic bandit test, the version displayed to visitors is only based on the results obtained and ignores the context.

More evolved versions of the algorithm exists: contextual bandits, extension of bandit tests, or simplified versions of reinforcement learning.

Contextual bandit algorithms are capable of taking into account the context (visitor journey, origin, geolocation, etc.) to refine the traffic allocation: if an initial visitor converts on the variation ‘x’, then other visitors with a similar profile will see the same variation.

Ultimately, these algorithms can personalize the content of each segment of your audience; however, this method requires a lot of time and many repetitions if it is to succeed.

vincent

Whether you use a classic A/B or a bandit test, be sure to follow best testing practices : whether these concern interpreting the data or the moment at which to stop the test .

Learn more about Kameleoon's A/B testing solution which provides a full range of features to let you run simple or complex tests quickly and easily.

New Call-to-action

Mastering executive buy-in for experimentation: Insights from industry experts

What "winners" look like, the difference between good and great companies when it comes to experimentation.

Thumbnail

Everything you need to know about Mobile App Testing with Booking.com

What marketing leaders can learn about growth with experimentation from product teams.

  • Discover Dynamic Yield
  • Take your knowledge to exponential levels
  • XP 2 Hub Take your knowledge to exponential levels
  • Learning Paths Curated courses on key skill areas
  • Talks Engaging discussions taking place in CX
  • Articles An expansive collection of in-depth playbooks
  • Encyclopedia A glossary of experience optimization terms
  • Inspiration Library Personalization examples from real brands
  • Personalization Maturity How global businesses prioritize personalization
  • Guides & reports Comprehensive topic-specific materials
  • Benchmarks Industry performance metrics and insights
  • XP 2 Newsletter

Sign up for the XP² newsletter

Thanks for signing up, a/b testing & optimization course.

  • An introduction to A/B testing and optimization Already read
  • A/A testing and decision making in experimentation Already read
  • Why reaching and protecting statistical significance is so important in A/B tests Already read
  • Choosing the right traffic allocation in A/B testing Already read
  • Understanding conversion attribution scoping in A/B testing Already read
  • Choosing the right conversion optimization objective Already read
  • Frequentist vs. Bayesian approach in A/B testing Already read
  • Guidelines for running effective Bayesian A/B tests Already read
  • Beyond A/B testing: Multi-armed bandit experiments Already read
  • Client-side vs server-side A/B testing and personalization Already read
  • Segmented A/B tests: Avoiding average experiences Already read
  • The impact of A/B tests and personalization on SEO Already read
  • There are no failed A/B tests: How to ensure every experiment yields meaningful results Already read
  • How to analyze and interpret A/B testing results Already read
  • Building an experimentation growth plan: A Kopari Beauty case study Already read

Guidelines for running effective Bayesian A/B tests

In this post, we describe the basic ideas behind bayesian statistics and how they feed into business decisions you will need to make at the end of a test..

Ron Kenett

Both professor Ron Kenett and David Steinberg of KPA Group sat down to talk to more candidly about some of the topics discussed in this article.

Read the full transcript

So the straightforward one is comparing two options, A and B. The more complex ones can compare combinations. So you could have A, which is a combination of two or three things, and B, which is a different combination, and C, which is still another combination. And if you apply methodology which is based on a statistical approach called design of experiments, in one test, you can learn about several factors, the effect of several factors.

So when you’re thinking about setting up an AB test, invariably, you have some alternatives that you wanna compare, and the tests usually follow methodology that’s been developed over many years in the statistical literature. What you’d like is to make sure that the people who see option A are, in some sense, as similar as possible to those who see option B. And it’s become well-established in the scientific literature, going back to work of the statistician Ronald Fisher, about 100 years ago, that the best way to do that is to make sure that you randomly allocate who gets which of the options that you want to compare.

So if you’re gonna start an experiment, you have to think about what are the options that are on the table. What do you want to compare? You have to have an engine at your disposal, framework that’s gonna let you make those random allocations. So if someone comes into your website, they’re not just gonna get a landing page, you have to decide what landing page they’re gonna get. You control that, and you want to control that by allocating it at random between the groups. That’s what’s gonna guarantee having a fair comparison.

Now, in order to validate the data that you’re capturing, you must ensure, for example, that this random allocation occurs. Because if you give the young people version A and the older people version B, and you see a difference, you will never know if it’s due to the age or to the web page design. That’s called confounding. So the random allocation, in a sense, establishes some causality on what is really impacting user behavior.

There are a number of ways that you can go about analyzing the data that you get from an AB test. Classical statistics takes a view that there is some true value for that KPI, and for example, it might be common if you’re comparing two options, to start with a framework that says both options are identical unless we get evidence that shows that they’re different. The Bayesian analyses take a somewhat different viewpoint, and rather than saying there’s some value, we just don’t know what it is, describe that value by putting a probability distribution on it. So you get more and more data. The probability distribution gets tighter and tighter about focusing on what we’ve now learned so that we have better information. It often gives a much more intuitive way, in particular in terms of business decisions, for looking at how to characterize what we know, rather than saying, “Well, it could be between this and this, and either it is, or it isn’t.” That’s sort of the classical way. And we give a more gradated answer by saying, “Well, this is the most likely.” And then there’s a distribution that describes how unlikely values become as we move away from what we think is the most likely value.

Bayesian statistics give you a, I think, a much more intuitive and natural way to express those understandings that you get at the end of an experiment in terms of say, what’s the probability that A is better, what’s the probability that B is better. So you bring in the data, use the data, and now Bayesian inference provides standard rules, and very strictly mathematical rules for taking the data, using that to update your prior to get what’s called a posterior distribution.

The posterior is, this is your current view. This is what you think after having seen the data. What describes your uncertainty about the KPIs after having seen the data, and you combine those two together in order to get your final statement about what it is you think. Can you really state a prior distribution? Is there a defensible way to state what you think in advance? As again, this has certainly been the source of friction between people in the different statistical camps. In the case of most AB tests, you’re getting lots and lots of data, and the data is going to wash out anything that you might’ve thought in the prior. So that for typical AB tests, again, because you have very large data volumes, it no longer becomes, in my mind, very controversial because whatever you put in to get the Bayesian engine going is essentially gonna be washed out by the data. And the data is really going to be the component that dominates what you get in the end.

So sample size is an important question in any study that’s going to be run. Whenever you go out to get the data. How long do I have to run my test? How many visitors to the website do I have to see before I can make conclusions? In order to get a test that gives a complete and honest picture, representative picture, of all the people who are long term gonna be exposed to whatever decisions you make, you wanna make sure that you include everyone within a cycle like that. You don’t wanna stop the test after having seen only visitors on Tuesday and Wednesday. And it may turn out that the decision you made is really catastrophic for those who come in on the weekends. So you always want to make sure that you pick up any potential time-cycles like this. And in terms of the sort of Bayesian descriptions that we were talking about later, I think this really gets to the heart of, in the end, you’re gonna get these posterior distributions that describe your KPIs, and how narrow do you want them to be? The tighter the interval, the more you know.

You can have a tight interval, but if the people who looked at the website and were part of the randomization subgroup, then whatever you’re going to get will not apply to the general group. So you really should work on both fronts. One is, how much information do you have? The tighter the interval, the more informative it is. But also how generalizable is the information that you provide?

Both of these aspects are important, and both of them can be the ones that really are the driver in dictating how large an experiment has to be. A website that has very heavy volume may quickly get to the needed sample size but without being able to see the full representative picture of all the visitors. And of course, it can happen just the other way, that by the time you get enough people in, enough visitors into the site, in order to get to the sample size you need, you’ve already gone through several of those time cycles. And so you have to see how both of these work out and balance against one another.

  • A/B testing involves showing different versions of a webpage to different visitors and measuring which version performs better.
  • Randomization is key to ensuring that the results of your A/B test are accurate and unbiased.
  • It’s important to define your goals and metrics before running an A/B test so that you can measure success.
  • Make sure your sample size is large enough to draw statistically significant conclusions from your A/B test results.
  • Pay attention to the statistical significance of your results to avoid making decisions based on random fluctuations.
  • Iterate on your A/B tests based on the results you learn to continuously improve your website.

A/B testing is a great way to compare alternative experiences or possible modifications to your website or digital properties. By running the two experiences in parallel and deciding at random which visitors land on which version, you get an honest comparison of which one delivers better performance.

Once you get the data from your test, you need reliable and informative ways to summarize them and reach conclusions. The Bayesian approach to doing statistics is a great way to accomplish this. 

Here we describe the basic ideas behind Bayesian statistics and how they feed into the business decisions that you will need to make at the end of a test. We first present the approach to data analysis. 

Then, we illustrate how data analysis leads to natural solutions for questions such as:

  • Which variant drives better results? 
  • How much better are those results?
  • Can we be confident in our conclusion?
  • What if we want to test more than two variants?
  • What sample size is needed for a Bayesian A/B test?
  • Can a test be terminated early if results look clear?

What is Bayesian Statistics?

Bayesian statistics is named after the Reverend Thomas Bayes who lived in Britain in the 18th century and was responsible for proving Bayes Theorem . The guiding principle in Bayesian statistics is that you can use the language of probability to describe anything that we don’t know and want to learn from data. As we will see, this is an ideal language for discussing many of the questions that are most important to business decisions.

When you run an A/B test, the primary goal is to discover which variant is most effective. To do so, you compare the results with the two methods. The Bayesian analysis begins by looking at the KPIs for each of the variants. The data gives us information about their value but always leaves some uncertainty. We describe uncertainty using a probability distribution. We can simulate results from the distribution to understand how it looks. 

The histogram in Figure 1. is typical of what you might get from your analysis. It shows the statistical distribution of difference in click-through rate (CTR) between A and B. Most of the curve is to the right of 0, providing evidence that A has a higher CTR. The fraction of the curve to the right of 0 is a great summary of that evidence – it is called the Probability to Be Best (P2BB). In Figure 1., P2BB is 0.699, in favor of version A.

traffic allocation option within a/b tests

Figure 1: Probability function for the CTR for A minus the CTR for B

If you have three or more variants, it is still easy to compute the probability of being best (P2BB), with probability split into three pieces, one for each variant. 

How does Bayesian analysis work? 

You need to start the Bayesian engine running with a prior probability distribution that reflects what you think about the KPI before seeing any data. The prior is then combined with the test data to obtain a posterior distribution for each variant. Figure 1. would be the posterior distribution for the difference between the A and B variants – this reflects the current view after having seen the data.

You need the prior to make things work, but when the volume of data is large – and that is almost always the case in A/B tests – the posterior is quickly dominated by the data and virtually “forgets” the prior; so you don’t need to invest too much thought about how to choose the prior. It is possible to work with some standard choices for priors without affecting the analysis. 

But can you really state a prior distribution and what you think in advance?

This has certainly been the source of friction between people in the different statistical camps, so it is helpful to contrast the Bayesian summary with the “classical,” Frequentist approach you might have studied in an introductory statistics course. In the classical approach, either A is better than B, B is better than A, or they are identical. The question “what is the probability that A is better” is not part of the lexicon – you can’t give an answer, and in fact, you’re not even permitted to ask the question! 

The classical paradigm does use probability, but only to describe the experimental data, not to summarize what you know about a KPI. That’s why you can’t make a summary like “the probability that B gives a positive uplift over A is 98%.

In most A/B tests, you’re getting lots and lots of data. Therefore, it no longer becomes very controversial, because whatever you put in to get the Bayesian engine going is essentially gonna be washed out by that information, which is really going to be the component that dominates what you get in the end. And that’s why we think the Bayesian summaries are so much better suited for business decisions.

For more on the different viewpoints when it comes to the Bayesian vs. Frequentist approach to A/B testing, check out this article .

