A/B Testing is one of the best way to compare two or more versions of an application or a web page. It enables you to figure out which one of them performs better and can produce better conversion rates. It is one of the simplest ways to analyze an application or a web page to create a new version that is more effective. This is a brief study notes that covers the fundamentals of A/B Testing with appropriate examples to illustrate how you can put it into practice.
This study notes has been designed to suit the requirements of all those professionals who are working in the software testing domain. It gives sufficient insight into the concepts of A/B Testing and how you can apply it to perform data analysis and maximize the conversion ratio of any site.
We assume that the readers of this study notes have fundamental knowledge of HTML and some experience of handling a website. In addition, it is going to help if the readers have an elementary knowledge of Data Analysis and Conversion ratio of sites and mobile applications.
A/B Testing (also known as Split testing) characterizes an approach to compare two versions of an application or a web page that enables you to figure out which one performs better. It is one of the easiest approaches to analyze an application or a web page to create a new version. Thereafter, both these versions can be compared to find the conversion rate, which further helps in finding the better performer of these two.
Let us expect that there is a web page and all the traffic is directed to this page. Presently as a part of A/B Testing, you have made some minor changes like headlines, numbering, etc. on the same page and half of its traffic is directed to the modified version of this web page. Now you have version A and version B of the similar web page and you can monitor the visitor’s actions using statistics and analysis to determine the version that yields a higher conversion rate.
A conversion rate is characterized as the instance, when any visitor on your site performs a desired action. A/B Testing enables you to determine the best online marketing strategy for your business. Take a look at the following illustration. It shows that version A yields a conversion rate of 15% and version B yields a conversion rate of 22%.
You can monitor the visitor’s activities using statistics and analysis to decide the version that yields a higher conversion rate. A/B Testing results are normally given in fancy mathematical and statistical terms, however the meaning behind the numbers are actually quite straightforward. There are two important methods through which you can check conversion rates using A/B Testing −
Let us discuss these two above methods in detail.
The number of samples depend upon the number of tests performed. The count of conversion rate is known as a sample and the process of collecting these samples is called as sampling.
Let us say you have two products A and B, you need to collect sample data as per its demand in the market. You can request a few people to choose from product A and B and then request them to participate in a survey. As the number of participant’s increase, it will begin showing a realistic conversion rate.
There are many different tools that can be used to determine the correct number of sample size. One such free tool available is −
Confidence interval is the measurement of deviation from the normal on the multiple number of samples. Let us accept that 22% of people prefer product A in the above example, with ±2% of confidence interval. This interval indicates the upper and lower limit of the individuals, who opt for Product A and is also called as margin of error. For best outcomes in this average survey, the margin of error should be as small as possible.
Let us assume that in Product B, we have included a minor change and then performed A/B Testing on these two products. Confidence interval product A and B are 10% with ±1% and 20% with ±2% respectively. So this shows that a minor change has increased the conversion rate. If we ignore the margin of error, conversion rate for test variation A is 10% and conversion rate for test variation B is 20%, for example a 10% increase in the test variation.
Now, if we divide the difference by control variation rate 10% ÷ 10% = 1.0 = 100%, it shows an improvement of 100%. Hence, we can say that A/B Testing is a technique based on mathematical strategies and analysis. There are various online tools that can be utilized to calculate A/B significance.
A/B Testing is used to settle business decisions based on the outcomes derived from data, rather than just making predictions. It permits you to create variations of your website or application and then helps you to confirm or discard your decision to make changes.
This testing permits you to enhance your site or application in such a way that it increases the conversion rates. A higher conversion rate means getting more value from your existing users instead of having to pay more on traffic acquisition. A/B Testing can also assist you to change other factors in business like changing work culture, and so on. It helps you to utilize mathematical data and statistics to determine the direction of your product variations.
Either you are a designer, a business analyst, or a developer, A/B Testing gives a simple way to use the power of data & statistics to reduce risks, improve results, and become more data-driven in your work.
To run an A/B Test, you should think about the following points −
Continuously perform A/B Testing if there is probability to beat the original variation by >5%.
