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Google Content Experiments is a tool that can be used to create A/B test from within Google Analytics.A superb A/B Testing tool for smaller teams.Google Content Experiments was one of the tools that we used when looking to do A/B testing within the organisation. Typically we would work with customers to determine what elements were important to their business and use this to come up with ways to validate the ideas or disprove them. Content Experiments was ideal as it was a free tool - that could be rolled up quite quickly and was free!,Has a great analytics engine in its backend which uses multi-arm bandit methodology- and thus can perform multiple variations at once. Multi-arm bandit means that it's also really effective in finding the winning solution. It can be based on Analytics Goals via Optimize so you can drive things that are important to the business.,Their documentation is not the best and it's quite a steep learning curve. They also don't tell you particularly well what sorts of things you should be testing. Compared to other suppliers of A/B testing tools- it needs a simpler interface. Optimize is starting to answer that - but is still quite Beta-like.,7,Doing good experiments/Optimize has helped to take out the guesswork of the things we want to implement. We have done fairly complex changes such as changing navigation and managed to see improvements outcomes immediately before we have to request developer. Our teams have become more data centric in how they approach changes.,,Google Tag Manager, Mouseflow, Tint, ObservePointGood tool for optimizing websitesWe use Google Content Experiments to regularly test and improve website performance, primarily on our lead generation websites. Content Experiments has also been used to settle what design elements to move forward with and what ones to let go when planning new updates.,Easy to use and implement. Easy to understand the reports. Seamless integration with Google Analytics.,No support for multivariate testing, a feature that was pulled from the product when it was rebranded as Google Content Experiments from Google Website Optimizer.,8,Improved goal conversion rates. Increased time spent on site.,Google Content Experiments: Quick and easy (and free) A/B/n testing you should already be using.We use Google Content Experiments to run A/B/n split test and optimize landing pages. Pros: * Easy to set up a test and use content experiments to monitor ongoing test metrics, if you already have Google Analytics installed. * Good entry level tool: Due to the relatively low skill required - so even entry level staff will be able to run a split test.with minimal training and supervision. Interpreting the results however...requires appropriate training. Caveat emptor! As with any other A/B test tool, badly designed tests result in useless data. * Fully integrated with Google Analytics, so you can use existing goals, all the metrics are the same as in Google Analytics, and are measured in the same way as the rest of the Google Analytics metrics. Makes life much easier when interpreting the results. * Run test only using a percentage of website traffic (current options are 1%, 5%, 0%, 25%, 50%, 75%, 100%) to reduce risk when testing radical changes. * Free * Huge volume of online resources to help with getting started and using it. Cons: * Tests cannot be paused - only ended. * Losing variants cannot be removed before the test is complete - although if you choose the default setup option to not split traffic evenly, Google will optimize throughout the test and reduce traffic to the losing page(s) while increasing it to the winner(s). * Same proportional split only - i.e. you cannot give one version 90% and the other 10% of the traffic (You might want to do this to confirm that your current champion page is consistently winning over the demoted loser over a long period to rule out seasonal variations, etc. Or you might not want to risk 50% of your traffic on a radically different landing page and subsequent revenue loss if it is a loser.) You can (partially) work around this by having multiple versions of one page and testing against a single version of the other (max 4:1 to get 80%:20% split) * A/B/n testing only: Google killed it's (in some ways better) Website Optimizer to replace it with Content Experiments - and in so doing dropped the ability to run multivariate tests. This could also be seen as a pro ;-) Multivariate testing requires much better design of experiments, more traffic, and more time than A/B testing. Since Google measures everything, they presumably found that their customers were doing A/B and needed a better integrated tool, and that MVT was not being used, or was not appropriate for this market sector. * Tests must run for 3 days minimum (works for us at SpamTitan, but in a previous job with much higher volume of traffic, and same day sales conversions, a valid winner could be called much sooner. Your sales cycle, traffic volume and conversion funnel will determine if this is a negative factor) * Tests cannot be run for more than 3 months (I believe!) so if you have a long, complex sale cycle, this may not work for you - you can only use it for micro-conversions. So you'd best be sure they are actually relevant factors influencing the sale. It probably goes without saying, but I'll say it anyway - it's easy to optimize for registrations at the expense of actual sales. Sometimes a lower conversion rate for a microconversion will result in higher profits. * Almost zero support from Google - you probably cannot get support direct from the vendor unless you are spending enough on Adwords to have an account manager. Luckily, you probably won't need help as the product is easy to use and limited in scope.,Free Easy to use if you already use Google Analytics - literally a few minutes to set up a test Fully integrated with Google Analytics - so you can use your existing conversion goals, and no confusion over metrics.,Multivariate testing Ability to drop losing variants during a test Ability to manually choose split of traffic between variants,8,Higher quality leads Reduced sales team effort (due to less low quality leads) Higher conversion from