How should traffic be distributed between variations?

What you’d like is to make sure that the people who see option A are, in some sense, as similar as possible to those who see option B. And it’s become well-established in the scientific literature, going back to work of the statistician Ronald Fisher, about 100 years ago, that the best way to do that is to make sure that you choose the right traffic allocation , or who gets which of the options that you want to compare. 

So, you have to think about what are the options that are on the table. What do you want to compare? You have to have an engine at your disposal – a framework that’s gonna let you make those random allocations. If someone comes to your website, they’re not just gonna get a landing page, you have to decide what landing page they’re gonna get. You control that, and you want to control that by allocating it at random between the groups. That’s what’s gonna guarantee having a fair comparison.

In order to validate the data that you’re capturing, you must ensure, for example, that this random allocation occurs. Because if you give the young people version A and the older people version B, and you see a difference, you will never know if it’s due to the age or to the web page design. That’s called confounding. So the random allocation, in a sense, establishes some causality on what is really impacting user behavior.

What length of time is needed to run an a/b test?

In making these decisions, it is important to think about “what might go wrong” with the testing engine. You want your test to be sensitive to possible bugs. Often bugs are related to failures of the random allocation over time so that natural time trends in your KPIs bias the results. If there are weekly trends in your data, then you need to run an A/B test for two weeks to make sure that the engine is successfully handling them. If there are diurnal trends (but not weekly), you can afford to run shorter experiments. There may also be special events that suddenly increase or decrease the number of visitors to your site, or change the composition of visitors, affecting the performance of an experience. You may want to hold off on making any decisions until you see whether such issues have an impact on your results. 

The considerations noted above will suggest a minimum time frame for running your experiment that ensures representative coverage in the A/B test of typical future site visitors. Often, these will dictate the length of time for the test.

How large a sample size is needed for a Bayesian test? 

The sample size paradigm for Bayesian testing asks how narrow you want your final probability functions to be. You will probably want to focus on the probability function for the difference in KPIs between the two variants, as shown in Figure 1. And you will already have an idea what sort of difference could be important for your business; that gives a guideline for how narrow you want the function to be – the tighter the interval, the more you know.

It’s important to note that you can have a tight interval, but if the people who looked at the website and were part of the randomization subgroup, then whatever you’re going to get will not apply to the general group. So you really should work on both fronts. One is, how much information do you have? The tighter the interval, the more informative it is. But also how generalizable is the information that you provide?

Both of these aspects are important and can be the driver in dictating how large an experiment has to be. A website that has very heavy volume may quickly get to the needed sample size but without being able to see the full representative picture of all the visitors. And this can happen the other way around too, so that by the time you get the sample size you need, you’ve already gone through several time cycles – you have to see how both of these work out and balance against one another.

You will need to provide information on likely values for the KPIs. Often one of the versions is already running, so you can use historical data for this purpose. For new versions, you will need to think about what effect they might have. If the test includes multiple versions, the same ideas apply and the same inputs are needed.

The basic inputs needed to compute a sample size depend on the nature of the KPI :

  • For binary KPIs like CTR or installation rate, all you need are the expected rates for each version
  • If you are looking at purely continuous KPIs like revenue per conversion, you will need to supply an estimate of both the typical conversion size and the variation in size among those who do convert 
  • For a “mixed” KPI like revenue per visitor, you will need the average, variation, and an estimate of the fraction who will convert.

There are straightforward formulas you can use to determine the sample size, and also helpful A/B test sample size calculators . 

What if there are more than two versions in the test? 

The same sample size formulas are relevant, but here we distinguish between two goals: (1) finding the best and (2) showing that the standard version is not the best. 

What is different about the second goal? 

Suppose you have two new versions that you want to compare to the standard. Your best guess gives almost the same expected KPI to each of the new versions, with both of them improving over the standard. With very similar KPI’s, you will need a large sample to know which of the two new versions is better than the other. But if you expect both to be better than the standard, you can “rule out” the standard version with a much smaller test.

To further illustrate the idea : 

The current experience is getting a CTR of 1%. A new version is proposed with the expectation that it will increase the CTR to 1.2%. The expected difference is 0.2%, so you need an A/B test that will tell you what is the true difference to a higher resolution. A good rule of thumb is to aim for a resolution that concentrates 95% of the Posterior Distribution in an interval that is the size of the expected difference. 

In this example, that means narrowing down the difference so that you are 95% certain about an interval of length 0.002. To achieve this level of accuracy, you will need a sample of 83,575 visitors to both pages. What if even a 10% improvement in CTR, to 1.1%, is critical? Then you would need to know the difference up to, at most, 0.1%. That will require a sample size of 319,290 for each version.

Now let’s look at an example involving revenue per visitor :

A common scenario (often referred to as the Pareto principle )is that most visitors don’t provide any revenue and, among those who do, a small number of “big fish” pull up the average, and also the standard deviation. 

The current landing page has a conversion rate of 1%; on average, converters spend $20; the standard deviation in the purchases of converters is $25. That means the average revenue per visitor is $0.20. Marketing thinks that the new version will bring in more purchasers, upping the conversion rate to 1.2%, but will decrease the proportion of “big fish,” so that the average conversion drops to $19 and the standard deviation to $22. 

That translates to an average revenue of $0.228, an expected improvement of $0.028 per visitor. The A/B test will need to be large enough to narrow down the 95% credible interval for the difference in average revenue to no more than the expected difference, i.e. to $0.028. We will need 397,830 visitors to each experience to achieve that level of accuracy.

What if we want a stronger guarantee that we will find the important difference? 

We advised above setting the sample size to control the width of the 95% credible interval. It is easy to reduce the chance of missing a valuable change by insisting on a higher level of probability, say 98% or 99%, but at the expense of increasing the sample size.

Can you stop a test early? 

The probability that A is better (or worse) than B provides a natural metric to track as the test progresses. And a strong signal from the P2BB statistic can be used to guide early stopping. However, early stopping needs to be done with caution. 

First, the Bayesian summary is not completely immune to data peeking. The P2BB will drift up and down during an experiment. This is especially true if there is no difference in performance between the variants, as in A/A testing . In that case, you will see that the P2BB increases and decreases as you collect more data. The fact that it crossed a threshold (like 95%) at some point during the test is not a guarantee that it would remain above the threshold if you wait until the planned termination time. 

Second, it is always risky to stop before observing at least one full time cycle. Version B might be better for visitors at the start of the cycle, but worse for those later in the cycle. If you stop before the end of a full cycle, your results may be biased.

If you want to stop early, think about using stricter criteria for an early stopping point. For example, if you see that the probability becomes extreme (say above 0.999 or below 0.001), you can feel safe in stopping and making a decision. If there really is a difference between A and B, you can still expect to reach those thresholds relatively quickly. 

Finally, we also reiterate the importance of waiting for time trends to reveal themselves in your test. You don’t want to decide on the basis of a short time window only to find that the results are very different later in the day or week – suppose weekday visitors respond better to A than B, but that trend flips on the weekend. Making a decision Wednesday from an experiment that began on Tuesday might lead to a bad decision.

Continue reading

Dynamic Allocation Optimization in A/B-Tests Using Classification-Based Preprocessing

Ieee account.

  • Change Username/Password
  • Update Address

Purchase Details

  • Payment Options
  • Order History
  • View Purchased Documents

Profile Information

  • Communications Preferences
  • Profession and Education
  • Technical Interests
  • US & Canada: +1 800 678 4333
  • Worldwide: +1 732 981 0060
  • Contact & Support
  • About IEEE Xplore
  • Accessibility
  • Terms of Use
  • Nondiscrimination Policy
  • Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

How to Do A/B Testing with Google Analytics: The Ultimate Guide

How to Do A/B Testing with Google Analytics: The Ultimate Guide

Table of Contents

Welcome to the world of A/B testing! If you’re curious about how to do A/B testing with Google Analytics, you’ve landed in the perfect spot. As one  powerful tool for optimizing  your website’s performance and user experience, A/B testing is crucial for any online business. In this comprehensive guide, you’ll learn the ins and outs of setting up, running, and analyzing A/B tests using  Google Analytics . Additionally, we’ll cover best practices and common pitfalls to avoid. Let’s dive in and explore the exciting world of A/B testing!

Why A/B Testing Is Important

At its core,  A/B testing  (also known as split testing) involves comparing two versions of a webpage or element to determine which one performs better. By analyzing data from user interactions, you can make data-driven decisions to improve your website’s  performance and user experience .

The importance of A/B testing cannot be overstated. Continually testing and optimizing your site helps increase conversion rates, enhance user engagement, and boost your bottom line. As an SEO expert, I assure you that knowing how to do A/B testing with Google Analytics is essential for your online success.

Setting Up A/B Testing in Google Analytics

Google Analytics offers a built-in A/B testing feature called “Google Optimize,” which allows you to easily create and manage your experiments. In this section, we’ll walk through how to set up A/B testing in Google Analytics and how to effectively split traffic for A/B testing:

 how to do a/b testing with google analytics

  • Sign up for a Google Optimize account and link it to your Google Analytics property.
  • Create a new experiment in Google Optimize by clicking on “Create Experiment.”
  • Choose the type of experiment (A/B test, multivariate test, or redirect test) and enter the page URL you want to test.
  • Set the traffic allocation for each variant. This determines how to split traffic for A/B testing. For example, you can assign 50% of your traffic to variant A and 50% to variant B.
  • Create the variants of the page you want to test, either by using the Google Optimize visual editor or by manually adding custom code.

Defining Goals and Metrics for A/B Testing

Before diving into how to do A/B testing with Google Analytics, defining your goals and key performance indicators (KPIs) is crucial. These metrics will help you evaluate the success of your experiments.

Consider the following best practices for setting up goals and metrics in Google Analytics:

Choose goals that align with your overall business objectives, such as increasing conversions, reducing bounce rate, or improving user engagement. Use specific, measurable, and actionable KPIs. Examples include conversion rate, time on page, or click-through rate.

Set up custom goals in Google Analytics to track your KPIs.

Creating and Running A/B Tests in Google Analytics

Now that you’ve set up your A/B testing experiment and defined your goals, it’s time to create and launch your tests in Google Analytics. Follow these best practices for designing and implementing A/B tests to ensure that your results are accurate and meaningful:

Keep your tests simple : Focus on testing one element at a time to isolate the impact of individual changes. This will help you understand which specific factors are influencing your results.

Test multiple variations : While A/B testing typically compares two versions of a page, consider testing multiple variations to explore different design options and increase your chances of finding the best-performing version.

Run your tests simultaneously : Running your tests simultaneously ensures that external factors, such as seasonal trends or  marketing campaigns , do not skew your results.

Test for a sufficient duration : A/B tests should run long enough to collect statistically significant data. This usually means running the test for at least a week or until you have a few hundred conversions per variation.

Don’t stop your tests too early : Let your tests run their full course to avoid making decisions based on incomplete data.

Once your tests are running, monitor their progress in Google Analytics. This will help you track your KPIs and understand how your variations are performing in real-time.

Tips for Interpreting and Analyzing A/B Testing Data

After running your A/B tests, you must interpret and analyze the data to make informed decisions. Here are some tips for effectively evaluating your results:

Focus on statistical significance : Use Google Analytics’ built-in statistical significance calculator to determine whether your results are statistically significant. This will help you avoid making decisions based on random fluctuations in the data. A commonly accepted threshold for statistical significance is a p-value of 0.05 or lower.

how to split traffic for ab testing

Consider the effect size : Statistical significance alone doesn’t tell the whole story. Look at the effect size, which measures the magnitude of the difference between your variations. A large effect size indicates a more substantial impact on your KPIs.

Analyze secondary metrics : While your primary KPIs are crucial, don’t overlook secondary metrics such as bounce rate, time on page, and pages per session. These can provide valuable insights into user behavior and help you identify areas for further optimization.

