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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Optimizely Feature Experimentation
Score 7.5 out of 10
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Optimizely Feature Experimentation combines experimentation, feature flagging and built for purpose collaboration features into one platform.
If you're using the Adobe stack and tools to power your website, Target is a great solution to implement. I've utilized Target within two organizations, one running on Adobe Experience Manager (AEM), and the other on Adobe Magento. I don't see how companies could harness the full capacity of Target without also having Adobe Analytics integrated. This is their 'secret sauce' and might not be a good solution for companies who are invested in Google Analytics 360. Integration was straightforward but did require support from the Adobe team to implement successfully. While Target is a great tool for digital teams to support, you'll need your tech team aligned and available to support implementation.
Optimizely Feature Experimentation works really well for setting up feature flags with an easy UI for turning them on and off or ramping up a gradual rollout. It also works really well to set up split tests where you can split your traffic by percentage as well as almost any custom data attribute you wish to define. This is more for robust features and less for visual changes - Optimzely Edge or Web are better suited for that.
This application gives us an incredible integration with Adobe Analytics that allows its operation to be the best and determine the performance of our website.
It offers us an analysis based on user behavior and a web page customization option to adapt and meet the needs of those users.
Its ability to run A/B tests and multivariate experiments simultaneously allows us to identify the best-performing options quickly.
Optimizely blends into our analytics tools, giving us immediate feedback on how our experiments are performing. This tool helps us avoid interruptions. With this pairing, we can arrive at informed decisions quickly.
Additionally, feature toggles enable us to introduce new features or modifications to specific user groups, guaranteeing a smooth and controlled user experience. This tool helps us avoid interruptions.
This is something a lot of testing tools struggle with, but I think the WYSIWYG ("What you see is what you get") editor - or Visual Experience Composer (VEC) in Adobe terminology - could definitely use some work. It's a struggle to execute many tests beyond simple copy, color, placement changes, and even the features that do exist are often clunky if not altogether broken.
The interface itself can be a bit counterintuitive in certain parts. If you are familiar with other tools, it's likely middle of the road in this respect; think much easier to understand than Monetate for instance, but a far cry from the simplicity of an Optimizely.
It can be a bit buggy from time to time. The worst example is the frequency at which the tool will fail to save due to an error, but not inform you of this until you try to save, at which point your only option is to log out, log back in, and make all of your updates once again. It can become an extreme pain point at times, and I personally have just gotten into the habit of saving every couple of minutes to avoid a massive loss of productivity.
Splitting feature flags from actual experiments is slightly clunky and can be done either as part of the same page or better still you can create a flag on the spot while starting an experiment and not always needing to start with a flag.
Recommending metrics to track based on description using AI
We have a team of people trained on how to use the application and it integrates well with the other Adobe products we use. Our future roadmap of testing will require some complex scenarios which we hope Target will be able to accomplish
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
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
We seriously considered another software but because we use so many other Adobe products this made the most sense for us. If you are not dependent on other Adobe software and are a smaller company, in my opinion, Target may not be the best fit.
Optimizely Feature Experimentation is better for building more complex experiments than Optimizely Web. However, Optimizely Web is much easier to kickstart your experimentation program with as the learning curve is much lower, and dedicated developer resources are not always necessary (marketers can build experiments quickly with Optimizely Web without developers' help).
We have been able to run specific A/B tests that have shown an increase in conversion, which in turn has led to very large banked sales numbers for the year.
We have been able to prove that using and automated Merchandising process did not decrease conversion. This allowed us to greatly increase efficiency by opening up resource time.
Experimentation is key to figuring out the impact of changes made on-site.
Experimentation is very helpful with pricing tests and other backend tests.
Before running an experiment, many factors need to be evaluated, such as conflicting experiments, audience, user profile service, etc. This requires a considerable amount of time.
