Difference between revisions of "AB Testing"
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| − | <seo title=" | + | <seo title="What is A/B testing and what can it be used for?" metadescription="A/B tests compare two versions of a website to determine which version is better suited to achieving a defined goal. Learn more ..." /> |
== Definition == | == Definition == | ||
| − | |||
[[File:AB-Testing.png|thumb|450px|right|alt=AB testing|'''Figure:''' AB Testing - Author: Seobility - License: [[Creative Commons License BY-SA 4.0|CC BY-SA 4.0]]|link=https://www.seobility.net/en/wiki/images/2/24/AB-Testing.png]] | [[File:AB-Testing.png|thumb|450px|right|alt=AB testing|'''Figure:''' AB Testing - Author: Seobility - License: [[Creative Commons License BY-SA 4.0|CC BY-SA 4.0]]|link=https://www.seobility.net/en/wiki/images/2/24/AB-Testing.png]] | ||
| − | A/B tests (sometimes referred to as split tests) compare two versions of a website to determine which version is better suited to achieving a defined goal. Such a goal could be, for example, increasing conversion rates or improving other performance indicators (KPIs). An A/B test compares two website versions by randomly [[Redirect|redirecting]] visitors to one of the two variants and measuring user behavior. This allows webmasters and companies to test how user behavior is affected by changing a particular variable. | + | A/B tests (sometimes referred to as split tests) compare two versions of a website to determine which version is better suited to achieving a defined goal. Such a goal could be, for example, increasing [[Conversion Rate|conversion rates]] or improving other performance indicators (KPIs). An A/B test compares two website versions by randomly [[Redirect|redirecting]] visitors to one of the two variants and measuring user behavior. This allows webmasters and companies to test how user behavior is affected by changing a particular variable. |
== The goal of A/B testing == | == The goal of A/B testing == | ||
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The goal of A/B testing is to find the best possible website version to achieve a specific goal and to learn which changes can push website visitors towards a desired action. | The goal of A/B testing is to find the best possible website version to achieve a specific goal and to learn which changes can push website visitors towards a desired action. | ||
| − | Compared to the costs of paid traffic, the costs of increasing conversion rates through A/B testing are rather low. The return on investment of A/B testing can be very high as even small changes to a website can lead to significant increases in leads and sales generated. | + | Compared to the costs of paid [[Website Traffic|traffic]], the costs of increasing conversion rates through A/B testing are rather low. The [[ROI (Return on Investment)|return on investment]] of A/B testing can be very high as even small changes to a website can lead to significant increases in leads and sales generated. |
== How does A/B testing work? == | == How does A/B testing work? == | ||
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== What can A/B testing be used for? == | == What can A/B testing be used for? == | ||
| − | You can use an A/B test for optimizing landing pages, emails or e-commerce pages with regard to conversion rate, leads, registrations, etc. The following elements, among others, can be modified and analyzed in an A/B test: | + | You can use an A/B test for optimizing [[Landing Page|landing pages]], emails or e-commerce pages with regard to conversion rate, leads, registrations, etc. The following elements, among others, can be modified and analyzed in an A/B test: |
| − | * CTA buttons: Size, shape, text, color, and placement | + | * [[Call to Action (CTA)|CTA]] buttons: Size, shape, text, color, and placement |
* CTA Texts: Length, content, and formatting | * CTA Texts: Length, content, and formatting | ||
* Headings: Content, length, font size, typography | * Headings: Content, length, font size, typography | ||
| Line 37: | Line 36: | ||
In addition, for websites with little traffic, the tests may have to be carried out over several weeks or months in order to obtain a sufficiently large database for meaningful results. | In addition, for websites with little traffic, the tests may have to be carried out over several weeks or months in order to obtain a sufficiently large database for meaningful results. | ||
| − | A/B testing also cannot measure or indicate whether there are usability problems on a website that may be responsible for results such as a low conversion rate. | + | A/B testing also cannot measure or indicate whether there are [[Usability|usability]] problems on a website that may be responsible for results such as a low conversion rate. |
If multiple variables are changed simultaneously, there also is a risk that the test results will be misinterpreted. | If multiple variables are changed simultaneously, there also is a risk that the test results will be misinterpreted. | ||