Segment your data : Break down your results by different segments, such as device type, traffic source, or demographic factors. This can help you understand how different user groups respond to your variations and tailor your website to their needs.

Optimizing and Iterating Based on A/B Testing Results

Once you’ve analyzed your A/B testing data, use the insights to optimize your website’s performance and user experience. Here are some best practices for iterating and improving A/B tests over time:

Implement the winning variation : If one of your variations outperforms the others, update your website with the winning design. This will help you capitalize on your testing efforts and benefit immediately from the improved performance.

Test further improvements : Don’t stop at one successful test. Continue to identify areas for improvement and run additional A/B tests to fine-tune your website’s performance and user experience.

Learn from unsuccessful tests : Not all tests will yield positive results. Use insights from unsuccessful tests to refine your hypotheses and improve your future experiments.

Keep an eye on the long-term impact : Regularly monitor your KPIs to ensure that the changes you’ve implemented based on A/B testing results continue to have a positive impact on your website’s performance over time.

Common A/B Testing Mistakes to Avoid

AB Testing Mistakes to Avoid

Testing too many elements simultaneously : Testing multiple elements simultaneously can make it difficult to determine which changes are driving the results. Stick to testing one element at a time for clearer insights.

 Ignoring statistical significance : Decisions based on statistically insignificant results may lead to incorrect conclusions. Always ensure that your results are statistically significant before changing your website.

Not running tests long enough : Stopping tests too early can result in misleading data. Run your tests for a sufficient duration to collect enough data for accurate analysis. Overlooking external factors: Be aware of external factors, such as marketing campaigns or seasonal trends, that may impact your results. Consider these factors when designing and analyzing your A/B tests.

The Power of A/B Testing with Google Analytics

A/B testing with Google Analytics is a powerful tool for optimizing your website’s performance and user experience. Following the steps outlined in this guide on how to do A/B testing with Google Analytics, you’ll be well-equipped to set up, run, and analyze A/B tests effectively.

At Oyova, we specialize in  web design ,  development , and  SEO services  that can help you optimize your website’s performance and user experience. Whether starting with A/B testing or looking to take your website to the next level, our team of experts can help you achieve your goals.  Contact us  today to learn how we can help you implement effective A/B testing with Google Analytics and achieve your business objectives.

' src=

Let’s talk about how we can take your company or agency to the next level.

  • First Name *
  • Last Name *
  • Adobe Target Business Practitioner Guide Home
  • Target announcements and events
  • Target release notes (current)
  • Target release notes (prerelease)
  • Target documentation overview
  • System status updates and proactive notifications
  • Documentation changes
  • Release notes for previous releases
  • Introduction to Target
  • Access Target from the Adobe Experience Cloud
  • Target key concepts
  • Understand the Target UI
  • Target welcome kit overview
  • Chapter 1: Introduction
  • Chapter 2: Target at a glance
  • Chapter 3: Develop your testing and personalization ideas
  • Chapter 4: Tips for using Target
  • Chapter 5: Inspiration for testing and personalization activities
  • Chapter 6: Easily avoidable pitfalls
  • Chapter 7: Create and run your first Target activity
  • Chapter 8: Communicate your activity results
  • Chapter 9: Next steps and resources
  • How Target works
  • Training and certification
  • Training videos for Target Standard and Premium
  • Target optimization and personalization FAQ
  • Administer Target overview
  • Administrator first steps
  • Configure the Visual Experience Composer
  • Configure reporting
  • Estimating lift in revenue
  • Scene7 configuration
  • Implementation
  • Environments
  • Response tokens
  • User management
  • Users overview
  • Troubleshoot user management
  • Enterprise user permissions
  • Configure enterprise permissions
  • Grant Adobe I/O integrations access to workspaces and assign roles
  • Implement Target overview
  • A4T overview
  • Before you implement
  • Analytics for Target implementation
  • User permission requirements
  • Create an activity that uses Analytics as the reporting source
  • A4T support for Auto-Allocate and Auto-Target activities
  • Use an Analytics tracking server
  • A4T reporting
  • Troubleshoot A4T
  • Initial provisioning - A4T FAQ
  • Activity settings - A4T FAQ
  • View reports - A4T FAQ
  • Redirect offers - A4T FAQ
  • Lift and confidence - A4T FAQ
  • Metric definitions - A4T FAQ
  • Classifications - A4T FAQ
  • Share metrics, audiences, and reports - A4T FAQ
  • Legacy SiteCatalyst to Test&Target Integration - A4T FAQ
  • Expected data variances between Target and Analytics when using and not using A4T
  • Use offer decisions
  • Experience Cloud Audiences
  • Integrate Target with AEM overview
  • AEM Experience Fragments and Content Fragments overview
  • AEM Experience Fragments
  • AEM Content Fragments
  • Integrate Target with Adobe Audience Manager (AAM)
  • Integrate with Real-time Customer Data Platform
  • Integrate Target with Adobe Campaign
  • Activities overview
  • Target activity types
  • A/B test overview
  • How long should you run an A/B test?

Ten common A/B testing pitfalls and how to avoid them

  • A/A testing
  • Create an A/B test
  • Activity URL
  • Add experience
  • Select audience
  • Goals and settings
  • Use Analytics Data
  • Set metrics
  • Multiple experience audiences in an A/B Test
  • Auto-Allocate overview
  • Create an Auto-Allocate activity
  • Interpret Auto-Allocate reports
  • Auto-Allocate can give you faster test results and higher revenue than a manual test
  • Auto-Target overview
  • Create an Auto-Target activity
  • Auto-Target FAQs and troubleshooting
  • Reporting and Auto-Target
  • Automated Personalization overview
  • Random Forest Algorithm
  • Create an Automated Personalization activity
  • Upload data for the Target personalization algorithms
  • Data collection for the Target personalization algorithms
  • Estimate the traffic required for success
  • Preview experiences for an Automated Personalization test
  • Target Automated Personalization offers
  • Manage exclusions
  • Offer reporting groups in Automated Personalization
  • Select the control for your Automated Personalization or Auto-Target activity
  • Automated Personalization FAQ
  • Troubleshoot Automated Personalization
  • Experience Targeting overview
  • Create an activity
  • Create an experience
  • Switching experiences in Experience Targeting
  • Multivariate Test overview
  • Multivariate Test best practices
  • Plan a Multivariate Test
  • Create a test
  • Create combinations
  • Preview experiences for a Multivariate Test
  • Estimate the traffic required for a successful test
  • Test summary
  • Troubleshoot Multivariate Tests
  • Recommendations activity
  • Edit an activity or save as draft
  • Activity settings
  • Success metrics
  • Click tracking
  • Capture score
  • Activity change log
  • Troubleshoot activities overview
  • Troubleshoot content delivery
  • Activity QA overview
  • Activity QA bookmarklet
  • Use Activity QA with server-side delivery
  • Audiences overview
  • Create audiences overview
  • Build audiences in Target
  • Categories for audiences overview
  • Custom parameters
  • Operating System
  • Target Library
  • Traffic Sources
  • Visitor Profile
  • Create a profile attribute comparison audience
  • Combine multiple audiences
  • Create an activity-only audience
  • Audience filters for reporting
  • Apply a reporting audience to a success metric
  • Visitor profiles overview
  • Visitor profile lifetime
  • Profile attributes
  • Use profile scripts to test mutually exclusive activities
  • Category affinity
  • Customer attributes
  • Real-time profile syncing for mbox3rdPartyId
  • Profile and variable glossary
  • Targets and audiences FAQ
  • Experiences and offers overview
  • Visual Experience Composer overview
  • Visual Experience Composer options
  • Include the same experience on similar pages
  • Multipage activity
  • Activity collisions
  • Modifications overview
  • Experience templates
  • Element selectors used in the Visual Experience Composer
  • Mobile viewports for responsive experiences
  • Visual Experience Composer best practices and limitations
  • Troubleshooting the Visual Experience Composer overview
  • Troubleshooting the Visual Experience Composer and Enhanced Experience Composer
  • Troubleshooting the Visual Experience Composer
  • Troubleshooting the Enhanced Experience Composer
  • Enabling mixed content in your browser
  • Page modification scenarios
  • Visual Editing Helper extension
  • Visual Experience Composer helper extension
  • Redirect to a URL
  • Creating carousels that work in the Visual Experience Composer
  • Form-Based Experience Composer
  • Single Page App (SPA) Visual Experience Composer
  • Offers overview
  • Create offer folder
  • Uploading content
  • Create redirect offers
  • Create remote offers
  • Create JSON offers
  • Work with content in the library
  • Search content
  • Pass dynamic data into offers
  • AEM Experience and Content Fragments
  • Reports overview
  • Report settings overview
  • View multiple metrics in a report
  • Exclude extreme values
  • Downloading data in a CSV file
  • Statistical calculations in A/Bn tests
  • Auto-Target Summary report
  • Automated Personalization Summary reports
  • Personalization Insights reports overview
  • Automated Segments report
  • Important Attributes report
  • Experience Performance report (MVT)
  • Location Contribution report (MVT)
  • Analytics for Target (A4T) reporting
  • Reporting FAQ
  • Recommendations overview
  • Introduction to Recommendations
  • Plan and implement Recommendations
  • Entities overview
  • Entity attributes
  • Custom entity attributes
  • Catalog search
  • Collections
  • Criteria overview
  • Create criteria
  • Create criteria sequences
  • Base the recommendation on a recommendation key
  • The science behind Target’s recommendations algorithms
  • Upload custom criteria
  • Use dynamic and static inclusion rules
  • Entity attribute matching
  • Profile attribute matching
  • Parameter matching
  • Static filter
  • Use a backup recommendation
  • Work with multi-value attributes
  • Use Adobe Analytics with Recommendations
  • Design overview
  • Create a design
  • Customize a design using Velocity
  • Create a Recommendations activity
  • Select criteria
  • Add promotions
  • Recommendations activity settings
  • Preview and launch your Recommendations activity
  • Recommendations as an offer
  • Recommendations FAQ
  • Integrate Recommendations with email
  • IP addresses used by Recommendations feed-processing servers
  • Recommendations Classic versus Recommendations activities in Target Premium
  • Recommendations Classic documentation
  • Troubleshoot Target
  • Adobe Target API overview
  • Resources and contact information
  • A/B Tests View more on this topic
  • Created for:

A/B testing in Adobe Target forms the backbone of most digital marketing optimization programs, helping marketers offer optimized and targeted experiences to their visitors and customers. This article outlines ten of the most significant pitfalls that companies fall prey to when performing A/B testing. It also includes ways to avoid them, so your company can achieve greater ROI through its testing efforts and have greater confidence in its reported A/B test results.

Pitfall 1: Ignoring the effects of the significance level

How likely is it that your test reports a significant difference in conversion rate between two offers when in fact there is none? That’s what the significance level of a test helps determine. Such misleading findings are often called a false positive, and in the world of statistics, are called a Type I error (if you incorrectly reject the null hypothesis that is true).

When you specify the significance level of an A/B test, you’re making a trade-off between your tolerance for accepting that one experience is better than the other when it really isn’t (Type I error or “false positive”) versus seeing no statistical difference between the experiences when there actually is a true difference (Type II error or “false negative”). The confidence level is determined before a test is run.

The confidence interval , which is determined after a test is complete, is impacted by three key factors:

  • Sample size of the test
  • Significance level
  • Population standard deviation

Because the marketer selected the significance level before the test is designed and the population standard deviation can’t be impacted, the only “controllable” factor is the sample size. The sample size required for a confidence interval you are comfortable with, and the resulting time it takes to reach that sample size, is a key decision a marketer must determine during the test design.