visitor to lead,2,10Limited Testing Solution, but Not Without ValueAs a digital marketing agency, we develop and optimize landing pages and websites for internal and client use. To aid in our conversion rate optimization efforts, we need a robust landing and website page testing solution that incorporates existing and new Google Analytics goals.,It is free Tests are easy to set up. Experiment creation is a 4-step process that can be completed with limited knowledge of Google Analytics Experiments is integrated with your Google Analytics account, utilizing existing goals or allows you to create new goals for testing directly within the set up area Allows for advanced segmentation, filtering, and traffic allocation,No multivariate tests: Google Content Experiments tests on an A|B|n platform. As such, you must create full iterations of landing pages to test against each other. If you are testing a full layout change, the A|B|n model will suit your needs just fine. However, if you’d like to test multiple elements at one time or a small element, like button color, you would need to create a new landing page for each different color button you will test. This can become quite cumbersome. Basic testing platform: This platform will also present challenges if you would like to test the influence of one element across multiple pages. You will need a much more sophisticated tool for this type of testing. Content Experiments defaults to a Multi-Armed Bandit algorithm. This automatically adjusts the amount of traffic allocated to each variation based on sample size and performance metrics, resulting in less traffic to "under-performing" variations. In theory the Multi-Armed Bandit is much more efficient than your classic A|B testing method, concluding tests much quicker while reducing potential revenue loss. Unfortunately, we have found that for testing with small sample sizes, like those commonly conducted for B2Bs or SMBs, the multi-armed bandit has the potential to create an invalid test that will never declare a winner. This is because traffic to the variation is typically reduced so severely and so quickly that there is not a significant enough sample size to give it a competitive opportunity. Testing on a specific segmentation is challenging: To test on a distinct segment of your visitors requires more advanced knowledge of Analytics to set up a new filter view and cannot be completed within Google Content Experiments. Not ideal for marketers who want independence from IT: Content Experiments requires a snippet of code being placed on one experiment page for each test. It also requires a full version of the test page with a unique URL.,7,Utilization of Google Content Experiments has allowed us to consistently conduct more tests, resulting in a 141% improvement in overall conversion rate.,UnBounce,16,8A great place to start your conversion rate optimization adventure. However the limited features will have you searching for a more advanced tool within a few months.I have used Google Content Experiments as a great way to introduce various businesses to the idea of conversion testing and to create a conversion culture. Its ease of use, the fact that it is available to anyone that uses Google Analytics and also its agreeable price tag (it is free,) have made it my go to tool when I need to demonstrate and educate businesses to the value of conversion testing.,Quick and easy to create and set up experiments Results are presented in a way that is familiar and easy for any Google Analytics user to understand. So is great for beginners to conversion testing Already integrated into Google Analytics and can measure results against your existing conversion goals Allows nine possible variants to be A/B tested Features more than one testing methodology, Bayesian (Multi-Armed Bandit,) and Full Factorial Allows the user to select one of three possible confidence thresholds to ensure that experiment results are robust Great value, it is free!,Should include a multivariate testing (MVT) option. Whilst the ability to test nine possible variants using A/B/n testing is great for strong bold tests, the ability test multiple elements on the same page is essential for a successful testing program Reliance on your IT department to code the variants you wish to test and also to insert the experiment code on the test page can be an issue which may slow the testing process for some businesses It is very easy to make the wrong decisions and find a false winner. A high level of conversion rate optimisation knowledge is needed to ensure that the results from the experiment are valid and of statistical significance Should not default to Bayesian (multi armed bandit,) methodology and a 95 percent confidence threshold as a those new to testing may not fully understand the impact of these setting and whilst these options can be changed in “Advanced Options” the combination of these two factors could easily find an incorrect winner Google Experiments should not declare the winning variant as it does not understand your business or business cycles, so can easily find a false winner. Although the option to set a minimum time for the experiment to run before declaring a winner goes some way towards mitigating this issue it is limited to a maximum of two weeks which may not be long enough for some businesses,6,Google Content Experiments has shown a great return on investment as it is a free tool that has been used to answer various business questions. I have Google Content Experiments to demonstrate various percentage uplifts to key metrics across the business,Maxymiser,Visual Website Optimizer,10,1,2,Improving landing page conversion rates Developing a testing culture within the business An always on back up testing tool should it be needed,No,Price Vendor Reputation Existing Relationship with the Vendor Analyst Reports,Ensure that we research all possible testing methodologies and available options, then map those onto a matrix to ensure that you are selecting the most appropriate tool for the job.
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Google Content Experiments
71 Ratings
Score 7.5 out of 101
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Google Content Experiments Reviews