Test should be run for a considerable amount of time, so that you should have enough example data to perform statistics and analysis.
A/B Testing also empowers you to gain maximum from your existing traffic on a webpage. The expense of increasing your conversion is minimal as compared to the cost of setting up the traffic on your website. The ROI (return on investment) on A/B Testing is huge, as a few minor changes on a site can result in a significant increase of the conversion rate.
A/B Testing is about creating various variations of an application or a site and then comparing all these versions utilizing samples to determine the best variation that has the maximum conversion rate. There are various types of variations, which can be applied to a website page or an application. A/B Testing isn't limited to an application or a website page only, as you can create variations for different products as well. Anything on a web page that can affect the behavior of a visitor while browsing on the site can be tested using A/B Testing.
Here is a list of A/B Testing variations that can be applied on a website page −
There are different tools that can be used to create variations that you will read in detail later in this study notes.
A/B Testing comprises of a set of cycles that one must follow sequentially in order to arrive at a realistic conclusion. In this section, we will talk in detail the steps of A/B Testing process that you can use to run tests on any website page −
Background research plays a critical role in A/B Testing. The initial step is to discover out the bounce rate of the website. This can be possible with the help of several widely available background research tools like Google Analytics and others.
Data from Google Analytics can assist you to discover visitor behaviors on the websites. It is always advisable to collect enough data from the site. Try to discover the pages with low conversion rates or high drop-off rates that can be further improved. Additionally calculate the number of visitors per day that are required to run this test on the website.
The next step is to set your business or conversion goals, which will help in understanding what the objective is. When that is done, then you can discover the metrics that determine whether or not a new version is more successful than its original version.
Once goal and metrics have been set for A/B Testing. The next step is to discover ideas on how to improve the original version and how to make it better than the current version. Once you have a list of thoughts, prioritize them in terms of expected impact and trouble of implementation.
For example, one of the most effective thing is to add pictures to a site, which will help in decreasing the bounce rate to some extent.
There are many A/B Testing tools in the market that has a visual editor to make these changes successfully. The key decision to perform A/B Testing successfully is by selecting the correct tool. Some of the most commonly available tools are −
There are various types of variations that can be applied to an object like using bullets, changing numbering of the key elements, changing the font and color, etc.
Present all the variations of your site or applications to the visitors. Their actions will be monitored for each and every variation. Besides, this visitor interaction for every variation is measured and compared to determine how a particular variation performs.
Once this experiment is finished, the next step is to analyze the results. A/B Testing tool will present the data from the experiment and will disclose you the difference between the performance and efficiency of various versions of a website page. It will also show if there is a important difference between variations with the help of mathematical methods and statistics.
For example, if the pictures on the website page have reduced the bounce rate, you can add in more pictures to increase the conversion. If you see no change in bounce rate because of this, return to the previous step to create a new hypothesis/variation to perform a new test.
The data from Google Analytics can assist you to find visitor behaviors. It is always advisable to gather enough data from the site. Try to discover the pages with low conversion rates or high drop-off rates that can be improved. In this section, we will talk about a few tools which can be utilized to collect data for A/B Testing.
Most of the websites have Google Analytics installed to get an idea of how visitors interact with the site. If you don’t have Google Analytics installed to monitor traffic, you can install it from the web. Google Analytics is one of best analytic tools available for free of cost.
To install Google Analytics on your site, you can simply copy the code and deploy it on your website and you will get a good amount of data to work with. You can also apply customization of the tool to meet your business objectives.
Replay tools are utilized to get a better insight of user actions on your website. It also permits you to click maps and heat maps of user clicks to check how far the users are browsing on the site.
Replay tools like Mouse Flow permits you to view a visitor's session in a way, as if you are with the visitor itself. Video replay tools give deeper insight into what it would be like for that visitor browsing the different pages on your site.
Survey tools are utilized to collect qualitative feedback from the website. This includes asking returning visitors some survey questions. This survey asks them general inquiries and also allows them to enter their views or select from pre-provided choices.