Another directly related term, the confidence level , takes more of a glass half-full approach. Rather than stating the likelihood that you get a false positive, as the significance level does, the confidence level represents the likelihood that your test does not make that mistake.

Confidence level and significance level are directly related because:

100% - confidence level = significance level

In A/B testing, marketers often use 95% confidence levels. Clearly, based on the above equation, that corresponds to a significance level of 5%. Testing with a 95% confidence level means you have a 5% chance of detecting a statistically significant lift, even when in reality, there’s no difference between the offers.

As the graph below illustrates, the more tests that you run, the more likely at least one of those tests results in a false positive. For example, if you run 10 tests using a 95% confidence level, there is approximately a 40% chance that you detect one or more false positives (given that there is no real lift: Pr(at least one false positive) = 1 - Pr(no false positives) = 1 - 0.95^10 = 40%).

pitfalls1 image

In a marketing organization, 95% usually constitutes a reasonable trade-off between the risk of a false positive and false negative.

However, two situations warrant paying close attention to the significance level and its implications for test results: post-test segmentation and testing multiple offers.

Post-Test Segmentation: Marketers often slice and dice the results of a test based on visitor segments after the A/B test concludes. Common segments include browser type, device type, geographic areas, time of day, and new versus returning visitors. This practice, known as post-test segmentation, provides excellent insight into visitor segments. In turn, marketers can use these insights to create better-targeted, more-relevant, and differentiated content.

If there is no real difference in conversion rate, each time you test a segment, the probability of a false positive equals the significance level. And, as mentioned, the more tests you run, the greater the likelihood that you experience at least one false positive among those tests. In essence, each post-test segment represents a separate test. With a significance level of 5%, on average you fall prey to one false positive every time you look at 20 post-test segments. The chart above shows how that likelihood increases.

The more tests that you run, the greater the likelihood that you experience at least one false positive among those tests. In essence, each post-test segment represents a separate test, which increases the likelihood of a false positive. This increase can be even more significant if segments are correlated.

Should you not do post-test segmentation? No, post-test segments are valuable. Instead, to avoid this cumulative false positive issue with post-test segmentation, after you’ve identified a post-test segment, consider testing it in a new test. Alternatively, you can apply the Bonferroni correction, discussed next.

Testing Multiple Offers: Marketers frequently test more than two offers (or experiences) against each other. That’s why you sometimes see A/B testing solutions called A/B/n testing, where n is the number of offers that you are testing simultaneously.

It’s important to note that each offer tested has a false positive rate equal to the significance level, as described above. Again, you are effectively running multiple tests when several offers are pitted against each other within a single test environment. For example, if you compare five offers in an A/B/C/D/E test, effectively you form four comparisons: control to B, control to C, control to D, control to E. With a confidence level of 95%, rather than the 5% probability of a false positive, you actually have 18.5%.

To keep your overall confidence level at 95% and avoid this issue, you apply what is known as the Bonferroni correction. Using this correction, you simply divide the significance level by the number of comparisons to come up with the significance level that you must achieve a 95% confidence level.

Applying the Bonferroni correction to the example above, you would use a 5%/4 = 1.25% significance level, which is the same as a 98.75% confidence level for an individual test (100% - 1.25% = 98.75%). This adjustment maintains the effective confidence level at 95% when you have four tests, as in the described example.

Pitfall 2: Declaring winners of multiple offer tests with no statistically significant difference

With multiple offer testing, marketers often declare the offer with the highest lift as the test winner, even though there is no statistically significant difference between the winner and the runner-up. This situation occurs when the difference between the alternatives is smaller than the difference between the alternatives and the control. The figure below illustrates this concept, with the black error bars representing 95% lift confidence intervals. The true lift for each offer relative to the control offer is 95% likely to be included within the confidence interval-the range shown by the error bars.

pitfalls2 image

Offers A and B have the highest observed lift during the test, and it would be unlikely that offer C would outperform those offers in a future test, because the confidence interval of C does not overlap with the confidence intervals of A or B. However, even though offer A has the highest observed lift during the test, it is possible that offer B could perform better in a future test because the confidence intervals overlap.

The takeaway here is that both offers A and B should be considered winners of the test.

It’s typically not feasible to run the test long enough to identify the true relative performance of the alternatives, and oftentimes the difference in performance between the alternatives is too small to substantially impact the conversion rate. In such cases, you can interpret the outcome as a tie and use other considerations, such as strategy or alignment with other elements of the page, to determine which offer to implement. With multiple tests, you must be open to more than one winner, which sometimes considerably opens up the possibilities for the direction to take with your website development.

If you do want to identify the offer with the highest conversion rate, you are comparing all offers to every other offer. In the example above, you have n = 5 offers—you have to make n(n-1)/2 comparisons, or 5*(5-1)/2 = 10 comparisons. In this case, the Bonferroni correction requires that the significance level of the test be 5%/10 = 0.5%, which corresponds to a confidence level of 99.5%. However, such a high confidence level might require you to run the test for an unreasonably long period.

Pitfall 3: Ignoring the effects of statistical power

Statistical power is the probability that a test detects a real difference in conversion rate between offers. Because of the random, or as statisticians like to call it, “stochastic,” nature of conversion events, a test might not show a statistically significant difference, even when a real difference exists in conversion rate between two offers in the end. Call it bad luck or by chance. Failing to detect a true difference in conversion rate is called a false negative or a Type II error.

There are two key factors that determine the power of a test. First is the sample size, that is, the number of visitors included in the test. Second is the magnitude of the difference in conversion rate that you want the test to detect. Perhaps this is intuitive, but if you are interested in detecting only large conversion rate differences, there’s a higher probability that the test will actually detect such large differences. Along those lines, the smaller the difference you want to detect, the larger the sample size, and therefore, time to get that larger sample size, you require.

Today’s marketers under-power a remarkable number of tests. In other words, they use a sample size that is too small. That means that they have a slim chance of detecting true positives, even when a substantial difference in conversion rate actually exists. In fact, if you continually run underpowered tests, the number of false positives can be comparable to, or even dominate, the number of true positives. This often leads to implementing neutral changes to a site (a waste of time) or changes that actually reduce conversion rates.

pitfalls3 image

To avoid under-powering your test, consider that a typical standard for a well-powered test includes a confidence level of 95% and a statistical power of 80%. Such a test offers a 95% probability that you avoid a false positive and an 80% probability that you avoid a false negative.

Pitfall 4: Using one-tailed tests

One-tailed tests require a smaller observed difference in conversion rates between the offers to call a winner at a certain significance level. This type of test seems to appeal because winners can be called earlier and more often than when using two-tailed tests. But in keeping with the saying, “There’s no free lunch,” one-tailed tests come at an expense.

In a one-tailed test, you test whether offer B is better than offer A. The direction of the test has to be determined before the test commences, or “a priori” in statistics-speak. In other words, you must decide whether to test for B being better than A or A being better than B before initiating the test. However, if you look at the results of the A/B test and see that B is doing better than A and then decide to do a one-tailed test to see whether that difference is statistically significant, you are violating the assumptions behind the statistical test. Violating the assumptions of the test means that your confidence intervals are unreliable and the test has a higher false positive rate than you would expect.

You might view a one-tailed test as putting an offer on trial with a judge who has already made up his or her mind. In a one-tailed test, you’ve already decided what the winning offer is and want to prove it, rather than giving each experience an equal chance to prove itself as the winner. One-tailed tests should only be used in the rare situations in which you are only interested in whether one offer is better than the other and not the other way around. To avoid the issue of the one-tailed test, use an A/B testing solution that always uses two-tailed tests, such as Adobe Target.

Pitfall 5: Monitoring tests

Marketers frequently monitor A/B tests until the test determines a significant result. After all, why test after you’ve achieved statistical significance?

Unfortunately, it’s not that simple. Not to throw a wrench in the works, but it turns out that monitoring the results adversely impacts the effective statistical significance of the test. It greatly increases the likelihood of false positives and makes your confidence intervals untrustworthy.

This might seem confusing. It sounds like we are saying that just from looking at your results mid-test, you can cause them to lose their statistical significance. That’s not exactly what’s going on. The following example explains why.

Let’s say you simulate 10,000 conversion events of two offers, with both offers having 10% conversion rates. Because the conversion rates are the same, you should detect no difference in conversion lift when you test the two offers against each other. Using a 95% confidence interval, the test results in the expected 5% false positive rate when it is evaluated after collecting all 10,000 observations. So if we run 100 of these tests, on average we get five false positives (in actuality, all positives are false in this example because there is no difference in conversion rate between the two offers). However, if we evaluate the test ten times during the test—every 1,000 observations—it turns out that the false positive rate jumps up to 16%. Monitoring the test has more than tripled the risk of false positives! How can this be?

To understand why this occurs, you must consider the different actions taken when a significant result is detected and when it is not detected. When a statistically significant result is detected, the test is stopped and a winner is declared. However, if the result is not statistically significant, we allow the test to continue. This situation strongly favors the positive outcome, and hence, distorts the effective significance level of the test.

To avoid this problem, you should determine an adequate length of time the test runs before initiating the test. Although it’s fine to look at the test results during the test to make sure that you implemented the test correctly, do not draw conclusions or stop the test before the required number of visitors is reached. In other words, no peeking!

Pitfall 6: Stopping tests prematurely

It is tempting to stop a test if one of the offers performs better or worse than the others in the first few days of the test. However, when the number of observations is low, there is a high likelihood that a positive or negative lift will be observed just by chance because the conversion rate is averaged over a low number of visitors. As the test collects more data points, the conversion rates converge toward their true long-term values.

The figure below shows five offers that have the same long-term conversion rate. Offer B had a poor conversion rate for the first 2,000 visitors, and it takes a long time before the estimated conversion rate returns to the true long-term rate.

pitfalls4 image

This phenomenon is known as “regression to the mean,” and can lead to disappointment when an offer that performed well during the initial days of a test fails to keep up this level of performance in the end. It can also lead to lost revenue when a good offer is not implemented because it happened to under-perform in the early days of a test just by chance.

Much like the pitfall of monitoring your test, the best way to avoid these issues is to determine an adequate number of visitors before running the test and then let the test run until this number of visitors has been exposed to the offers.

Pitfall 7: Changing the traffic allocation during the testing period

We recommend that you do not change the traffic allocation percentages during the testing period because this can skew your test results until the data normalizes.

For example, suppose you have an A/B test in which 80% of the traffic is assigned to Experience A (the control) and 20% of the traffic is assigned to Experience B. During the testing period, you change the allocation to 50% for each experience. A few days later, you change the traffic allocation to 100% to Experience B.

In this scenario, how are users assigned to experiences?

If you manually change the allocation split to 100% for Experience B, visitors who were originally allocated to Experience A (the control) remain in their initially assigned experience (Experience A). The change in traffic allocation impacts new entrants only.

If you want to change percentages or greatly affect the flow of visitors into each experience, we recommend that you create a new activity or copy the activity, and then edit the traffic allocation percentages.

If you change the percentages for different experiences during the testing period, it takes a few days for the data to normalize, especially if many purchasers are returning visitors.

As another example, if your A/B test’s traffic allocation is split 50/50, and then you change the split to 80/20, for the first few days after that change the results might look skewed. If the average time to conversion is high, meaning it takes someone several hours or even days to make a purchase, these delayed conversions can affect your reports. So, in that first experience where the number went from 50% to 80%, and the average time to conversion is two days, only visitors from 50% of the population are converting on the first day of the test, although today 80% of the population is entering into the experience. This makes it look like the conversion rate plummeted, but it will normalize again after these 80% of visitors have taken two days to convert.