| Line 47: | Line 46: | ||
=== Google Analytics - Experiments === | === Google Analytics - Experiments === | ||
| − | The "experiments" of Google Analytics make the tool a complete A/B test platform. Users can perform split tests for up to 10 versions of a single page. | + | The "experiments" of [[Google Analytics]] make the tool a complete A/B test platform. Users can perform split tests for up to 10 versions of a single page. |
=== KISSmetrics === | === KISSmetrics === | ||
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Google allows and encourages webmasters to perform A/B testing. The company has stated that performing an A/B or multivariate test does not pose an inherent risk to the ranking of your website. However, it is possible that your ranking may be compromised if you use A/B test tools in the wrong way. You should, therefore, follow these tips for A/B testing: | Google allows and encourages webmasters to perform A/B testing. The company has stated that performing an A/B or multivariate test does not pose an inherent risk to the ranking of your website. However, it is possible that your ranking may be compromised if you use A/B test tools in the wrong way. You should, therefore, follow these tips for A/B testing: | ||
| − | A/B tests should not be misused for cloaking. Cloaking means that [[Search Engine Crawlers|search engine crawlers]] are redirected to a different version of the site than users to deceive the search engine. But even if you do not have such intentions, you should not fundamentally change the content of your page. If Google notices that the variation of your page differs significantly from the original not only in design but also in scope and content, Google may misunderstand this change as cloaking and your site may be penalized. For this reason, you should never perform an A/B test longer than necessary, as Google may also perceive this as an attempt to deceive. | + | A/B tests should not be misused for [[Cloaking|cloaking]]. Cloaking means that [[Search Engine Crawlers|search engine crawlers]] are redirected to a different version of the site than users to deceive the search engine. But even if you do not have such intentions, you should not fundamentally change the content of your page. If Google notices that the variation of your page differs significantly from the original not only in design but also in scope and content, Google may misunderstand this change as cloaking and your site may be penalized. For this reason, you should never perform an A/B test longer than necessary, as Google may also perceive this as an attempt to deceive. |
Furthermore, you should use [[Canonical Tag|canonical tags]] to refer to the original page if the A/B test has multiple URLs. | Furthermore, you should use [[Canonical Tag|canonical tags]] to refer to the original page if the A/B test has multiple URLs. | ||
| − | Setting up 302 redirects is not harmful unless they redirect to unexpected or unlinked content. 301 redirects, on the other hand, should not be used as they would signal to Google that your page has been moved permanently which does not apply to A/B testing. | + | Setting up [[HTTP 302|302 redirects]] is not harmful unless they redirect to unexpected or unlinked content. 301 redirects, on the other hand, should not be used as they would signal to Google that your page has been moved permanently which does not apply to A/B testing. |
== Related links == | == Related links == | ||
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[[Category:Web Development]] | [[Category:Web Development]] | ||
[[Category:Online Marketing]] | [[Category:Online Marketing]] | ||
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| + | | [[File:Seobility S.jpg|link=|100px|left|alt=Seobility S]] The Seobility Wiki team consists of seasoned SEOs, digital marketing professionals, and business experts with combined hands-on experience in SEO, online marketing and web development. All our articles went through a multi-level editorial process to provide you with the best possible quality and truly helpful information. Learn more about <html><a href="https://www.seobility.net/en/wiki/Seobility_Wiki_Team" target="_blank">the people behind the Seobility Wiki</a></html>. | ||
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Latest revision as of 14:38, 4 December 2023
Contents
Definition
A/B tests (sometimes referred to as split tests) compare two versions of a website to determine which version is better suited to achieving a defined goal. Such a goal could be, for example, increasing conversion rates or improving other performance indicators (KPIs). An A/B test compares two website versions by randomly redirecting visitors to one of the two variants and measuring user behavior. This allows webmasters and companies to test how user behavior is affected by changing a particular variable.
