Integrates well with Google Analytics
We use Google Content Experiments for AB testing in our Digital Marketing Department. Google Content Experiments allows us to test …
Overview
What is Google Content Experiments (discontinued)?
Google Content Experiments was a tool that can be used to create A/B test from within Google Analytics. It has been discontinued since 2019, and Google now recommends using its Google Optimize service for A/B testing.
Recent Reviews
Pricing
N/A
Unavailable
What is Google Content Experiments (discontinued)?
Google Content Experiments was a tool that can be used to create A/B test from within Google Analytics. It has been discontinued since 2019, and Google now recommends using its Google Optimize service for A/B testing.
Entry-level set up fee?
- No setup fee
Offerings
- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
Would you like us to let the vendor know that you want pricing?
Alternatives Pricing
What is Oracle Marketing?
Oracle CX Marketing (formerly Oracle Marketing Cloud) is a solution designed to enable marketers to plan and execute automated marketing campaigns via email, display search, video advertising, and mobile while delivering a personalized customer experience for their prospects.
N/A
Unavailable
What is Kameleoon?
Kameleoon boasts users among 500 corporate and enterprise companies across North America, Europe, and Asia Pacific to help brands deliver digital experiences and products to their customers. GDPR, CPPA, and HIPPA compliant, Kameleoon’s A/B testing, full stack, and AI-powered personalization…
Product Details
- About
- Competitors
- Tech Details
What is Google Content Experiments (discontinued)?
Google Content Experiments was a tool that can be used to create A/B test from within Google Analytics. It has been discontinued since 2019, and Google now recommends using its Google Optimize service for A/B testing.
Google Content Experiments (discontinued) Competitors
Google Content Experiments (discontinued) Technical Details
| Operating Systems | Unspecified |
|---|---|
| Mobile Application | No |
Comparisons
Compare with
Reviews and Ratings
(74)Attribute Ratings
Reviews
(1-3 of 3)Companies can't remove reviews or game the system. Here's why
February 04, 2015
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.
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
- Higher quality leads
- Reduced sales team effort (due to less low quality leads)
- Higher conversion from visitor to lead
2
1. Adwords Advertising exec.
2. Marketing Manager.
and ....3rd person in training - Marketing Exec.
2. Marketing Manager.
and ....3rd person in training - Marketing Exec.
February 04, 2015
Limited Testing Solution, but Not Without Value
Score 7 out of 10
Vetted Review
Verified User
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.
- Utilization of Google Content Experiments has allowed us to consistently conduct more tests, resulting in a 141% improvement in overall conversion rate.
Google Content Experiments provides significantly more insight, historical data and analysis than Unbounce. However, if you do need a solution that offers a WYSIWYG editor, landing page hosting, and limited reporting and testing, Unbounce is a good all-in-one solution and that can incorporate Analytics tracking.
16
Client account managers, optimization expert.
Score 6 out of 10
Vetted Review
Verified User
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 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
If you are looking for a more advanced great value for money solution I would recommend investigating Visual Website Optimizer. For a more powerful enterprise level solution with the option to have a fully managed service I would recommend Maxymiser.
1
Used by Marketing to ensure that all landing pages perform at their highest potential
2
The two main skills need to use Google Analytics Content Experiments are:
- A strong understanding of conversion rate optimisation and testing methodologies
- Some coding knowledge to build the alternative test variants and deploy the test code to the test page
- 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
The most important factor in choosing Google Content Experiments was the fact that is was already available to us through Google Analytics
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.