Pitfall 8: Not considering novelty effects

Other unexpected things can happen if we don’t allow enough time for running a test. This time the problem is not a statistics problem; it’s simply a reaction to change by the visitors. If you change a well-established part of your website, returning visitors might at first engage less fully with the new offer because of changes to their usual workflow. This can temporarily cause a superior new offer to perform less optimally until returning visitors become accustomed to it—-a small price to pay given the long-term gains that the superior offer delivers.

To determine if the new offer under-performs because of a novelty effect or because it’s truly inferior, you can segment your visitors into new and returning visitors and compare the conversion rates. If it’s just the novelty effect, the new offer wins with new visitors. Eventually, as returning visitors get accustomed to the new changes, the offer wins with them, too.

The novelty effect can also work in reverse. Visitors often react positively to a change just because it introduces something new. After a while, as the new content becomes stale or less exciting to the visitor, the conversion rate drops. This effect is harder to identify, but carefully monitoring changes in the conversion rate is key to detecting this.

Pitfall 9: Not considering differences in the consideration period

The consideration period is the time period from when the A/B testing solution presents an offer to a visitor to when the visitor converts. This can be important with offers that affect the consideration period substantially, for example, an offer that implies a deadline, such as “Time-limited offer. Purchase by this Sunday.”

Such offers nudge visitors to convert sooner and will be favored if the test is stopped immediately after the offer expires, because the alternative offer might have a longer deadline or no deadline, and therefore, a longer consideration period. The alternative would get conversions in the period after the termination of the test, but if you stop the test at the end of the deadline, further conversions do not get counted toward the test conversion rate.

The figure below shows two offers that two different visitors see at the same time on a Sunday afternoon. The consideration period for offer A is short, and the visitor converts later that day. However, offer B has a longer consideration period, and the visitor who saw offer B thinks about the offer for a while and ends up converting Monday morning. If you stop the test Sunday night, the conversion associated with offer A is counted toward offer A’s conversion metric, whereas the conversion associated with offer B is not counted toward offer B’s conversion metric. This puts offer B at a significant disadvantage.

pitfalls5 image

To avoid this pitfall, allow some time for visitors who were exposed to the test offers to convert after a new entry to the test has been stopped. This step gives you a fair comparison of the offers.

Pitfall 10: Using metrics that do not reflect business objectives

Marketers might be tempted to use high-traffic and low-variance conversion metrics in the upper funnel, such as click-through rate (CTR), to reach an adequate number of test conversions faster. However, carefully consider whether CTR is an adequate proxy for the business goal that you want to attain. Offers with higher CTRs can easily lead to lower revenue. This can happen when offers attract visitors with a lower propensity to buy, or when the offer itself, for example, a discount offer-simply leads to lower revenue.

pitfalls6 image

Consider the skiing offer below. It generates a higher CTR than the cycling offer, but because visitors spend more money on average when they follow the cycling offer, the expected revenue of putting the cycling offer in front of a given visitor is higher. Therefore, an A/B test with CTR as the metric would pick an offer that does not maximize revenue, which might be the fundamental business objective.

pitfalls7 image

To avoid this issue, monitor your business metrics carefully to identify the business impact of the offers, or better yet, use a metric that is closer to your business goal, if possible.

Conclusion: Success with A/B testing by recognizing and stepping around the pitfalls

After learning about the common A/B testing pitfalls, we hope you can identify when and where you might have fallen prey to them. We also hope we’ve armed you with a better understanding of some of the statistics and probability concepts involved in A/B testing that often feel like the domain of people with math degrees.

The steps below help you avoid these pitfalls and focus on achieving better results from your A/B testing:

  • Carefully consider the right metric for the test based on relevant business goals.
  • Decide on a confidence level before the test starts, and adhere to this threshold when evaluating the results after the test ends.
  • Calculate the sample size (number of visitors) before the test is started.
  • Wait for the calculated sample size to be reached before stopping the test.
  • Adjust the confidence level when doing post-test segmentation or evaluating more than one alternative, for example, by using the Bonferroni correction.

On this page

  • Recommended courses
  • Certification
  • Instructor-led training
  • Browse content library
  • View all learning options

Documentation

  • Documentation home
  • Experience Cloud release notes
  • Document Cloud release notes
  • Community home
  • Advertising Cloud
  • Audience Manager
  • Campaign Standard
  • Experience Cloud
  • Experience Manager
  • Experience Platform
  • Marketo Engage
  • Feedback Program
  • Experience Cloud support
  • Document Cloud support
  • Community forums
  • Adobe Developer
  • Adobe status

Adobe Account

  • Log in to your account
  • Manage my account
  • Corporate responsibility
  • Investor Relations
  • Supply chain
  • Trust Center
  • Diversity & Inclusion
  • COVID-19 Responses
  • All Test Patterns
  • Guides & eBooks
  • Templates & Blueprints
  • Conversion Case Study Packages
  • Conversion Rate Optimization Service
  • A/B Testing Services
  • Conversion Rate Optimization Training for Corporate Sector
  • Highly Tailored Coaching Sessions for CRO Needs
  • Hire a Speaker For A/B Testing & Conversion Rate Optimization
  • Exceptional conversion Copywriting Services for CRO & A/B Testing
  • A/B Test Case Studies
  • White Label Ebooks for clients Ebooks

Google Optimize: Creating and Launching an A/B test

The complete guide: a comprehensive step-by-step tutorial to create and launch a/b tests.

By: Deborah O'Malley, M.Sc | Last updated December, 2021

traffic allocation option within a/b tests

In the free A/B testing platform , Google Optimize , it's easy to set-up your first A/B test .

But, so that you can accurately, and confidently, perform the set-up, this article breaks down exactly what you need to do -- and know -- with definitions, examples, and screenshots.

Setting-Up a Test Experience

Imagine that you're about to set-up and run your first A/B test. Exciting times!

Let's say you've decided to test the button copy on your sign-up form. You want to know if the text "sign up now" converts better than "get started".

Well, by running an A/B test in Google Optimize , you can definitively determine if one text variant will outperform.

Creating Your Experience

To begin, you simply need to open Google Optimize , ensure it's set-up for your specific site and click the "Create" button in the upper right hand corner of your screen in Google Optimize.

Doing so will create a new test experience:

traffic allocation option within a/b tests

A screen will slide out from the right side, asking you both to name your experiment, as well as define what kind of test you’re going to run:

traffic allocation option within a/b tests

Naming Your Test

You'll first need to name the test.

The test name should be something that is clear and recognizable to you. 

*Hint* - the name should be both memorable and descriptive. A title both you and your colleagues or clients will immediately understand and recognize when you come back to it in several months or even years time.

For this example, we’ll call the test “CTA Button Test”

You’ll next be prompted to type in the URL of the webpage, or website , you’d like to use. 

This URL is very important to get right. 

It’s crucial you input the URL of the control -- which is the original version you want to test against the variant(s).

If you’re running a re-direct test and you select the URL of the variant, the variant will be labeled as the control in Google Optimize and it will totally mess up your data analysis -- since you won’t be able to accurately compare the baseline, or performance of the original version against the variant.

Trust me! I’ve seen it happen before; it’s a mistake you want to avoid.

So, make sure you properly select the URL of the page where your original (the control) version currently sits on your site. 

If the element you want to test is a global element, for example, a top nav bar button that shows on all pages across the site, you’ll use the homepage URL.

In this example, the button we’re testing is in the top nav header and shows on every page of the site. So, we’ll use the website's homepage URL: https://convertexperts.com/

We’ll enter that URL into the URL field:

traffic allocation option within a/b tests

Defining Your Test Type

In order to accurately run the proper test, you'll need to choose the correct type of test to run.

In Google Optimize, there are four different test type options:

  • Multivariate test
  • Redirect test
  • Personalization

traffic allocation option within a/b tests

Most of the time, you'll be a running a straight-up A/B test. But to know for sure which test type is best for your needs, check out this article .

For our example, we just want to set-up a simple A/B test looking at the effect of changing the CTA button.

For your test, once you've confirmed the type of test you're going to run, you're ready to officially set-up the test, or as Google Optimize calls it, "create the experience."

To do so, simply press the blue "Create" button in the upper right hand corner:

traffic allocation option within a/b tests

Adding the Variants

Once you've created the experience, you’re now ready to continue setting up the test, starting with adding your variant(s).

The variant, or variants, are the versions you want to test against your control. Typically, in an A/B test, you have one variant (Version B).

But, as long as you have enough traffic , it's perfectly legitimate to run an A/B/n test with more than one variant (where n stands for any n umber of other variants.)

To set-up your variant(s), sSimply click the blue “Add variant” button:

traffic allocation option within a/b tests

Next, name the variant with a clear, descriptive, memorable title that make sense. Then click “Done”:

traffic allocation option within a/b tests

Woo hoo! You’ve just set-up your A/B test! 🙂

But, your work isn’t done yet. . . now you need to edit your variant. 

Editing the Variant

Remember, the variant is the version you want to test against the control.

To create the variant, you can use Google Optimize’s built-in WYSIWYG visual editor.

Or, for more complex tests and redesigns, you might want to inject code to create the new version.

To do so, you click “Edit”:

traffic allocation option within a/b tests

Note, the original is the version currently on your site. You can not edit this page in anyway through Optimize. The name cannot be changed, nor can the page URL.

You’re now brought into an editor where you can inject code or get a visual preview of the webpage itself. 

Using the visual editor, you can click to select the element you want to edit, and make the change directly. 

Optimize’s visual editor is pretty intuitive, but if you’re unsure what elements to edit, you can always refer to this guide .

In this example, you see the visual editor.

To make changes, you'd first click to select the “Get a Free Analysis” button text, and then click to edit the text:

traffic allocation option within a/b tests

Now, type in the new text, “Request a Quote” and click the blue “Done” button at the bottom right of the preview editor screen:

traffic allocation option within a/b tests

When you're happy with all changes, click the top right "Done" button again to exit out of the preview mode :

traffic allocation option within a/b tests

You're now brought back into the Optimize set-up:

traffic allocation option within a/b tests

Here, you could continue adding additional variants, in the same way, if you wanted.

You could also click the "Preview" button to preview the variants in real-time.

Assigning Traffic Weight

Once you've assigned and defined your variants, you're going to want to state the weight of traffic, or what percentage of traffic will be allocated to each variant.

The default percentage is a 50/50% split meaning half of visitors (50%) will see the original version and the other half (50%) will see the variant.

As a general testing best practice, traffic should be evenly split, or when testing two versions, weighted 50/50.

As explained in this article unequal allocation of traffic can lead to data discrepancies and inaccurate test results.

So, as a best practice, don't change this section.

But, if for some reason, do you need to change the traffic weight, you can do so by clicking on the link that says "50% weight":

traffic allocation option within a/b tests

A slide-out will then appear in which you can click to edit the weight to each variant. Click the "Custom percentages" dropdown and assign the weight you want:

traffic allocation option within a/b tests

If you were to assign an 80/20% split, for example, that would mean the bulk, 80% of traffic, would be directed towards the control and just 20% of visitors would see the variant.

This traffic split is very risk adverse because so much of the traffic is diverted to the control -- where there is no change.

If you find yourself wanting to allocate traffic in this way, consider if the test itself should be run.

Testing is itself a way to mitigate risk.

So, if you feel you further need to decrease risk by only showing a small portion of visitors the test variant, the test may not actually be worth doing.

After all, testing takes time and resources. So, you should do it properly. Start with evenly splitting traffic.

Setting-Up Page Targeting

You're now ready to set-up the page targeting.

Page targeting means the webpage that is being "targeted" or tested in the experiment.

You can target a specific, single page, like the homepage, a subset of pages, like all pricing pages, or all pages on the site.

In this test example, we want to select any URL that contains, or includes a certain URL path within the site.