Google Content Experiments
71 Ratings
Score 7.5 out of 101
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January 22, 2018

Google Content Experiments Review: "A superb A/B Testing tool for smaller teams."

Score 7 out of 10
Vetted Review
Verified User
Review Source
Google Content Experiments was one of the tools that we used when looking to do A/B testing within the organisation. Typically we would work with customers to determine what elements were important to their business and use this to come up with ways to validate the ideas or disprove them. Content Experiments was ideal as it was a free tool - that could be rolled up quite quickly and was free!
  • Has a great analytics engine in its backend which uses multi-arm bandit methodology- and thus can perform multiple variations at once.
  • Multi-arm bandit means that it's also really effective in finding the winning solution.
  • It can be based on Analytics Goals via Optimize so you can drive things that are important to the business.
  • Their documentation is not the best and it's quite a steep learning curve.
  • They also don't tell you particularly well what sorts of things you should be testing.
  • Compared to other suppliers of A/B testing tools- it needs a simpler interface. Optimize is starting to answer that - but is still quite Beta-like.
Google Content Experiments (along with Optimize) are best suited to get your team started on the path of A/B testing. It's a cheap and low-risk way to test, and also ties well into Google Analytics. Its integration with Optimize is built on top of Google Tag Manager so again this is well-understood technology and chances are most businesses would have someone who is familiar with it.
Read this authenticated review
October 30, 2015

Google Content Experiments Review: "Good tool for optimizing websites"

Score 8 out of 10
Vetted Review
Verified User
Review Source
We use Google Content Experiments to regularly test and improve website performance, primarily on our lead generation websites. Content Experiments has also been used to settle what design elements to move forward with and what ones to let go when planning new updates.
  • Easy to use and implement.
  • Easy to understand the reports.
  • Seamless integration with Google Analytics.
  • No support for multivariate testing, a feature that was pulled from the product when it was rebranded as Google Content Experiments from Google Website Optimizer.
I'm a believer in ongoing website optimization. This being said, I would suggest every website owner run A/B tests at all times. Google Content Experiments is an easy and free way to get started.
Read Mark Castaldo's full review
February 04, 2015

Review: "Google Content Experiments: Quick and easy (and free) A/B/n testing you should already be using."

Score 8 out of 10
Vetted Review
Verified User
Review Source
We use Google Content Experiments to run A/B/n split test and optimize landing pages.

Pros:
* Easy to set up a test and use content experiments to monitor ongoing test metrics, if you already have Google Analytics installed.
* Good entry level tool: Due to the relatively low skill required - so even entry level staff will be able to run a split test.with minimal training and supervision. Interpreting the results however...requires appropriate training. Caveat emptor! As with any other A/B test tool, badly designed tests result in useless data.
* Fully integrated with Google Analytics, so you can use existing goals, all the metrics are the same as in Google Analytics, and are measured in the same way as the rest of the Google Analytics metrics. Makes life much easier when interpreting the results.
* Run test only using a percentage of website traffic (current options are 1%, 5%, 0%, 25%, 50%, 75%, 100%) to reduce risk when testing radical changes.
* Free
* Huge volume of online resources to help with getting started and using it.