Live chat facility permits visitor to get quick answers from customer service team and help resolve the situation quicker. This also helps you to get the general inquiries from the visitors and to gather data for testing.
The next step is to set your conversion goals. Discover the metrics that decide whether or not the variation is more successful than the original version. Goals come from your business objectives, so as an example, if you have to increase the sale of garments in terms of objectives, it can be as −
Next is to characterize metrics that meet your business objectives. A metric becomes a KPI (Key Performance Indicator) only when it is measuring something associated to your objectives.
Your Garment store’s business goal is to sell garments, so the KPI of this business objective could be the number of garments sold on the web. You need to have your business objectives clearly defined otherwise you won't able to identify your KPI’s. If you set the KPIs correctly and measure them periodically, you will keep your strategy on track to create variations and perform A/B Testing. Next is to discover the target metrics for your business objectives.
Your cloth store sold 100 products last week. Is this OK or bad? For your KPIs to mean something for you, they need target metrics. Define a target for each KPI that is essential to you. Once you define business goals and target metrics then you have a framework, which will assist to determine if the work you will be doing is relevant to your business objectives or not.
After identifying your business goals, the next step is to generate A/B Testing ideas and hypothesis for why you think they will be superior than the current version. Create a list of all hypothesis that you think you can create, prioritize all variations in terms of the expected impact and how to implement them utilizing different tools.
You can decrease the bounce rate by adding more pictures at the bottom. You can add links of different social sites to increase the conversion rate also.
As A/B Testing is about creating new versions of an application or a website page and then comparing all versions to see the conversion rate. You can improve the conversion rate by investigating the statistics to check new variations.
There are various types of variations that can be applied to an object like utilizing bullets, changing numbering of the key elements, changing the font and color, etc. There are many A/B Testing tools in the market that has a visual editor to make these changes effectively. The key decision to perform A/B Testing successfully is by choosing the correct tool. Some of the most normally available tools are −
Visual Website Optimizer empowers you to test different versions of a same page. It also contains ‘what you see is what you get’ (WYSIWYG) editor that enables you to make the changes and run tests without changing the HTML code of the page. You can update headlines, numbering of elements and run a test without making changes to IT assets.
To create variations in VWO for A/B Testing, open your website page in the WYSIWYG editor and then you can apply the below changes to the website page −
This permits you to create up to five variations of any web page and then load all pages to Google Analytics to perform A/B Testing. Google Content Experiment is utilized to measure the results of all the variations and to decide the variant with the maximum conversion rate. The primary advantage of using Google Content Experiments is that it is a freeware from Google, but you have to load the variants into Google Analytics to perform the test.
Optimizely is a device used to perform A/B Testing, multivariate testing on a web page or on a mobile app and permits you to compare different versions of a web page or an application to decide, which variety provides a better conversion rate for your business.
To test a mobile application utilizing Optimizely, it runs via a Software Development Kit for iOS and/or Android. Optimizely running on your site collects data of site visitors and conversion rate and runs them on Stats Engine to determine, which variation is a winner or a loser. Once these stats are compared to target goals and set metrics, they help you to make decisions about the variation to be applied on the site.
Optimizely permits you to perform these tests −
It involves introducing all variations of your site or an application to the visitors and their actions are monitored for every variation. Visitor interaction for each variation is measured and compared to determine how this variation performs.
As discussed in the previous chapter, there are different tools that can be utilized to generate hypothesis and to run the variations −
There are different A/B Testing tools that permits marketing professionals to create multiple variations of their website pages by using a point-and-click editor. It doesn’t need any HTML knowledge and you can check which version produces the maximum conversion rate or sales.
Executing VWO split testing software is very simple as you just need to copy paste the code snippet in your site and you can easily make it available to visitors. Visual Website Optimizer also gives an option of multivariate testing and contains other number of tools to perform behavioral targeting, heat maps, usability testing, and so on.
There are various features in VWO that ensures all your conversion rate optimization activities are secured by this tool. Many enterprises and small scale online stores are using A/B Testing VWO software for landing page optimization and for increasing their website sales and improving conversion rates too.