We're, therefore, going to set-up our Google Optimize test with a URL that “Contains” the substring match. We'll do so by going to the pencil or edit symbol, clicking on it:

traffic allocation option within a/b tests

And, selecting “Contains” from the dropdown menu:

traffic allocation option within a/b tests

This rule is saying, I want all the URLs that contain, or have, the www.ConvertExperts.com URL to be part of the test. 

In contrast, if we had selected “Matches”, the test would only be on the homepage www.ConvertExperts.com , because it would be matching that URL.

If you’re unsure which parameter to select for your test, you can consult this Google article .

If you want to verify the URL will work, you can check your rule. Otherwise, click “Save”:

traffic allocation option within a/b tests

Audience Targeting

Now, you can customize the test to display for only certain audiences, or behavior.

To do so, simply click “Customize”:

traffic allocation option within a/b tests

Here you can set a variety of parameters, like device type and location, if you only want certain viewers taking part in the test:

traffic allocation option within a/b tests

Note, if you want to parse out results by device type, that reporting is done in Google Analytics , and should NOT be activated within Google Optimize.

However, if you only wanted mobile visitors, for example, to take part in the test, then you’d select the “Device Category” option and choose only mobile visitors.

In this test example, we don’t have any rules we’d like to segment by, so we’ll leave everything as is.

Describing the Test

Next, you can add a “Description” about the test.

This step is optional, but is a good practice so you can see your test objective and remind yourself of the hypothesis . 

Adding a description also helps keep colleagues working with you on the same page with the test.

To add a “Description” simply, click the pencil to edit:

traffic allocation option within a/b tests

Then, add your description text:

traffic allocation option within a/b tests

Defining Test Goals

You're now ready to input your test goals.

"Goals" are defined as the conversion objectives or Key Performance Indicator (KPI) of what you're measuring from and hoping to improve as a result of the experiment.

You may have one single goal, like to increase form submissions. Or many goals, like increasing Clickthrough Rates (CTRs) and form submissions.

Your goals may be conversion objectives that you've newly set, or might tie-in to the existing goals you've already created, defined, and are measuring in Google Analytics.

To set-up a new goal, or select from your existing goals, simply click the “Add experiment objective” button:

traffic allocation option within a/b tests

You'll then have the option to either choose from already populated goals in Google Analytics, or custom create new goals.

Note, if you're using existing goals, they need to have already been set-up in Google Analytics and integrated with Google Optimize. Here's detailed instructions on how to link Google Analytics into Google Optimize.

traffic allocation option within a/b tests

For this example, we want to “Choose from list” and select from the goals already created in Google Analytics (GA):

traffic allocation option within a/b tests

The GA goals now show-up as well as other default goals that you can select:

traffic allocation option within a/b tests

In this example, we want to measure those people who reached the thank you page, indicating they filled out the contact form. We, therefore, select the "Contact Us Submission" goal:

traffic allocation option within a/b tests

We can now add an additional objective. Again, we’ll “Choose from list”:

traffic allocation option within a/b tests

In this case, we also want to see if the button text created a difference in Clickthrough rate (CTR) to the form page.

Although this goal is very important it's labelled as the secondary goal because contact submissions are deemed more important than CTR conversions:

traffic allocation option within a/b tests

Email Notifications

It's completely optional, but under the “Settings” section, you can also select to receive email notifications, by sliding the switch to on:

traffic allocation option within a/b tests

Traffic Allocation

Traffic allocation is the percentage of all visitors coming to your site who will take part in the test. 

Note, this allocation is different than the weight of traffic you assign to each variant. As described above, weight is the way you split traffic to each variant, usually 50/50.  

Of that weighted traffic, you can allocate a percentage of overall visitors to take part in the test.

As a general best practice, you should plan to allocate all (100%) of traffic coming to your site to the test experiences, as you’ll get the most representative sample of web visitors. 

Therefore, you shouldn't need to change any of the default settings.

However, if you’re not confident the test will reveal a winner, you might want to direct less than 100% of the traffic to the experiment.

In this case, you can change the traffic allocation from 100% of visitors arriving at your site to a smaller percentage by clicking the pencil to edit the value here (simply drag the slider up or down):

traffic allocation option within a/b tests

Note that the smaller the percentage of total traffic you allocate to your test, the longer it will take for you to reach a statistically significant result.

As well, as explained in this article unequal allocation of traffic, or reallocation of traffic mid-test can lead to data discrepancies and inaccurate test results. So once you've allocated, ideally, 100% of your traffic to the test, it's best to set it and forget it.

Activation Event

By default, “Page load” is the activation event, meaning the experiment you've set-up will show when the webpage loads. 

So long as you want your test to show when the page loads, you’ll want to stick with the default “page load” setting.

If you’re testing a dynamic page -- one which changes after loading -- or a single page application that loads data after the page itself has populated, you’ll want to use a custom “Activation event” by clicking the pencil tool and selecting the activation event from the dropdown menu that fits best for you:

traffic allocation option within a/b tests

An activation event requires a data layer push using this code: dataLayer.push({‘event’: ‘optimize.activate’}); You can learn more here .

Note, in the free version of Google Optimize, you can choose up to one primary objective and two secondary objectives. 

Once these objectives are selected and your experiment launched, you can’t go back and change them. 

So, make sure you think about how you want to track and monitor conversions before you launch your test!

Prior to Launch

With all your settings optimized, you’re nearly ready to start your test! 

Preview Your Experiences

But, before launching, it’s always a good idea to preview your experiences to make sure everything looks good.

To do so, click on the “Preview” button in the Variants section and select the appropriate dropdown menu for the view you want to see:

traffic allocation option within a/b tests

My recommendation is to individually preview each variant in web, tablet, and mobile preview mode:

traffic allocation option within a/b tests

Confirm and Debug Your Tags

Next, you’ll want to click the “Debug” option within the Preview mode:

traffic allocation option within a/b tests

Clicking into the Debug mode will bring up the website you’re testing and will show you a Google Tag Manager (GTM) screen, with the targeting rules for the tags that will be firing:

traffic allocation option within a/b tests

If there are any issues, you can debug them now -- before launching your experiment.

Get Stakeholder Approval

If you’re working with stakeholders, or clients, on the test, it’s a good idea to let them know the test is set-up and ready to launch, then get their approval before you start the test.

Previewing the test variants, and sending them screenshots of the preview screens will enable you to quickly and efficiently gain stakeholder approval.

You're then ready to launch the test! 🙂

Launching the Test

With all your i’s dotted and t’s crossed, you’re ready to launch your test!

To do so, simply click the “Start” button at the top right of the screen.

traffic allocation option within a/b tests

And, ta da! You’ve just set-up an A/B test in Google Analytics.

Congratulations. Well done!

Your Thoughts

Hope you found this article helpful!

Do you have any thoughts, comments, questions?

Share them in the Comments section below.

guest

Other Posts You Might Enjoy

Can you trust large uplifts in your test results.

One of the most debated testing topics is how large does my sample size need to be to get trustworthy test results? Some argue samples of more than 120,000 visitors per variant are needed to begin to see trustworthy test results. Ishan Goel of VWO disagrees. What does he think is needed to get trustworthy test results? Listen to this webinar recording to find out.

Buttons vs. links: which wins?

To get users clicking your content, which format works best: buttons or links. A series of 8 real-life A/B tests suggests one format consistently outperforms. Can you guess which version wins? Checkout the mini meta analysis to find out.

Overcome GA4 limitations with Google Tag Manager

Learn how using Google Tag Manager (GTM) helps you overcome many of the limitations with Google Analytics 4 (GA4). Find out the best ways to use GTM to manage and derive the most meaningful data from GA4.

checkbox_transparent-new-green-512

User name/email address:

Remember me

Reset Password

Username or Email

FREE SIGN UP

Get free a/b test case studies, and more valuable optimization content, sent to you every other week., now, just enter your first name and create a password. then, you're all set, get money-making a/b tests, ideas and insights to increase conversions.

adt

Group Annual Plan

Individual annual plan, individual monthly plan.

Stripe

Adobe Test & Target: An Adobe A/B Testing Guide [2022 Update]

traffic allocation option within a/b tests

Adobe Target, also known as Test and Target, is Adobe’s optimisation solution hosted within the  Adobe Experience Cloud  platform.

As well as offering a powerful solution for A/B testing, Adobe Target also enables multivariate testing, personalised and dynamic content solutions and mobile app optimisation.

Recent updates including the recommendation engine and integration with Adobe Experience Platform provide even more capability to A/B test with known and unknown visitors and provide better customer experiences.

A Brief Adobe Target A/B testing Tutorial

If you’re not familiar with A/B testing (also known as bucket tests and split-run testing), it’s a process commonly used by marketers to assess two or more variants of information across websites, emails or mobile apps:

  • Content  – copy, imagery etc.
  • Navigation  – how the user moves through the site or across a page.
  • Styling  – the look and feel.

The purpose of Adobe Target A/B testing is usually to identify the best performing variant in terms of conversion, revenue or engagement. This could be the viewing of a specific webpage, time spent on the website, click-through rate, or any other variable right through to the completion of an order.

How Adobe Target handles A/B testing

A/B tests are handled in two main ways within Adobe Test and Target feature:

  • Using a single URL  where different experiences are created for each test by amending the content, navigation or styling for the page. This is the most popular Adobe Target A/B testing option.
  • Using a URL redirect  where each experience has its own dedicated URL, e.g. Experience A uses homepage.com and Experience B uses www.homepage.com/version2.

URL redirects are more common when evaluating two differing purchase funnels, where the steps the customer takes between selecting a product or service and completing the purchase are substantially different, or where multiple versions of a page have been developed.

In both cases, Experience A is usually set as the standard version and control experience, and Experience B is used to test different variables.

Adobe Test and Target includes an online editor to create simple A/B tests by altering content, such as changing the page heading or using an alternative hero image. The online editor also caters for basic style changes of a page, such as amending the background colour of a text box or changing the text colour.

The online editor also offers the option to change the HTML and CSS where more complex changes to the content, styling or navigation are required.

Targeting / Audiences

Adobe Test and Target provides the option to specify audiences the A/B test is appropriate for. Tests can either be set to run for  All Visitors , or only to run when the visitor is identified as being part of a specified audience.

The platform includes several predefined audiences including the following:

  • Browser specific  – Goole Chrome, Firefox, Internet Explorer, Safari etc.
  • Operating System specific  – Mac OS, Windows OS etc.
  • Tablet Device specific  – Using a tablet device
  • Search engine referrals  – Arrived at the website via Google, Yahoo, Bing etc.

Audiences created elsewhere in the Adobe Experience Cloud are also available for use within Adobe Test and Target. For example, integrating Adobe Experience Platform audiences in Adobe Target allows targeting for known CRM contacts as well as unknown website visitors.

Audiences can be combined and used as the targets for the test or set as exclusions.

For example, you may wish to run Adobe Target’s A/B testing capabilities for the home page to change the hero image but only if the visitor was not referred to the site via a search engine. In this scenario you would  target All Visitors  but exclude the audiences related to search engine referrals.

In addition to the predefined and Adobe Experience Cloud audiences. Adobe Target also allows users to create new audiences based on several additional rules, such as the pages the visitor has viewed and referring sources, such as specific campaign URLs.

Adobe Test and Target features also provide an option to create more complex audiences using Profile Scripts written in JavaScript. A typical scenario for using a Profile script is where an organisation offers an affiliate program with many participants. The Profile Script can be developed to group all visitors referred from affiliate URLs into a single audience.

New Adobe Target feature: Recommendation engine

Audiences today expect to be recommended relevant content. Media companies like Netflix, Spotify and YouTube do this exceptionally well, but personalised recommendations are also core to the consumer experience in travel, retail, B2B sales, web publishing and more industries.

Adobe Target introduced a recommendation engine that uses AI to provide relevant and personalised recommendations in real-time.