Cons:
* Tests cannot be paused - only ended.
* Losing variants cannot be removed before the test is complete - although if you choose the default setup option to not split traffic evenly, Google will optimize throughout the test and reduce traffic to the losing page(s) while increasing it to the winner(s).
* Same proportional split only - i.e. you cannot give one version 90% and the other 10% of the traffic (You might want to do this to confirm that your current champion page is consistently winning over the demoted loser over a long period to rule out seasonal variations, etc. Or you might not want to risk 50% of your traffic on a radically different landing page and subsequent revenue loss if it is a loser.) You can (partially) work around this by having multiple versions of one page and testing against a single version of the other (max 4:1 to get 80%:20% split)
* A/B/n testing only: Google killed it's (in some ways better) Website Optimizer to replace it with Content Experiments - and in so doing dropped the ability to run multivariate tests. This could also be seen as a pro ;-) Multivariate testing requires much better design of experiments, more traffic, and more time than A/B testing. Since Google measures everything, they presumably found that their customers were doing A/B and needed a better integrated tool, and that MVT was not being used, or was not appropriate for this market sector.
* Tests must run for 3 days minimum (works for us at SpamTitan, but in a previous job with much higher volume of traffic, and same day sales conversions, a valid winner could be called much sooner. Your sales cycle, traffic volume and conversion funnel will determine if this is a negative factor)
* Tests cannot be run for more than 3 months (I believe!) so if you have a long, complex sale cycle, this may not work for you - you can only use it for micro-conversions. So you'd best be sure they are actually relevant factors influencing the sale. It probably goes without saying, but I'll say it anyway - it's easy to optimize for registrations at the expense of actual sales. Sometimes a lower conversion rate for a microconversion will result in higher profits.
* Almost zero support from Google - you probably cannot get support direct from the vendor unless you are spending enough on Adwords to have an account manager. Luckily, you probably won't need help as the product is easy to use and limited in scope.
  • Free
  • Easy to use if you already use Google Analytics - literally a few minutes to set up a test
  • Fully integrated with Google Analytics - so you can use your existing conversion goals, and no confusion over metrics.
  • Multivariate testing
  • Ability to drop losing variants during a test
  • Ability to manually choose split of traffic between variants
Do you already have Google Analytics? If so content experiments is a good, free, starting point to dip your toes in A/B testing. Do you need to run Multivariate experiments? If so, Google Content Experiments is not going to fit your needs.
Read Salvatore Polizzi McDonagh's full review
February 04, 2015

Google Content Experiments Review: "Limited Testing Solution, but Not Without Value"

Score 7 out of 10
Vetted Review
Verified User
Review Source
As a digital marketing agency, we develop and optimize landing pages and websites for internal and client use. To aid in our conversion rate optimization efforts, we need a robust landing and website page testing solution that incorporates existing and new Google Analytics goals.
  • It is free
  • Tests are easy to set up. Experiment creation is a 4-step process that can be completed with limited knowledge of Google Analytics
  • Experiments is integrated with your Google Analytics account, utilizing existing goals or allows you to create new goals for testing directly within the set up area
  • Allows for advanced segmentation, filtering, and traffic allocation
  • No multivariate tests: Google Content Experiments tests on an A|B|n platform. As such, you must create full iterations of landing pages to test against each other. If you are testing a full layout change, the A|B|n model will suit your needs just fine. However, if you’d like to test multiple elements at one time or a small element, like button color, you would need to create a new landing page for each different color button you will test. This can become quite cumbersome.
  • Basic testing platform: This platform will also present challenges if you would like to test the influence of one element across multiple pages. You will need a much more sophisticated tool for this type of testing.
  • Content Experiments defaults to a Multi-Armed Bandit algorithm. This automatically adjusts the amount of traffic allocated to each variation based on sample size and performance metrics, resulting in less traffic to "under-performing" variations. In theory the Multi-Armed Bandit is much more efficient than your classic A|B testing method, concluding tests much quicker while reducing potential revenue loss. Unfortunately, we have found that for testing with small sample sizes, like those commonly conducted for B2Bs or SMBs, the multi-armed bandit has the potential to create an invalid test that will never declare a winner. This is because traffic to the variation is typically reduced so severely and so quickly that there is not a significant enough sample size to give it a competitive opportunity.
  • Testing on a specific segmentation is challenging: To test on a distinct segment of your visitors requires more advanced knowledge of Analytics to set up a new filter view and cannot be completed within Google Content Experiments.
  • Not ideal for marketers who want independence from IT: Content Experiments requires a snippet of code being placed on one experiment page for each test. It also requires a full version of the test page with a unique URL.
If you want to conduct very simple (and free) A|B tests, Google Content Experiments is an appropriate solution. Experiments has certain limitations for pages with flow traffic and is not the best solution for people who don't have the ability to code a page from scratch, or need to test without the help of IT. Experiments also (obviously) assumes that you are a current Google Analytics user and does not take into account tracking with a different tool.
Read Jenny DeGraff's full review
June 02, 2014

Google Content Experiments: "A great place to start your conversion rate optimization adventure. However the limited features will have you searching for a more advanced tool within a few months."