Company also provides a 30 days’ trial that can be downloaded free from − https://vwo.com/.
Some of the key features of VWO are as follows −
Optimizely running on your website page collects data of site visitors, conversion rate and runs them on Stats Engine to decide, which variation is a winner and which one is a loser. Once these stats are compared with target goals and set metrics, it will assist you to make decisions about the variation to be applied on the site.
It permits you to create up to five variations of a page and then load all these pages to Google Analytics to perform A/B Testing.
To begin with Google Analytics, you need to have a Google Analytics account and a tracking code to be installed on your site. If you don’t have an account, you can join using the following tool − http://www.google.com/analytics/
Adding tracking code directly to a website
To finish this process, you must have access to your website source code, you should also be comfortable editing HTML (or have a webmaster/developer, who can assist you with this), also you should have a Google Analytics account and property already set up.
To set up tracking code into your webpage
Discover the tracking code snippet and sign in to your Google Analytics account, and select the Admin tab at the top.
Go to the ACCOUNT and PROPERTY tab, select the property you’re working with. Click on Tracking Info → Tracking Code. Picture of where you discover your tracking code in your Analytics account → Click to expand this image and see where these options appear in the interface.
The tracking code contains a unique ID that corresponds to each Google Analytics property. Don’t mix up tracking code snippets from various properties, and don’t reuse the same tracking code snippet on multiple domains.
Copy the snippet and paste into each website page you want to track. Paste it immediately before the closing </head> tag.
If you use templates to dynamically generate pages for your site, you can paste the tracking code snippet into its own file, then include it in your page header.
Verify if the tracking code is working
You can confirm if the tracking code is working, check real time reports, you can also monitor user activity as it happens. If you see data in these reports, it means that your tracking code is currently gathering the data.
Content Experiments is one of the fastest method to test web pages - landing pages, homepage, category pages and it requires fewer code implementations. It can be utilized to create A/B Tests inside Google Analytics.
Some of the most common features of Content Experiments are −
You need to utilize original page script to run tests, the standard Google Analytics tracking code will be utilized to measure goals and variations.
Target goals that are defined on Google Analytics can be utilized as the experiment goal, including AdSense revenue.
The Google Analytics segment builder can be used to segment results dependent on any segmentation criteria.
It permits you to set tests that automatically expires after 3 months to prevent leaving tests running, if they are unlikely to have a statistically significant winner.
How to utilize Content Experiments to create A/B Tests?
Go to the Behavior section and click on the Experiments link. It will also show you a table with all the existing experiments. Click on the “Create experiment” choice at the top of this table.
Enter → Name of the experiment, objective of the experiment, percentage of site traffic to take part, any mail notification for important changes, for distributing the traffic to all variations, set up time that investigation will run and also threshold values.
You can add URLs of unique page and all the variations that you want to create and click on the next button. Select the implementation method and click on the next button → Click on validation (If you have one code implemented it will approve. If there is no code, it will show an error message) → Start Experiment.
Once this experiment is run, you will see the following options −
Segmentation − It permits you to see how each variation has performed for each segment of visitors on your webpage.
Once the experiment is finished, next step is to analyze the results. A/B Testing tool will present the data from the experiment and will tell you the difference between how the various variations on a web page performs, and also if there is a important difference between variations, using the assistance of mathematical techniques and statistics.
If the images on a website page has reduced the bounce rate, you can decide whether it has a good conversion or not, when you upload more pictures on a web page. If you see no change in the bounce rate because of this, go back to the previous step and create a new hypothesis/variation to perform a another test.
Tools like VWO and Optimizely are utilized to run tests, but Google Analytics is most suited to run post-test analysis. This analysis is utilized to decide the way going forward. A/B Testing tools tell about the outcome of a test result, but there is a need to perform post analysis as well. To do post analysis you need to integrate each test with Google Analytics.
Both VWO and Optimizely gives built-in Google Analytics integration capability. The data for each test from both these tools should be sent to Google Analytics. By doing this, it upgrades your analysis capabilities and ensures testing data. There is a possibility that your testing tool might be recording the data incorrectly, and if you have no other source for your test data, you can never be sure whether to trust it or not.