Marketers provide the context for what kind of recommendations to provide, then Adobe Target analyses millions of data points to recommend the most relevant items from the company’s catalogue, such as:

  • Further reading/related articles

Adobe Target’s recommendation engine lets you define the audience, criteria and design for recommendations.

  • Audience – You can use existing audiences (and those brought over from Adobe Experience Cloud) or one of 14 built-in audiences.
  • Criteria – Think of criteria like a recipe (albeit a highly advanced one) that determines which content, product or service recommendations a visitor receives.
  • Design – Adobe Target features 10 built-in design templates, or you can create your own design for recommendations.

Read more about implementing Adobe Target’s recommendation engine here .

Traffic Allocation

Allocating traffic during the A/B test can be managed in two ways within Adobe Target:

  • Manual/user defined  – This is set by the user when building the A/B test and is based on percentage splits between each experience. For example, 50% of visitors to Experience A and 50% to Experience B is the default method.
  • Auto-allocate  – This option automatically allocates traffic to the best-performing experience. Adobe Test and Target will split the traffic equally between each experience and after a period of analysis, will allocate the traffic to the best performing experience, based on the goals for the A/B test. This option is commonly used when there is a revenue-based goal such as an e-commerce order or an online donation.

Goals & Reporting

Alongside its leading Test and Target features, the platform has in-built reporting capabilities to track the performance of each A/B test. Goals are split into three measures:

  • Conversion  – Viewed a specific page(s) or clicked a specific element(s)
  • Revenue  – Revenue per visitor or order.
  • Engagement  – Page views or time spent on the site.

A primary goal is set for each A/B test which is used in the auto-allocate traffic allocation mentioned above (if selected). Multiple additional metrics can also be added.

An example of a primary goal could be:

  • The visitor reached the order confirmation page;

and a supporting additional metric could be:

  • The visitor viewed the account details page.

Managing Tests

When planning A/B tests, it’s important to avoid conflicts between multiple tests, and it’s worth considering any upcoming events or campaigns that could impact the measurement of success of each test.

How long an Adobe Target A/B test should run for varies depending on the number of experiences included in that test, the traffic volume and the type of allocation. In general, lower traffic volumes or tests with more than two experiences, usually require longer test durations.

We hope this brief Adobe Target A/B testing tutorial has given you a better understanding of how Adobe Test and Target features works. Once you get to grips with all the platform’s features then it can help you enhance the performance of your campaigns considerably.

Learn about TAP CXM’s Adobe Target training services.

Related posts

traffic allocation option within a/b tests

A review of Adobe Campaign Classic V8

traffic allocation option within a/b tests

How To Assign Permissions In Adobe Campaign

traffic allocation option within a/b tests

What are Personalisation Blocks in Adobe Campaign?

Start the Martech Procurement Assessment

traffic allocation option within a/b tests

Submit to see your results

clock.png

Almost there, please reconfirm your details.

For the MarTech assessment tool.

  • First Name *
  • Last Name *
  • How can we help?

TAP Academy Training

traffic allocation option within a/b tests

How does Traffic Allocation works in an Activity in Adobe Target ?

某些 Creative Cloud 应用程序、服务和功能在中国不可用。

traffic allocation option within a/b tests

Let 's take up a use-case where we have an A/B activity such that 80% of traffic is assigned to (Control A) & 20% (Treatment B) and the we move to 50% (Control A) / 50% (Treatment B), then eventually  0% (Control A) / 100% (Treatment B).Hence After we went to 100% (Treatment B), How does  users get assigned to experiences ?

If you manually change the % split to 100% for Experience B,  Visitors who had previously been allocated to A (Control)  will stay in their initially-assigned experiences.  The traffic change will impact new entrants only.

It is recommended that, if you want to change percentages or greatly affect the flow of people into each experience, you should create or copy a new activity. Otherwise, if you change the percentages on different experiences, it will take a few days for the data to normalize again if many purchasers are return visitors. For example, if your A/B test is split 50/50, and then you change the split to 80/20, for the first few days after that change, the results might look skewed. If the average time to conversion is high, meaning it takes someone several hours or even days to make a purchase, these delayed conversions can affect the reports. So, in that first experience where the number went from 50 to 80, and the average time to conversion is two days, only people from the 50% of the population are converting on the first day of the test, although today 80% of the population is entering the experience. This makes it look like the conversion rate plummeted, but it will normalize again after these 80% of people have the two days to convert

Legal Notices   |   Online Privacy Policy

Language Navigation

  • History of cooperation
  • Areas of cooperation
  • Procurement policy
  • Useful links
  • Becoming a supplier
  • Procurement
  • Rosatom newsletter

© 2008–2024Valtiollinen Rosatom-ydinvoimakonserni

traffic allocation option within a/b tests

  • Rosatom Global presence
  • Rosatom in region
  • For suppliers
  • Preventing corruption
  • Press centre

Rosatom Starts Life Tests of Third-Generation VVER-440 Nuclear Fuel

  • 16 June, 2020 / 13:00

This site uses cookies. By continuing your navigation, you accept the use of cookies. For more information, or to manage or to change the cookies parameters on your computer, read our Cookies Policy. Learn more

  • / BUSINESS DIRECTORY
  • / WHOLESALE TRADE
  • / MERCHANT WHOLESALERS, NONDURABLE GOODS
  • / MISCELLANEOUS NONDURABLE GOODS MERCHANT WHOLESALERS
  • / RUSSIAN FEDERATION
  • / MOSCOW REGION
  • / ELEKTROSTAL
  • / STELS, OOO
  • Aleksei Dmitrievich Latynin Director

Dynamic search and list-building capabilities

Real-time trigger alerts

Comprehensive company profiles

Valuable research and technology reports

  • Bahasa Indonesia
  • Slovenščina
  • Science & Tech
  • Russian Kitchen

Why were so many metro stations in Moscow renamed?

Okhotny Ryad station in Soviet times and today.

Okhotny Ryad station in Soviet times and today.

The Moscow metro system has 275 stations, and 28 of them have been renamed at some point or other—and several times in some cases. Most of these are the oldest stations, which opened in 1935.

The politics of place names

The first station to change its name was Ulitsa Kominterna (Comintern Street). The Comintern was an international communist organization that ceased to exist in 1943, and after the war Moscow authorities decided to call the street named after it something else. In 1946, the station was renamed Kalininskaya. Then for several days in 1990, the station was called Vozdvizhenka, before eventually settling on Aleksandrovsky Sad, which is what it is called today.

The banner on the entraince reads:

The banner on the entraince reads: "Kalininskaya station." Now it's Alexandrovsky Sad.

Until 1957, Kropotkinskaya station was called Dvorets Sovetov ( Palace of Soviets ). There were plans to build a monumental Stalinist high-rise on the site of the nearby Cathedral of Christ the Saviour , which had been demolished. However, the project never got off the ground, and after Stalin's death the station was named after Kropotkinskaya Street, which passes above it.

Dvorets Sovetov station, 1935. Letters on the entrance:

Dvorets Sovetov station, 1935. Letters on the entrance: "Metro after Kaganovich."

Of course, politics was the main reason for changing station names. Initially, the Moscow Metro itself was named after Lazar Kaganovich, Joseph Stalin’s right-hand man. Kaganovich supervised the construction of the first metro line and was in charge of drawing up a master plan for reconstructing Moscow as the "capital of the proletariat."

In 1955, under Nikita Khrushchev's rule and during the denunciation of Stalin's personality cult, the Moscow Metro was named in honor of Vladimir Lenin.

Kropotkinskaya station, our days. Letters on the entrance:

Kropotkinskaya station, our days. Letters on the entrance: "Metropolitan after Lenin."

New Metro stations that have been opened since the collapse of the Soviet Union simply say "Moscow Metro," although the metro's affiliation with Vladimir Lenin has never officially been dropped.

Zyablikovo station. On the entrance, there are no more signs that the metro is named after Lenin.

Zyablikovo station. On the entrance, there are no more signs that the metro is named after Lenin.

Stations that bore the names of Stalin's associates were also renamed under Khrushchev. Additionally, some stations were named after a neighborhood or street and if these underwent name changes, the stations themselves had to be renamed as well.

Until 1961 the Moscow Metro had a Stalinskaya station that was adorned by a five-meter statue of the supreme leader. It is now called Semyonovskaya station.

Left: Stalinskaya station. Right: Now it's Semyonovskaya.

Left: Stalinskaya station. Right: Now it's Semyonovskaya.

The biggest wholesale renaming of stations took place in 1990, when Moscow’s government decided to get rid of Soviet names. Overnight, 11 metro stations named after revolutionaries were given new names. Shcherbakovskaya became Alekseyevskaya, Gorkovskaya became Tverskaya, Ploshchad Nogina became Kitay-Gorod and Kirovskaya turned into Chistye Prudy. This seriously confused passengers, to put it mildly, and some older Muscovites still call Lubyanka station Dzerzhinskaya for old times' sake.

At the same time, certain stations have held onto their Soviet names. Marksistskaya and Kropotkinskaya, for instance, although there were plans to rename them too at one point.

"I still sometimes mix up Teatralnaya and Tverskaya stations,” one Moscow resident recalls .

 “Both have been renamed and both start with a ‘T.’ Vykhino still grates on the ear and, when in 1991 on the last day of my final year at school, we went to Kitay-Gorod to go on the river cruise boats, my classmates couldn’t believe that a station with that name existed."

The city government submitted a station name change for public discussion for the first time in 2015. The station in question was Voykovskaya, whose name derives from the revolutionary figure Pyotr Voykov. In the end, city residents voted against the name change, evidently not out of any affection for Voykov personally, but mainly because that was the name they were used to.

What stations changed their name most frequently?

Some stations have changed names three times. Apart from the above-mentioned Aleksandrovsky Sad (Ulitsa Kominterna->Kalininskaya->Vozdvizhenka->Aleksandrovsky Sad), a similar fate befell Partizanskaya station in the east of Moscow. Opened in 1944, it initially bore the ridiculously long name Izmaylovsky PKiO im. Stalina (Izmaylovsky Park of Culture and Rest Named After Stalin). In 1947, the station was renamed and simplified for convenience to Izmaylovskaya. Then in 1963 it was renamed yet again—this time to Izmaylovsky Park, having "donated" its previous name to the next station on the line. And in 2005 it was rechristened Partizanskaya to mark the 60th anniversary of victory in World War II. 

Partizanskaya metro station, nowadays.

Partizanskaya metro station, nowadays.

Another interesting story involves Alekseyevskaya metro station. This name was originally proposed for the station, which opened in 1958, since a village with this name had been located here. It was then decided to call the station Shcherbakovskaya in honor of Aleksandr Shcherbakov, a politician who had been an associate of Stalin. Nikita Khrushchev had strained relations with Shcherbakov, however, and when he got word of it literally a few days before the station opening the builders had to hastily change all the signs. It ended up with the concise and politically correct name of Mir (Peace).

The name Shcherbakovskaya was restored in 1966 after Khrushchev's fall from power. It then became Alekseyevskaya in 1990.

Alekseyevskaya metro station.

Alekseyevskaya metro station.

But the station that holds the record for the most name changes is Okhotny Ryad, which opened in 1935 on the site of a cluster of market shops. When the metro system was renamed in honor of Lenin in 1955, this station was renamed after Kaganovich by way of compensation. The name lasted just two years though because in 1957 Kaganovich fell out of favor with Khrushchev, and the previous name was returned. But in 1961 it was rechristened yet again, this time in honor of Prospekt Marksa, which had just been built nearby.

Okhotny Ryad station in 1954 and Prospekt Marksa in 1986.

Okhotny Ryad station in 1954 and Prospekt Marksa in 1986.

