Adobe Test and Target is an A/B, multi-variate testing platform which Adobe acquired as part of the Omniture platform in 2009. It is now part of the Adobe Marketing Cloud. It offers tight integration with Adobe analytics and content management products.
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Google Content Experiments (discontinued)
Score 7.3 out of 10
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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.
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Pricing
Adobe Target
Google Content Experiments (discontinued)
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Adobe Target
Google Content Experiments (discontinued)
Free Trial
No
No
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Community Pulse
Adobe Target
Google Content Experiments (discontinued)
Features
Adobe Target
Google Content Experiments (discontinued)
Testing and Experimentation
Comparison of Testing and Experimentation features of Product A and Product B
Adobe Target
8.6
Ratings
5% above category average
Google Content Experiments (discontinued)
9.2
Ratings
11% above category average
a/b experiment testing
10.00 Ratings
9.00 Ratings
Split URL testing
8.50 Ratings
10.00 Ratings
Multivariate testing
9.50 Ratings
10.00 Ratings
Multi-page/funnel testing
8.00 Ratings
9.00 Ratings
Cross-browser testing
8.60 Ratings
8.00 Ratings
Mobile app testing
8.60 Ratings
8.00 Ratings
Test significance
7.40 Ratings
9.00 Ratings
Visual / WYSIWYG editor
8.50 Ratings
10.00 Ratings
Advanced code editor
8.00 Ratings
9.00 Ratings
Page surveys
9.00 Ratings
8.00 Ratings
Visitor recordings
8.50 Ratings
8.00 Ratings
Preview mode
9.50 Ratings
8.00 Ratings
Test duration calculator
9.50 Ratings
10.00 Ratings
Experiment scheduler
9.00 Ratings
10.00 Ratings
Experiment workflow and approval
7.90 Ratings
8.00 Ratings
Dynamic experiment activation
8.00 Ratings
10.00 Ratings
Client-side tests
9.50 Ratings
10.00 Ratings
Server-side tests
8.00 Ratings
10.00 Ratings
Mutually exclusive tests
7.50 Ratings
10.00 Ratings
Audience Segmentation & Targeting
Comparison of Audience Segmentation & Targeting features of Product A and Product B
Adobe Target
8.5
Ratings
0% below category average
Google Content Experiments (discontinued)
10.0
Ratings
16% above category average
Standard visitor segmentation
8.50 Ratings
10.00 Ratings
Behavioral visitor segmentation
8.00 Ratings
10.00 Ratings
Traffic allocation control
8.50 Ratings
10.00 Ratings
Website personalization
9.00 Ratings
10.00 Ratings
Results and Analysis
Comparison of Results and Analysis features of Product A and Product B
We recommend this application because it allows us to segment and track the traffic of our domain under an analysis of their behavior, ranging from counting the number of clicks they make on a single element to the most complete action within our page in real time.
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.
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.
There should be some more clarity around what makes a test significant. While this can be decided by the client themselves, some direction from the tool would be helpful.
Also, if there was an easier way to organize campaigns and search for them it would be helpful. Right now there is just a long list of campaigns and you have to rely on search to find a specific campaign. What if you don't know the name of the test?
Once you get started with your testing program, you realize that it is necessary to continue. You must keep optimizing in order to remain a vital competitor in today's marketing world. Even if you're not using Test & Target or any other user experience testing software, you ought to be performing comparison tests on your own, simply by routing your audience to different experiences and quantifying the aggregate of the results.
Content Experiments just makes it is simple and easy to implement A|B tests. We will be evaluating other tools in search of a more robust system for multivariate and cross-page testing, such as Optimizely or Visual Website Optimizer. However, for basic testing, you can't really beat it.
The recent UI update is a complete mess. It is difficult to navigate and find features that previously existed. The reactiveness of the page depending on window size is also ridiculous and it is absurd that depending on how large your window is, entire columns of functions will disappear with no indication that they are missing. The usability of the tool has fallen off a cliff.
On several occasions, we have had the need to ask for help from the Adobe Target support team, and I must say that they have provided us with an excellent experience, as they take care of solving the problems quickly and with high precision
Using the free tool, overall "live support" is limited. However, there are plenty of online resources to get started. If you need handheld support, it is best to upgrade the service or hire a developer through one of Google's partner agencies. There could be more support for understanding what makes a test useful or not.
The instructor that came to train us was awesome and this training was very useful. I would recommend it for anyone who is going to be using this software. I only mark it lower because it is an added expense to an already expensive product, and a lot of the training covered the "Target" portion of the software (which again, we didn't use)
The training was very easy to understand, however it would have been more useful to my development team than me. It was also primarily over-the-phone, which is never as easy to follow as in-person. We ended up scheduling and paying for an in-person training session to supplement the online/phone training because it wasn't helpful enough.
Implement using a global mBox on the page so you can change any and everything over the traditional method. Traditional method is good if you do not have technical web dev resources, do not know Javascript/jQuery, or you have money to blow on mBox calls. Global deployment reduces mBox calls and allows you to touch many parts of the page easily. A lot more customizable
For us, the decision was very straightforward. We chose to invest in the Adobe stack and utilize tools that are developed to integrate together and complement each other. Ex: Adobe Target 'A4T' integration within Adobe Analytics. Optimizely appears to be a great tool, but for us aligning with the Adobe suite, ensuring that future product enhancements and tools would work well together was a very important key factor in our decision
Google Website Optimizer was a better product but has been discontinued. We have also used Test and Target , which has more features but we have been doing fine with Google Content Experiments. Most testing situations can be handled with Google Content Experiments.
This is something we've been working to improve on, as far as how we're calculating and tracking this, but Target has had a substantial ROI on our business.
I will say specific to our efforts, we could have probably done similar work if not the same work using a different testing tool (Optimizely for example), but Target has been good for us.
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.