The goal of A/B testing
The goal of A/B testing is to find the best possible website version to achieve a specific goal and to learn which changes can push website visitors towards a desired action.
Compared to the costs of paid traffic, the costs of increasing conversion rates through A/B testing are rather low. The return on investment of A/B testing can be very high as even small changes to a website can lead to significant increases in leads and sales generated.
How does A/B testing work?
In an A/B test, a second version of a web page is created in which a specific element is modified. This change can affect headings, buttons, images, or colors, for example. The change rarely involves a complete redesign of a page.
The next step in A/B testing is to redirect half of the visitors to the original version of the page, and the other half to the modified version. Visitor behavior is measured and analyzed on both sides. This way, you can determine whether the change had a positive, negative or no impact on visitor behavior (for example, in terms of conversion rate).
What can A/B testing be used for?
You can use an A/B test for optimizing landing pages, emails or e-commerce pages with regard to conversion rate, leads, registrations, etc. The following elements, among others, can be modified and analyzed in an A/B test:
- CTA buttons: Size, shape, text, color, and placement
- CTA Texts: Length, content, and formatting
- Headings: Content, length, font size, typography
- Texts: length and content
- Images: static or carousel, size, and placement
Advantages and disadvantages of an A/B test
A/B testing is very useful for testing new ideas and determining whether they lead to results such as an improvement in conversion rate, a reduction in bounce rate, or an increase in retention time, for example. You can test possible changes on your page step by step. Thereby, you can analyze whether different colors, buttons, layouts or images influence the behavior of your visitors.
A disadvantage of A/B tests, however, is the time required to prepare and set up two different versions of a website.
In addition, for websites with little traffic, the tests may have to be carried out over several weeks or months in order to obtain a sufficiently large database for meaningful results.
A/B testing also cannot measure or indicate whether there are usability problems on a website that may be responsible for results such as a low conversion rate.
If multiple variables are changed simultaneously, there also is a risk that the test results will be misinterpreted.
Tools for A/B testing
There is a whole range of professional tools available for A/B testing on the internet, some of which are free and some of which are subject to a fee:
Google Analytics - Experiments
The "experiments" of Google Analytics make the tool a complete A/B test platform. Users can perform split tests for up to 10 versions of a single page.
KISSmetrics
KISSmetrics is a powerful analysis platform. The KISSmetrics JavaScript library provides a feature that helps users set up their A/B test. Users can also integrate KISSmetrics into their internal test code or into another A/B test platform.
Unbounce
With Unbounce, you can create, publish and test responsive landing pages without HTML knowledge. Unbounce can be integrated into a variety of other tools. You also have the possibility to assign roles to team members, capture leads, and include videos, social feeds, and widgets to optimize the conversion rate of a website or email.
Optimizely
Optimizely is one of the most popular WYSIWYG A/B testing tools. Optimizely enables tests based on ad campaigns, geography, or cookies to facilitate the design and optimization of personalized content based on target audience segments.
A/B testing and Search Engine Optimization
Google allows and encourages webmasters to perform A/B testing. The company has stated that performing an A/B or multivariate test does not pose an inherent risk to the ranking of your website. However, it is possible that your ranking may be compromised if you use A/B test tools in the wrong way. You should, therefore, follow these tips for A/B testing:
A/B tests should not be misused for cloaking. Cloaking means that search engine crawlers are redirected to a different version of the site than users to deceive the search engine. But even if you do not have such intentions, you should not fundamentally change the content of your page. If Google notices that the variation of your page differs significantly from the original not only in design but also in scope and content, Google may misunderstand this change as cloaking and your site may be penalized. For this reason, you should never perform an A/B test longer than necessary, as Google may also perceive this as an attempt to deceive.
Furthermore, you should use canonical tags to refer to the original page if the A/B test has multiple URLs.
Setting up 302 redirects is not harmful unless they redirect to unexpected or unlinked content. 301 redirects, on the other hand, should not be used as they would signal to Google that your page has been moved permanently which does not apply to A/B testing.
Related links
| About the author |
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