In 1990, two historical street names—Teatralny Proyezd and Mokhovaya Street—were revived to replace Prospekt Marksa, and the station once again became Okhotny Ryad.

Okhotny Ryad in 2020.

Okhotny Ryad in 2020.

If using any of Russia Beyond's content, partly or in full, always provide an active hyperlink to the original material.

to our newsletter!

Get the week's best stories straight to your inbox

  • 7 things that the USSR unexpectedly put on WHEELS
  • Why did the USSR build subway stations inside residential buildings? (PHOTOS)
  • How Russian trains deal with winter

traffic allocation option within a/b tests

This website uses cookies. Click here to find out more.

traffic allocation option within a/b tests

Russia’s BN-800 refuelled with mox: full mox core planned for 2022

!{Model.Description}

The first full refuelling of Russia’s BN-800 fast reactor at unit 4 of the Beloyarsk NPP with only uranium-plutonium mixed oxide (mox) fuel was completed during the recent scheduled maintenance outage, fuel company TVEL (part of Rosatom) announced on 24 February. The unit, which was shut down on 8 January, has been reconnected to the grid and has resumed electricity production. The first 18 serial mox fuel assemblies were loaded into the reactor in January 2020, and another 160 fuel assemblies have now been added to them. Thus, the BN-800 core is now one-third filled with innovative fuel and in future only mox fuel will be loaded into the reactor.

“Beloyarsk NPP is now one step closer to implementation of the strategic direction for the development of the nuclear industry - the creation of a new technological platform based on a closed nuclear fuel cycle,” said Ivan Sidorov, Director of the Beloyarsk NPP. “The use of mox fuel will make it possible to involve in fuel manufacture the isotope of uranium that is not currently used. This will increase the fuel base of the nuclear power industry tenfold. In addition, the BN-800 reactor can reuse used nuclear fuel from other NPPs and minimise radioactive waste by “afterburning” long-lived isotopes from them. Taking into account the planned schedule, we will be able to switch to a core with a full load of mox fuel in 2022.”

The fuel assemblies were manufactured at the Mining and Chemical Combine (MCC, Zheleznogorsk, Krasnoyarsk Territory). Unlike enriched uranium, which is traditional for nuclear power, the raw materials for the production of mox fuel pellets are plutonium oxide produced in power reactors and depleted uranium oxide (obtained by defluorination of depleted uranium hexafluoride - DUHF, the secondary "tailings" of the enrichment plant.

“In parallel with loading the BN-800 core with mox fuel, Rosatom specialists are continuing to develop technologies for the production of such fuel at the MCC,” said Alexander Ugryumov, vice president for research, development and quality at TVEL. “In particular, the production of fresh fuel using high-background plutonium extracted from the irradiated fuel of VVER reactors has been mastered: all technological operations are fully automated and are performed without the presence of personnel in the immediate vicinity. The first 20 mox-FAs incorporating high-background plutonium have already been manufactured and passed acceptance tests, and they are planned to be loaded in 2022. Advanced technologies for recycling nuclear materials and refabrication of nuclear fuel in the future will make it possible to process irradiated fuel instead of storing it, as well as to reduce the amount of high-level waste generated.”

Serial production of mox fuel began at the end of 2018 at MCC. To achieve this, broad industry cooperation was organised under the coordination and scientific leadership of TVEL, which supplies the mox-fuel to Beloyarsk NPP. Initially, the BN-800 reactor was launched with a hybrid core, partly equipped with uranium fuel produced by Mashinostroitelny Zavod in Elektrostal (Moscow Region), and partly with experimental mox assemblies manufactured at the Research Institute of Atomic Reactors (NIIAR) in Dimitrovgrad, Ulyanovsk region).

  • Terms and conditions
  • Privacy Policy
  • Newsletter sign up
  • Digital Edition
  • Editorial Standards

traffic allocation option within a/b tests

IMAGES

  1. Choosing the right traffic allocation in A/B testing

    traffic allocation option within a/b tests

  2. Choosing the right traffic allocation in A/B testing

    traffic allocation option within a/b tests

  3. Sample Ratio Mismatch: What Is It and How Does It Happen?

    traffic allocation option within a/b tests

  4. Dynamic traffic allocation: optimize your A/B tests

    traffic allocation option within a/b tests

  5. Dynamic traffic allocation: optimize your A/B tests

    traffic allocation option within a/b tests

  6. Don't lose out on conversions with dynamic traffic allocation by AB Tasty

    traffic allocation option within a/b tests

VIDEO

  1. OES profile blank/info is missing*Last date of traffic allocation*Edit option for crypto withdrawal

  2. Traffic Sheet 1 Problems 1 & 2

  3. Traffic Sheet 1 Intoduction for Problem 3

  4. TRAFFIC PROBLEM 1

  5. Determining Traffic Rules at Specific Locations 0.10 Exam Accept 💯 Start 0.20 #toloka @Gullutips

  6. BFC34303 CIVIL ENGINEERING STATISTICS

COMMENTS

  1. Choosing the right traffic allocation in A/B testing

    The behavior of each traffic allocation option is as follows: Manual traffic allocation (The classic A/B testing approach) In a nutshell, with manual allocation, traffic is split evenly between variations until a single winner is declared.

  2. Dynamic traffic allocation: optimize your A/B tests

    Dynamic traffic allocation is sometimes presented as a revolutionary alternative to , or even as a competitor to predictive targeting. The reality is more straightforward: it is an optimized testing approach which does not address the same issues as "classic" A/B testing. Dynamic traffic allocation can resolve what is known as the multi ...

  3. Guide: Defining Traffic Allocation

    Traffic allocation is the fifth step of the campaign creation flow. When creating a test or a personalization campaign, you need to define the percentage of visitors allocated to the variation (s) (in case of a test) or scenario (s) (in case of a personalization) and the original version of your website.

  4. Creating an A/B Test

    An A/B Test consists of modifying one element on one page (e.g. homepage, basket page, etc.) or a string of equivalent pages that share the same layout (e.g. all of your product pages, all of your results pages, etc.), and measuring if the new variation (s) will be more or less performant than the original.

  5. Unequal Allocation of Traffic in A/B Tests: Pros and Cons

    Equal allocation is the default approach in which we allocate equal amounts of users to each test variant in an A/B test. If there is one variant, and one control group, we assign 50% of users to the control group and 50% to the variant. If there are 3 variants and one control, we would allocate 25% of users to each of them, under equal allocation.

  6. Guidelines for running effective Bayesian A/B tests

    The expected difference is 0.2%, so you need an A/B test that will tell you what is the true difference to a higher resolution. A good rule of thumb is to aim for a resolution that concentrates 95% of the Posterior Distribution in an interval that is the size of the expected difference. In this example, that means narrowing down the difference ...

  7. Dynamic allocation

    Dynamic allocation is an option on the Traffic allocation step of all A/B Tests. 📖 Definition Dynamic traffic allocation is based on a statistical research topic called " multi-armed bandit ". The goal of this algorithm is to limit loss due to the lowest performing variations.

  8. How to split the traffic in an A/B Test

    Option 1: Splitting the traffic when a new user lands on any of the website requires 92,448 users. Option 2: Splitting the traffic when a new user enters the shopping cart (where the test is set up) requires 63,220 users. Compared to Option 2, Option 1 requires 46.23 % more users. Option 2 is superior because it detects the same outcome faster ...

  9. Dynamic Allocation Optimization in A/B-Tests Using Classification-Based

    Recent development efforts around A/B-Tests revolve around dynamic allocation. They allow for quicker determination of the best variation (A or B), thus saving money for the user. However, dynamic allocation by traditional methods requires certain assumptions, which are not always valid in reality.

  10. PDF Beyond A/B testing: Automation within testing in Adobe Target

    In Adobe Target, when setting up an A/B test activity, you can allocate traffic to a test using one of three different options—Manual, Auto-Allocate, and Auto-Target.

  11. How To Do A/B testing With Google Analytics

    Create a new experiment in Google Optimize by clicking on "Create Experiment.". Choose the type of experiment (A/B test, multivariate test, or redirect test) and enter the page URL you want to test. Set the traffic allocation for each variant. This determines how to split traffic for A/B testing. For example, you can assign 50% of your ...

  12. How Do I Avoid Common A/B Testing Mistakes?

    Topics: A/B Tests A/B testing in Adobe Target forms the backbone of most digital marketing optimization programs, helping marketers offer optimized and targeted experiences to their visitors and customers. This article outlines ten of the most significant pitfalls that companies fall prey to when performing A/B testing.

  13. Google Optimize: Creating and Launching an A/B test

    The complete guide: a comprehensive step-by-step tutorial to create and launch A/B tests. By: Deborah O'Malley, M.Sc | Last updated December, 2021 Overview. Good news! In the free A/B testing platform, Google Optimize, it's easy to set-up your first A/B test.. But, so that you can accurately, and confidently, perform the set-up, this article breaks down exactly what you need to do -- and know ...

  14. Adobe Test & Target: An Adobe A/B Testing Guide

    2 Aug 2022 As well as powerful A/B testing features, Adobe Target enables multivariate testing, AI-driven recommendations and more to deliver the best user experience.

  15. Run A/B tests

    With A/B tests, you define two or more and then implement a different code path for each variation. From the Optimizely interface, you can determine which users are eligible for the experiment and how to split traffic between the variations, as well as the you will use to measure each variation's performance. Specify an experiment key.

  16. Get started with A/B test

    The Statistical Significance value for an A/B test in Acoustic Personalization should be within the range 50% to 100%. By default, the value is set to 90%. ... Need for traffic allocation. Traffic Allocation in A/B test is useful when, you are not confident of the A/B test being run. In this case, you can run A/B test with a few users (10%) and ...

  17. How does Traffic Allocation works in an Activity in Adobe Target

    Let 's take up a use-case where we have an A/B activity such that 80% of traffic is assigned to (Control A) & 20% (Treatment B) and the we move to 50% (Control A) / 50% (Treatment B), then eventually 0% (Control A) / 100% (Treatment B).Hence After we went to 100% (Treatment B), How does users get assigned to experiences ?

  18. Guide: Creating an AA Test

    Part 6: Traffic allocation . The traffic allocation step enables you to choose if you want to test all your targeted traffic or just a small part of it. In the case of an AA Test, you have to direct 100% of your traffic to the variation. To learn more about traffic allocation, read our complete guide. Part 7: Advanced options

  19. Rosatom Starts Life Tests of Third-Generation VVER-440 Nuclear Fuel

    The work is carried out within the contract between TVEL Fuel Company of Rosatom and Czech power company ČEZ a.s., which includes design and introduction of this fuel modification at Dukovany NPP in the Czech Republic. ... Life tests are carried out on a full-scale research hot run-in test bench V-440 and will last for full 1500 hours.

  20. STELS, OOO

    See other industries within the Wholesale Trade sector: Apparel, Piece Goods, and Notions Merchant Wholesalers , Beer, Wine, and Distilled Alcoholic Beverage Merchant Wholesalers , Chemical and Allied Products Merchant Wholesalers , Drugs and Druggists' Sundries Merchant Wholesalers , Farm Product Raw Material Merchant Wholesalers , Furniture and Home Furnishing Merchant Wholesalers , Grocery ...

  21. Why were so many metro stations in Moscow renamed?

    The Moscow metro system has 275 stations, and 28 of them have been renamed at some point or other—and several times in some cases. Most of these are the oldest stations, which opened in 1935.

  22. Russia's BN-800 refuelled with mox: full mox core planned for 2022

    The first 20 mox-FAs incorporating high-background plutonium have already been manufactured and passed acceptance tests, and they are planned to be loaded in 2022. Advanced technologies for recycling nuclear materials and refabrication of nuclear fuel in the future will make it possible to process irradiated fuel instead of storing it, as well ...