Google Content Experiments (discontinued)

We use Google Content Experiments for AB testing in our Digital Marketing Department. Google Content Experiments allows us to test bounces, duration, click-throughs and session duration of users that enter our consumer websites. This tool allows us to test different marketing messages and designs to enhance the users experience, and helps us convert shoppers into buyers.

Google Content Experiments is suited for large and small organizations, no matter your organizational goals. It is not recommended for organizations that are only interested in qualitative data, as there are other tools for receiving specific user experience feedback. It is also not recommended that you implement tests without some sort of goal in mind.

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!

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.

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.

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.

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.

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.

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.

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.

We are utilizing Google Content Experiments to simply and freely test content changes on a small scale before implementing them site-wide.

Google Content Experiments are a great gateway into web content testing.

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.

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.

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.

I would recommend it with the caveat that they do some additional manual work to get the required results for our organisation.

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.

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.

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.

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.