There are different tools that can be used to generate hypothesis and to run the variations, these include −
All these tools are capable to run A/B Tests and to discover the winner, but to perform post analysis these tools should be integrated with Google Analytics.
Google Analytics has two options for analyzing the data −
New Universal Analytics feature permit you to use 20 concurrent A/B Tests sending data to Google Analytics, however the Classic version permits only up to five.
To integrate Optimizely in to Universal Google Analytics, first select the ON button on its side panel. Then you must have an available Custom to populate with Optimizely experiment data. Then the tracking code must be placed at the bottom of the <head> section of your pages. Google Analytics integration won't function properly unless the Optimizely snippet is above this Analytics snippet.
Optimizely utilizes Universal Google Analytics' "Custom Dimensions" to tag your visitors with the experiments and variations to which they've been included. Configuring Optimizely to begin sending this information to Universal Analytics requires four stages −
// Optimizely Universal Analytics Integration window.optimizely = window.optimizely || ; window.optimizely.push("activateUniversalAnalytics");
In the Optimizely Editor, go to Options → Integrations then click on the Universal Analytics checkbox to enable the integration.
Select the custom dimension you would like Optimizely to use. You need to ensure that the Custom Dimension should not be in use already by any other part of your site, or by another currently-running Optimizely experiment.
Select a Custom Tracker if you are using a custom event tracker other than the default. This will change Optimizely's integration call to utilize the custom tracker rather than the default.
Let us say your site is using the following call −
In this case you will be entering tracker3 in specifying a custom tracker field, and Optimizely would integrate with tracker3 rather than the default tracker.
First step is to sign into your Universal Analytics account and click the Customization tab at the top. You should see a Custom Reports list.
Next is to set up a Custom Report for each experiment that you have integrated Universal Analytics with.
Click on the New Custom Report → Enter the report title and add the metric groups you wish to see in the report.
To filter this report for only your Optimizely experiment, choose the Custom Dimension you set up previously as one of the Dimension Drilldowns.
Include this dimension in the Filters section and utilize a Regex match on the experiment ID for the experiment you want to filter.
Click on Save.
Like A/B Testing, Multivariate Testing depends on the same mechanism, but it compares higher number of variables, and provides more information about how these variables behave. In A/B Testing, you split the traffic of a page between various versions of the design. Multivariate Testing is utilized to measure the effectiveness of each design.
Let us say there is a webpage that has received enough traffic to run the test. Now the data from every variation is compared to check the most successful variation, but it additionally includes the elements, which have the maximum positive or negative effect on a visitor's interaction.
Multivariate Testing is an effective tool to enable you target as well as redesign the elements of your page and show the areas that will have the most impact. Multivariate method is helpful for creating landing page campaigns.
Data about the effect of a certain element's design can be applied to future campaigns, even if the context of the element has changed.
Limitations of Multivariate testing is the traffic needed to finish the test. As all the experiments are completely factorial, too many changing elements at once can quickly add up to a very huge number of possible combinations that must be tested. Even a site with fairly high traffic might have trouble completing a test with more than 25 combinations in a feasible amount of time.
A/B Testing also known as Split Testing is a strategy of website optimization, where you compare the conversion rates of two versions of a page namely, A and B. All visitors are divided into one version or the other. Once the visitors visit either of these versions (A or B), they click on various buttons or even sign-up for the newsletter. This permits you to determine which version of the page is more powerful.
SEO is a technique to display your website at the top of the page, when a search is performed for those relevant items. It includes the information that your website offers to the visitors and why webpage content is relevant to come at the top in a query result. Many potential customers feel that A/B Testing or Multivariate Testing will have an effect on their search engine rankings.
There are four different ways that ensure you to run A/B Tests without worrying about losing the potential SEO Value.
Cloaking is called when you show one version of your webpage to Googlebot agent and other version to your site visitors. Google says that you shouldn’t cloak and is very strict with this. It can even lead to your site being excluded from the search results or demoted in SEO ranking. You need to ensure that you don’t divide your visitors among the different versions of your A/B Test based on a user agent. Google doesn’t care if their bot sees one version or another, it just cares that its bot has the same user experience as that of a random visitor.