Score 6 out of 10
Vetted Review
Verified User
Review Source
I have used Google Content Experiments as a great way to introduce various businesses to the idea of conversion testing and to create a conversion culture. Its ease of use, the fact that it is available to anyone that uses Google Analytics and also its agreeable price tag (it is free,) have made it my go to tool when I need to demonstrate and educate businesses to the value of conversion testing.
  • Quick and easy to create and set up experiments
  • Results are presented in a way that is familiar and easy for any Google Analytics user to understand. So is great for beginners to conversion testing
  • Already integrated into Google Analytics and can measure results against your existing conversion goals
  • Allows nine possible variants to be A/B tested
  • Features more than one testing methodology, Bayesian (Multi-Armed Bandit,) and Full Factorial
  • Allows the user to select one of three possible confidence thresholds to ensure that experiment results are robust
  • Great value, it is free!
  • Should include a multivariate testing (MVT) option. Whilst the ability to test nine possible variants using A/B/n testing is great for strong bold tests, the ability test multiple elements on the same page is essential for a successful testing program
  • Reliance on your IT department to code the variants you wish to test and also to insert the experiment code on the test page can be an issue which may slow the testing process for some businesses
  • It is very easy to make the wrong decisions and find a false winner. A high level of conversion rate optimisation knowledge is needed to ensure that the results from the experiment are valid and of statistical significance
  • Should not default to Bayesian (multi armed bandit,) methodology and a 95 percent confidence threshold as a those new to testing may not fully understand the impact of these setting and whilst these options can be changed in “Advanced Options” the combination of these two factors could easily find an incorrect winner
  • Google Experiments should not declare the winning variant as it does not understand your business or business cycles, so can easily find a false winner. Although the option to set a minimum time for the experiment to run before declaring a winner goes some way towards mitigating this issue it is limited to a maximum of two weeks which may not be long enough for some businesses
Google Content Experiments is best suited to businesses that have a limited budget but have the passion and knowledge to experiment with conversion rate optimisation.
Read John Mills's full review
June 13, 2014

Google Content Experiments: "FREE and integrated, but no advanced testing possibilities and harder setup"

Score 7 out of 10
Vetted Review
Verified User
Review Source
Google Content Experiment is used to optimize our websites through A/B/n testing. It is utilized by a user in Marketing and IT. It allows our company to improve our visit to lead conversion rate, which translates to higher sales and ROI of all marketing campaigns.
  • It's FREE
  • It ties into Google Analytics
  • Requires code setup for each experiment (less of an issue if also using Google Tag Manager)
  • Does not allow for multivariate testing
  • Does not allow for advancing testing and targeting
I would recommend it, especially for beginners, because it is free, so there is nothing to lose. For robust website optimization and testing, Google Content Experiment is probably not going to be sufficient.
Read Antonio Segovia's full review
June 10, 2014

Review: "Google Content Experiments: Your Free Gateway to A/B Web Content Testing"

Score 9 out of 10
Vetted Review
Verified User
Review Source
We are utilizing Google Content Experiments to simply and freely test content changes on a small scale before implementing them site-wide.
  • Seamless integration with Google Analytics
  • Absolutely free of charge
  • Once Google's Universal Analytics supports Content Experiments and requires no additional javascript to implement, it will be a truly seamless experience.
Google Content Experiments are a great gateway into web content testing.
Read Eric Olsen's full review
June 02, 2014

Google Content Experiments: "Another Google Freebie, but could be improved for real testing strategies"

Score 4 out of 10
Vetted Review
Verified User
Review Source
We use Google Content experiments to run in house A/B testing as part of our Google Analytics package. The tool enables us to test significant web page variation changes default vs. new test variant. This allows us to identify the performance changes of new pages if they increase/decrease conversion rate.
  • Integrates well with Google Analytics to perform segmentation analysis
  • Capability of using server redirects rather than java script redirects unlike most testing tools.
  • Easy to set up
  • It doesn't handle multivariate testing
  • Basic test configuration compared to other testing tools in the market
  • Still requires a developer to code new pages rather than CMS capabilities of some products
It's well suited to A/B testing in house, and best of all it's free. The setup process is straight forward and developers appear to be happy using it. There are ample resources for instructions to use the tool on Google Forums. For complex tests the tool wouldn't be used to replace a dedicated MVT testing tool.
Read Lee Duong's full review
May 13, 2014

