Dynamic Yield vs. Google Content Experiments (discontinued)

Dynamic Yield is great for just about any sized organization, though to get the best bang for your buck, I recommend having a front-end web developer well-versed in JavaScript. Additionally, a front-end web designer would be advisable as well as their templates have great functions but some have lackluster UI's that can't be tweaked without developer assistance. Were it not for the above + the occassional slowness on the console/admin-side of the platform, I'd give it a 10. If you have a front-end dev/designer, then it's closer to a 9.5. Ideal utilization scenarios could include: Personalization, CRO/UX/UI testing, and audience or user-level tailored digital experience.

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.

Incentivized

• Provide fantastic support, both in relation to strategy/best practice and troubleshooting.
• An easy to use interface, as a user who is relatively new to Dynamic Yield I find that it is an intuitive platform to use.
• The ability to segment and drill down on data allows for really specific insights which, whilst not necessarily being leveraged on a testing basis, can be super valuable from a greater marketing perspective.

Incentivized

• 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!

• The impact (either positive or negative) of potentially overlapping campaigns, especially the UX personalization or custom code campaigns, may not be easily identifiable.
• It would make more sense for the new deep-learning and machine learning (ML) driven strategies be made part of the standard offering, as opposed to positioning them as add-on subscription, given that many other completing services are baking in ML as part of their platform evolution.
• The documentation on the API and custom code implementation can be fleshed out further.

• 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.

Incentivized

implementation took a long time but also, DY has really proven that they are transforming and adapting their platform to be more user friendly and the right technology choice for their brand or company

Incentivized

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.

Setting up strategies, audiences, and experiences is simple and fast. It is incredibly easy to modify the appearance of your site and optimize every aspect with the Dynamic Yield Personalizations. However, while the data visualization on an experience level is easy to modify and analyze, exporting the data in meaningful ways is time consuming.

Incentivized

Overall, the support is very good. If you are a partner (my case), they assign you a customer success manager, that helps a lot. Also, there is a technical person to provide support to the partners, again a great help.

My only "complain" is that with some complex issues, the support may delay in providing you with a solution. Sometimes that can cause some tension with your client.

Incentivized

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.

Incentivized

• Oracle Maxymiser is very clunky and hard to code with. Previewing changes was a challenge and development for fixes were slow
• Optimizely - Great for coding. Fast and efficient. Everything worked great. They were limited at personalization triggers though and their costs were expensive.
• Monetate - Evaluated but their UI was hard to use.

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.

Incentivized

• Most tests have had a positive impact on either revenue or conversion rate - quite often in double digits.
• Dynamic Yield has also helped us to stop some particular initiatives through direct interaction with the customer base via questionnaires or by a test proving negative quicker than rolling out a permanent feature.

Incentivized

• CE has made efficient time use easy and fool-proof when it comes to learning the software
• Because it integrates with other Google programs there is a benefit to track with Google Analytics
• The learning curve can create initiate time investment that may go beyond what companies are willing to dedicate.