When you have A/B Tests with different URL’s, you can add ‘rel=canonical’ to the webpage to indicate to Google, which URL you need to index. Google suggests to use canonical element and it’s a noindex tag as it is more in line with its aim. You are only indicating about which content is unique. In this way Google can group and index pages accordingly.
Note − If it is not possible to use canonical, then you have to guarantee that there is a noindex tag in HTML or HTTP Header, if not you should ensure it at least has a robots.txt.
Google recommends to utilize the temporary direction method − a 302 over the permanent 301 redirect. As in any A/B Test, it is not a permanent relocation, but just a temporary one. It is always advisable to use 302 redirect as it is a notice of a temporary redirect. So if you’re utilizing a redirect for A/B Testing, ensure you use a 302 header.
Most significant point to consider for SEO is that you need to make it clear to search engines that they shouldn’t eliminate your original URL from their index and just put it temporarily on the hold. When the spiders come back for their next indexation, they will check again, if the redirect is still applicable, and if not, the old URL will be restored again.
Please note that when your A/B Test is completed, you should eliminate the variations as soon as possible and make changes to your website and begin using the winning conversion. You have to ensure that you eliminate all the elements of the tests − like alternative URLs and test scripts.
If you run the test for a longer period, Google takes this as a way to fool search engines. This can happen when you are showing a test variant to a large number of visitors for a longer period of timeframe.
A/B Testing (also known as Split testing) defines an approach to compare two versions of an application or a website page that enables you to determine, which one performs better. A/B Testing is one of the simplest ways, where you can change an application or a web page to create a new version and then comparing both these versions to find the conversion rate. This also lets us know, which is the better performer of the two.
The number of samples depend on the quantity of tests performed. The count of conversion rate is called a sample and process of collecting these samples is called sampling.
Confidence interval is called measurement of deviation from the average on the various number of samples. Let us assume that 22% of people prefer product A, with +/- 2% of confidence interval. This interval indicates the upper and lower limit of the people, who opt for Product A and is also called margin of mistakes. For best outcomes in this average survey, the margin of error should be as small as possible.
Always perform A/B Testing if there is probability to beat the original variation by >5%. Test should be run for considerable amount of time, so that you should have enough sample data to perform statistics and analysis. A/B Testing also enables you to increase maximum from your existing traffic on a web page.
The expense of increasing your conversions is minimal as compared to the cost of setting up the traffic on your site. The ROI (return on investment) on A/B Testing is huge, as a few minor changes on a website can result in a significant increase of the conversion rate.
Like A/B Testing, Multivariate testing depends on the same mechanism, but it compares higher number of variables, and gives more information about how these variables behave. In A/B Testing, you split the traffic of a page between various versions of the design. Multivariate testing is used to measure the effectiveness of each design.
The issue with testing multiple variables at once is that it would be difficult to accurately determine which of these variables have made the difference. While you can say one page performed better than the other, if there are three or four variables on each, you can’t be certain as to why one of those variables is actually a detriment to the page, nor can you replicate the good elements on different pages.
Here are a few A/B Testing variations that can be applied on a website page. The list includes − Headlines, Sub headlines, Pictures, Texts, CTA text and button, Links, Badges, Media Mentions, Social mention, Sales promotions and offers, Price structure, Delivery options, Payment options, Site navigations and user interface.
Background Research − First step in A/B Testing is to discover out the bounce rate on your website. This can be done with the help of any tool like Google Analytics.
Collect Data − Data from Google Analytics can assist you to discover visitor behaviors. It is always advisable to collect enough data from the site. Try to discover the pages with low conversion rate or high drop-off rates that can be improved.
Set Business Goals − Next step is to set your conversion goals. Discover the metrics that determines whether or not the variation is more successful than the original version.
Construct Hypothesis − Once the objective and metrics have been set for A/B Testing, next is to find ideas to improve the original version and how they will be better than the current version. Once you have a list of ideas, organize them in terms of expected impact and difficulty of implementation.
