Maze is a rapid user testing platform from Maze.design in Paris, designed to give users actionable user insights, in a matter of hours. The vendor states that with it, users can test remotely, autonomously, and collaboratively.
$75
per month
Optimizely Web Experimentation
Score 8.6 out of 10
N/A
Whether launching a first test or scaling a sophisticated experimentation program, Optimizely Web Experimentation aims to deliver the insights needed to craft high-performing digital experiences that drive engagement, increase conversions, and accelerate growth.
Maze User Testing is great if you're interested in doing user research from the comfort of your own desk. You can easily setup usability tests, surveys, card sorting and tree tests among other things to get a better understanding of how customers use your product. The only limitation at the moment with Maze that I can identify is only being able to do unmoderated tests, so if you'd like to be able to ask follow up questions in the moment, Maze is not the tool for you.
Best for CRO initiatives, including testing variations of landing pages, user flows, and product pages. Optimizely may not be suitable for more complex machine learning models. To analyze the effect of the feature usage the way we will do in our solution. Using the audience to measure the success of the features Also, to the best of my knowledge, Optimizely is the only tool that can do all this.
Powerful Stats Engine that drives conclusivity of outcomes and helps generate trust in results when shared to leadership and stakeholders.
Customizable metrics with various tags, properties, and attributes that allow users flexibility in what and how they architect their Optimizely analytics.
Flexibility for different levels of tech expertise, I live in the tool as an expert JavaScript and front-end developer, someone else might use solely the visual editor to click and make changes without knowledge of how to code.
Google Tag Manager. Our challenge, it's a strange use case, but our challenge is that we don't have Google Tag Manager, so we can't integrate with GA4. And that's been a bit of a bummer. So I would like to be able to integrate with GA4 even without Google Tag Manager.
I rated this question because at this stage, Optimizely does most everything we need so I don't foresee a need to migrate to a new tool. We have the infrastructure already in place and it is a sizeable lift to pivot to another tool with no guarantee that it will work as good or even better than Optimizely
Optimizely Web Experimentation's visual editor is handy for non-technical or quick iterative testing. When it comes to content changes it's as easy as going into wordpress, clicking around, and then seeing your changes live--what you see is what you get. The preview and approval process for sharing built experiments is also handy for sharing experiments across teams for QA purposes or otherwise.
I would rate Optimizely Web Experimentation's availability as a 10 out of 10. The software is reliable and does not experience any application errors or unplanned outages. Additionally, the customer service and technical support teams are always available to help with any issues or questions.
I would rate Optimizely Web Experimentation's performance as a 9 out of 10. Pages load quickly, reports are complete in a reasonable time frame, and the software does not slow down any other software or systems that it integrates with. Additionally, the customer service and technical support teams are always available to help with any issues or questions.
They always are quick to respond, and are so friendly and helpful. They always answer the phone right away. And [they are] always willing to not only help you with your problem, but if you need ideas they have suggestions as well.
The tool itself is not very difficult to use so training was not very useful in my opinion. It did not also account for success events more complex than a click (which my company being ecommerce is looking to examine more than a mere click).
In retrospect: - I think I should have stressed more demo's / workshopping with the Optimizely team at the start. I felt too confident during demo stages, and when came time to actually start, I was a bit lost. (The answer is likely I should have had them on-hand for our first install.. they offered but I thought I was OK.) - Really getting an understanding / asking them prior to install of how to make it really work for checkout pages / one that uses dynamic content or user interaction to determine what the UI does. Could have saved some time by addressing this at the beginning, as some things we needed to create on our site for Optimizely to "use" as a trigger for the variation test. - Having a number of planned/hoped-for tests already in-hand before working with Optimizely team. Sharing those thoughts with them would likely have started conversations on additional things we needed to do to make them work (rather than figuring that out during the actual builds). Since I had development time available, I could have added more things to the baseline installation since my developers were already "looking under the hood" of the site.
A Lookback is an alternative option if you think Maze User Testing is quite expensive for you, but look back has a different approach to Maze User Testing. Lookback focuses on qualitative usability testing instead of quantitative UserTesting. And also, Maze User Testing has a free option but Lookback doesn't have it, but Lookback has a cheaper option at $19/month than Maze.
Optimizely Web Experimentation was more robust and able to handle the broad array of sites we run than VWO. It has been a great platform to easily add additional sites onto, but still providing a universal overview of all of them, making management a simple task.
It's incredibly flexible and adapts well to organizations of all sizes, whether you’re running a single site or managing multiple departments and platforms. The ability to deploy experiments seamlessly across different environments is a huge plus, especially for growing businesses. While it’s highly scalable, the last point would depend on the right team leveraging its full potential.