Google Content Experiments Review: "Google CE Impressed Me"

Score 7 out of 10
Vetted Review
Verified User
Review Source
My organization was interested in examining how we could redesign webpages to reduce bounce rate. We thought rewriting product descriptions was as far as we would need to go, however we learned differently. Google CE taught us that we needed to mix good design with good content and streamline product searching on our site.
  • Google CE really gives in-depth insight into why some pages perform better than others. You can see where the readers eyes travel most often.
  • Talk about putting yourself in the audience's shoes! Thats exactly what A/B testing does
  • There is also multivariate testing for advanced changes.
  • There was a somewhat high learning curve which I would like to see flattened.
  • Google needs a more robust help section integrated in CE to help users navigate better.
  • There should be more mock testing windows during evaluation stages.
It is most appropriate for webpage redesign and email marketing. There are many forums that help clarify concepts that Google takes for granted having an advanced knowledge of. People who just want to reduce bounce rate may want to go somewhere else, but to truly build a robust website with strong pages CE is the trick!
Read David Jackiewicz's full review
June 10, 2014

"Google Content Experiments Review #58,733 (probably)"

Score 7 out of 10
Vetted Review
Verified User
Review Source
We are using it for A/B testing web pages & content for conversions listed in Google Analytics. It is only currently being used by our UK office.
  • It is free to get going - better than the competitors!
  • You use your Google Analytics conversions as your objectives, so as long as your goals are set up in GA then it's a breeze.
  • You can only optimise for one goal, so if you have several conversions, like phone call and email you need to do it manually.
  • It seems not to work that well for pages with lower amounts of traffic - not great for new or niche sites.
I would recommend it with the caveat that they do some additional manual work to get the required results for our organisation.
Read Sam Wiltshire's full review
May 13, 2014

Google Content Experiments Review: "Not a bad tool, but not for the code-shy"

Score 6 out of 10
Vetted Review
Verified User
Review Source
I used to train marketing and development folks on how to use Google Content Experiments. We used it primarily when we needed to tie success of the test to a non-destination URL type goal (e.g. event triggered). Given that these non-destination URL goals were already setup in GA, it was easier to configure content experiments to measure against these goals than other testing tools. Additionally, we could see how the testing groups performed against other goals on the site, including both destination URL goals, engagement goals, and event-based goals.
  • When you need to measure against event-based goals
  • If you need to see how the test variations performed against secondary goals
  • Given that the the platform requires you actually code a new page with a unique URL, this tool can be good for radical redesigns.
  • Great insights into other information about your testing groups, like whether or not they're mobile, screen size, browser, or really any dimension available in GA.
  • Not a great solution for people who don't have the ability to code a page from scratch, or need to implement a test without the help of IT. This tool requires implementation of a few different code snippets on different pages and the ability to code a new page. If you're looking for a WYSIWYG editor - try Optimizely.
Are you doing multivariate tests? If so - try Visual Website Optimizer. Are you looking for a GUI to let you change a CTA or button color? Try Optimizely. GCE is good for A/B testing primarily, not great for multivariate, but pretty cool for radical redesigns.
Read this authenticated review
June 10, 2014

Review: "Google Content Experiments - Adequate and free"

Score 7 out of 10
Vetted Review
Verified User
Review Source
It is used to create A / B tests inside Google Analytics to test and optimise pages on websites, identify issues in flow on websitses and to find ways to rectify these issues.
  • A / B testing: You define a control page (page A) and a variation (page B) of that original page to test against. The purpose of this test is to expose your audience to the different versions of a page to determine which version will result in more conversions for your site.
  • Does not support multivariate testing
It is useful if you already have GA set up and need to do A / B tests. If you are not currently using GA it is not necessarily worth switching just for Content Experiments.
Read this authenticated review

Google Content Experiments Scorecard Summary

About Google Content Experiments

Google Content Experiments is a tool that can be used to create A/B test from within Google Analytics.
Categories:  A/B Testing

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