Create Variations/Hypothesis − There are many A/B Testing tools in the market that has a visual editor to make these improvements effectively. The key decision to perform A/B Testing successfully is by selecting the right tool.
Running the Variations − Present all variations of your site or an application to the visitors and their actions are monitored for each variation. Visitor interaction for each variation is measured and compared to determine how that variation performs.
Analyze Data − Once an experiment is finished, next is to analyze the results. A/B Testing tool will present the data from the experiment and will tell you the difference between how the different variations of web page is performed. Also if there is any important difference between variations with the help of mathematical methods and statistics.
The most common type of data collection tools includes the Analytics tool, Replay tools, Survey tools, Chat & Email tools.
Replay tools are utilized to get better insight of user actions on your site. It also permits you to click maps and heat maps of user click and to check how far user is browsing on the website. Replay tools like Mouse Flow permits you to view a visitor's session in a way you are with the visitor.
Video replay tools give deeper insight into what it would be like for that visitor browsing the different pages on your site. The most commonly used tools are Mouse Flow and Crazyegg.
Survey tools are utilized to collect qualitative feedback from the website. This involves asking returning visitors some survey questions. The survey asks them general questions and also permits them to enter their views or select from pre-provided choices.
You can reduce the number of bounce rate by adding more pictures at the bottom. You can add links of social sites to further increase the conversion rate.
There are various types of variations that can be applied to an object like using bullets, changing numbering of the key elements, changing the font and color, etc. There are numerous A/B Testing tools in the market that has a visual editor to make these improvements effectively. The key decision to perform A/B testing successfully is by selecting the right tool.
Most commonly available tools are Visual Website Optimizer, Google Content Experiments and Optimizely.
Visual Website Optimizer or VWO empowers you to test multiple versions of the similar page. It also contains ‘what you see is what you get’ (WYSIWYG) editor that enables you to make the changes and run tests without changing the HTML code of the page. You can update headlines, numbering of elements and run a test without making changes to IT assets.
To create variations in VWO for A/B Testing, open your website page in WYSIWYG editor and you can apply many changes to any website page. These include Change Text, Change URL, Edit /Edit HTML, Rearrange and Move.
Visual Website Optimizer also gives an option of multivariate testing and contains other number of tools to perform behavioral targeting, heat maps, usability testing, and so on.
These tests can be applicable on few other places like Email, Mobile Apps, PPC and CTAs as well.
Once an experiment is completed, next is to analyze the outcomes. A/B Testing tool will present the data from the experiment and will tell you the difference between how the various variations of that site page are performed. It will also show if there is a important difference between variations using mathematical methods and statistics.
To integrate Optimizely to Universal Google Analytics, first select the ON button on the side panel. Then you must have an available Custom to populate with Optimizely experiment information.
The Universal Google Analytics tracking code must be placed at the bottom of the <head> section of your pages. Google Analytics integration won't function properly unless the Optimizely snippet is above the Analytics snippet.
Google Analytics has two options for analyzing the data, which are Universal Analytics and Classic Google Analytics. New Universal Analytics features permit you to use 20 concurrent A/B tests sending data to Google Analytics, however the Classic version allows only up to five.
This is a myth that A/B Testing hurts search engine rankings because it could be classified as copy content. The following four ways can be applied to ensure that you don’t lose the potential SEO value, while running A/B Tests.
Don’t Cloak − Cloaking is when you show one version of your webpage to Googlebot agent and other version to your website visitors.
Use ‘rel=canonical’ − When you have A/B Tests with different URL’s, you can add ‘rel=canonical’ to the webpage to indicate to Google which URL you want to index. Google recommends to use canonical element and not noindex tag as it is more in line with its intention.
Use 302 redirects and not 301’s − Google recommends to use the temporary direction method − a 302 over the permanent 301 redirect.
Don’t run experiments for a longer period of time − Please note that when your A/B Test is finished, you should eliminate the variations as soon as possible and make changes to your webpage and start using the winning conversion.